esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics Analysis of a Variable Stiffness Tuned Mass Damper Enhanced by an Inerter
## 5 Conclusions  
In this paper, a novel tuned mass damper with variable stiffness and an inerter (VS-TMD-I) is proposed. The dynamic equations of the primary structure with the VS-TMD-I are established. The frequency responses are obtained from a harmonic balance method and verified by numerical simulations. The main conclusions are drawn as follows:  
1. The performance of the vibration reduction of the VS-TMD-I is much better than the traditional VS-TMD with the same mass ratio. The VS-TMD-I and the traditional VS-TMD have almost the same vibration reduction performance with the same inertance ratio under a small external excitation. The enhancement performance is more obvious with the increase of external excitations. Moreover, the VS-TMD-I has less added mass. The natural frequency of the primary structure is only slightly changed by the VS-TMD-I.
2. The primary structure exhibits variable nonlinear dynamic phenomena and unstable response, and the range of unstable band widens as the negative stiffness ratios in  
Figure 13: Responses of the relative displacement \(u_{3}\)-\(u_{2}\) and the corresponding wavelet transform spectra with various initial impulses: (**a**,**b**) \(X=0.2\); (**c**,**d**) \(X=0.5\).  
crease and inertia ratios decrease. Fortunately, the amplitude of the primary structure becomes lower.
3. A frequency change indicator is used as a constraint for the optimal parameter analysis. When the positive stiffness ratio is constant, the optimum negative stiffness ratio and inertance ratio of the VS-TMD-I are searched. The vibration reduction rate can reach 87.09% with the optimal parameters of the VS-TMD-I \(\beta_{3}=0.027\), \(\beta_{4}=0.06\), and \(\lambda_{3}=15\).
4. The VS-TMD-I can improve the energy absorption rate of the primary structure under different initial impulse values. The VS-TMD-I can reduce the time for the primary structure to reach steady state. The primary structure and the new vibration absorber engage in 1:1 internal resonance, 1:2 internal resonance, 1:3 internal resonance, and low-frequency resonance with different initial impulse values based on the wavelet transform spectra.  
K.-F.X.: Conceptualization, Methodology, Software, Writing-original draft, Visualization. Y.-W.Z.: Supervision, Funding acquisition. M.-Q.N.: Resources, Supervision, Funding acquisition. L.-Q.C.: Conceptualization, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Project Nos. 62188101, 12272103, and 12022213) and Guangdong Basic and Applied Basic Research Foundation (2022A1515012054).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 4 Experimental Evaluation
### Experimental Setting  
We have chosen a realistic layout to compare our proposal with other well-known channel assignment techniques. This layout is the first floor of the Polytechnic School of the University of Alcala (Spain). This building is square-shaped, with sides approximately 130 m long. We have used the real positions whereat the 26 APs are located in the building, which are shown in the heat map displayed in Figure 4. In this figure, signal coverage ranges from light blue (low coverage) to red (high coverage). The reason for having such a low coverage in the center of the setting is because it is a central courtyard. Regarding the positions of STAs, they are not included in Figure 4, because their positions will depend on the occupancy of the building. In a university campus, students will be mainly attending classes, but there are also a number of students in the buildings involved in different activities (studying, preparing assignments, labs, etc.). For that reason, in all of our settings we have considered that there are 100 randomly located STAs in the plant. Moreover, we have considered a varying number of classrooms under use. In the first floor plant of the building there are 48 classrooms, so in our different settings we have considered 12, 24, 36 and 48 classrooms simultaneously under use, to represent different occupancy settings, being: low, medium, high and total occupancy, respectively. For each of this occupancy settings we have considered two scenarios. For example, in the low occupancy setting, where 12 from a total of 48 classrooms are in use (25%), these 12 classrooms are chosen randomly, so we have tested two different choices. In each classroom that is under use, we have considered that there are 25 students randomly located in that classroom using a normal distribution around the center and a standard deviation of 6.5 m. To summarize, and for example, the two scenarios of low occupancy (25% of the classrooms under use) have 100 STAs randomly located and \(12\cdot 25\ =\ 300\) STAs, giving a total of 400 STAs. With a similar reasoning, for the medium, high and total occupancy settings, we will have 700, 1000 and 1300 STAs, respectively. In Figure 5 we show the resulting graph for one the scenarios for each classroom occupancy. Note that in those figures we have overlapped both connectivity and interference layers into a single picture. Again, we have used large green circles to represent APs, small black squares to represent STAs and red lines to represent interferences. To distinguish interferences from STA-AP associations in the same picture, we have used black-dashed lines to represent the association between APs and STAs.  
Figure 4: Polytechnic school building plan.
## 3 Numerical Model and Simulation Scheme of the Strain-Structural Plane
### Constitutive Model and Material Mechanical Parameters  
The Mohr-Coulomb constitutive model is a yield criterion that can describe the mechanical behavior of hard rock, and the yield function was used as follows [30]:  
\[f_{\text{s}}=(\sigma_{1}-\sigma_{3})-2c\cos\varphi-(\sigma_{1}+\sigma_{3})\sin\varphi \tag{4}\]  
where \(c\) and \(\varphi\) are the cohesion force and friction angle, respectively.  
The failure envelope of the criterion corresponds to the shear and tensile stress yield functions, which is a flow rule related to tensile failure. When \(f_{\text{s}}<0\), the rock shear failure occurs. To truly reflect the stress state of surrounding rock and the interaction of contact between blocks, the Mohr-Coulomb model is adopted for surrounding rock mass (change \(\text{Cons}=2\)), and the Coulomb-slip model is adopted for virtual joints and structural plane (change \(\text{Jcons}=1\)). In a 3 DEC-BBM model, the mechanical properties of the blocks and contacts need to be calibrated so that the blocks can represent the actual mechanical behavior of a given rock. This study adopted a \"trial-and-error method\" for calibrating the rock mechanical parameters [31]: The rock mechanical parameters are directly obtained from the laboratory test results in the research report from the research group. The numerical simulations are conducted, and the input parameters are varied until the mechanical behavior of the numerical simulation closely matches the data from the laboratory test. The fictitious contact parameters are derived based on the laboratory test data and the empirical formula, and the real joint-like structural plane's contact parameters are set according to the previous study [32]. The rock calculation parameters and contact parameters are shown in Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elastic Modulus** & **Bulk Modulus** & **Shear Modulus** & **Internal Friction** & **Cohesion Force** & **Tensile Strength** \\
**(GPa)** & **(GPa)** & **(GPa)** & **Angle (\({}^{\circ}\))** & **(MPa)** & **(MPa)** \\ \hline
33.57 & 20.72 & 13.64 & 53.51 & 24.67 & 7.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rock calculation parameters.  
Figure 2: The 3D discrete element numerical model of rockburst: (**a**) Dimensions of the tunnel and structural plane; (**b**) Boundary conditions (Colors represents the rock mass, Black arrows represents the vertical stress, and circles represent the displacement constraint boundaries).
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 4 Simulation Set-Up and Evaluation Metrics  
For the purpose of evaluating the different schemes considered in this study, we used the ndnSIM 2.1 simulator [44,45], as ndnSIM is a modular NDN simulator that implements all basic NDN operations and is based on the NS-3 network simulator. We implemented AFIRM, Map-Me, Map-Me-IU, and a relaxed version of Kite (which we refer to as R-KITE) to compare their performances using the same network scenarios and mobility settings.  
We evaluated all three schemes in the exact same settings to guarantee evaluation fairness.  
We relaxed some specifications of Kite to represent the general anchor-based solutions.  
Our decision was based on the following observations and considerations:  
* Kite's trace-based mechanism is useful in situations where the consumer interest arrivals are sparse and the producer is moving at a low mobility speed. However, when these conditions are not met (either high consumer interest arrival rates or a high producer mobility speed), the consumer interest may arrive before expiry of the old trace. Hence, the trace-based mechanism downgrades the scheme's operation. This can be seen in the case where consumer interests are mistakenly forwarded to the  
Figure 4: Example Kite operation.  
producer's old PoA instead of the anchor. Retransmitted interests will continue to be mistakenly forwarded until the old trace expires [46].
* Even when new routing information to the producer's new PoA is available at the anchor and a new trace is established, the aforementioned situation still holds. Specifically, for all consumers in the same sub-tree of the producer's old PoA (rooted at the anchor), interests are mistakenly forwarded to the old PoA until the associated trace expires. This is a direct result of Kite's design choice in passively managing stale traces and leaving it up to the expiry of the trace, instead of providing an active mechanism to remove the stale traces upon producer dissociation.
* Although a forwarding strategy was proposed to reduce the negative affect of the described situation, we aim to provide a fair comparison of the schemes, where the default NDN strategies and setting are applied.  
Therefore, the relaxed version of Kite is indeed a general representative of anchor-based solutions which require a special node to handle producer mobility. Consequently, this allowed us to make general conclusions about the performance of the broader anchor-based category.  
We describe the simulation topology, applied parameters, and evaluation metrics in the following sections.
# A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery
## 3 Proposed Methodology
### Comparison Between R-PRIM-Cl and CART  
[PERSON] et al. introduced classification and regression trees in 1984 [12]. This rule-based classifier divides the dataset recursively into subsets according to feature thresholds in order to create decision trees for classification or regression tasks. Within the resulting groups, each split seeks to maximize the target variable's homogeneity. In this section, we compare R-PRIM-Cl and CART on the same Iris dataset for predicting if a flower is a \"setosa\". To test if CART can detect subgroups of \"setosa\" in the input space with the four features, we left the parameters at their default values and did not limit the tree's depth. Notably, the purpose of this comparison is to see if CART and R-PRIM-Cl are able to identify as many subgroups of the iris flower \"setosa\" as possible. As seen in Section 3.3, R-PRIM-Cl detected twelve rules thanks to the search in different feature spaces. R-PRIM-CL allowed the discovery of small groups with rule 7 with only 5% of support and 7% of coverage, rule 3 with 7% of support and 8% of coverage, and rule 2 with only 10% of support and 15% of coverage. These rules are used to find new trends and patterns in the dataset that subject-matter experts can identify. On the other hand, CART only detected one rule (IF petal length <= 2.45 THEN flower ='setosa') in the feature space. When the goal is subgroup discovery, the original PRIM algorithm outperforms CART (classification and regression trees) because it is designed to concentrate on identifying and characterizing subgroups in data. The identification of condensed, interpretable regions (or boxes) where the target outcome is enriched is given priority by PRIM, in contrast to CART, which strives for global predictive accuracy by creating an extensive decision tree. This targeted approach allows R-PRIM-Cl to isolate meaningful subgroups and, at the same time, build an accurate predictive model. In contrast, CART may fail to differentiate finer patterns.  
Furthermore, R-PRIM-Cl offers more straightforward and implementable rules for every subgroup that has been identified, while CART's output can become excessively complicated with deep trees that do not focus on particular regions.
## 5 Experimental Simulation Analysis
### Confidentiality Analysis  
The RSST-SR-DCSK communication method utilizes scrambling to alter the reference signal, making it difficult for non-cooperative parties to demodulate the original chaotic signal without the inverse transformation used in the scrambling process. The scrambler essentially encrypts the chaotic reference signal, thereby increasing the complexity of signal decryption for non-cooperative  
Figure 9: Comparison of DBR between RSST-SR-DCSK system and DCSK system.  
parties and improving the confidentiality of the physical layer communication signal. In Figure 10, simulation results depict the cross-correlation between the reference signal and the information signal for both the RSST-SR-DCSK and SR-DCSK systems. These results demonstrate that the correlation peaks of RSST-SR-DCSK are notably weakened after scrambling, thereby reducing the correlation between the reference signal and the information signal within the system.  
RSST-SR-DCSK is a system that scrambles the reference signal, resulting in a modified waveform compared to the original chaotic signal. This modification reduces the correlation between the reference signal and the information signal, thereby enhancing the confidentiality of the communication signal to a certain degree. Simulations have shown that the RSST-SR-DCSK system demonstrates improved resistance to interception and enhanced confidentiality.
# Impact of Chitosan-Genipin Films on Volatile Profile of Wine along Storage
## 2 Materials and Methods
### Analysis of Volatile Compounds
#### 2.4.2 Determination of Volatile Compounds in Wine Model Solutions  
The content of each volatile compound in the wine model solutions was determined by HS-SPME combined with gas chromatography using a flame ionisation detector (GC-FID). After the extraction and concentration step, the SPME coating fibre was manually introduced into the GC-FID injection port (220 \({}^{\circ}\)C). The GC was equipped with 30 m column DB-FFAP (J&W Scientific, Folsom, CA, USA) with 0.25 mm internal diameter (i.d.) and 0.32 um thickness. The separation of each volatile compound, namely benzaldehyde, furfural, hexanol, hexanal, and phenylacetaldehyde, was carried out with the oven temperature program reported by [PERSON] et al. [21]. The separation of octan-3-one was done using the following oven temperature program: initial temperature 35 \({}^{\circ}\)C during 3 min, an increase in temperature with a rate of 45 \({}^{\circ}\)C/min until 220 \({}^{\circ}\)C, maintaining for 1 min. The separation of ethyl hexanoate and hexanoic acid occurred with the following temperature program: initial temperature at 40 \({}^{\circ}\)C, an increase in temperature at a rate of 3 \({}^{\circ}\)C/min until 120 \({}^{\circ}\)C, followed by a rate of 40 \({}^{\circ}\)C/min until 250 \({}^{\circ}\)C and maintain at this temperature for 1 min. In all separations, the flow rate of the hydrogen (H\({}_{2}\)) was 1.7 mL/min and the detector temperature was set at 230 \({}^{\circ}\)C.  
The quantitative analysis was obtained by interpolating the peak area on the external calibration curves made with five different concentrations for each standard. The retention of the volatile compounds (benzaldehyde, ethyl acetate, ethyl hexanoate, hexanoic acid, hexanol, hexanol, and octan-3-one) by Ch-Ge films was expressed based on the decrease in content in the vapour phase in comparison with the reference (without films). The formation of volatile products of Maillard reaction (benzaldehyde, furfural, 3-methylbutanal, and phenylacetaldehyde) by Ch-Ge films was expressed based on their content in the vapour phase in comparison with the reference (without films).
## 4 The Proposed ECPSO-MTP
### Communication Rules
#### 4.3.1 Forward Communication Rules  
Under the hierarchical structure of the proposed ECPSO-MTP, forward communication rules are applied to establish transitive relationships between elementary membranes. There are two types of communication rules in the onward direction, including \(\sigma_{o}\) (\(1\leq o\leq m\)) to \(\sigma_{m+1}\) and \(\sigma_{m+1}\) to \(\sigma_{m+2}\). It is noted that there is no interaction between elementary membranes at the same level of membrane structure.  
The forward communication rules from \(\sigma_{o}\) to \(\sigma_{m+1}\) are given in the form, \(R_{o,m+1}^{{}^{\prime}}:\ \left[u_{o}^{\textit{gbest}}(t)|_{p}\right]_{o} [\lambda]_{m+1}\rightarrow\left[\lambda\right]_{o}[u_{o}(t)]_{m+1}\), for \(1\leq o\leq m\). It only can be applied on a configuration if both \(u_{o}^{\textit{gbest}}(t)\) and \(p\) appear in \(\sigma_{o}\) at \(t\). When this communication rule associated with \(\sigma_{o}\) and \(\sigma_{m+1}\) is applied, under the presence of \(p\), \(u_{o}^{\textit{gbest}}(t)\) in \(\sigma_{o}\) is simultaneously sent to \(\sigma_{m+1}\) and evolve into \(u_{o}(t)\). Therefore, the totality of objects from \(\sigma_{1}\) to \(\sigma_{m}\) in \(\sigma_{m+1}\) is \(m\).  
The forward communication rules from \(\sigma_{m+1}\) to \(\sigma_{m+2}\) are given in the form, \(R_{m+1,m+2}^{{}^{\prime}}:\ \left[u_{o}^{\textit{lbest}}(t)|_{p}\right]_{m+1}[ \lambda]_{m+2}\rightarrow\left[\lambda\right]_{m+1}[u_{o}(t)]_{m+2}\), for \(1\leq o\leq m\). It only can be applied on a configuration if both \(u_{o}^{\textit{gbest}}(t)\) and \(p\) appear in \(\sigma_{m+1}\) at \(t\). When this communication rule associated with \(\sigma_{m+1}\) and \(\sigma_{m+2}\) is applied, under the presence of \(p\), \(u_{o}^{\textit{lbest}}(t)\) in \(\sigma_{m+1}\) is simultaneously sent to \(\sigma_{m+2}\) and revised to \(u_{o}(t)\). Thus, the best object from \(\sigma_{m+1}\) is selected and stored as the global best \(u_{m+2}^{\textit{gbest}}(t)\) of \(\sigma_{m+2}\) at \(t\).
## 3 Results Analysis
### Polarization Insensitivity and Oblique Incident Angle Stability  
The polarization insensitivity of the proposed MMA was investigated to verify its absorption efficiency. Figure 8 shows the absorption property of different polarization incident angles (b) for TE and TM modes. The TE mode z-axis is the direction of wave propagation, and the magnetic field vector Hz is along the z-axis. The electric field vector (Ex) and magnetic field vector (Hy) are along the x- and y-axis, respectively. On the other hand, in the TM mode, the electric field vector (Ez) is along the direction of wave propagation, and the magnetic (Hx) and electric (Ey) field vectors are along the x- and y-axis, respectively. Due to the axial and rotational symmetricity, the proposed MMA achieves unique absorption properties for polarization incident angles (\(\updownarrow\)) up to 180\({}^{\circ}\). In all the above discussions, all results have been obtained from a normal incident angle (\(\updownarrow\) = 0\({}^{\circ}\)), but the EM wave occurs at an oblique incident angle on the MMA. Therefore, discussion of the absorption behavior of oblique incidents is also vital. The oblique incident angle (\(\updownarrow\)  
Figure 6: (**a**) Co- and cross-polarization component, and (**b**) PCR value of x and y polarization of the incident wave.  
Figure 7: (**a**) Metamaterial property of TM mode, and (**b**) metamaterial property of TE mode.  
is made between the direction of wave propagation and the z-axis of the MMA structure. Figure 9a,b show the absorption curve of both TE and TM modes for the oblique incident angles (\(\theta\)) up to \(60^{\circ}\) and \(70^{\circ}\), respectively, where an operating frequency above 70% absorption is found. Absorption properties of various oblique incident angles are listed in Table 2.
## 4 Discussion
### Convergence Rapidity  
The stability distance and the stability time were used to evaluate the convergence speed. According to Figure 8 and Table 2, with variable look-ahead distance, the stability distances for the three different initial states were 64.6 cm, 204.9 cm, and 342.3 cm, respectively, and the average value of stability distances was 203.9 cm. Stability times were 3.2 s, 4.2 s, and 7.8 s, respectively, and the average value was 5.1 s. In contrast, with the fixed look-ahead distance, stability distances for the three different initial states were 274.7 cm, 275.5 cm, and 372.9 cm, respectively. Stability times were 5.6 s, 5.2 s, and 8.4 s, respectively. The method used in this paper can be used to achieve a more rapid convergence to the desired path and has a more sensitive response. In the early period of path tracking, the small look-ahead distance shortens the \"rise time\" in the control process, thus shortening the time required for the prototype vehicle to converge from the initial state to the steady state. In real agricultural production situations, the characteristics of rapid convergence can reduce the idle time to obtain a higher operational efficiency.