esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Airborne Sound Insulation  
The investigation of the sound insulation to airborne noise is useful in order to understand if the actions of the floating floor and of the suspended ceiling, applied for impact noise reduction, can influence also soundwaves propagation in air. For this reason, in Figure 14, the measured trends are reported.  
It is evident how the bare floor provides very poor frequency performances, while the addition of a suspended ceiling (configuration 2) positively affects sound insulation. In this case, also floating floor (configuration 3) positively affects sound insulation, showing a very similar trend compared to configuration 2. When combining the two solutions, some significant improvements can be seen at middle-low frequencies, until 1600 Hz.  
In Table 5, the results of the calculated frequency sound insulation are reported. In addition, the difference \(\Delta_{\mathrm{n}}\) between the measured and the calculated values (using Equation (9) are included after each configuration. It is interesting to notice that configuration 1 (bare floor) is not fitted robustly. Accordingly, differences up to \(-19.8\) dB are verified, with a mean value of \(-13.9\) dB. When considering the suspended ceiling addition  
Figure 14: Airborne sound insulation for CLT floors (configurations 1, 2, 3, and 4).  
(configuration 2), we can verify a significant improvement of the sound insulation prediction. A maximum of 9.8 dB difference is provided, featuring a mean value of \(-\)1.6 dB. It is worthy to highlight that from 100 to 2000 Hz, the prediction falls within a range of \(\pm\) 3 dB, with the exception of 315 Hz (3.7 dB) and 400 Hz (3.5 dB). This fact demonstrates how low, middle, and middle-high frequencies can be successfully calculated using [PERSON]'s theory.  
When considering only floating floor addition to the bare floor (configuration 3), the prediction is not to be considered robust and reliable. Differences are significant (up to \(-\)12.4 dB) with a mean value of \(-\)6.3 dB. A different trend is verified for the complete layering (configuration 4). For low frequencies (100 Hz-500 Hz), a good prediction is provided, falling within the \(\pm\) 3 dB range, with the exception of 125 Hz (4.2 dB) and 160 Hz (3.2 dB). From 630 Hz, a constant increase in the difference is verified, reaching its maximum at 5000 Hz (\(-\)15.0 dB) and a mean value of \(-\)5 dB.  
As an overall result, we can say that the [PERSON]'s model provides a reliable prediction only for configuration 2 up to middle frequencies, while for the other configurations, no consistent trends are found. For all configurations, the [PERSON]'s model tends to overestimate the sound insulation and the worst range is represented by high frequencies (>1600 Hz).
## 3 Reduced Dynamic Orbit Determination of COSMIC-2  
### Reduced Dynamic Method  
LEO satellites are affected by various perturbations when they move around the earth, including conservative forces and non-conservative forces. The conservative forces mainly include non-spherical gravitation perturbations, multi-body perturbation, earth tide perturbation, relativistic effects, and non-conservative forces include solar radiation pressure, earth's albedo, and atmospheric drag force. The motion equation of an LEO satellite considering these perturbations in the geocentric inertial system is [30; 38]:  
\[\ddot{r}=-GM\frac{r}{r^{3}}+f_{1}(t,\dot{r},\ddot{r},q_{1}\ldots,q_{d}) \tag{7}\]  
where: \(r,\dot{r},\ddot{r}\) are the position, velocity and acceleration of the satellite, respectively; The initial condition is \(r_{0}(t)=r(a,e,i,\Omega,\omega,T_{0};t_{0})\), \(\dot{r}(t_{0})=\dot{r}(a,e,i,\Omega,\omega,T_{0};t_{0})\), Where, \((a,e,i,\Omega,\omega,T_{0};t_{0})\) are Kepler six elements at the reference epoch \(t_{0}\); \(f_{1}\) is disturbance acceleration; \(q_{1}\ldots,q_{d}\) are the unknown dynamic orbit parameters; \(GM\) is the gravitational constant of the earth.  
Assuming that the prior orbit \(r_{0}(t)\) of the satellite is known, the Taylor series expansion of \(r(t)\) is carried out at the reference epoch \(t_{0}\) to eliminate the unknown disturbing force parameters. The corrected value of the prior orbit parameter \(p_{i0}\) is obtained in the least square processing of GPS carrier-phase observation data of the LEO satellite. The expression of the final orbit \(r(t)\) is [38]:  
\[r(t)=r_{0}(t)+\sum_{i=1}^{n}\frac{\partial r_{0}(t)}{\partial p_{i}}(p_{i}-p_{ i0}) \tag{8}\]  
where: \(p_{i}\) is the track parameter; \(p_{i0}\) is a priori orbit parameter.  
LEO satellites will be affected by atmospheric drag force when they move around the earth. Due to the complex space environment, it is difficult to model the atmospheric drag force. Therefore, the un-modelled atmospheric drag force will degrade the orbit determination accuracy with the dynamic method [39]. The RD orbit determination method is to introduce pseudo-random pulse parameters into the satellite motion equations, adopts the geometric information of the GPS observations and the dynamic information of the satellite, and balances the geometric and kinematic information by a priori weights [39; 40]. A priori weight can keep the pseudo-random pulse parameters from deviating from the expected value, and it can effectively absorb the dynamic model errors and the un-modelled perturbation errors, so as to improve the RD orbit determination accuracy for LEO satellites [4; 39].
## 4 Experimental Results
### Performance Evaluation.
The time required for each step of our method is shown in Table 3.  
Finally  
The above figure clearly shows that the salient maps of our method are visually much better than those of the other methods with full resolution. Our method can not only successfully highlight the salient object/region and uniformly differentiate them from the complex background, but also functions on salient maps with inherent contour and local texture. For instance, take the original horse image in the third line: in our method, the salient object \"horse\" produced is clearly of better quality, without grassland, and the horse's mane and the other characteristics are clearly shown. Although the SF method can properly detect the horse with complete contour information in the grassland scene, the detailed characteristics of the horse have poor salient features. Meanwhile, the horse in the salient map using the FASA method is not clearly distinct from the grassland. The salient map of the RC method is not at full resolution. Therefore, the results of our method visually satisfy the criteria introduced in Section 1.
## 3 Ultra-Low-Frequency Broadband Characteristics  
### Ultra-Low-Frequency Broadband Structure Analysis  
In order to explore the band gap properties of the quasi-static metamaterials, the finite element method is used to calculate the dispersion relationship of the quasi-static metamaterial structure and the results are presented in Figure 2. Here, the blue solid line represents the real part of the wave vector, i.e., the wave propagation mode, and the red dotted line represents the imaginary part of the wave vector, i.e., the wave evanescent mode. It is worth noting that the absolute value of the imaginary part of the wave vector represents the attenuation of the elastic wave amplitude in the space, i.e., there is an evanescent wave mode. The metamaterial geometry parameters are given as follows: the lattice constant \(a\), the waist height \(h=a\), the waist thickness \(d=0.6a\), and the average leg thickness \(e=0.2a\). The material is epoxy resin, and the material parameters are: the elastic modulus \(E=4.35\times 10^{9}\) Pa, Poisson's ratio \(\mu=0.368\), the mass density \(\rho=1180\) kg/m\({}^{3}\), the reduced frequency \(\Omega=\omega a/2\pi c_{t}\), and the shear wave velocity of the epoxy resin matrix \(c_{t}=1160.8\) m/s.  
Figure 2 shows the dispersion relation curves of the quasi-static radial metamaterial structure. As shown in Figure 2, the ultra-low-frequency broadband appears in the quasi-static I-shaped radial elastic metamaterials. There are only two energy band curves below the parsimonious frequency \(\Omega=0.50\). Its first band gap (\(0<\Omega<0.20\)) is below the first energy band. It is worth noting that the imaginary part of the energy band has a part greater than zero in the band gap frequency range, indicating the generation of evanescent  
Figure 2: Dispersion relationship curve of the quasi-static REMs.  
waves. Furthermore, the imaginary part of the band in the first band gap changes sharply with the frequency. This is the hallmark of a typical local resonance. Similar changes have been observed in one-dimensional metamaterials [34]. In this case, the dispersion relation is significantly different from the typical Bragg scattering band gap dispersion curve.  
In order to further verify the correction of the band structure, the 5/10-period quasi-static metamaterial frequency response curves are calculated as shown in Figure 3. It can be observed that there is a good agreement between the band gap position and the band gap results obtained from the dispersion curve.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 1 Introduction
Considering the different customers' equipment, orbits, and frequency bands, the channel models were studied through trade-off analysss and classifications. Although many researchers have conducted a variety of work on mega-constellation design and development problems, there are still some questions that need to be answered. The low orbit environment and collision risk are discussed and the influence is analyzed in the previous work; however, the quantitative evaluation is absent and the relationship between the project failure factors and deployment decisions needs to be demonstrated. This work pays attention to the system states and optimal deployment strategy under the  
Figure 1: The current status of LEO environment. risk of orbital space debris collision. Through cost-effectiveness analysis, the optimal action could be made to realize the best performance. The structure of this article is as follows: the space orbital debris flux model and collision analysis are introduced in Section 2; the Markov decision process is modeled and cost-effectiveness evaluating results are analyzed in Section 3; in Section 4, an example of a constellation is simulated and the optimal strategy is obtained; and Section 5 contains the conclusions.
## 5 Experimental Results
### Results and Discussions
Figure 11: Illustration of the results of estimating VO of TQU-SLAM benchmark dataset when using the SHI_TOMASI features. The results are presented on FO-D data. The black points belong to the ground-truth trajectory, and the red points are the estimated camera position. Figure 12: Illustration of the results of estimating VO of the first FO-D of the TQU-SLAM benchmark dataset when using the features extracted by VGG. The black points belong to the ground-truth trajectory, and the red points are the estimated camera position. The results in Figure 12 also show that the features extracted are limited when using VGG; there are many frames whose features cannot be extracted. Therefore, the camera's position cannot be estimated. Figure 13 shows the difficult feature extraction in frame pairs when using VGG for feature extraction. The results of the camera motion trajectory estimation error (\(Err_{d}\)) based on the DPVO and TartanVO networks on the TQU-SLAM benchmark dataset are shown in Table 4.  
Figure 13: Illustration of the feature matching extracted from the VGG of frame pair of 1 st FO-D of the TQU-SLAM benchmark dataset. The lines connecting two features on two consecutive frames represent two corresponding locations on two consecutive frames detected by the feature descriptor. The results of the camera motion trajectory estimation error (\(ATE\)) based on the DPVO and TartanVO networks on the TQU-SLAM benchmark dataset are shown in Table 5.  
The ratio of the number of detected and extracted frames characterized to estimate the VO of the DPVO is shown in Table 6. This rate is low, only 49.68%, while the characteristic extraction rate of the TartanVO is 100%. As shown in Table 4, the average estimation error (\(Err_{d}\)) of DPVO is 13.7 m, and the (\(Err_{d}\)) of TartanVO is 14.96 m. Table 5 also shows that the average estimation error (\(ATE\)) of DPVO is 15.96 m, and the (\(ATE\)) of TartanVO is 17.46 m. These results show that DPVO is better than TartanVO in the estimation error, but the estimated realized frame rate of DPVO is only 49.68%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Dataset/** \\ **Measure/** \\ **Methods/** \\ \end{tabular} } & \multirow{2}{*}{**Experimental**} & \multicolumn{6}{c}{**Aspect Ratio Fails to Detect Keypoints**} \\ \cline{3-10}  & & & \multicolumn{3}{c}{**of the TQU-SLAM Benchmark Dataset (\(r_{d}\)(\%))**} & \\ \cline{3-10}  & & & **1 st** & \multicolumn{2}{c}{**2 nd**} & \multicolumn{2}{c}{**3 rd**} & \multicolumn{2}{c}{**4 th**} \\ \cline{3-10}  & & & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** \\ \hline \multirow{3}{*}{DPVO} & Average & 49.85 & 49.44 & 50.00 & 50.00 & 50.00 & 50.00 & 47.23 \\ \cline{2-10}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The ratio of frames with detected features/keypoints on the TQU-SLAM benchmark dataset when using the DPVO model to extract the features.
## 5 Results
### Measuring Diagnostic Accuracy
**Actual negative:** Possible diagnosis that was not identified as cardiovascular disease by a doctor or an existing application. **Predicted positive:** Possible CVD diagnosis made by the app, based on its analysis and algorithm. **Predicted negative:** This denotes other diagnoses that were not detected or identified as cardiovascular diseases by the app. These diagnoses may include conditions or diseases not related to CVD. Parameters for Calculating Accuracy  
1. Positive predictive value (PPV), also known as precision, tells the proportion of correctly predicted positive instances out of all instances predicted as positive by the application. A higher PPV indicates that the application is more reliable for correctly identifying positive instances. It suggests that when the application predicts a positive result, there is a relatively higher chance that it is accurate. \[PPV=\frac{TP}{TP+FP}\]
2. The negative predictive value (NPV) tells us the proportion of correctly predicted negative instances out of all instances predicted as negative by the application. \[NPV=\frac{TN}{TN+FN}\]
3. Sensitivity, also known as the recall or true positive rate, indicates the proportion of correctly predicted positive instances out of all of the actual positive instances. \[Sensitivity=\frac{TP}{TP+FN}\] \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & \(4^{\text{TP}}\) & \(0^{\text{FP}}\) \\ \hline
**Actual Negative** & \(0^{\text{FN}}\) & \(0^{\text{TN}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of Test 1 (comparison of the suggested diagnosis by an app with the diagnosis conducted by a medical doctor.) \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & \(5^{\text{TP}}\) & \(2^{\text{FP}}\) \\ \hline
**Actual Negative** & \(0^{\text{FN}}\) & \(1^{\text{TN}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of Test 2 (comparison of the suggested diagnosis by an app with the diagnosis suggested by an existing AI-based diagnosis Android app. )Specificity tells us the proportion of correctly predicted negative instances out of all actual negative instances. \[Specificity=\frac{TN}{TN+FP}\]
5. Accuracy is a performance metric that tells us the overall correctness of the predictions made by the application or system being evaluated. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
We observed that the accuracy of the application was 1.0 or 100%. This high accuracy was achieved because the three cases were accurately diagnosed with cardiovascular disease, matching the predictions made by the application. Furthermore, the positive predictive value was 100%, indicating that all positive diagnoses made by the application were true positives. However, the negative predictive value was undefined because there were no negative diagnoses; all cases were positively diagnosed as cardiovascular disease, according to the evaluations of medical doctors.
## 3 Preparation of MIPs/SIPs for Electrochemical Biosensor
### Electropolymerization or UV Light-Induced Polymerization  
Electropolymerization is a simple and convenient deposition technique with a conductive polymer layer produced on an electrode surface combined with the template. The layer thickness can be controlled easily. The high-affinity binding sites can be formed by direct doping of templates into the polymer matrix [50]. Usually, the thickness of the film controlled by the electropolymerization conditions and can be characterized by electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The charge-transfer resistance of the surface would be increased with the thicknesses added. It is mainly because the polymer holds a low-conductive nature. In order to fabricate a layer of effective MIPs/SIPs, it is critical to control the polymeric film so that it does not cover the whole template so that it can be removed easily and rebound later. If the MIPs/SIPs are too thin, there are no stable microcavities formed on the electrode. It also lowers sensitivity/affinity for the template, since a lower number of binding sites are available. In turn, if the MIPs/SIPs film is too thick, it may entrap the template within the polymeric matrix, hence make its removal/rebinding more difficult. Imprinted artificial capture antibodies (cAbs) for _Staphylococcus aureus (S. aureus)_ were fabricated by electropolymerization [55]. By formation of a Schiff base linkage, _S. aureus_ was fixed on the aldehyde functionalized ITO electrode surface first. Then, an in-situ electrochemically assisted polycondensation strategy was applied to deposit a silica film on the electrode surface around the _S. aureus_. Finally, a calcination treatment was used to achieve the cAbs. The cAbs with lots of regular cavities were observed after the removal of the template. The circular shape of the cavities was proven by images of higher magnification. The AFM image (Figure 5A) revealed that the depths of the cavities were \(\sim\)160 nm. Apparently, the pathogen template was imprinted on the ITO surface successfully and the three-dimensional spheroidal architecture was observed. All the preparation process was also characterized by using electrochemical methods in the study.  
[PERSON] et al. [56] applied a MIPs film consisting of overoxidized polypyrrole (OPPy) to recognize bacilliform bacteria specifically and rapidly. Polypyrrole (PPy) was synthesized using electrochemical polymerization combined with dielectrophoresis (DEP) technique. The DEP resulted in the _P. aeruginosa_ being oriented in one direction, perpendicular to the film surface. The number of bacteria doped in the film was counted to be \(1.8\times 10^{9}\) cm\({}^{-2}\).  
UV light-induced polymerization also can be used to prepare MIPs/SIPs on the electrode. It has been used to prepare SIPs-graphene oxide composites on the electrode for Zika virus (ZIKV) detection (Figure 5B) [52]. [PERSON] et al. fabricated SIPs under UV-polymerization for _E. coli_ detection [57].
## 5 Discussion
### Advantages of Adaptive Task Traffic-Shaping Algorithm
Our method outperforms the classical algorithm, which is up to 1.08 times that of our method in mixed task scenarios, showcasing its robustness against bursty or resource-heavy tasks. These advancements align with the growing emphasis on QoS in resource-constrained GIS systems, offering a self-tuning solution that eliminates manual parameterization and enhances stability under diverse workloads. Future research may explore integrating edge computing resources or hierarchical task prioritization to further extend the framework's applicability in distributed GIS environments.
## 2 Methods  
### Differential Evolution Algorithm  
The Differential Evolution Algorithm (DE) is a population-based heuristic global optimization algorithm [34]. The DE algorithm is often used to solve continuous optimization problems, and its basic idea is to find the optimal solution by continuously evolving the individuals in the population. The DE algorithm generates new solution vectors by introducing a differential mutation strategy, which performs mutation operations on the individuals in the population. The new solution vectors are filtered using operations, such as crossover and selection, to retain the excellent solutions and gradually converge to the global optimal solution [35]. In this process, the DE algorithm has better global search abilities and convergence performance, so it is widely used in many optimization and search problems [36]. Its expression formula is shown in Equations (1)-(3) as follow:  
\[v_{i}(g)=x_{r1}(g)+F*(x_{r2}(g)-x_{r3}(g)) \tag{1}\]  
\[u_{i,j}(g)=\left\{\begin{array}{l}v_{ij}(g),rand(0,1)\leq CR,\ j=j_{rand}\\ x_{ij}(g),rand(0,1)>CR,\ j\
eq j_{rand}\end{array}\right. \tag{2}\]  
\[x_{i}(g+1)=\left\{\begin{array}{l}u_{i}(g),(u_{i}(g))\leq f(x_{i}(g))\\ x_{i}(g)\end{array}\right. \tag{3}\]  
where Equation (1) represents the variation operation of differential evolution. Here,\(x_{r1}(g)\), \(x_{r2}(g)\), \(x_{r3}(g)\) are the three individuals randomly selected from the current population, \(v_{i}(g)\) is the variant individual, and \(F\) is the scaling factor. Equation (2) represents the crossover operation of differential evolution, where \(CR\) is the crossover factor and \(u_{i,j}(g)\) is the experimental individual. Equation (3) represents the selection operation of differential evolution, where \(f(x_{i}(g))\) is the \(x_{i}(g)\) fitness. DE selects individuals from the experimental population \(u_{i}(g)\) as individuals in the next generation population \(x_{i}(g+1)\), according to the fitness value following the greedy criterion. The steps of DE include population initialization, mutation operation, crossover operation, selection operation, and termination judgment. The above steps can be iterated several times until the stopping condition is reached.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 2 Related Work
We also introduce a model for message dropping by relying on probabilistic techniques. Lastly, to represent IoT systems that consider the data recipients' preferences [32; 33; 34] to deliver different types of data (message sizes, important, etc.) [35; 36; 38; 39], multiclass and priority queues are introduced where different classes of messages can be assigned with different priorities. To summarize, application designers are able to analyze and configure certain system aspects (middleware QoS delivery modes, network and user connectivity, message dropping rates, system resources, priority levels, message sizes) by combining the provided queueing models in order to tune and guarantee the appropriate response time and delivery success rate between IoT devices.