esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 4 Experimental Results and Discussion
### Model Training Results  
During the training process, the model's loss function value exhibits a significant downward trend and gradually converges to a relatively stable level as the number of training iterations continues to increase, ultimately achieving a satisfactory convergence effect, as illustrated in Figure 5. However, it is noteworthy that the model's loss value and accuracy may experience a certain degree of fluctuation in different iteration stages. To obtain optimal model performance, a common practice is to retain the model weights that yield the highest accuracy on the test set during the training process and consider them the final optimal model parameters. This accuracy-based model parameter selection strategy effectively captures the model's best state during the training process, thereby ensuring the model's generalization ability on the test data. Concurrently, by monitoring the changing trend of the loss function value, a comprehensive assessment of the model's convergence can be conducted, providing crucial reference information for model optimization and parameter tuning.  
As presented in Table 1, through comparative analysis, it can be observed that the LSTM model exhibits superior performance across multiple evaluation metrics. Its accuracy (0.764) and other metric values surpass those of machine learning and deep learning models such as GRU, SVM, KNN, and RF (with accuracy ranging from 0.68 to 0.74). From the perspective of the AUC metric, the LSTM model (AUC: 0.766) continues to demonstrate outstanding performance, indicating that the LSTM model possesses a strong ability to distinguish between positive and negative samples at various classification thresholds. In contrast, the AUC values of other models are lower than that of the LSTM model. The exceptional performance of the LSTM model in classification tasks can be attributed to its deep network architecture and abundant trainable parameters. The deep network structure endows the LSTM with the capability to learn complex feature patterns and high-level abstract representations from data, facilitating more accurate classification decisions.  
Figure 5: Loss function training.  
In our analysis, the superior performance of the LSTM model compared to GRU, SVM, KNN, and RF can be attributed to its ability to capture temporal dependencies in the data. LSTMs are specifically designed to handle sequential data, making them well suited for modeling the temporal aspects of road collapse incidents. In contrast, models like SVM, KNN, and RF are more static and may not effectively capture time-based patterns, which are crucial for this application. While GRU is also a recurrent neural network, LSTM's architecture, with its additional gates, may offer more nuanced control over long-term dependencies, leading to improved accuracy in our specific dataset.
## 3 Routing Protocols for UWSN
### Regional Sink Mobility (RSM) and Vertical sink Mobility (VSM)  
In SEEC, the throughput was low due to multi-hopping of the clustering head in the dense region. To increase the throughput [PERSON] et al. proposed two routing protocols. One is called the Regional Sink Mobility (RSM) where the sinks move regionally to extract the data. Another scheme is called Vertical Sink Mobility (VSM) in which the sink travels vertically to collect data from the nodes [64]. In both the schemes, it is not needed to know the information of each and every node rather one node needs to know the information of its neighboring nodes.
## 2 Numerical Method and Simulation Conditions
For temperatures above 100 \({}^{\circ}\)C, the tissue water content may decrease to less than 20% by a mass due to evaporation, leading to drastic changes in tissue dielectric parameters and greater penetration of microwaves [82; 85]. Since a large fraction of liver tissue consists of water, the tissue's thermal properties are similar to those of water and vary with temperature and water content [86]. It is shown in [87] that the influence of internal water evaporation can be included in bioheat Equation (4) by replacing specific heat \(c\) with an effective value \(c^{\prime}\), given by the relation:  
\[c^{\prime}=c-\frac{\alpha}{\rho}\frac{\partial W}{\partial T} \tag{9}\]  
where \(\alpha\) is the water latent heat constant equal to 2260 (kJ/kg) [85]. The derivative of the \(W(T)\) appearing in the above relation is shown in Figure 4b. The biological damage depends on both temperature and time. Although tissue damage can be associated with many different reactions, it may be approximated in a single process characterized by a single rate constant of the Arrhenius form [88]. An arbitrary function of tissue injury \(\Omega\) is defined as [89]:  
\[\Omega(t)=\int\limits_{0}^{t}A\exp\left(-\frac{\Delta E}{RT}\right)\text{d}t, \tag{10}\]  
Figure 4: (**a**) The time dependence of the water content \(W(T)\) of the tissue according to expression (8), taken from reference [85] and (**b**) the first derivate of \(W(T)\) which is then used to calculate an effective specific heat given by Equation (9). where \(A\) and \(\Delta E\) represent the frequency factor and the activation energy for irreversible damage reaction, respectively, \(T\) is the temperature calculated at each point of the model region and \(R\) is the gas constant. Fraction of necrotic tissue, \(\theta_{\rm d}\), can be determined from the degree of tissue injury \(\Omega\)[65]:  
\[\theta_{\rm d}=1-\exp(-\Omega). \tag{11}\]  
Numerical simulations were performed for the microwave frequency of 2.45 GHz and the input power of 13 W, including the temperature dependence of the dielectric properties of tissues, thermal conductivity, heat capacity, and blood perfusion. Parameters that characterize healthy liver, tumoral tissue, and blood are found in the literature [68] and listed in Table 1.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Temporal Constraints and Temporal Reasonings  
The temporal constraints are defined as follows [10]:  
1. The time-point constraint (\(T\)(a) = [\(t_{\mathrm{a}}\)\({}^{-}\), \(t_{\mathrm{a}}\)\({}^{+}\)]) indicates that event a occurs in the time interval [\(t_{\mathrm{a}}\)\({}^{-}\), \(t_{\mathrm{a}}\)\({}^{+}\)]. If \(t_{\mathrm{a}}\)\({}^{-}\) = \(t_{\mathrm{a}}\)\({}^{+}\), event a occurs at a specific time.
2. The time-distance constraint (\(D\)(a, b) = [\(\Delta t_{\mathrm{ab}}\)\({}^{-}\), \(\Delta t_{\mathrm{ab}}\)\({}^{+}\)]) indicates that the time distance between the occurrence times of events a and b is within the time interval [\(\Delta t_{\mathrm{ab}}\)\({}^{-}\), \(\Delta t_{\mathrm{ab}}\)\({}^{+}\)]. According to the above definitions, the following are temporal reasonings that utilize the interval calculation rules.  
1. Reverse reasoning: The time-point constraint \(T\)(b) and the time-distance constraint \(D\)(a, b) are known. The following definition can be employed to infer the time-point constraint \(T\)(a): \[T\)(a) = \(T\)(b) - \(D\)(a, b) = [\(t_{\mathrm{b}}^{-}\) - \(\Delta t_{\mathrm{ab}}^{+}\), \(t_{\mathrm{b}}^{+}\) - \(\Delta t_{\mathrm{ab}}^{-}\)] (1)
2. Merge reasoning: If event a is known to satisfy multiple time-point constraints \(T_{1}\)(a), \(T_{2}\)(a), \(T_{3}\)(a), \(\ldots\), \(T_{k}\)(a) simultaneously, the following definition can be employed to infer the time-point constraint \(T\)(a). \[T\)(a) = \(T_{1}\)(a) \(\cap\)\(T_{2}\)(a) \(\cap\)\(\cdots\)\(\cap\)\(T_{k}\)(a)
## 4 Research Methodology  
The research methodology was based on preventing linear cryptanalysis attacks by mapping **GF (\(\mathbf{2^{32}}\))** and long irreducible polynomials on the building blocks that we found to be the weakest links during a linear cryptanalysis attack. The research was conducted as follows:  
1. We collected all ten algorithms (AES, BLOWFISH, CAMELLIA, CAST, CLEFIA, DES, MMB, RC5, SERPENT, and SKIPJACK) from different IoT devices using FileDisassembler and analyzed all the.dll cryptographic files using dotPeek and iLSpy.
2. We checked all ten algorithms' correctness using test vectors from their developers. Test vectors are sets of inputs and outputs for any system to check the system's correctness [26]. For example, test vectors of the AES algorithm are shown in [26] (p. 35). For more information about test vectors, refer to Appendix A.
3. We analyzed the way all algorithms are attacked by intruders using the linear cryptanalysis attack method.
4. We mapped or applied **GF (\(\mathbf{2^{32}}\))** and long irreducible polynomials to the building blocks that are the weakest link during the linear cryptanalysis attack.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(\mathbf{X}\) & \(\mathbf{S(X)=Y}\) \\ \hline
0 & 16401A11 \\ \hline
1 & 119D04D3 \\ \hline
2 & 15C964 DE \\ \hline
3 & 1038E4 DA \\ \hline
4 & 10 AF9A2D \\ \hline
5 & 16B6 CF6A \\ \hline
6 & 14 DBFA18 \\ \hline
7 & 1377 DAIF \\ \hline
8 & 11264780 \\ \hline
9 & 146544C5 \\ \hline A & 12846F79 \\ \hline B & 1552 AF6B \\ \hline C & 1213 BA26 \\ \hline D & 13 EE872 \\ \hline E & 0 FC22787 \\ \hline F & 130124 CC \\ \hline \hline \end{tabular}
\end{table}
Table 6: New S-Box generated by GF (\(\mathbf{2^{32}}\)) and long irreducible polynomial of simplified DES’s S-Box.  
* We checked whether it was still possible for the linear cryptanalysis attack to be successful after **GF (232)** and long irreducible polynomials were applied. If it was still possible, we went back to steps (iv) and (v).
* If the linear cryptanalysis attack is blocked on steps (iii), (iv), and (v), then we accept and rebuild a new algorithm mapped with **GF (232)** and long irreducible polynomials as an algorithm that is resistant to the linear cryptanalysis attack.  
For example, we investigated how to make LAT more difficult for attackers to construct and more cumbersome to guess the key of cryptographic algorithms mapped with **GF (232)** and long irreducible polynomials. It is already stated that the security of algorithms relies on the output bit size of S-Box; if the output bit size is small, it is easy for attackers to attack algorithms. That is the theory we had before, but we did not know how possible it was. We mapped **GF (232)** and long irreducible polynomials to increase the size of the output bits of S-Boxes. **GF (232)** and long irreducible polynomials always yield 32 bits of output when applied to S-Box or any building block of an algorithm. The research methodology is summarized by the schematic diagram in Figure 4.  
Figure 4: Schematic diagram of research methodology.
## 3 Results and Discussion
### Acoustic Emission Characterization
#### 3.3.1 AE Characteristics under Monotonic Loading  
Figure 17 illustrates the evolutions of stress, the AE counts and the cumulative AE counts over time for specimens at different loading-foliation angles under monotonic loading. The whole loading process can be divided into three stages. In the first stage, the micropores and microcracks inside the specimen are compacted and a large number of acoustic emission signals are generated, with a rapid increase in cumulative AE counts, which corresponds to the compaction stage. In the second stage, few acoustic emission signals are generated, the growth rate of cumulative AE counts slows down, and some microcracks sprout, corresponding to the elastic stage. In the third stage, cracks within the specimen are propagated and coalesced, and a large number of acoustic emission signals are generated, corresponding to the stage of expansion and failure. It is worth noting that the specimen at \(\beta=0^{\circ}\) has a clear precursor before the complete failure, with a sudden increase in AE counts at certain moments when a slight stress drop can be observed from the stress-time curve. Moreover, considerably more AE counts in the second stage are observed in the specimen at \(\beta=45^{\circ}\) than at \(\beta=30^{\circ}\). This can be attributed to the fact that when the shear fracture occurs, the fracture through foliation planes often produces more AE counts than that along the foliation plane, which is consistent with the experimental finding of [PERSON] et al. [47]. Although in this study the shear fracture dominates in specimens at \(\beta=30^{\circ}\) and \(\beta=45^{\circ}\), the positions of the shear fracture plane in relation to the foliation plane are different, which will be further analyzed in Section 3.4.
## 2 Related Work  
### Continual Learning  
#### 2.1.1 Traditional Continual Learning Methods  
Continual learning methods are commonly divided into four categories: regularization-based, distillation-based, architecture-based, and rehearsal-based. Regularization-based methods [22; 24; 25; 26] impose constraints on the update of crucial network parameters for prior tasks by integrating regularization terms into the loss function. While effective, these methods often significantly inhibit the network's ability to learn new information. Distillation-based methods [23; 27; 28] synchronize the output spaces of current and previous tasks, becoming integral to various incremental learning strategies as knowledge distillation techniques evolve. Architecture-based methods [29; 30; 31; 32; 33] mitigate catastrophic forgetting by dynamically expanding network architectures or modifying internal structures for new tasks. Rehearsal-based methods [8; 9; 34; 35; 36; 37], on the other hand, integrate old and new knowledge by training with a mix of representative samples from both past and current tasks. However, these methods face limitations in privacy-sensitive and security-focused scenarios due to the need to store samples from previous tasks.
## 1 Introduction
Although the consensus problems of multi-agent systems were studied in [35; 36], the authors further considered high-power nonlinear dynamics and communication delays. In addition, it is well known that the control objective of practical industrial systems needs to be achieved in a finite time to be meaningful. However, in most existing works, such as [37; 38; 39; 40] and references therein, the control methods designed are usually used to solve the infinite time control problem. Therefore, designing effective control laws to realize the finite-time control of complex systems is worth considering. Inspired by the above observations, we consider the finite-time asymptotic tracking control problem of uncertain multi-agent systems with unknown control gains. Given the existence of uncertain dynamics and unknown control gains, the dynamic surface control technique and the Nussbaum gain function technique are used, respectively. To deal with unknown uncertain nonlinear dynamics, the radial basis function neural network (RBFNN) is applied to the final control law design. The main contributions of this paper are highlighted as follows:  
1. Different from the multi-agent systems considered in [13; 36], the control gain of each subsystem in multi-agent systems considered in this work is unknown. Moreover, the Nussbaum gain function technique is applied to deal with the unknown control gain in recursive design. 2. The unknown nonlinear dynamics of multi-agent systems are approximated by using the RBFNN. Unlike the direct estimation of ideal weights in [24; 37], this paper redesigns the weights of RBFNN to implement estimation and design adaptive control laws. 3. A nonlinear compensating term with the estimation of an unknown bounded parameter is designed to avoid repeated differentiation of each virtual control law. Furthermore, a class of first-order nonlinear filters is designed to overcome the issue of \"explosion of complexity\". This consideration simplifies the analysis process compared with [26; 31]. 4. An adaptive neural network finite-time dynamic surface control law is constructed to solve the finite-time asymptotic tracking control of given multi-agent systems. It is proven that all signals of the closed-loop system are semi-global practical finite-time stable, and by selecting appropriate design parameters, the tracking error of each follower agent can converge to a small neighborhood of zero in a finite time. The rest of this paper is structured as follows. In Section 2, some useful preliminaries and the problem description of uncertain multi-agent systems are introduced. In Section 3, the detailed control law design and stability analysis are shown. In what follows, the simulation example and brief conclusions are given in Sections 3 and 4, respectively.
## 3 Wind Farm Controller
### Wake Control Strategy  
In wind farms, owing to the economic, logistic, and space limitation, it is not feasible to place turbines far enough from each other and this results in the various interactions between turbines. These interactions have a significant impact on downstream turbines in the wake, which affects the entire performance of the turbines, including their efficiency and structural loading. These wake interactions cause a velocity deficit, resulting in a drop in the downstream turbine power production.  
The power loss is associated with spacing between turbines in the wind farm and some studies show a power reduction of up to 23% [32]. The power loss is more severe concerning the first downstream turbine that immediately follows the upstream turbine. Nearly the same effect can be observed for the remaining downstream turbines, but this effect is gradually reduced downstream [33]. This can be observed in Figure 9, which shows the normalized power along the rows of the Horns Rev wind farm in Denmark [34] as an example.  
The wake also increases the fatigue loading on the downstream turbines, thereby potentially reducing the lifespan of the turbines [3]. To mitigate these effects to some degree, the following wind farm control strategies are employed: (i) AIC and (ii) WRC [18].  
Figure 8: Flowchart of dynamic wind farm controller.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 3 Non-Uniform Deployment Framework
### Optimization Problem Solving  
In this section, we present a detailed description of the optimization problem solving process using mixed-integer nonlinear programming (MINLP). We use Gurobi [18] as the solver to maximize the system's minimum throughput \(Thmin\) while satisfying the computational and communication constraints of the devices.  
First, we define the system parameters, including: \(M\), \(N\), \(O_{i}\), \(Mem_{j}\), \(Req_{i}\), \(B_{j,j}\), and \(T_{comp}^{i,j}\). Next, we establish the optimization model and define the decision variables. These variables include binary variables \(x_{i,j}\), \(y_{j,s}\), and \(u_{j}\). In addition, we define execution time and communication time variables, including \(T_{j,s}\) and \(T_{s}\). Here, \(T_{s}\) represents the execution time of stage \(s\). Furthermore, we introduce throughput-related variables, such as \(Th_{ls}\), \(Th_{lj,s}\), and \(Th_{min}\). \(Th_{lj,s}\) represents the throughput of device \(j\) in stage \(s\). Subsequently, we define the constraints consistent with the mathematical modeling.  
Before solving the optimization problem, we calculate the execution time and throughput for each device in each stage and compute the throughput for each stage. The execution time of a device in a stage includes computation time and communication time. The computation time is determined by the time required for the device to process its assigned sub-model, while the communication time is the time for transmitting the output of the sub-model from the device to the device in the next stage that will process the next sub-model. The communication time is calculated as the output size of the sub-model on the device divided by the communication bandwidth between the two devices. The total execution time is the sum of the computation time and the communication time. The throughput of a device in a stage is defined as the inverse of the execution time of the device in that stage, and the throughput of each stage is the sum of the throughputs of all devices in that stage. Subsequently, we set the optimization objective to maximize the minimum throughput \(Th_{min}\) across all stages to ensure that the system achieves the highest possible throughput even at the slowest stage. Once the optimization problem is formulated, we solve it using the solver.
## 5 Limitations and Future Perspectives  
Due to the fact that data analysis with our experimental tool takes a considerable amount of time, and because of the volume of daily clinical work, it is difficult to convince a specialist to devote additional time and effort to performing image analysis for research purposes. While we were happy that we managed to obtain annotations from four experienced clinicians, we had to limit the number of images analyzed and perform the LGE quantification on a single preselected SAx slice for each patient, in contrast to larger studies [6; 10; 11]. The consequences are the low statistical significance of the statistical testsarising from the low number of images analyzed and a lack of insight into the pathology distribution at the whole-heart level.  
Unfortunately, the follow-up data for our patients were not available. Therefore, we could not evaluate which quantification method would have provided the best information on the clinical outcome. Further research should determine the precise relationship between LGE extent and adverse events. Doing so would likely improve risk stratification and clinical decision-making.  
Our images were all acquired by using the same field strength of 3T, the same imaging sequence, and the same amount of contrast agent per patient (0.1 mmol/kg). Because all these factors can affect the signal-to-noise (SNR) ratio and contrast of the images [9; 10; 25], future research should be conducted using different field strengths or imaging sequences (e.g., balanced steady-state free precession (bSSFP) [26]).
# Enhanced Specific Mechanism of Separation by Polymeric Membrane Modification--A Short Review
## 5 Adsorptive Membranes
### Surface Modification  
Within the adsorptive membrane, the three main types could be distinguished. There are mixed matric adsorptive membranes, pore-filled adsorptive membranes, and surface adsorptive membranes. Hence, several methods of preparation could be delivered. Among them, the most popular are surface-coated adsorptive membrane, surface-deposited adsorptive membrane, surface-grafted adsorptive membrane, and surface-assembled adsorptive membrane (Figure 6).  
The mixed matrix membranes are the most common adsorptive membranes. However, their preparation process is simple; however, not free from difficulties such as agglomeration of adsorbents. Adsorbents embedded in a polymer matrix have a lower adsorption capacity and longer adsorption equilibrium time. The following four types of surface adsorptive membranes are fabricated by coating, depositing, grafting, and assembling. Two steps usually prepare the surface-coated adsorptive membrane: the particles can first be loaded on the surface of the membrane by dipping and filtering and then be covered with the polymer layer formed by crosslinking or coating. The surface-deposited adsorptive membranes could be prepared by filtration deposition, the deposition of hydrothermal techniques, and the vapour deposition method.  
Figure 5: The mechanism of transportation through the adsorptive membranes. Reproduction from [75].  
On the other hand, the surface-grafted adsorptive membranes can be manufactured with the grafting method and a photo-induced post-synthetic polymerisation strategy to immobilise adsorbents on the membrane surface via a covalent link. The surface-assembled adsorptive membrane can be prepared by assembling polyelectrolytes via the electrostatic interaction. In general, the surface adsorptive membranes have a high adsorptive capacity and a short equilibrium time. However, the surface-deposited adsorptive membrane and the surface-assembled adsorptive membrane have the detachment risk of the deposition and assembly layers during application and regeneration. The surface-grafted adsorptive membranes may have a complicated process and harsh reaction conditions [75; 83; 84].