esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Analysis
### Computational Complexity  
The number of floating-point operations serves as a complexity metric for comparing the computational complexity between the proposed algorithm and the point-by-point cancellation adaptive pre-equalization algorithm [10]. The computational complexity of the point-by-point cancellation adaptive pre-equalization algorithm is as follows:  
Figure 12: Relationship between BER and SNR under different acceleration factors.  
Figure 13: Relationship between acceleration factor and BER.  
**Author Contributions**: Conceptualization, M.C., Q.Y., G.Z. and H.W.; Methodology, M.C., H.W. and Q.Y.; Validation, M.C., Q.Y. and G.Z.; Investigation, G.Z. and Y.Z.; Resources, Q.Y. and X.Z.; Data Curation, X.Z.; Writing--Original Draft Preparation, G.Z. and Q.Y.; Writing--Review and Editing, M.C., H.W., Q.Y. and Y.Z.; Visualization, Q.Y. and H.W.; Supervision, M.C. and H.W.; Project Administration, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was supported by NSFC Program under Grant 62265010, 62261033 and Natural Science Foundation of Gansu Province, China (24 JRRA183) and Youth Science and Technology Foundation of Gansu Province, China (24 JRRA974) and Natural Science Foundation of Shandong Province, China (ZR2022 MF284).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We gratefully acknowledge the assistance of [PERSON] in preparing and debugging the program. We also thank [PERSON] and [PERSON] for help with the English writing and details. All authors have consented to and are informed by this acknowledgement.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 6 Experimental Setup  
For our experiments, we split the datasets into training, validation and test data. Training data are used to learn the model parameters (i.e., weights), validation data to check the goodness of training hyperparameters, and test data are used to assess generalization and real-world performance. Before target and attack model training, so-called hyperparameters have to be set manually before training (e.g., learning rate, batch size). We used Bayesian hyperparameter optimization for all target model experiments to ensure that we found good hyperparameters that yield high accuracies on the respective models and data. Bayesian optimization is more efficient than grid search, since it considers past trials during the hyperparameter search. An overview of all hyperparameters, dataset size for training, validation and test, and the overall \(\epsilon\) per training is provided in Table 2. For the attack model, we reused the original hyperparameters of [PERSON] et al. [8] which already performed well. The majority of experiments were conducted on EC2 ([[https://aws.amazon.com/ec2/](https://aws.amazon.com/ec2/)]([https://aws.amazon.com/ec2/](https://aws.amazon.com/ec2/)), (accessed on 2 June 2022)) GPU optimized instances of type \"p3.8 xlarge\" with the \"Deep Learning AMI\" machine image, building on a Linux 4.14 kernel, Python 3.6 and TensorFlow 2.2.  
In all experiments, we assume that the data owner would also want converging target models even when training with DP. Thus, all HTC models leverage early stopping with a patience of _three_ epochs to terminate the training process before overfitting. Furthermore, we set the DP parameter \(\mathcal{C}\) (i.e., the sensitivity \(\Delta f\)) in our experiments to the median of the norms of the unclipped gradients over the course of original training as suggested by [PERSON] et al. [12]. For all executions of the experiment, CDP noise is sampled from a Gaussian distribution (cf. Definition 1) with scale \(\sigma=noise\)_multiplier_\(z\times clipping\)_norm_\(\mathcal{C}\). According to [PERSON] et al. [52], values of \(z\approx 1\) will provide reasonable privacy guarantees. We evaluate increasing noise regimes per dataset by evaluating noise multipliers \(z\in\{0.1,0.5,1.0,3.0\}\) and calculate the resulting \(\epsilon\) at a fixed \(\delta=\frac{1}{\eta}\).
## 3 Proposed Method
### Lexicon Update  
We next update the phoneme-based lexicon to the proposed unit-based lexicon. The mapping from the phoneme sequence of the word to the corresponding proposed unit sequence is carried out using the decision tree. To build the proposed lexicon, we select the leaf node of the decision tree corresponding to the CD phoneme in question. To do this, the new pronunciation sequence of each word is generated by examining the word from left-to-right one CD phoneme symbol at a time. Based on the context information, the CD phoneme starts at the root node of the decision tree and goes down until the left node is found. The index of the leaf node corresponding to the CD phoneme is output in the new subword unit. The process then moves onto the next CD phoneme unit, and the corresponding CD phoneme unit is found in a similar way. The subword sequence of each word is constructed by concatenating the proposed unit that has been found.  
Figure 4a shows a block diagram of the lexicon update, and Figure 4b shows an example of a phoneme-based lexicon consisting of \(N\) words. Figure 4c shows the updated lexicon obtained by using the decision tree. In Figure 4b, the first word \(W_{1}\) has \([a\ b]\) as the pronunciation sequence. Here, the tri-phone of the first phoneme '\(a\)' has a word boundary symbol '$' on the left and the phoneme symbol '\(b\)' on the right. As shown in Figure 4d, we can express this CD phoneme unit as \"$ - \(a+b\)\". Here, the \"$ - \(a+b\)\" symbol denotes that the '$' symbol (i.e., the left-hand symbol) precedes the '\(a\)' phoneme, and the '\(b\)' phoneme (i.e., the right-hand phoneme) follows it. The decision tree first asks what the center phoneme of the unit is and then goes to the left node corresponding to phoneme '\(a\)' (red line). After that, the decision tree checks whether the left symbol '$' belongs to the question \([a,c]\). In this case, we seek out a node corresponding to '\(No\)', since the symbol does not belong to the question. The decision tree then checks again whether the right symbol '\(b\)' belongs to the question \([a,b]\). In this case, we go to a node corresponding to \([a,b]\), which means '\(Yes\)'. Finally, we reach the leaf node '\(U\)\(2\)'. The leaf node '\(U\)\(2\)' is then output as a new subword unit for the CD phoneme \"\(\$-a+b\)\". This process then moves on to the next CD phoneme \"\(a-b+\)\", and the CD phoneme that corresponds to it is found in a similar way. As a result, we constructed the proposed lexicon consisting of 120 subword units from the original lexicon consisting of 1.8 million words and 40 phoneme units. Note that 64,000 (= \(40\times 40\times 40\)) CD phonemes, rather than 40 CI phonemes, are mapped to 120 subword units. Further, this is a crude computation; in most ASR systems, the left and right contexts are binned into, for example, 10 broader classes, leaving \(10\times 40\times 10\) CD phonemes. We use the proposed lexicon for the acoustic model training and speech recognition experiments.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 3 Model Design  
### Overall Process of the System  
The subject of this paper is the presentation of an analytical model for the prediction of rainfall intensity of the SAGIN system. Figure 5 is the overall process of the system.  
First of all, the system collects weather-related data through IoT sensor devices to form a dataset. Next, the obtained dataset undergoes a series of data preprocessing procedures. The extended ELM model inputs the collected weather-related data, outputs the prediction of blockage which affects the satellite routing design, and improves the reliability and effectiveness of data transmission.
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 1 Introduction
As mentioned above, BA was successfully applied to many application domains including FS. However, one of the limitations with many meta-heuristic algorithms, including BA, is their deficient performance with high-dimensional problems. This problem is most likely to appear as the search space is not effectively explored due to losing population diversity during the search process [28]. Many methods were proposed in the literature to control population diversity including cooperative algorithms [29; 30]. However, for high-dimensional problems, co-evolutionary algorithms are preferred as they can divide the dimension of the solution into multiple parts, and optimize each part separately [31; 32]. Moreover, as the text data are represented as a sequence of terms where each term is considered as one feature, this aggravates the problem of high dimensionality. The coevolutionary strategy was successfully employed in several evolutionary computations, such as job-shop scheduling [33], path-planning problem [34], supply chain-gap analysis [35], flow-shop scheduling problem [36], large-scale optimization [37], hierarchized Steiner tree problems [38] and sensor ontology meta-matching [39]. However, the majority of these applications are continuous problems. Applying this technique to a discrete problem such as text feature selection is still challenging and needs to be further studied. In this paper, a cooperative coevolutionary BBA is proposed and evaluated as a TFS method, that provides the following contributions:  
1. Controlling the population diversity during the search process using the multi-population BBA;
2. Handling the high dimensionality of the feature space by using the divide and conquer strategy;
3. Initializing a diverse population using the modified Latin Hypercube Sampling (LHS) initialization method;* Better evaluation of the solutions using the adapted Rough Set (RS)-based fitness function that is independent of any classification method. The rest of this paper is organized as follows: Section 2 provides a summary of the related work. Then, the details of the proposed algorithm are given in Section 3, followed by the experimental setup in Section 4. After that, the discussion and analysis of the experimental results are shown in Section 5. Finally, the work is concluded in Section 6 of this paper.
###### Abstract  
During the operation of space gravitational wave detectors, the constellation configuration formed by three satellites gradually deviates from the ideal \(60^{\circ}\) angle due to the periodic variations in orbits. To ensure the stability of inter-satellite laser links, active compensation of the breathing angle variation within the constellation plane is achieved by rotating the optical subassembly through the telescope pointing mechanism. This paper proposes a high-performance robust composite control method designed to enhance the robust stability, disturbance rejection, and tracking performance of the telescope pointing system. Specifically, based on the dynamic model of the telescope pointing mechanism and the disturbance noise model, an \(H_{\infty}\) controller has been designed to ensure system stability and disturbance rejection capabilities. Meanwhile, employing the method of an \(H_{\infty}\) norm optimized disturbance observer (HODOB) enhances the nonlinear friction rejection ability of the telescope pointing system. The simulation results indicate that, compared to the traditional disturbance observer (DOB) design, utilizing the HDOB method can enhance the tracking accuracy and pointing stability of the telescope pointing system by an order of magnitude. Furthermore, the proposed composite control method improves the overall system performance, ensuring that the stability of the telescope pointing system meets the 10 nrad/Hz\({}^{1/2}\) @0.1 mHz\(\sim\)1 Hz requirement specified for the TianQin mission.  
space gravitational wave; breathing angle variation; telescope pointing mechanism; \(H_{\infty}\) controller; HDOB; pointing stability
## 4 Conclusions  
The highlights of this work can be summarized as follows:  
1. Co-precipitation and solid-solid methods have been studied and compared as possible strategies to obtain low-cost tricalcium aluminate hexahydrate materials. The solid-solid reaction pointed out that it is not possible to obtain a material with appropriate thermochemical features without passing through a preliminary synthetic route that leads to a better chemical interaction between Ca\({}^{2+}\) and Al\({}^{3+}\).
2. The structural and thermochemical characterization showed higher katoite content (98.6%) if a 1:1 Ca/Al ratio was initially used in co-precipitation, which also involves a greater heat of dehydration (807.0 kJ/kg), compared to the other products.
3. The study on dehydration/hydration cycled materials evinced that the initial samples underwent a structural change during the cycles. As a consequence, a decrease in conversion yields and, therefore, in stored-released heat, is observed, with respect to the starting materials.
4. Calcium hydroxide appears to be important on the stabilization of the material, partially preventing decomposition phenomena.
5. Once a cycle stability is reached, the amount of stored/released heat per mass unit is similar for both co-precipitated products (about 260 kJ/kg of hydrated material), while in terms of storage density, CA-11H exhibits the highest capacity (224.2 MJ/m\({}^{3}\)).  
In future development, further studies will aim to enhance the obtained promising results. Cyclic stability will be investigated and improved in order to make this low-cost and nontoxic material a worthy competitor to current thermochemical energy storage systems.  
Figure 12: SEM images, magnification = 100 k, for CA-32H (**a**), CA-11H (**b**) and CA-SH (**c**) after five dehydration-hydration cycles.  
**Author Contributions:** Conceptualization: C.M., methodology: L.C. and E.P., validation: F.A., E.P. and L.C., formal analysis: F.A., E.L.M. and M.L.; investigation: F.A. and E.L.M.; data curation: F.A., L.C. and E.P.; writing original draft: F.A.; writing review: F.A., L.C. and E.P.; supervision: C.M. 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:** The data presented in this study are available within the article.  
**Acknowledgments:** This study was conducted as part of the project, \"Thermochemical materials for heat storage: development and characterization,\" sponsored by INSTM (Consorzio Interuniversitario Nazionale per la Scienza e Tecnologia dei Materiali), within the IEA SHC Task 58 \"Material and Component Development for Thermal Energy Storage.\" Authors thank [PERSON] for helpful discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The complex influence of various disturbances on an inertially stabilized platform (ISP) restricts the further improvement of its servo performance. This article investigates the mapping relationship between internal and external disturbances and servo performance by establishing a high-precision dynamics model of the servo device with simulation and experiment. For internal disturbances, a nonlinear model of friction and backlash is established based on a BP neural network, and the transmission error is reconstructed based on the principle of main order invariance. For external disturbances, the road disturbance torque, changing inertia, and mass imbalance torque are modeled. The quantitative mapping relationship between internal and external disturbances and servo performance is obtained through simulation, in which friction and road disturbance are the largest internal and external factors affecting the servo performance, respectively. These conclusions are verified by load simulation experiments on a certain type of servo device, in which the servo performance is improved by 17% for every 25% reduction of friction torque, and the servo performance is reduced by 12% for every 33% increase of road disturbance torque. The research results provide a reference for servo device selection, performance indicator assignment, and performance prediction of the ISP.  
inertially stabilized platform (ISP); dynamic model; servo performance; friction torque; road disturbance torque +
Footnote †: journal: Applied sciences
## 4 The Simplified Theoretical Model of the LPCB with One PCS  
For the convenience to analyze the experimental results and simulate the influence of the design parameters of the LPCB on the electric output of the piezoelectric brick, the simplified theoretical model of the LPCB displayed in Figure 6a is proposed. Due to the trajectory of the shaft just along the shaft slot shown as Figure 1d and the axial stiffness of the PCS of the LPCB is tremendous, a hypothesis is assumed that the horizontal, vertical, and angular displacements (\(x_{1}=l_{p}\)) of PCS are zero. In this case, the force arm segment can be considered as a cantilever shown as Figure 6b. According to the moment equilibrium principle, the bending moment (\(x_{2}=0\)) of the cantilever should be equal to the bending moment (\(x_{1}=l_{p}\)) of the PCS. Therefore, given the bending moment (\(x_{2}=0\)) of the cantilever is solved, the charge and the voltage on the PCS can be obtained. Some necessary theoretical formulas are listed as below:  
\[EI\frac{\partial^{4}w}{\partial x^{4}}+\overline{m}\frac{\partial^{2}w}{ \partial t^{2}}=F(t) \tag{1}\]  
\[w(x_{2},t)=\sum_{n=1}^{\infty}W_{n}(x_{2})q_{n}(t) \tag{2}\]  
where \(EI\) denotes the bending rigidity of the force arm segment; \(\overline{m}\) is the mass of unit length of the force arm segment; \(q_{n}(t)\) and \(W_{n}(x_{2})\) are the nth generalized coordinate and vibration shape function of the force arm segment. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Substrate Beam** & **Piezoelectric Patches (PZT4)** \\ \hline \(L\) (mm) & 19.5 & \\ \(A\) (mm) & 7 & \\ \(t_{1}\) (mm) & 1.5 & \\ \(t_{2}\) (mm) & 5 & \\ \(t_{3}\) (mm) & 4 & \\ \(l_{p}\) (mm) & & 50 \\ \(l_{p}\) (mm) & & 1 \\ Young’s modulus \(E\) (GPa) & 72 & 75.1–77.3 \\ Mass density (kg/m\({}^{3}\)) & 2742 & 7640–7683 \\ \(C\) (nF) & & 42.4–43.5 \\ \(d_{31}\)(pC/N) & & 615–623 \\ \hline \end{tabular} Note: \(C\) and \(d_{31}\) respectively denote the capacitance and piezoelectric coefficient of PZT4 patches.  
\end{table} 
Table 1: Dimensions and physical parameters of piezoelectric bricks. Figure 5: The view of the experimental setup and the electric circuit of piezoelectric brick made of a pair of LPCBs. (**a**) Experimental setup; (**b**) Electric circuit. \[W(x_{2})=C_{1}\cos{sx_{2}}+C_{2}\sin{sx_{2}}+C_{3}\cosh{sx_{2}}+C_{4}\text{sinhs}x _{2} \tag{3}\]  
where \(C_{1}\)-\(C_{4}\) is the constant; is frequency parameter; \(C_{1}\)-\(C_{4}\) and \(s\) can be solved through the boundary condition.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 2 Materials and Methods
### Design of KANs  
To fully understand KANs, exploring the MLP configuration is crucial. The MLP is characterized by having nonlinear fixed activation functions on its nodes, such as the ReLU function [50]. While some studies have used trainable activation functions such as [51; 52; 53; 54], for now, we are focusing on an MLP with a fixed activation function. When a specific input \(x\) is fed into the MLP, it initiates operations involving the composition of weight matrices across multiple layers (ranging from layer 0 to layer \(L-1\)) and the application of the activation function \(\sigma\). The result of this process is the generation of the final output, as described in the work by [PERSON] (1999) [55].  
\[\text{MLP}(x)=\sigma(W_{L-1}\sigma(W_{L-2}\cdots\sigma(W_{1}\sigma(W_{0}x)))) \tag{2}\]  
Inspired by KART, KANs were designed by [PERSON] et al. [1], who adopted a more optimistic perspective on the utility of the Kolmogorov-Arnold theorem for machine learning by implementing the following:  
1. Generalizing the Network Structure: Instead of adhering strictly to the original two-layer structure with \(2n+1\) hidden units In Equation (1), they generalize KANs to have arbitrary widths and depths. The main challenge is to find the appropriate functions \(\Phi_{q}\) and \(\varphi_{q,p}\). A general KAN network with \(L\) layers produces the output, as In Equation (3).
2. Leveraging Smoothness and Sparsity: Many real-world functions are smooth and possess sparse compositional structures, facilitating more effective Kolmogorov-Arnold representations. This aligns with the physicist's approach of focusing on typical cases rather than worst-case scenarios, assuming that physical and machine learning tasks inherently possess useful or generalizable structures [56]. \[\text{KAN}(x)=(\Phi_{L-1}\circ\Phi_{L-2}\circ\cdots\circ\Phi_{1}\circ\Phi_{0}) (x)\] (3)  
In the context of a KAN, \(\Phi_{l}\) represents the function matrix of the \(l\)th KAN layer or a set of preactivations. We use \(\Phi_{l}\) to denote the connections between the \(i\)th neuron of layer\(l\) and the \(j\)th neuron of layer \(l+1\). The activation function \(\varphi_{l,j,i}\) connects the neuron at position \((l,i)\) to the neuron at position \((l+1,j)\).  
\[\varphi_{l,j,i},\quad l=0,\dots,L-1,\quad i=1,\dots,n_{l},\quad j=1,\dots,n_{l+1} \tag{4}\]  
The number of nodes in the \(l\)th layer is denoted as \(n_{l}\). The function matrix \(\Phi_{l}\) can be expressed as a \(n_{l+1}\times n_{l}\) matrix of activations.  
\[\Phi_{l}=\begin{pmatrix}\varphi_{l,1,1}(\cdot)&\varphi_{l,1,2}(\cdot)&\dots& \varphi_{l,1,n_{l}}(\cdot)\\ \varphi_{l,2,1}(\cdot)&\varphi_{l,2,2}(\cdot)&\dots&\varphi_{l,2,n_{l}}(\cdot )\\ \vdots&\vdots&\ddots&\vdots\\ \varphi_{l,n_{l+1},1}(\cdot)&\varphi_{l,n_{l+1},2}(\cdot)&\dots&\varphi_{l,n_ {l+1},n_{l}}(\cdot)\end{pmatrix} \tag{5}\]