esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### Experimental Setup and the Evaluation Metrics
For example, for two users \(u_{i}\) and \(u_{j}\), we count the number of recommendations of the corresponding top \(N\) items, \(Rec_{i}(N)\) and \(Rec_{j}(N)\), and further normalize this number using the threshold value \(N\) to obtain the degree of overlap between two sets of recommendations. It is clear that using an approach of higher recommendation diversity result in larger MP. As suggested by Gan and Jiang in Gan and Jiang in [32], we set \(N=20\) in calculating this metric. \[RMP(N)=1-\frac{1}{N}\frac{2}{|L|(|U|-1)}\sum_{1\leq i\leq j\leq|U|}\Big{|}Rec_{ i}(N)\cap\ Rec_{j}(N)\Big{|}, \tag{18}\]  
MN indicates the novelty of recommendations provided to the users. First, it calculates the fraction of users who have ever rated each recommendation, and then computes the sum over all recommendations in \(Rec_{m}(N)\) to obtain the novelty for user \(u_{m}\). Finally, we calculate the average novelty over all users as  
\[MN(N)=-\frac{1}{|U|}\sum_{1\leq m\leq|U|}\sum_{m\in Rec_{m}(N)}log_{2}f_{n}, \tag{19}\]  
where \(f_{n}\) indicates the fraction of users who rated the n-\(th\) item. We also set \(N=20\) in calculating this metric as suggested in [32], and an approach will have a larger MN if it can be used to make newer recommendations.
# High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance
## 4 Discussion  
In this work, we employed the impressive recognition and generalization of convolution neural networks for complex multidimensional data to implement entanglement detection. This classification model achieved an accuracy exceeding 97.5%, making it a reliable tool for determining entanglement solely based on measurement results, without requiring complete information about the quantum states. Despite the impact of noise, this classification model maintains its excellent performance, demonstrating resilience against a certain degree of noise influence. Additionally, the generalization of convolutional neural networks enables this classification model, which was trained on a certain range of quantum states, to a certain extent, to extend to different states, making it an exceptional tool for entanglement detection. The model schemes hold great potential for future applications in the field of entanglement detection, and we are exploring optimization schemes to make the model more effective to higher dimensional systems. Furthermore, although our model has yielded positive results, we recognize that the quantum states we are currently studying have relatively simple density matrix forms. In future research, we can further consider improving the model structure or mixing noisy samples into the training data, aiming to enhance the performance of this noise-resistant model in handling quantum states with greater randomness and expand its applicability.  
Conceptualization, Y.S. and Q.S.; methodology, Q.S. and Z.L.; software, Q.S.; formal analysis, Q.S. and Y.S.; data curation, Y.S.; writing--original draft preparation, Q.S. and Y.S.; writing--review and editing, Z.L. and N.J.; supervision, N.J.; project administration, N.J.; funding acquisition, N.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 12204055 and Fundamental Research Funds for the Central Universities grant number 2243100012.  
Not applicable.  
The data presented in this study are available from the corresponding author on reasonable request due to privacy protection concerns.  
We would like to thank [PERSON] at Beijing Normal University, who provided some space for this research.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional neural network \\ PPT & Positive partial transpose \\ TP & True positive \\ FP & False positive \\ TN & True negative \\ FN & False negative \\ ReLU & Rectified linear unit \\ Acc & Accuracy \\ Pre-Ent & Precision of entangled states \\ Pre-Sep & Precision of separable states \\ Rec-Ent & Recall of entangled states \\ Rec-Sep & Recall of separable states \\ \end{tabular}
# A Highly Glyphosate-Resistant EPSPS Mutant from Laboratory Evolution
## 3 Results
### Kinetic Parameters of the GR79(Y40I)-EPSPS Mutant  
The _GR79-EPSPS_ and _GR79(Y40I)-EPSPS_ genes were cloned into the pET-28a vector and expressed proteins for enzyme assays. The kinetic constants of wild-type GR79-EPSPS and GR79(Y40I)-EPSPS mutants are shown in Table 2. Under the condition of 1 mM substrate, the specific enzyme activities of the GR79(Y40I)-EPSPS mutant and GR79-EPSPS were 17.436 (U)/mg and 14.331 (U)/mg, respectively. The IC\({}_{50}\) value of GR79(Y40I)-EPSPS was approximately twice as high as that of GR79-EPSPS (13.995 mM and 6.8685 mM). The _K_m value of GR79(Y40I)-EPSPS was nearly 1.8-fold lower than that of GR79-EPSPS (22.012 \(\upmu\)M and 39.341 \(\upmu\)M), indicating that the mutant had higher PEP affinities than the wild-type protein. The Ki value of GR79(Y40I)-EPSPS was 1.7-fold that of the wild-type protein (127.343 \(\upmu\)M and 75.360 \(\upmu\)M), indicating that the mutant can tolerate high glyphosate concentrations (Figure 1, Table 2). Ki/Kmis is the crucial value for estimating enzymatic activity in the presence of glyphosate inhibitor because glyphosate competes with PEP for binding in the active site of EPSPS. The Ki/Km value of the GR79(Y40I)-EPSPS mutant was approximately three-fold that of wild-type EPSPS (Table 2), indicating that the 40 th amino acid is one of the critical sites of the substrate-binding enzyme. Since this site has never been reported before, its glyphosate resistance mechanism needs to be further explored.
# Optimizing Field Body Fat Percentage Assessment in Professional Soccer Players
## 2 Materials and Methods
### Body Fat Mass Measurement
#### 2.2.2 Bioelectric Impedance Analysis  
To measure body mass and composition, BIA measurements were taken using a Tanita BC-545N device. Prior to the measurements, participants' palms and soles were wiped with an electrolyte tissue and they were then asked to stand on the Tanita scale, making sure their soles were in contact with the foot electrodes. Prior to measurements, data including sex, age, and height were entered into the device and participants were asked to place the thumb and fingers in the set location and grasp firmly. During %BF measurement, the subject remains motionless to avoid errors in the results. Measurements were repeated twice in each participant, and the means of the two measures entered in the analysis. If the first two measures were farther than 0.05% apart, a third measurement was taken and the median of the three measures was considered in the analysis.
# Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance
## 3 Analysis of Strut Friction Effect on Touchdown Performance Using RSM
### Design Optimization  
Design optimization applies the method of mathematical optimization to design projects, which involves the selection of variables and objectives and the determination of constraints and an optimal value set of variables [26]. The design optimization is carried out to prove that adjusting the friction load can improve the touchdown performance based on the response surface model. The design variables for design optimization are the same as those mentioned in Table 6. According to the RS functions, the overloading of sprung mass in two landing conditions is selected as the optimization objective. The constraint boundary of the design parameters is defined in the design space \(V\) as shown in Table 6. To maintain the comparability of the result, the shock absorber stroke is limited to be smaller than the original value of stroke in Section 2.3. As the two optimization objectives, the multi-objective optimization (MDO) is carried out using the elitist non-dominated sorting genetic algorithm version II (NSGA-II) [27] for the design optimization. The mathematical model of the design optimization can be expressed as  
\[\begin{array}{l}\min\;OL_{LC-1},\;OL_{LC-2}\\ \text{s.t.}\left\{\begin{array}{l}[r_{k},\;L_{d},\;A_{d},\;d_{B},\;L_{b},\; I_{1},\;I_{2},\;I_{w}]\in V,\\ \;S_{LC-1}-S_{LC-1}^{b}\leq 0,\\ \;S_{LC-2}-S_{LC-2}^{b}\leq 0,\end{array}\right.\end{array} \tag{16}\]  
where the superscript \"\(b\)\" denotes the original value of stroke. After the design optimization, the Pareto front of the optimization result fitted by the two optimization goals is shown in Figure 12. The Pareto front is non-convex, which is most obvious at the right end. As these non-convexities do not cause a dominated solution [28; 29], the Pareto optimal solutions in the Pareto front are all non-inferior solutions. Because of the more probability of the LC-1 of all landing conditions, the optimum parameter values of landing gear are selected comprehensively from the left area of the Pareto front as shown in Table 10. The numerical model of the landing gear is modified according to the selected parameter values. Then, the dynamics simulations of the two landing conditions are carried out.  
Figure 12: The Pareto front of two optimization goals.  
Table 11 compares the landing response results before and after design optimization. It can be seen that the max friction load decreased by 12.39% in LC-1, while the shock absorber stroke is at the same level as the original value. The max overloading of sprung mass decreased by 4.84% as the shock-strut efficiency increased by 4.03% in LC-1. The design optimization results in LC-2 are inefficient as the selection of optimum parameter values takes the touchdown performance in LC-1 as the principal object. The touchdown performance in LC-1 still gets some improvement after design optimization, and even the shock-strut efficiency is already at a high level in the original layout of the landing gear.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Windowing Based Methods
#### 3.2.4 Rakshit's Method  
In [129], a new form of adjustable window function combining a tangent hyperbolic function and a weighted cosine series is proposed. In this paper, the modified tangent hyperbolic function is given as  
\[y_{1}=\tan\left\{\frac{n-\frac{N-1}{2}+\cosh^{2}(\alpha)}{B}\right\}\] \[\qquad\qquad\qquad\qquad-\tan\left\{\frac{n-\frac{N-1}{2}-\cosh^{2 }(\alpha)}{B}\right\}, \tag{49}\]  
and the weighted cosine function is expressed as  
\[y_{2}=0.375-0.5\cos\left(\frac{2\pi n}{N-1}\right)+0.125\cos\left(\frac{4\pi n }{N-1}\right), \tag{50}\]  
where \(\alpha\) and \(B\) are the constants, and the symbol \(n=0,1,2,3,\cdots,(N-1)\). Then the new window function can be expressed as  
\[w(n)=\begin{cases}(y_{1}\times y_{2})^{\gamma\frac{1}{5}},&0\leq n\leq N,\\ 0,&\text{otherwise},\end{cases} \tag{51}\]  
where \(\gamma\) is a variable which controls the shapes and frequency response of window function, and \" \(\times\) \" denotes dot product.  
Figure 5 shows the frequency responses of Gaussian window and the new window proposed in [129]. The length of the filters is \(N=65\), and \(B=1\), \(\alpha=2.5\), \(\gamma=1.5\) for the new window in [129]. It can be observed that the new window has much less side-lobe peak level than Gaussian window while its main-lobe width keeps exactly the same.
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 4 Results and Discussion  
### Ship Shore Transmission Network  
This paper takes the network system of CPS of an intelligent ship as an example, as shown in Figure 4. Intelligent ship CPS integrates information communication technology and all function-oriented subsystems to develop a networked, information-based and intelligent integrated monitoring and management system. The network system consists of satellite communication, 4G tracking antenna, 4G array antenna, Beidou antenna and display and control terminal, VEDS communication antenna, external communication management platform, on-site integrated control network, and life network. Multiple business subnets are set up according to different business needs, and safety equipment is used to isolate the subnets to ensure the security of data and business. The interaction between the ship end network system and the vase area network adopts the border security gateway to provide the security protection of the network layer. At the exit of the on-site  
Figure 4: Combined transform.  
integrated control network and living network, the ship shore communication behavior management server provides data transfer and caching, flow monitoring, filtering, user authority management, and other functions. The service function layer is responsible for ship monitoring and business processing, which is the most complex part of the network structure in CPS. The CPS network of the intelligent ship realizes the collection of information resources and equipment management of the whole ship through the interaction of internal application services and external access systems.  
In the network structure in Figure 5, the communication connection between the business subnets in the ship LAN and the communication connection with the WAN are the risk points of the intelligent ship CPS. The data security risks it faces are similar to other IoT systems, such as malware attacks, DDoS attacks, hacker attacks, malicious insiders, etc. At the same time, the lack of a risk assessment scheme for complex business subnet of intelligent ships CPS will lead to exposing potential risk points to attackers [25]. To ensure data security, data encryption algorithms are usually applied in border security gateways or other security protection systems, combined with identity recognition and other mechanisms to ensure that data is not tampered with, stolen and forged by attackers [26].
## 3 Results
### PDE Test Problems and Related Results  
For the three ODE test problems in Section 3.1, there was no significant difference in runtime between the sequential and MGRIT-based parallel-in-time algorithms using eight processors. However, a notable runtime advantage was observed in the case of partial differential equations (PDEs).  
Our test problems for PDEs are special cases of one-dimensional, possibly nonlinear diffusion equations with Dirichlet boundary conditions. Specifically, we consider the following formulation:  
\[\partial_{t}u(t,x) =\frac{1}{10}\partial_{xx}(u^{p}(t,x)),\quad x\in(0,1),\,t\in(0,5) \tag{10}\] \[u(0,x) =g(x),\quad x\in(0,1)\] \[u(t,0) =g(0)\text{ and }u(t,1)=g(1),\quad t\in[0,5],\]  
where \(g(x)=10e^{-(x-0.435576)^{2}}-7.27185\). The linear case corresponds to \(p=1\), while we take \(p=2\) in the nonlinear case. Based on the ODE test results in Section 3.2, we only consider the modified refinement factor \(r_{mod}\) using \(c=0.4\). In space, we use the classical second-order central difference scheme. For time integration, we use a two-stage, stiffly accurate, L-stable, singly diagonally implicit Runge-Kutta method (see [31], Butcher tableau (221), with the choices \(\gamma=(2-\sqrt{2})/2\) and \(\hat{b}_{2}=0\)). When selecting the spatial and temporal step sizes, we make sure that the necessary CFL condition is satisfied, both for \(p=1\) and \(p=2\) in (10). The results related to the tolerance proportionality, achieved accuracy, and runtime can be seen in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem** & **tol** & \(\mathbf{10^{-5}}\) & \(\mathbf{10^{-7}}\) & \(\mathbf{10^{-9}}\) & \(\mathbf{10^{-11}}\) & \(\mathbf{10^{-13}}\) \\ \hline \multirow{3}{*}{(6)} & \(r\) & 128 & 191 & 718 & 3997 & 22,562 \\  & \(r_{mod}\) & 128 & 169 & 620 & 2828 & 13,076 \\  & \(r\) & 218 & 855 & 4237 & 22,371 & 107,982 \\  & \(r_{mod}\) & 184 & 754 & 3449 & 15,963 & 74,047 \\  & \(r\) & 931 & 3777 & 20,432 & 100,043 & 475,284 \\  & \(r_{mod}\) & 773 & 3070 & 14,090 & 65,258 & 302,692 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of time steps after the final refinement for different tol values, using refinement factors \(r\) and \(r_{mod}\), for the ODE test problems (6)–(8).  
Figure 13 demonstrates that both the linear and nonlinear diffusion Equation (10) show good tolerance proportionality and computational stability, as well as the expected qualitative behavior of achieved accuracy versus tolerance. In addition, the expected reduction in runtime due to parallelization is also observed.
# Regeneration of Articular Cartilage Using Membranes of Polyester Scaffolds in a Rabbit Model
## 2 Materials and Methods
### Methods
#### 2.2.1 Implantation  
All surgical procedures were performed under aseptic conditions of the operating room at the Animal Breeding Laboratory of the Medical University of Warsaw. The surgical operations were performed according to the scheme (Figure 3):  
1. We administered general anesthesia intramuscularly (xylazine and ketamine--dose calculated according to the animal's body weight; ketamine--0.4 mg/kg, xylazine 0.5 mg/kg).
2. We shaved the area to be operated.
3. We placed the animal on the side opposite the one to be operated, with the limb in abduction. Skin decontamination with iodine solution. We covered the operating area with sterile drapes, which were attached to the skin.
4. We prepared the scaffold (membrane \"PVP\" or \"Z\"--depending on group). Under sterile conditions, we removed the membrane immersed in alcohol in transport packaging and placed the membrane in sterile petri dishes filled with a 0.9% NaCl solution. Rinsed the membranes twice at 10-min intervals with a 0.9% NaCl solution (to get rid of alcohol). For each animal, membranes were prepared in a separate petri dish. This point was omitted in the control group (where no membrane was used).
5. Skin incision on the lateral side at the level of the knee joint, then the incision of subcutaneous tissue and the joint capsule. Dislocation of the kneecap to the medial side, providing access to the articular surface of the femur. We created 2 defects located symmetrically at both condyles (each condyle had one defect) of the femur (load bearing area), using a chisel (Figure 3). Defect (0.1 cm\({}^{2}\)) of cartilage and bone to a depth of 3 mm. Confirming a full-depth defect penetrating the bone marrow by the occurrence of bleeding from the base of the defects.
6. We rinsed the defects with sterile 0.9% NaCl solution and dried them (to get rid of any debris). We cut a scaffold to the size of the defect and inserted a scaffold inside the defect so that the more porous surface of the membrane was in direct contact with the bone marrow.
7. After filling the defects with membranes, we applied the tissue glue (TisselLyo) to the surface of the defects according to the manufacturer's instructions.
8. We waited for the adhesive to bond with the surrounding tissues. We moved back the kneecap into the correct position. We sutured the articular capsule with a Vicryl 3/0 suture, subcutaneous suture--Vicryl 4/0, skin suture--Ethilon 4/0 (to be removed in 14 days). We covered the postoperative wound with iodoform. Sterile dressing. We applied soft dressing from the ankle to the groin for 24 h.
9. Postoperative administration of antibiotics (Enrofloxacin) and analgesics (Metamizole) for 2 days.
10. Due to animal welfare reasons, we operated the second knees of the animals after the previously planned time (8 or 16 weeks, depending on the group).
## 1 Introduction
The present study proposes a sliding mode control design based on the penalty approach and actuated by shape memory alloys to stabilize a flexible bar system in the presence of uncertainties. The control law is based on the system model which includes the one-input multi-output equation of motion of the flexible bar and the mathematical model of the shape memory alloy. The equation of motion of the system is obtained using a physical discretization approach. Moreover, the SMA actuation is described using Choi model [23], based on the step-input response of the SMA actuator. Although other models have been proposed to describe the dynamic behavior of shape memory alloys [24; 25; 26], Choi model is selected due to the description of the force-current relationship of the SMA actuator in a straightforward way which is computationally efficient (The shape recovery of SMA happens under temperature control and highly depend on potential alloys materials (such as NiTi, Cu-based and some others), as well on compositions, transformation temperatures (depending on the composition), ambient temperatures and so on. Obviously, the considered controlled model depends on the variations of the parameter at the heating and cooling stages. These changes of variation are considered here as uncertainty which can be suppressed using sliding mode approach.). In summarize, the novelty of this work, compared to previous literature, consists in two points: the complete knowledge of the parameters of the considered model (especially the parameters of the shape memory alloy) is not required as the sliding mode control (SMC) is applied to overcome this trouble; the state variables in the desired regime are connected by a set of algebraic constrains, which make it impossible to apply direct application of SMC; therefore a special Lyapunov-like function, including the penalty term, is introduced to realize the desired dynamics.
## 2 Antenna Structure
### Performance Enhancement  
Although [PERSON]'s procedure can be effectively extended to design a 2-D SIW SAA, all mutual couplings, particularly the \(E\)-plane external mutual couplings, are only taken into account at the design frequency. Consequently, for slot arrays with wide bandwidths, the external mutual coupling in the \(E\)-plane is inaccurately accounted for at off-design frequencies, requiring time-consuming optimizations using commercial full-wave simulation software. In our design, for simplicity, we configured the 6 \(\times\) 16 slot array by extending the 1 \(\times\) 16 slot array directly. Then, to reduce the \(E\)-plane external mutual coupling between adjacent linear slot arrays, metallic decoupling walls were implemented. As shown in Figure 6a, the decoupling walls have a width of 5 mm and a height of 3 mm and are arranged in \(Y\!OZ\)-plane (\(E\)-plane) with a distance of 14.54 mm. Figure 6b illustrates the simulated \(|S_{11}|\) and the broadside realized gains of the proposed antenna with and without decoupling walls. As observed, the impedance matching (for \(|S_{11}|\leq-10\) dB) is improved in the frequency range of 9.56 to 9.83 GHz owing to the \(E\)-plane external mutual coupling reduction. The proposed antenna with decoupling walls exhibits a \(-10\) dB reflection bandwidth ranging from 9.56 to 10.28 GHz (7.26%), which is wider than that of the antenna without a decoupling wall (only 9.83-10.31 GHz (4.77%)). Moreover, the broadside gain of the proposed antenna is also enhanced by adding the decoupling walls. A maximum gain enhancement of approximately 0.61 dBi is achieved at 9.8 GHz.