esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Data Set 1 Benchmarking Instances  
Table 3 shows the performance comparisons against those by the MILP model and LAHC heuristic of [PERSON] [18] and Genetic Algorithm and Tabu Search heuristic (GATS) of [PERSON] [13] and MSB of [PERSON] [14].  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{Characteristics} & \multicolumn{4}{c}{**2-Phase**} & \multicolumn{4}{c}{**MILP**} & \multicolumn{4}{c}{**LAHC (1000)**} & \multicolumn{4}{c}{**LAHC (100)**} & \multicolumn{4}{c}{**MSB**} & \multicolumn{1}{c}{**GATS**} \\ \hline
**Instance** & \(M\) & \(J\) & \(O\) & \(A\) & **Nodes** & **CAXX** & \(\sigma\) \% & \(\overline{T}\) & \(T_{\text{\emph{medium}}}\) & \(Iter_{\text{\emph{best}}}\) & \(C_{\text{\emph{MAX}}}\)  
The two-phase heuristic obtained six of the optimal solutions provided by the MILP model of [PERSON] [18] while providing solutions that are at least as good as those by LAHC or GATS or MSB.  
While the two-phase heuristic does not dominate in terms of solution quality, it is more robust than LAHC, as evidenced by having zero-percentage makespan standard deviation.  
As mentioned in [PERSON] and [PERSON] [18], the MILP model solution time is directly dependent on the number of operations of each instance. This is also observed in the two-phase heuristic--which employs the solver to solve each of the two MILP subproblems--where fjsp7 and fjsp10 require the most time and where fjsp10 is ran for 100 iterations.  
The processor used for this work is slightly slower than that used by [PERSON] and [PERSON] [18], as seen from the CPU marks reported in PassMark(r) CPU marks [33]. Comparing both the MILP model timings and that of the two-phase heuristic for instances attaining optimality, it is observed that the latter is much faster than the former. This supports the usage of the decomposition approach as opposed to the monolithic approach.
## 3 Finite-Time ANFTSMC Design
### Proof of Stability  
**Theorem 1**. : _For the translation subsystem (14a) and the nonsingular fast terminal sliding-mode surface (19a) under consideration, if the controller and the adaptive law of the translation system are selected as (24a) and (26), respectively, the system converges to the sliding-mode surface in a finite time, and, finally, the tracking errors of the outer loop converge to zero in a finite time._  
Proof of Theorem 1.: In order to prove Theorem 1, the following Lyapunov function is selected for the translation subsystem:  
where \(\rho_{\Theta}=min\Big{\{}\sqrt{2}\rho_{1},\sqrt{2}\rho_{3},\sqrt{2}\rho_{5},\sqrt{2 \mu_{i1}/\gamma_{i1}\beta_{1}b_{2}}\rho_{i1},\sqrt{2\mu_{i3}/\gamma_{i3}\beta_{ 3}b_{4}}\rho_{i3}\,,\sqrt{2\mu_{i5}/\gamma_{i5}\beta_{5}b_{6}}\rho_{i5}\Big{\}}\). \(\square\)  
In this part, a translation subsystem controller based on the ANFSMC algorithm is designed. It can be seen from the above Lyapunov analysis that the finite-time stability of the outer ring can be guaranteed. Similarly, the finite-time stability of the attitude loop is proven in the following sections. **Theorem 2**. : _Considering the coaxial HAUIV's rotation subsystem (14b), if the controller and adaptive law are designed as Equations (24b) and (25), attitude tracking errors converge to zero in a finite time._  
**Proof. ** Next, the Lyapunov function of the rotation subsystem is determined as shown below:  
\[ \begin{array}{ll}V_{A}&=\frac{1}{2}\mathbf{S}_{A}^{\mathrm{T}}S_{A}+\sum \limits_{i=0}^{2}\frac{\gamma_{i1}\beta_{1}b_{2}}{2\mu_{i1}}\tilde{a}_{i1}^{2} +\sum\limits_{i=0}^{2}\frac{\gamma_{i2}\beta_{3}b_{4}}{2\mu_{3}}\tilde{a}_{i3 }^{2}+\sum\limits_{i=0}^{2}\frac{\gamma_{i2}\beta_{5}b_{6}}{2\mu_{3}}\tilde{a}_ {i5}^{2}\\ &=\frac{1}{2}s_{1}^{2}+\frac{1}{2}s_{3}^{2}+\frac{1}{2}s_{5}^{2}+\sum\limits_ {i=0}^{2}\frac{\gamma_{i2}\beta_{1}b_{2}}{2\mu_{i1}}\tilde{a}_{i1}^{2}+\sum \limits_{i=0}^{2}\frac{\gamma_{i2}\beta_{3}b_{4}}{2\mu_{3}}\tilde{a}_{i3}^{2}+ \sum\limits_{i=0}^{2}\frac{\gamma_{i2}\beta_{5}b_{6}}{2\mu_{3}}\tilde{a}_{i5} ^{2}\end{array}  \tag{35}\]  
By calculating the derivative of Equation (35) and substituting the controller Equation (24b) of the rotation subsystem, we can obtain the following:  
\[
## 3 Analysis of Torque Load Transfer Mechanism
### Influence of Bottom Flange Width on Foundation Ring Bottom  
Using the same analysis method, the torque load transfer process of specimen ZT-3, with reduced flange width and thickness of the foundation ring bottom, was analyzed. Under various loads, the stress changes at the measurement points on the planes above or under the opening of specimen ZT-3 were obtained, as shown in Figure 12.  
Using the simplified analysis method, the force in the top radial reinforcement and the foundation ring of specimen ZT-3 during the whole loading process was analyzed, as shown in Figure 13.  
The torque load is still mainly sustained by the foundation ring, which is the same as those for ZT-1 and ZT-2. The sharing ratio of the foundation ring fluctuates at the early stage and tends to be stable afterward. Under 0.6-1.0 \(T\), about 90% of the torsional load is sustained by the foundation ring. After 1.2 \(T\), this ratio changes to about 78%.  
After the width and thickness of the flange on the foundation ring bottom were both reduced by 13%, the load transfer process and mechanism of the torsional load of the foundation ring were not obviously affected, and the torque load was still mainly sustained and transferred by the foundation  
Figure 12: Torsional shear stress on the foundation ring of ZT-3.  
ring. The top radial reinforcements tend to be stable after 1.0 \(T\), and the torque load sharing ratio is about 22%.
## 4 Experimental Results and Analysis
### Differential Privacy Impact on Deep Learning Training Process  
We analysed the effects of the battery ageing model without and with different levels of data protection using differential privacy. The battery ageing model was trained to predict the gradients (i.e., the rate of change) of battery capacity based on the training data. Figure 4 shows the loss of mean squared error (MSE) over 20 epochs for three different training scenarios in a transformer-based model.  
In the first scenario (a), shown by the blue line, the model is evaluated without using the DP mechanism. The MSE loss decreases smoothly and reaches almost zero at the end of training, indicating a good performance of the model without privacy constraints. In the second scenario (b), we investigate the behaviour of the model using DP with the noise multiplier 1.1, represented by the green line. We chose the noise multiplier 1.1 that is noise with standard deviation 1.1 times the gradient clipping norm because it represents a moderate level of noise added to the gradients: high enough to provide privacy, but not so high that it destroys the learning signal. With the noise multiplier 1.1, we still protect individual training sequences (battery degradation profile) and allow our model to learn meaningful ageing dynamics. The loss rises from 0.01 to 0.05 MSE in the worst case, compared to the non-DP model, and the curve shows a more oscillating pattern and slower convergence. This is to be expected due to the noise that makes the gradients stochastic  
Figure 4: MSE loses with respect to the epoch of the training without and with differential privacy.  
and unstable. In the third scenario (c), we increase the noise level in the model with a noise multiplier of 1.15, which is represented by the orange line. This leads to a higher variance of the noise added to the gradients and more gradient perturbations during training.
## 4 Empirical Results
### Results of Hypotheses Testing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model 3**} & \multicolumn{2}{c}{**Model 4**} \\  & \multicolumn{2}{c}{**Healthiness**} & \multicolumn{2}{c}{**Intention to purchase**} \\ \cline{2-5}  & \(\beta\) & **t Value** & \(\beta\) & **t Value** \\ \hline Constant & \(-0.948\) & \(-1.17\) & \(-0.080\) & \(-0.39\) \\ \hline Brand trust & \(0.922\) & \(6.85\)* & \(0.496\) & \(7.89\)* \\ \hline Nutrition disclosure & \(0.847\) & \(3.79\)* & & \\ \hline Interaction & \(-0.130\) & \(-2.28\)* & & \\ \hline Healthiness & & & \(0.528\) & \(9.17\)* \\ \hline F-value & \(83.43\)* & & \(191.15\)* & \\ \hline R\({}^{2}\) & \(0.4540\) & & \(0.4611\) & \\ \hline Conditional effect of the & & & & \\ focal predictor & & & & \\ \hline Nutrition disclosure & & & & \\ \(3.00\) & \(0.532\) & \(7.08\)* & & \\ \(3.75\) & \(0.435\) & \(7.02\)* & & \\ \(4.75\) & \(0.305\) & \(3.62\)* & & \\ \hline Index of mediated & & & & \\ \(\text{moderation}\) & & & & \\ \hline \multicolumn{5}{c}{\(-0.0687\)*} & \(-0.1346\) & \(-0.0067\) \\ \hline \end{tabular} Note: * \(p<0.05\), interaction: brand trust \(\times\) nutrition disclosure (test of interaction: F = 5.20)*).  
\end{table} 
Table 6: Results of hypotheses testing focusing on brand trust. Figure 3: The moderating effect of healthiness on the association between the anti-oxidant effect and healthiness. Figure 4 illustrates the slope of nutrition disclosure. The impact of brand trust on healthiness is strongest in the low-nutrition disclosure group (\(\beta=0.532,p<0.05\)), while it is weakest in the high-nutrition disclosure group (\(\beta=0.305,p<0.05\)).
# Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission
## 5 Conclusions  
The lack of literature in the monitoring of the wear of pin connections motivated this research work. In this paper, a new approach to monitor the wear process of pin connections using the AE technique was proposed. An apparatus to conduct accelerated low rotational speed pin connections wear test was designed and fabricated. The AE sensor was mounted on the test apparatus in a nondestructive way and was capable of real time monitoring. In addition, to verify the results of AE technique, a digital microscope was employed to observe the micrographs of the tested pins. The experimental results show that AE activity existed throughout the entire wear process, and it was the most prominent in the serious wear phase. The wear degree of the low rotational speed pin connections can be reflected qualitatively by the signal strength and the accumulative signal strength of the AE signals. Experiments also reveal that the main wear forms of the low rotational speed pin connections in this research include the abrasive wear and the adhesive wear. Detailed research found that different AE signal strengths from four specimens meant different wear forms, and the micrographs of the four specimens confirmed this result, and adhesive wear and abrasive wear both occurred on the surface of pins. Further analyses of the AE signals demonstrated that the adhesive wear was the main mode of wear of the low rotational speed pin connection. The signal strength of the adhesive wear was around 190 times larger than that of abrasive wear, although the wear depths of them are similar. In summary, the research demonstrates that the developed AE method, though simple to use, is an effective nondestructive method to monitor the wear degree of low rotational speed pin connections in real time. Further research will focus on the relationship between the roughness of the wear surface, and the AE signals. Moreover, for applying the results of this paper to actual engineering, the effect of different rotational speed and different types of steel on AE results also will be studied.  
[PERSON], [PERSON], and [PERSON] conceived the original ideal; [PERSON], [PERSON], and [PERSON] proposed the conceptual experimental design; [PERSON] and [PERSON] performed the detailed design; [PERSON] conducted the experiments and performed the data analyses under the guidance of [PERSON], [PERSON], and [PERSON]; [PERSON], [PERSON], and [PERSON] wrote the paper; [PERSON], [PERSON], and [PERSON] proofread the paper.  
This research was funded by the Major State Basic Development Program of China (973 Program, grant number 2015 CB057704).  
The authors thank the Collaborative Innovation Center of Major Machine Manufacturing in Liaoning for the support given during the development of this work.  
The authors declare no conflict of interest.
## 2 Project Profile
### Numerical Model and Boundary Conditions  
The distribution relationships between the existing goaf and the tunnel are mainly divided into parallel, oblique, and orthogonal, and the influence of different goaf distribution forms on the mechanical characteristic of the tunnel is different, considering that the Huaying Mountain High-Speed Rail Tunnel and the goaf have a certain angle, and these goafs are located above the tunnel. Therefore, in this paper, the relationship between the location of the goaf and the tunnel is simplified, as shown in Figure 2. Based on the distribution characteristics of the surrounding rock and the goaf in the Huaying Mountain High-Speed Rail Tunnel segment, a 3D numerical model of the tunnel was constructed using FLAC\({}^{\text{3D}}\) (Version 6.0) numerical simulation software, according to the actual dimensions of the Huaying Mountain High-Speed Rail Tunnel project. The tunnel span is 13.23 m, the height is 12.31 m, and the depth is 60 m. With the Saint-Venant principle, the numerical model's boundary conditions were determined, taking into full consideration the actual engineering cross-section of the tunnel and eliminating the boundary effects generated during numerical simulation. The dimensions of the 3D numerical model in the x, y, and z directions were set as 120 m, 60 m, and 100 m, respectively (Figure 2). The monitoring points at the tunnel cross-section are labeled A-H (as shown in Figure 2). The goat section surrounding rock of the tunnel was treated as isotropic elastoplastic material using the Mohr-Coulomb theory. The upper boundary of the model was set as a free boundary; the bottom was fixed; and the remaining boundaries were subjected to normal displacement constraints. The surrounding rock is affected by its own gravity, and the unit weight of the surrounding rock is \(\gamma=24\) kN/m\({}^{3}\). The lateral pressure coefficient \(K=0.5\) is set in the scheme, that is, the vertical stress is equal to the horizontal stress in the x direction, and the y direction stress is half of the x direction stress.
Applied sciences Article  
A Modelling Study on the Comparison of Predicted Auditory Nerve Firing Rates for the Personalized Indication of Cochlear Implantation [PERSON]  
Division of Phoniatrics-Logopedics, Department of Otorhinolaryngology, Medical University of Vienna, 1090 Vienna, Austria; [EMAIL_ADDRESS]  
A potential future application is the simulation-based personalized indication of cochlear implantation in hearing-impaired individuals.  
The decision of whether to perform cochlear implantation is crucial because implantation cannot be reversed without harm. The aim of the study was to compare model-predicted time-place representations of auditory nerve (AN) firing rates for normal hearing and impaired hearing with a view towards personalized indication of cochlear implantation. AN firing rates of 1024 virtual subjects with a wide variety of different types and degrees of hearing impairment were predicted. A normal hearing reference was compared to four hearing prosthesis options, which were unaided hearing, sole acoustic amplification, sole electrical stimulation, and a combination of the latter two. The comparisons and the fitting of the prostheses were based on a 'loss of action potentials' (LAP) score. Single-parameter threshold analysis suggested that cochlear implantation is indicated when more than approximately two-thirds of the inner hair cells (IHCs) are damaged. Second, cochlear implantation is also indicated when more than an average of approximately 12 synapses per IHC are damaged due to cochlear synaptopathy (CS). Cochlear gain loss (CGL) appeared to shift these thresholds only slightly. Finally, a support vector machine predicted the indication of a cochlear implantation from hearing loss parameters with a 10-fold cross-validated accuracy of 99.2%.  
hearing; cochlear synaptopathy; cochlear gain loss; loss of inner hear cells; auditory modelling; treatment effect prediction; personalized medicine
# A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems
## 3 Energy Efficiency Techniques in Wireless Communication Systems
### Heterogeneous Networks  
A developing and encouraging means to solve the shortcomings in future electronic communication is the deployment of heterogeneous networks, which keeps up with the most significant amount of future system performance gains, considering the insufficiency in existing wireless networks, and the integration of supporting technologies within the network.  
To enhance the wireless communication systems, heterogeneous network modelling of the framework shown in Figure 2[19], the picocells, femtocells, microcells, etc., would have to work together for better performance [49]. A femtocell is a small, low-power base transceiver station situated in homes or small businesses. Figure 3 depicts femtocells configuration, showing dead zones in wireless networks [50]. Figure 4 describes femtocell-consumer installed wireless data access points inside homes [51]. Figure 5 depicts microcells installed cell towers, which improve coverage in environments with limited signal reception [50].  
Figure 1: Photovoltaic cell, module, and array for various applications.  
Figure 2: Heterogeneous wireless network support various wireless applications.  
Figure 4: Femtocell: Consumer installed wireless data access points inside homes.  
Figure 3: Femtocells configuration showing dead zones in wireless networks.  
The internet service providers connect to the femtocell via network cables or digital subscriber lines (DSL) [49]. The range of a femtocell is around 10 m; a picocell is around 200 m or less; while microcells are less than two kilometres wide [52]. A picocell is a small base transceiver station that covers small areas such as offices, stores, banks, etc., to increase the network capacity in those areas where it would have been difficult for macrocell to reach [49,53].
## 5 Conclusions  
Through this paper, we have proposed an improved parallel timing synchronization architecture to solve the urgent problem of enhancing transmission capacity in real-time wireless communication systems. Besides, we have demonstrated that the proposed architecture can be successfully implemented on FPGA. In addition, our work saves 67% LUTs resources on FPGA compared with earlier results. Meanwhile, the NCO is further improved to meet the FPGA timing requirements by direct-calculation instead of comparator based structure in related work. The key technology is parallel signal processing, both in theory and FPGA implementation. Parallelization of m channels could reduce the system clock to 1/2 m of that required in serial processing, which makes it feasible to achieve real-time performance with current existing hardware devices. Accordingly, a parallel digital timing synchronization theoretical model was established. Simulation result of 64-parallel channels, 20 Gbps rate and 16 QAM system shows high consistency with the theoretical model. The BER deterioration is less than 0.5 dB and 1 dB in float and fix point simulation respectively. Simultaneously, FPGA implementation shows excellent agreement with simulation. Furthermore, the proposed algorithm is not limited to 64-parallel, higher capacity can be achieved with faster clocks or more parallel channels. The proposed structure would be potentially optimized in future work of high capacity wireless communication.  
X.H.: Methodology, Software, Validation, Formal Analysis, Data Curation, Writing--Original Draft. C.L.: Conceptualization, Investigation, Writing--Review & Editing, Supervision, Project Administration, Funding Acquisition. Q.W.: Resources, Writing--Review & Editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China Grant (2018 YFB18000500), The President Funding of China Academy of Engineering Physics with No. YZJILX2018009.  
The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for helpful discussions.  
The authors declare no conflict of interest.  
Figure 9: Constellation diagram on FPGA and difference value (image part) of interpolator’s output with MATLAB fix point simulation and FPGA implementation.
## 3 Single Linear System  
Before dealing with the IO-FTS of hybrid systems, let us first consider a single linear time-varying system which is described as follows:  
\[\left\{\begin{array}{l}\dot{x}(t)=A(t)x(t)+B(t)u(t)+H(t)v(t)\\ y(t)=C(t)x(t)\end{array}\right. \tag{6}\]  
where \(x(t)\in\mathbb{R}^{n}\) is the system state, \(v(t)\in\mathbb{R}^{r}\) is the particular exogenous input, \(u(\cdot)\in\mathbb{R}^{\kappa}\) is the control input, and \(y(t)\in\mathbb{R}^{h}\) is the output. \(A(\cdot):\mathbb{R}^{+}\to\mathbb{R}^{n\times n}\), \(B(\cdot):\mathbb{R}^{+}\to\mathbb{R}^{n\times\kappa}\), \(H(\cdot):\mathbb{R}^{+}\to\mathbb{R}^{n\times r}\), and \(C(\cdot):\mathbb{R}^{+}\to\mathbb{R}^{h\times n}\) are continuous matrix-valued functions.  
The problem which will be solved is the design a state feedback control law \(u(t)=K(t)x(t)\) for system (6), such that the closed-loop system  
\[\left\{\begin{array}{l}\dot{x}(t)=\tilde{A}(t)x(t)+H(t)v(t)\\ y(t)=C(t)x(t)\end{array}\right. \tag{7}\]  
is positive and there is IO-FTS w.r.t. \((\omega,\eta(\cdot),T)\), where \(\tilde{A}(t)=A(t)+B(t)K(t)\), \(K(t):\mathbb{R}^{+}\to\mathbb{R}^{\kappa\times n}\) is the control gain, and \(T\) is a positive scalar.