esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 2 Materials and Methods
### Discretization of the Equation  
The formula for converting the system of Equation (5) into a quantum chain can be written as follows:  
1. Representation of the input vector \(b\): \[\left\lvert\,x\rangle\,\middle\lvert\,0\rangle\rightarrow\,\left\lvert\,x \rangle\,\middle\lvert\,b\rangle\right.\] (6) where \(\left\lvert\,x\rangle\) is the state of the qubits representing the solution vector \(x\), and \(\left\lvert\,b\rangle\) is the state of the qubits representing the vector \(b\). And the expression \(\left\lvert\,x\rangle\,\middle\lvert\,0\rangle\right\rangle\) itself represents the state of the system, in which the decision vector \(x\) is represented by qubits, and the qubits representing the vector \(b\) are initialized to zero.
2. Conversion of the matrix \(A\) into a quantum operation: \[\left\lvert\,x\rangle\,\middle\lvert\,y\rangle\,\middle\lvert\,0\rangle \rightarrow\,\left\lvert\,x\rangle\,\middle\lvert\,y\rangle\,\middle\lvert \,A(x,y)\rangle\right.\] (7) where \(\left\lvert\,x\rangle\) and \(\left\lvert\,y\rangle\) are the states of the qubits representing the indices of the matrix \(A\), and \(\left\lvert\,A(x,y)\rangle\right)\) is the state of the qubits representing the element of the matrix \(A\).
3. Calculation of the solution vector x: \[\left\lvert\,x\rangle\,\middle\lvert\,y\rangle\,\middle\lvert\,A(x,y)\rangle \,\middle\lvert\,b\rangle\rightarrow\,\left\lvert\,x\rangle\,\middle\lvert \,y\rangle\,\middle\lvert\,A(x,y)\rangle\,\middle\lvert\,b\rangle\,-\,A(x,y) \,\middle\lvert\,x\rangle\] (8)where the first three qubits contain information about the matrix \(A\) and the last qubit contains information about the vector b. The operation \(A(x,y)\ket{x}\) subtracts from the vector \(b\) the product of \(A\) and the vector \(x\).
4. Measurement of the states of the qubits representing the solution vector x: \[\ket{x}\ket{y}\ket{A(x,y)}\ket{b}\rightarrow\ket{x}\ket{y}\ket{A(x,y)}\ket{b} -A(x,y)\ket{x}\] (9)  
The result of the measurement is a solution vector \(x\) that can be used to solve the Poisson equation. Note that Equation (9) assumes that the matrix \(A\) can be efficiently represented in a quantum chain, which can be difficult for large matrices. Now, the HHL algorithm is used to implement all these steps.
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 4 Simulation Results and Analysis  
In order to verify the effectiveness of the proposed algorithm in this paper, this paper implements the ET-GM-PHD filtering algorithm, based on the algorithm proposed in this paper, distance partition [14] and directed graph SNN partition [18]. The simulation platform is MATLAB, R2018b, Win10 64-bit professional operating system, [EMAIL_ADDRESS] GHz, 16.00 GB RAM.  
The bird's eye view of the multi-object tracking scenario considered in this paper is shown in Figure 3.  
The evaluation method of OSPA [23] is adopted to calculate the OSPA error of the filtering algorithm based on different division methods. In this simulation experiment, OSPA parameters are set as \(p=2\) and \(c=60\), respectively. The number of Monte Carlo simulation experiments is 100. For fair comparison, the subsequent simulation experiments are the same as the simulation scenarios in the literature [18]. In the simulation, the target  
Figure 3: The bird’s eye view of the multi-object tracking scenario.  
Figure 2: The flowchart of the computational complexity involved in each block.  
kinematic model adopts the CV (constant velocity) model, and the state transition matrix is given by the following:  
\[F=\begin{bmatrix}1&T_{s}&0&0\\ 0&1&T_{s}&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{11}\]  
where the sampling interval \(T_{s}=1\) s in this simulation.  
The process noise covariance matrix is \(Q=\sigma_{v}^{2}\begin{bmatrix}\frac{T_{s}^{4}}{T_{s}}I_{2}&\frac{T_{s}^{3}}{ T_{s}}I_{2}\\ \frac{T_{s}^{2}}{T_{s}}I_{2}&\frac{T_{s}^{2}}{T_{s}}I_{2}\end{bmatrix}\), the measurement noise covariance matrix is \(R=\sigma_{e}^{2}I_{2}\), where \(I_{2}\) is a \(2\times 2\) identity matrix, the process noise standard deviation is set as \(\sigma_{v}=2\), and the measurement noise standard deviation is set as \(\sigma_{z}=20\).  
The size of the area monitored by the sensor is \([-1000,1000]\) m \(\times[-1000,1000]\) m. To verify the performance of the algorithm, the survival probability of each target is set as \(P_{s}=0.95\), with the detection probability of \(P_{d}=0.95\). The number of extended target measurements is a Poisson distribution with an expectation value of 10. The clutter measurements are uniformly distributed in the detection area, while the clutter count follows a Poisson distribution with an expected value of 20.  
To ensure the feasibility of the algorithm, the Gaussian terms need to be pruned and merged. The maximum number of Gaussian components is \(J_{\max}=100\), with the pruning threshold \(T=10^{-6}\), the merging threshold \(U=4\), and the distance threshold \(\sigma=50\).
### Web Vulnerabilities Countermeasures
#### 1.3.3 Dynamic Analysis  
It runs the application web and tries to identify security violations by using techniques such as code instrumentation (i.e., inserting checks into the program) and fuzzing (i.e., it inputs random test data to a target program in order to explore all possible paths).  
Ref. [10] improved the detection of XSS attacks in web applications by using dynamic analysis and a fuzzy engine. They extracted Application Entry Points (AEP) using a web crawler and then used a fuzzy engine that generates invalid strings for each AEP until the Web Application Firewall is defeated, in which case its signatures database is updated. Ref. [11] presented a method for the detection of DOM-XSS attacks: they used dynamic taint analysis of JavaScript code (i.e., taint traces are obtained while parsing web pages), and fuzzing to automatically derive attack vectors based on those taint traces, and then, they verify DOM-XSS vulnerability by rendering HTTP responses on the browser. Ref. [12] used code instrumentation to generate models that describe how and with whom client-side components interact, which allows protecting JavaScript-based web applications against client-side validation attacks. Refs. [13,14] presented a concolic (concrete + symbolic) execution-based approach for the detection of XSS attacks, and SQL injection and OS command injection, respectively.  
Dynamic analysis-based approaches incur no false positives but can not achieve high code coverage.
## 4 Evaluation Metrics
### Mobility Models' Metrics
#### 4.1.1 Network Diameter  
In [42], the network diameter is defined as follows. It is the maximum distance in hops between two nodes. More formally, let lg(_a,b_) be the minimum number of edges required to find a connected path between \(a\) and \(b\) in the two-dimensional space \(V^{2}\). Then,  
\[\text{Diameter}=\max\left\{\text{lg}(a,b)\right\}\ \forall\ (a,b)\in\ V^{2} \tag{1}\]  
As claimed in [42], this diameter is representative of the average length of the paths of the network. Thus, a mobility model that creates a low diameter will, for example, improve routing performanceand minimize interference. Although this metric can reflect routing performances, it is related only to the mobility model by itself, independent of any particular protocol.
## 3 Results and Discussion
### Dissociative Ionization of CHBrCl\({}_{2}\) Molecules in 800 nm Femtosecond Laser Fields
For Cl\({}^{+}\) ion, molecular ions CHBrCl\({}_{2}\)\({}^{+}\) are firstly excited to state S\({}_{10}\), which is determined by the E\({}_{\rm{AP}}\) of the channel (1), then quickly dissociated into fragments. Thus, the influence of molecular rotation on the ejection of fragment ions was very little. Angular distribution of fragment Cl\({}^{+}\) ion should be highly anisotropic. However, that assumption is contrary to the experimental result. We guess that the angular distribution of fragment Cl\({}^{+}\) ion is not only related to the excited state but also related to the molecular structure. For dissociation along with the C-Cl bond, the possibility of any breaking of the C-Cl bonds is almost equal. Therefore, the position of the Cl atom in the molecular structure establishes the initial angular distribution of Cl\({}^{+}\) ions, which weakens the anisotropy of the angular distribution of fragment Cl\({}^{+}\) ion. According to the appearance energy of channel (2), the molecular ions will be populated to the excited state S\({}_{6}\). Then, there are three possible dissociation pathways to produce Br\({}^{+}\) ion. For the dissociation pathway (1), electronic wave packets still evolve along the potential energy curve. Then, the molecular ions quickly dissociate along with the C-Br bond. For the dissociation pathway (2), the evolution of electronic wave-packets firstly follows the same route as pathway (1). Due to the strong coupling between states S\({}_{6}\) and S\({}_{5}\) in the range of 2.5-3.0 A, some of these wave-packets can transfer from the upper state down to the lower state, and then the dissociation processes occur along the potential energy curve of state S\({}_{5}\). For the dissociation pathway (3), the prepared wave-packets in  
Figure 4: The angular distribution of the ions Cl\({}^{+}\) (blue solid circle) and Br\({}^{+}\) (red hollow circle) in the lower laser intensity, where the double arrow represents the laser polarization direction. Figure 5: Potential energy curves along with the C-Cl (**a**) and C-Br (**b**) bonds, where G\({}_{0}\) (black solid line) and S\({}_{0}\) (red solid line) represent the ground state of molecule CHBrCl\({}_{2}\) and molecular ion CHBrCl\({}_{2}\)\({}^{+}\), and S\({}_{\rm{x}}\) (x = 1–10, blue and green solid line) represents the excited state x of molecular ion CHBrCl\({}_{2}\)\({}^{+}\), respectively. The pink arrows represent the field excitation processes. the coupling area will oscillate along the potential well of bound state S\({}_{5}\) periodically. Some of the wave-packets' energy exceeds the energy barrier of the S\({}_{5}\) state, which can directly cross the energy barrier. Finally, some of the molecular ions dissociate into fragment Br\({}^{+}\) ion. Due to the existence of multiple dissociation pathways, the dissociation period actually increases, which strengthens the influence of molecular rotation on the angular distribution of ions. Therefore, the angular distribution of Br\({}^{+}\) ions is almost isotropic.
## 4 Design of Proposed Scheme: FAPR
### FAPR Overview  
As mentioned before, our proposed link failure recovery scheme is called flow-aware pro-recover (FAPR). Note that \"flow-aware\" represents that our proposed scheme is \"flow-aware\", which means that it allocates backup paths based on the characteristics of flows. \"Pro-recover\" represents that our proposed scheme combines the advantages of both the \"proactive\" scheme and \"reactive\" scheme. More specifically, \"pro\" refers to the pro-VLAN method [19]. The overall architecture of FAPR is shown in Figure 4, which is divided into two main parts. The proactive part mainly includes the data collection module and the backup path generation module, and the reactive part consists of five modules: data collection, backup path generation, failure detection, flow analysis, and backup path allocation.  
Figure 3: An example of an FAPR scheme.  
In the proactive part, the pro-VLAN method from our previous research is adopted, which will be discussed in detail in the following subsection. In the reactive part, the backup paths are pre-calculated and stored in the controller, which can reduce the delay caused by calculating the backup paths after failures. Moreover, the selection of backup paths in FAPR is more sophisticated. Most current failure recovery schemes only consider the basic usability of backup paths for the affected flows, without taking into account potential issues flows might encounter in the following forwarding process from a global perspective. As stated in Section 3, FAPR assigns the most suitable backup path for each flow affected by the faulty link, and different flows are often assigned to different backup paths, avoiding the phenomenon that all the flows forward along the same backup path after failures. In the architecture, the data collection module collects network information and passes it to the backup path generation module to generate and save backup paths. When a link fault occurs, the failure detection module of the reactive part can detect the malfunction and pass the flow information to the flow analysis module for traffic classification. Finally, the backup path allocation module assigns the backup paths adaptively based on the result of classification and analysis of backup paths. The details of all modules in both reactive and proactive parts will be explained in the following subsections.
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 2 General Aspects of Carbon Dot/Polymer Composites
### Synthesis of Carbon Dot/Polymer Composites  
Although CD sources and syntheses have been extensively reviewed, it is noteworthy to briefly highlight the sources and preparation as a prelude to the topic. CDs can be derived from the following two main sources: (1) natural sources, such as carrots, lemons, and funnel seeds, and (2) synthetic sources, such as, phenylalanine and citric acid [4; 16]. More studies are needed to determine whether the source of CDs affects their effectiveness and properties. Top-down and bottom-up strategies were employed to prepare the CDs (Figure 2). While the top-down approach involves breaking down the carbon material structures (e.g., chemical oxidation process, plasma reactor, microwave, and laser ablation), the bottom-up process involves the carbonizations of tiny molecules to synthesize CDs (e.g., hydrothermal method, solvothermal method, carbonization, and thermal decomposition) [4; 18; 19; 20; 21; 22]. CD/polymer hybrid composite materials are prepared by several methods such as mixing, crosslinking reactions, polymerizations, and thermal treatments [23; 24; 25; 26; 27]. For instance, simple mixing of CD and polymer solutions [24], incorporation of CD into crosslinked polymer networks [25], and interfacial polymerizations with/without the polymers on the thin-film membranes [26; 27] are used to prepare CD/polymer hybrid composite materials.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 1 Introduction
Knowledge distillation is a technique to transfer the information of a network (teacher) to another network (student) by minimizing the gap between the SoftMax outputs of the two networks. In our framework, we use a pretrained original network as the teacher and provide its SoftMax output to a student network being compressed. We summarize our contribution as follows:  
* We propose a new robust weight compression framework for CNNs that uses pruning and knowledge distillation jointly within the adversarial training procedure. Our method is described as an optimization problem which deals with pruning, knowledge distillation, and adversarial training concurrently. * We show that our optimization problem can be solved with the proximal gradient method. Although the popular ADMM approach can also solve our optimization problem, it must keep two auxiliary tensors during optimization which can be a burden for a memory-constrained environment. Our proximal gradient-based approach solves the optimization problem without using any auxiliary tensor. * In experiments, we demonstrated that the knowledge distillation in our framework improves the adversarial robustness of the compressed CNNs. In addition, our method showed a better trade-off between adversarial robustness and compression rate compared to the state-of-the-art methods [15; 19; 22].
## 3 Results
### Expression and Purification of Recombinant DgeKer and DgeKer-C  
_E. coli_ is an ideal strain for protein heterologous expression. In order to obtain purified DgeKer and DgeKer-C (truncated form of the DgeKer without the C-terminal extension), recombinant plasmids carrying keratinase genes without signal peptide were constructed for _E. coli_ expression system. As shown in Figure 3, DgeKer and DgeKer-C were expressed in _E. coli_ BL21-codonplus (DE3) strains and purified successfully. The cloned DgeKer and DgeKer-C have expected molecular weight of 50.0 kDa and 38.4 kDa, respectively. However, the protein bands were detected in the SDS-PAGE after purification by Ni-affinity chromatography and migrated at approximately 38 kDa and 27 kDa, which correspond to the molecular mass of the mature keratinases without N-propeptide. Beside the major bands, protein bands at lower molecular weights (approximately 10-15 kDa) were detected.  
Figure 1: Alignment of the amino acid sequences of DgeKer. The keratinases used were KerA from _B. licheniformis_ PWD-1, MtaKer from _M. taiwanensis_ WR-220, etc. Sequence alignment showing structural elements of DgeKer were generated with ESPript3.The identical residues of all aligned proteins are shaded red, conserved residues are presented in red, and conserved regions are presented in blue boxes. \(\alpha\), \(\beta\), \(\eta\), TT above the sequences represent \(\alpha\) helix, \(\beta\) sheet, 310-helices and \(\beta\)-turns, respectively. Positions of the starting residues of the signal peptide (pre), N-terminal pro-peptide (N-pro), mature protease (mature), and C-terminal pro-peptide (C-pro) are marked by black arrows at the bottom of the sequences.  
Figure 3: Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis of DgeKer (**a**) and DgeKer-C (**b**). (**a**) M, protein MW maker; lane 1, _E. coli_ BL21-codonplus (DE3) harboring pET22b for control; lane 2, pET22b-DgeKer for control; lane 3, DgeKer crude extract; lane 4, purified keratinase DgeKer (arrow). (**b**) M, protein MW maker; lane 1, _E. coli_ BL21-codonplus (DE3) harboring pET22b for control; lane 2, pET22b-DgeKer-C for control; lane 3, DgeKer-C crude extract; lane 4, purified keratinase DgeKer-C (arrow).  
Figure 2: Phylogenetic analysis of amino acid sequence of keratinases. The keratinases used were KerA from _B. licheniformis_ PWD-1, MtaKer from _M. tutiumensis_ WR-220 and other homologous protein based on amino acid sequences. The name and GenBank accession No. for each protease are in front of the Latin name of each strain. The phylogenetic tree derived by the neighbor-joining method. The percentages of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches.
## 3 Results and Discussion
### Analysis of Flow and Heat Transfer Characteristics Under Sloshing Conditions  
#### 3.4.1 Sloshing Analysis  
In this section, the sloshing characteristics are analyzed under a sloshing period of 6 s and an amplitude of 3\({}^{\circ}\). Figure 18 compares the fluid temperature contours under sloshing and static conditions. It is evident that a pronounced thermal stratification exists in the vapor region, while the temperature distribution in the liquid region remains nearly uniform. Notably, under sloshing conditions, the degree of thermal stratification in the vapor region is significantly reduced compared to static conditions. Additionally, under sloshing conditions, the maximum temperature near the bulkhead is relatively lower, and the overall fluid temperature does not exhibit a significant increase.  
The primary reason for these observations is that the sloshing motion induces fluctuations at the vapor-liquid interface, which significantly enhance heat and mass transfer between the vapor and liquid phases. This enhanced interfacial dynamics under sloshing conditions promotes more efficient thermal mixing, thereby reducing thermal stratification and moderating temperature extremes within the vapor region.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 3 Experimental Results
### Simulated Example
#### 3.1.2 Considered Models  
We compare the proposed structured models to their univariate versions in order to study the contribution of the structured prior to the recovery performance. More specifically, we consider:  
* Continuous models: we compare the proposed group sparse model (5) (SMG-block) with univariate models, namely the univariate Student's t distribution (denoted by t-univ) and the Horseshoe prior defined by (A2) for Inverse Gamma and Half Cauchy mixing densities, respectively (denoted by H-univ).  
* Discrete models: we consider the univariate spike-slab that is defined in (A3), where \(\lambda_{k}\) is assumed to follow an Inverse Gamma density and the (unknown) prior probability of being non-zero is the same for all coefficients. We also compare six versions of the proposed structured spike-and-slab, namely _S&S_-1A, _S&S_-1B, _S&S_-2A, _S&S_-2B, _S&S_-3A, and _S&S_-3B, where the number \(i\in\{1,2,3\}\) denotes the prior assumption (from weaker to stronger) given by models (6)-(8), respectively, and the letter \(B\) or \(A\) denotes the use or not of additional prior assumption on energy contribution (i.e., \(\lambda_{k}\) can take any value as for the univariate model for \(A\) and \(\lambda_{k}=\gamma_{q}v_{b}\) for \(B\)).  
Regarding the hyper-parameters of the structured Spike-and-Slab models, we have set the mean \(\mathbf{m}\) to correspond to a prior probability of being non-zero of 0.15 in order to promote ultra-sparse spectrum. We further choose a Gaussian kernel to build the covariance matrix \(\mathbf{R}\) i.e., \(R_{ij}=a_{R}\exp(-b_{R}^{-1}|i-j|^{2})\) with \(a_{R}=4\) and \(b_{R}=0.5\), so that non-zero correlations span over two neighboring bins. It is worth noting that most of structured priors proposed for vibration monitoring deal with group sparse signals, where non-zero coefficients appear to be clustered rather than isolated peaks. To our knowledge, there is no proposed models that take the symmetrical structure of the spectrum of smoothly modulated signals into account, so we are not able to undertake a comparison with other structured models, as they are non-adapted to the structure of our signals.