esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Study of the Mechanical and Tribological Properties of NiCr-Al Detonation Coatings
###### Abstract  
This article presents a comprehensive study of the mechanical and tribological properties of detonation coatings in the NiCr-Al system. Using the detonation spraying technology, NiCr-Al homogeneous (HC) and gradient coatings (GCs) were produced, and their characteristics were determined. Modern analytical instruments were used in the course of the study. The results showed that the microhardness of the NiCr-Al GC was approximately 30% higher compared to the NiCr-Al HC. According to nanoindentation results, the elasticity modulus and nanohardness of the NiCr-Al GC were twice as high as those of the NiCr-Al homogeneous coating. Tribological tests conducted using the rotational ball-on-disk contact geometry showed that the wear rate of the NiCr-Al GC was significantly lower, while the friction coefficients of both coatings were approximately similar. According to the adhesion strength tests, the strength of the NiCr-Al GC was recorded at 38.7 \(\pm\) 6.9 MPa, while that of the NiCr-Al HC was approximately 25.4 \(\pm\) 3.1 MPa. High-temperature tribological tests revealed that the wear resistance of the NiCr-Al GC was 2.5 times higher than that of the NiCr-Al HC. The conducted studies demonstrated that the coating structure, particularly the distribution of elements, has a significant influence on its mechanical and tribological properties. Overall, the NiCr-Al GC exhibited superior mechanical and tribological performance.  
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Footnote †: journal: Applied Sciences  
Article
## 5 Neural Control Strategies
### Model Reference Control
With this diagram, we simulated the responses for the three references (step, sawtooth, and sinusoidal signals) for the model reference and internal model controllers, as shown in Figure 29. We observe that both controllers have a similar response to the different references, except in the case of the step, where the model reference controller has less oscillation around the reference value. Analyzing the performance indexes ISE, IAE, and ITAE of Table 13 we clear see the better performance of the model reference controller. Figure 30 shows the control signal applied to the system by the model reference controller for the sinusoidal reference. We verify that the control signal has little noise except when the disc is further away from the reference, requiring more current. An important point to notice is that this controller has almost no steady-state error without using an adaptive block unlike the internal model. This is given by the fact that the network was trained to make the system follow the reference and not to produce a control signal for a given reference. Due to the high computing power needed by both the internal model and model reference controllers, it was not possible to implement them in real-time in the Arduino with the Simulink Support Package for the Arduino Hardware Add-on. We verified that the clock frequency of the microcontroller was not high enough to compute the control cycles at the required \(T=0.003\) s, even though the Simulink Add-on deployed the code in the microcontroller, meaning the ATmega2560 had enough memory. However, due to the good performance of the identification network demonstrated in the experiments, it is expected that the response of the real system would be similar to the simulated ones. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Step** & **Sine** & **Saw** & **Total** \\ \hline \multirow{2}{*}{ISE} & Internal Model & 0.4950 & 0.2193 & 0.2446 & 0.9589 \\ \cline{2-6}  & Model Reference & 0.0786 & 0.1273 & 0.1447 & 0.3506 \\ \hline \multirow{2}{*}{IAE} & Internal Model & 3.9383 & 2.4808 & 2.4100 & 8.8291 \\ \cline{2-6}  & Model Reference & 0.9126 & 1.6756 & 1.3950 & 3.9832 \\ \hline \multirow{2}{*}{ITAE} & Internal Model & 76.4735 & 49.0349 & 48.2459 & 173.7543 \\ \cline{2-6}  & Model Reference & 16.8913 & 32.2026 & 27.2355 & 76.3294 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: ISE, IAE, and ITAE values for the internal model and the model reference controller. Figure 29: Comparison of the system responses with the internal model and reference model controllers.
# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 3 Overview of the Proposed Method
### Camera Pose Optimization  
Due to the motion error of the manipulator, the position of the eye-in-hand camera will deviate from the target position. If the point cloud of the current frame is directly spliced into the map, it will lead to point cloud model misalignment, so the registration method is necessary to be employed to optimize the camera pose and the SICP method is applied to calculate the camera pose deviation.  
Supposing that the point cloud of the current frame in the camera coordinate system is \(P_{0}^{c}\), the point cloud in the world coordinate system is \(P^{w}\), the transform matrix at the end of the manipulator of thecurrent frame relative to the world coordinate system is \(T_{w,t}\), the transform matrix of the camera relative to the end of manipulator is \(T_{t,c}\), the transform matrix of the current frame in the world coordinate system is \(T_{w,c}=T_{w,t}T_{t,c}\). After being transformed by \(T_{w,c}\), the current frame object point cloud matches with the map point cloud by the SICP algorithm \(P^{w}\) to obtain the optimization transformation \(T_{ICP}\), and the point cloud \(P^{w}_{0}\) of current frame object after compensation in the world coordinate system is:  
\[P^{w}_{0}=T_{ICP}T_{w,t}T_{t,c}P^{c}_{0} \tag{4}\]
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 3 Methodology  
The framework of this paper is shown in Figure 2. Firstly, static data \(D\) is used to train Model1, obtaining the outputs of Modell \(a^{(L)}\) as training data to be passed to Model3. After training is completed, the trained weights \(W,b\) are transferred to Model2, which is initialized with those weights. Then, the merged Model2 processes the stream data \(S\) to obtain a weight list \(o^{(L)}\), which is passed as test data to Model3. Model3 uses the previously obtained training data \(a^{(L)}\) to train the model, calculate the training error \(e_{a}\), and compute a threshold as the basis for the 3\(\sigma\) judgment criterion. Finally, the loss \(e_{s}\) obtained from the test dataset \(o^{(L)}\) is combined with the 3\(\sigma\) criterion to determine the occurrence of concept drift. For the DNN+AE-DD drift detection model, Model1 and Model2 are implemented using DNN networks, while Model3 is implemented using an autoencoder. Algorithm 1 outlines the overall process of the DNN+AE-DD.  
This section divides the work of this paper into three stages. Section 3.1 provides a detailed explanation of the pretraining process, including the model's inputs and outputs during training. The handling of stream data and the process of weight replication are discussed in Section 3.2. Finally, Section 3.3 focuses on the core contribution of this paper: the use of autoencoders in conjunction with the 3\(\sigma\) principle for anomaly detection.  
Figure 2: The overall workflow.  
```
0: Non-drift data \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{m},y_{m})\}\), Stream data \(S=\{(x_{1},y_{1}),(x_{2},y_{2}),(x_{3},y_{3}),\ldots,(x_{t},y_{t}),\ldots\}\).
0: Drift position.
1: Processing non-drift data \(D\) with Model1 according to Algorithm 2 and generate train data \(a^{(L)}\).
2: Utilize stream data S and weights \(W\), \(b\) from Model1 to initialize the Model2 according to the Algorithm 3 and generate test data \(o^{(L)}\).
3: Utilize the train data \(a^{(L)}\) from Model1 to train Model3 and obtain reconstruction error \(e_{a}\) to provide threshold for \(3\sigma\) rules.
4: Utilize the test data \(o^{(L)}\) from Model2 and obtain reconstruction error \(e_{s}\) integrate \(3\sigma\) rules obtain drift position (to see the details in Algorithm 4).
```  
**Algorithm 1** DNN+AE-DD operational workflow
## 3 Analysis of Numerical Simulation Result
### Determination of Optimal Operating Condition  
The simulation results could then be comprehensively evaluated to determine the TBM's optimal operating conditions at the fixed advance rate with 20 mm/min. As the EPB shield TBM must balance the pressure conditions at the tunnel face during excavation, the number of particles in the chamber and the surface pressure in the chamber were selected as evaluation indices. The SD of the ratio of compressive force and torque was also chosen as an evaluation index to reflect the basic operational management of TBM. Based on these three indices, Table 10 ranks the top five operating conditions.  
Figure 23: Particle mass flow rate of screw conveyor during simulation with respect to (**a**) the TBM’s advance and (**b**) screw conveyor and cutterhead RPM.  
Figure 24: Average driving power of the screw conveyor with respect to (**a**) the TBM’s advances and (**b**) the screw conveyor RPM.  
In terms of pressure balance in the chamber, Case 7 with cutterhead rotation at 2.0 rev/min and screw conveyor rotation at 12.0 rev/min was best (average, 2.5). However, the driving power of the screw conveyor exceeded its maximum capacity of 75 kW at very few points when the screw conveyor rotated at 12.0 rev/min. The screw conveyor was thus likely to break as it exceeded its maximum capacity. Therefore, it was necessary to slow the screw below 12.0 rev/min under the given simulation conditions. Without rotating the screw conveyor at 12.0 rev/min, Case 5 (1.5 rpm cutterhead rotation and 9.0 rev/min screw conveyor rotation) was best in terms of pressure balance in the chamber.  
Regarding fluctuation of the ratio of the compressive force to torque, Case 11 was best. However, the driving power of the cutterhead exceeded the maximum capacity (1440 kW) at 3.0 rev/min cutterhead rotation. Considering operational management of the TBM, Case 5 (1.5 rev/min cutterhead rotation and 9.0 rev/min screw conveyor rotation) was also best at the fixed advance rate with 20 mm/min.  
As a result, it was concluded that the operating condition with 1.5 rev/min cutterhead rotation and 9.0 rev/min screw conveyor rotation was the most optimal for TBM operation at the fixed advance rate with 20 mm/min in this TBM simulation.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
###### Abstract  
Session initiation protocol (SIP) is the most widely used application layer control protocol for creating, modifying, and terminating session processes. Many authentication schemes have been proposed for SIP aimed at providing secure communication. Recently, a new authentication and key agreement scheme for SIP has been proposed, and it was claimed that it could resist a variety of attacks. However, in this paper, we show that this scheme is vulnerable to an offline password guessing attack and a stolen memory device attack. Furthermore, we show that it lacks the verification mechanism for a wrong password, and that the password updating process is not efficient. To mitigate the flaws and inefficiencies of this scheme, we design a new robust mutual authentication with a key agreement scheme for SIP. A security analysis revealed that our proposed scheme was robust to several kinds of attacks. In addition, the proposed scheme was simulated by the automatic cryptographic protocol tool ProVerif. A performance analysis showed that our proposed scheme was superior to other related schemes.  
key agreement; mutual authentication; SIP; cryptanalysis Article
## 4 Conclusions  
1. The results of the conducted experiments were generalized as collision regime maps for droplets and solid particles in the systems of \(B\)(We) and We(Oh) coordinates. Ag-glameration and stretching separation regimes were distinguished. It was established how the liquid properties shift the transition boundaries between the collision regimes on the \(B\)(We) and We(Oh) maps. Approximations for a mathematical description of the transition boundaries between the agglomeration and stretching separation regimes were obtained. They took the form: \(B=\rm a\cdot We^{2}+b\cdot We+c\), \(\rm We=a\cdot Oh+c\).
2. It was established that an increase in the liquid viscosity from 1.4 to 6.3 mPa-s increases the critical Weber number by 20-30% between the agglomeration and stretching separation regimes, and decreases the ratio of the free surface areas after and before the droplet-particle collision almost by 40%. A decrease in the surface tension from 72.69 to 36.1 mN/m shifts the boundary between agglomeration and stretching separation towards 25% lower We, as well as increases the ratios of the free surface areas after and before the droplet-particle collision by 45%. A decrease in the interfacial tension from 42.57 to 3.41 mN/m increases the ratios of the free surface areas after and before the collision of a droplet with a solid particle by almost 40% and decreases the critical Weber numbers for stretching separation by 20-30%.
3. The results are of interest for the development of secondary atomization technologies. They can be used to predict the conditions that are sufficient for the transition between the agglomeration and stretching separation regimes for liquids with varying dynamic viscosities, densities, surface and interfacial tensions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122110747/s1](https://www.mdpi.com/article/10.3390/app122110747/s1)]([https://www.mdpi.com/article/10.3390/app122110747/s1](https://www.mdpi.com/article/10.3390/app122110747/s1)), Video S1: Video-grams of a droplet-particle collision in the stretching separation regime when varying the liquid viscosity, Video S2: Video-grams of a droplet-particle collision in the stretching separation regime when varying the liquid surface tension; Video S3: Images of a droplet-particle collision in the stretching separation regime when varying the interfacial tension of liquid.  
Conceptualization, G.K. and N.S.; methodology, N.S. and A.I.; investigation, P.T., N.S. and A.I.; writing--original draft preparation, A.I, N.S., P.T. and G.K.; writing--review and editing, A.I., N.S., P.T. and G.K.; funding acquisition, P.T., N.S. and A.I. All authors have read and agreed to the published version of the manuscript.  
The Research was supported by the Russian Science Foundation (project 18-71-10002-\(\pi\), [[https://rscf.ru/en/project/21-71-03001/](https://rscf.ru/en/project/21-71-03001/)]([https://rscf.ru/en/project/21-71-03001/](https://rscf.ru/en/project/21-71-03001/)), accessed on 30 July 2021).  
The authors declare no conflict of interest.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 2 An Overview on Multi-Objective Optimization and Multi-Objective Evolutionary Computation
### Multi-Objective Problem  
A MOP can be formulated as follows in Equation (1).  
\[\max F(x)=(f_{1}(x),\cdots,f_{m}(x))^{T}\quad x\in\Omega. \tag{1}\]In Equation (1), \(\Omega\) is a decision space or a parameter space, is composed of \(m\) objective functions, and is called the objective space. However, as most objectives tend to conflict with each other, it is not possible to maximize all objectives simultaneously at any point in \(\Omega\). Consequently, a balance was sought between these conflicting objectives.  
As opposed to single-objective optimization problems, which typically have a single optimal solution that can be found using standard deterministic mathematical methods, MOP involves multiple competing objectives [45]. In these types of problems, finding a single solution that optimizes all objectives at the same time is difficult because improving the performance of one objective may come at the cost of decreasing the performance of another. In contrast, the solution to a MOP is typically a Pareto solution set, which represents weighing up conflicting objectives [56].  
When there are multiple Pareto optimal solutions, it can be challenging to determine which solution is more desirable without additional details. As a result, all Pareto optimal solutions can be considered equally valid. Consequently, finding the most Pareto optimum solutions to a given optimization problem is the primary subject of multi-objective optimization [48]. To achieve this, multi-objective optimization typically focuses on the following two tasks.  
* Identify a set of solutions that are as near in terms of probability to the Pareto optimal domain.
* Identify a set of solutions that are as diverse as possible.  
The first task must be completed in any optimization task, and a convergence that is not close to one of the real Pareto optimal solution sets is undesirable. Only when a group of solutions has convergence that is close to the real Pareto optimal solution is the set of solutions guaranteed to approach the optimal property [57].  
In addition to converging to the approximate Pareto optimal region, the solutions obtained through multi-objective optimization should also be uniformly and sparsely distributed within that region. A good set of solutions for multiple objectives is represented by a diverse set of solutions. In an MOEA, diversity can be defined in both the decision variable space and the objective space [58]. For example, if the Euclidean distance between two solutions is large in the decision variable space, they are considered different in that space. Similarly, solutions that are far apart in the objective space are considered to be diverse. However, for complex and nonlinear optimization problems, good diversity in one space does not necessarily translate to good diversity in the other space. A set of solutions with good diversity must be identified in the relevant space [49].
## 2 Materials and Methods
### Software Tool Effectiveness Assessment
#### 3.2.1 Pedagogical Experiment for Student Success
The results of the ANCOVA show robust evidence that the proposed interactive software-based training method significantly affects the performance of the students (\(p<0.05\)). The overall model accounts for 30.2% of the variation in test scores, which means that although other external variables could affect learning outcomes, the chosen independent variables contribute significantly to determining the success of the students. The other key predictor of final performance is students' pre-existing knowledge, as indicated by their pre-training grades, which accounts for 13.9% of the variance. This indicates that students who have a better knowledge base are more likely to achieve greater levels of performance post-training irrespective of the instructional method. Nevertheless, the experimental group, while their pre-existing knowledge differed, still maintained significantly higher final scores, thereby substantiating the additional advantage offered by the software tool. Gender accounted for 5% of the variance in test scores, suggesting an influence on learning outcomes. Care must be taken, however, to interpret this finding since the study had a disproportionate male and female sample in the various groups. This may cause bias in the results, making additional research with a balanced sample needed to see if gender differences still hold across various settings. It is interesting to note that the training year was not identified as a statistically significant factor, which suggests that variations between groups of students or potential variations of curriculum across years did not have a detectable impact on learning outcomes. This stability over several years also adds to the validity of the proposed methodology. The most important finding was that the training method (control vs. experimental group) was a determining factor in student success. The use of the interactive software explained 4.4% of the variance in test scores, thus depicting a noteworthy effect on learning efficiency. Experimental group students outperformed their counterparts in the control group consistently, as per the analysis of results. This variation in performance indicates the effectiveness of the software tool in establishing theoretical concepts and strengthening problem-solving skills in coding theory. Figure 17: Estimated Marginal Means of Hamming Code (General Algorithm) Average Test Scores.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process
#### 3.1.4 Edge Model Aggregation  
To address the straggler effect in the synchronous FL framework, our approach introduces asynchronous aggregation. In this method, each edge server independently performs edge model aggregation using the collected model parameters and then uploads the aggregated parameters to the cloud server. It is unnecessary to wait for the model parameters from all the participating clients; thus, asynchronous aggregation can significantly reduce waiting time and potential delays caused by slow clients.  
However, a potential challenge lies in the possibility of clients uploading outdated parameters. To address the challenge, we introduce an obsolescence function as a hyperparameter to gradually reduce the weight of outdated parameters during the aggregation process. The obsolescence function of each client is determined by the current round of cloud aggregation, denoted as \(t\), and the round of model uploaded from client \(i\), denoted as \(t^{\prime}\). The function for the uploaded parameters of client \(i\) can be expressed as \(H_{i}^{(t)}=\lambda^{t-t^{\prime}}\), where \(\lambda\in(0,1)\) is an attenuation coefficient, which makes the influence of outdated parameters diminish over time.  
The model aggregation on edge server \(j\) can be expressed as  
\[\mathbf{\omega}_{j}^{(t)}=\frac{\sum_{i\in S_{j}^{(t)}}H_{i}^{(t)}|D_{i}|\mathbf{ \omega}_{i}^{(t)}}{\sum_{i\in S_{j}^{(t)}}H_{i}^{(t)}|D_{i}|}, \tag{4}\]  
where \(S_{j}^{(t)}\) is the set of clients associated with edge server \(j\) that participates in edge aggregation in round \(t\).
# Military Equipment Entity Extraction Based on Large Language Model
## 4 Experimental Results
### Datasets  
In the context of publicly available datasets, this study selected widely used datasets including CoNLL03, OntoNotes5.0, ACE2004, and ACE2005. Here is a brief description of each dataset:  
**CoNLL03**[21]: An English dataset that includes entities categorized into four types: locations, organizations, person names, and miscellaneous.  
**OntoNotes5.0**[22]: An English dataset comprising text from various sources. It includes entities classified into eighteen categories, which encompass eleven text categories (such as person names, organizations, etc.) and seven numeric categories (such as dates, percentages, etc.).  
**ACE2004**[23]** **and ACE2005**[24]: Each of the two datasets contains seven entity categories, with annotations indicating their locations in the text and corresponding head words.  
The military equipment dataset originates from unstructured text data on ships and aircrafts from a global military website, manually annotated to construct a dataset of 3000 high-quality military equipment records. This dataset encompasses twelve entity categories, including nine equipment categories and three others. The corresponding relationship between entity categories is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Category** \\ \hline \hline  & Ship \\  & Aircraft \\  & Missile \\  & Shells \\ Equipment & Radar \\  & System \\  & Submarine \\  & Torpedo \\  & Engine \\ \hline  & Person \\  & Location \\  & Country \\ \hline \hline \end{tabular}
\end{table}
Table 1: Entity category relationship correspondence.  
The dataset is partitioned into training, validation, and test sets in a 7:2:1 ratio. Following international conventions, submarine and ship categories are treated as parallel categories. Statistical information about this experimental dataset is provided in Table 2.