esa-sceva/satcom-chunk-collection huggingface dataset:
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
###### Abstract  
In this paper, a novel hysteresis-based current control approach is presented. The basis of the developed control approach is the theory of switched systems, in particular, the system class of switched systems with multiple equilibria. The proposed approach guarantees the convergence of the state trajectory into a region around a reference trajectory by selective switching between the individual subsystems. Here, the reference trajectory is allowed to be time varying, but lies within the state space spanned by the subsystem equilibria. Since already published approaches only show convergence to a common equilibrium of all subsystems, the extension to the mentioned state space is a significant novelty. Moreover, the approach is not limited to the number of state variables, nor to the number of subsystems. Thus, the applicability to a large number of systems is given. In the course of the paper, the theoretical basics of the approach are first explained by referring to a trivial example system. Then, it is shown how the theory can be applied to a practical application of a voltage source converter that is connected to a permanent-magnet synchronous motor. After deriving the limits of the presented control strategy, a simulation study confirms the applicability on the converter system. The paper closes with a detailed discussion about the given results.  
p _applied_  
23 August 2021  
29 September 2021  
Published: 2 October 2021  
frequency and the angular frequency of the reference voltage. In addition, in the case of hysteresis-based controllers, no fixed sampling rate can be defined, but only an average period duration [7].  
To overcome the limitations of the present modeling approaches, the theory of switched systems [14] can be applied. Following this theory, the use of averaged models can be avoided, as the system's dynamical behavior in every switching state is inherently included in the model. Given a switched system, the Lyapunov theory is a proven tool for stability analysis [14; 15; 16; 17]. Based on this theoretical background, refs. [18; 19] analyzed the stability of switched systems with unique stable equilibria at every switching state, which can finally also be used to design hysteresis-based current controllers with guaranteed stability properties.  
To demonstrate the basic ideas, this research focuses on an exemplary two-level converter system with inductive-resistive load and extends the general concept of [19] to a control law, which can also cope with time-varying reference trajectories. The paper is structured as follows. First, an introduction to the stability theory of switched systems is given; then, a hysteresis-based control is derived. Subsequently, the model of a voltage-source converter (VSC) plus load is introduced and the proposed control scheme is demonstrated by example. The paper concludes with simulations and a discussion of the results.
## 5 Conclusions  
The interaction of a drilled shaft with competent calcite deposits in heterogeneous soil was investigated following the results of full-scale Osterberg tests and the conducted finite element simulation. It was shown that the installed location of the O-cell hydraulic jack, with respect to the calcite layers, affects the interpretation of the test and reported load-settlement values.  
The finite element analysis indicates that the preferred location for the installation of the O-cell in soil containing a competent calcite layer is the closest distance to this competent deposit. In this installation setting, the load-settlement curves resulting from the upward load application in the bidirectional Osterberg test are comparable to the downward load application of the conventional top-down test. Therefore, the measured values from the Osterberg test are more reliable with such an installation setting. The selection of a proper installation location for the O-cell alongside the pile length leads to considering the effect of calcite layers' presence in the measurements of pile's side resistance capacity, which, in turn, results in shorter required pile lengths.  
Based on the outcome of this study, it is recommended to locate the most competent calcite layers in the soil profile prior to installation of the O-cell hydraulic jack, and to install this apparatus as close as possible to the competent calcite. This will enhance the design procedure of the drilled shaft in interaction with highly cemented soil layers, such as calcite deposits, and lead to a more economical pile design approach by considering the bearing capacity of calcite and reducing the required pile length.  
Conceptualization, R.A. and M.K.; Formal analysis, R.A., M.K., and V.F.; Software, R.A. and V.F.; Supervision, M.K.; Validation R.A. and V.F.; writing--original draft preparation, V.F. and R.A.; Data curation, R.A. and M.K.; Writing--review & editing, V.F. and M.K.; Funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by NOVA Geotechnical & Inspection Services, NV, USA.  
The authors declare that they have no conflict of interest.  
Figure 16: Unit side resistance and load–settlement comparison for the Osterberg test and conventional test (Case Study II).
## 3 Result and Discussion
### Material Features Study
If the Poisson's ratio lies in the range 0.1-0.2, the cross-link density will be high for the system with the better tightly packed structure to capture radiation [32]. For the present proposed glasses, the Poisson's ratio lies in the range 0.268-0.278, depicting better cross-link density of the tiled glasses that signifies better shielding performance. From the table, the observed reduction in the softening temperature (SBT-Ti > SBT-Zn > SBT-Ba > SBT-Pb) denotes the rise in the production of NBOn that causes a loosely bounded glass structure with reduced lattice vibrations. The micro-hardness usually depicts the essential stress to remove the glass matrix's free volume (distortion of the system) [33]. The free volume denotes the loosely  
Figure 2: The correlation between density and molar volume of the fabricated SBT glasses. packed structure, which should be minimum for a shielding medium. Figure 5 portrays the correlation between Poisson and micro-hardness of the studied glasses that expresses higher values of the SBT-Ti glass, indicating reduced free volume and better compactness of the structure than other samples. The bond fractal connectivity is a vital feature relating the mechanical attributes of glasses to their networks. The effective dimensionality and cross-link density of glassy systems can be expressed by means of bond fractal connectivity. The evaluated values may lie in the range of 1,2,3 for the chain, layer structure, and 3D structure depending upon the complexes. It is apparent from Table 2 that the assessed values lie in the range 2.087-2.201 for the as-polished samples symbolizing their 2D layer structure. Figure 4: The relationship among the various elastic moduli of the fabricated SBT glasses. Figure 3: The relationship among the dissociation energy (\(G_{t}\)) and packing density (\(V_{t}\)) of the fabricated SBT glasses.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### NetLogo  
NetLogo excels in modelling dynamic interactions within a community, representing agent-based systems with adaptability and responsiveness [83]. It incorporates learning mechanisms for agents to adapt based on experiences, contributing to dynamic simulations [84]. NetLogo integrates socio-economic variables and demographic factors, allowing for a realistic portrayal of community dynamics [85; 86]. Participatory modelling is facilitated through NetLogo's HubNet, enabling community members to actively participate in model development and testing [87]. Collaborative decision-making and scenario planning are supported, fostering the exploration of different scenarios [88]. NetLogo handles diverse data types and supports scalability for different community sizes [89]. It addresses uncertainties and risks associated with adaptation strategies, offering flexibility for various community contexts [90]. Sensitivity analysis and scenario testing are facilitated through the NLRX package [91]. NetLogo incorporates feedback loops for modelling the impact of adaptation measures over time, allowing users to observe and influence future states. It supports monitoring and evaluating the effectiveness of strategies, enabling a comprehensive assessment of their performance [92]. In conclusion, NetLogo is a versatile and accessible tool for community-based adaptation modelling, offering dynamic interaction modelling, learning processes, socio-economic integration, participatory modelling, scalability, uncertainty assessment, and comprehensive strategy evaluation across various domains, including social and natural sciences, teaching, and research.
# Overflow Energy Loss Recovery System Based on Hydraulic Motor-Electric Generator
## 3 Performance Analysis
### Stability Analysis of Outlet Back Pressure System of Proportional Relief Valve
After numerical calculation, the closed-loop transfer function of traditional proportional relief valve system \(M_{1}\)(s) and the closed-loop transfer function of system with back pressure disturbance at the outlet of proportional relief valve \(M_{5}\)(s) are obtained  
\[M_{1}(s)=2.46\times 10^{11}\times\frac{s^{2}+6.52\times 10^{4}s+6.66\times 10^{8 }}{s^{4}{+}3.95\times 10^{4}s^{3}+4.04\times 10^{7}s^{2}+6.26\times 10^{11}s+3.85 \times 10^{14}} \tag{40}\]  
\[M_{5}(s)=0.965\times\frac{s^{4}{+}4.02\times 10^{4}s^{3}+2.55\times 10^{11}s^{2} +1.66\times 10^{12}s+1.70\times 10^{20}}{s^{4}{+}3.95\times 10^{4}s^{3}+4.04 \times 10^{7}s^{2}+6.26\times 10^{11}s+3.85\times 10^{14}} \tag{41}\]  
The Routh array of the control system is as follows  
\[ \begin{array}{cccc}s^{4}&1&4.04\times 10^{7}&3.85\times 10^{14}\\ s^{3}&3.95\times 10^{4}&6.26\times 10^{11}\\ s^{2}&2.45\times 10^{7}&3.85\times 10^{14}\\ s^{1}&5.28\times 10^{9}&\\ s^{0}&3.85\times 10^{14}&\\ \end{array} \]  
It can be seen that  
1. The coefficients of the characteristic equation are not equal to zero;
2. All the coefficient symbols of the characteristic equation are the same, and all the items in the first column of the Routh array are positive signs; and
3. The proportional relief valve control system with back pressure interference is still stable, which lays a theoretical foundation for the follow-up research on energy recovery of overflow loss.
## 3 Methodology  
The estimation of parameters for the analytical models ([PERSON], [PERSON], [PERSON], and [PERSON]) of fixed-bed adsorption for hydrogen sulfide (H\({}_{2}\)S) and carbon dioxide (CO\({}_{2}\)) gasses was conducted using experimental data from [29], who analyzed the efficiency of babassu biochar as an adsorbent for H\({}_{2}\)S removal from air under low-pressure conditions in a fixed-bed column, and [30], who conducted an experimental and simulation study using a fixed-bed column for CO\({}_{2}\) adsorption using activated carbon. The authors employed the Linear Driving Force (LDF) model for parameter estimation, which is based on a system of partial differential equations that requires greater complexity for equation solving and subsequent estimation. The development of the model used by the authors is detailed in the Supplementary Material [31]. In this perspective, analytical breakthrough curve models of [PERSON], [PERSON], [PERSON], and [PERSON] (dose-response) were used, aimed at assessing the models' ability to achieve good agreement with experimental data, reducing computational costs, enabling faster analysis, and avoiding the complexity of partial differential equations. This approach is advantageous when working with large datasets or when multiple simulations are required [32]. Each model involves unknown parameters that cannot be directly determined from the observed or available data. Thus, these parameters were inferred through Bayesian inference using the Markov Chain Monte Carlo method (MCMC) with the Metropolis-Hastings algorithm. In Table 1 are the operating conditions used in the adsorption process; Table 2 presents a summary of the models used with their respective equations, parameters, and assumptions; and Table 3 presents the models with the parameters to be estimated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Initial Concentration (mg/L)**} & \multicolumn{2}{c}{**H\({}_{2}\)S**} & \multicolumn{2}{c}{**CO\({}_{2}\)**} \\ \cline{2-6}  & **1.327** & **2.577** & **18** & **29** & **35** \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Adsorbent mass (g) \\ Maximum adsorption capacity (mg/g) \\ Flow rate (L/min) \\ \end{tabular}  } & 18.4052 & \multirow{3}{*}{18.3893} & \multirow{3}{*}{7.26165} & 230 \\  & & 20.61 & & 10.43037 & 12.14676 \\ \cline{1-1} \cline{3-6} \cline{5-
# An Interface Pressure Detection Method of Cable Silicone Rubber-XLPE Based on Nonlinear Ultrasound
## 1 Introduction
found that the second harmonic generated by ultrasonic waves passing through a metal contact interface increases and then decreases with the interface pressure, reaching a maximum at a specific pressure value [17]. [PERSON] et al. used both linear ultrasound and nonlinear ultrasound based on the transmission method to detect the pressure at metal contact interfaces and showed that the ultrasonic nonlinear coefficient decreases with increasing interface pressure [21], and that the nonlinear characteristic parameter (nonlinear coefficient) is more sensitive to changes in interface pressure than the linear ultrasound characteristic parameter (first-order stiffness coefficient) over a small pressure variation range [22]. However, most of the current research on nonlinear ultrasonic detection of stresses at contact interfaces is aimed at metal-metal contact interfaces or metal-nonmetal interfaces. Nonlinear ultrasonic detection of stresses at nonmetal-nonmetal contact interfaces is less common. The authors based this study on nonlinear ultrasonic technology to detect the stress at the silicone rubber-XLPE interface. It not only expands the nonlinear ultrasonic detection of stress at the nonmetal-nonmetal contact interface, but also provides a possibility for the non-destructive detection of pressure at the interface between cable accessories and the body. A non-destructive testing method for the interface pressure of cable accessories based on nonlinear ultrasound is proposed to address the limitations of existing measurement methods for measuring the interface pressure of cable accessories. Firstly, the propagation mechanism of ultrasonic waves at the interface of silicone rubber-XLPE is simulated and analyzed, and the relationship between the nonlinear coefficient and the interfacial pressure is obtained. Then, the curved structure of the cable accessory and the body is simplified to a flat structure, and the nonlinear ultrasonic test platform is built based on the ultrasonic pulse reflection method, with silicone rubber and XLPE as the research objects. The ultrasonic response under different pressures is then evaluated.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 2 Background and Related Works
### Trust Management in MANET and VANET over NDN  
Trust establishment and management are well studied in distributed environments, such as MANETs and VANETs [16; 17]. Trust models are generally classified into entity-based, data-based, and hybrid models depending on the revocation target. In addition, there also exist trust establishment solutions for both MANET and VANET contexts.  
[PERSON] et al. [18] propose a social trust-based security scheme to verify the public-key and producer identity binding in Mobile Information Centric environment. Their proposal allows user to verify the content producer identity and its public-key binding relationship by retrieving the identity bundle from a trust social network. However, as revealed by the authors, this proposal works only for low mobility scenarios. In addition to the trust establishment issues, [PERSON] et al. [19] presented the features of information-centric vehicular networks together with the main open challenges. [PERSON] and [PERSON] [20] proposed the concept of Context Aware Routing Protocol (CARP) in NDN based VANETs which aims to transmit trusted information in a secure and reliable way. This proposal is designed such that it supports the traditional NDN Daemon in every vehicle, consisting of NDN data structures and forwarding strategies, and implements rules to check the trustworthiness of messages. However, this proposal sustains the same NDN hierarchical data authentication process which is not suitable for highly dynamic environment like VANET. Through extensive simulations, [PERSON] et al. [21] conclude that the classical geo-based routing protocols do not sustain the same performance in vehicular named data networks as in VANETs, that is why they propose to use a trust-based routing strategy instead. However, they only discussed the expected performance of their solution without implementing the proposal.
# Conductive Textiles for Signal Sensing and Technical Applications
## 4 Testing for Conductive Textiles
### Electromagnetic Shielding (EMS) Testing
#### 4.2.2 Shielded Box Method  
This method is widely implemented for comparative studies of different shield materials [278]. The test involves a metal box with a port to set samples. A transmitting antenna is placed outside a box, and the receiving antenna is set inside the box. The transmitter sends the signal, and the receiver receives the signal through an open port or a sample fitted over the port. The standard for shielded box methods frequently mentioned is MIL-STD-285-1956--for the frequency range between 100 kHz to 10 GHz [283]. The measurement ranges in this method are given below [284]:  
* Low range--9 kHz to 20 MHz--applicable for magnetic components (H);
* Resonant range--20 MHz to 300 MHz--applicable for electrical components (E);
* High range--300 MHz-18 GHz (100 GHz)--in case of plane wave power (P).  
The main limitation of this method is the difficulty of achieving proper electrical contact between the shielded box and the specimen [278].
## 4 Methodology
### Model Architecture  
Each edge node hosts a lightweight GRU-based Autoencoder ([22]) specifically designed to detect temporal anomalies in IoT traffic sequences with minimal resource usage. The architecture is composed of:  
* Input Layer: A time-series window of shape (\(T\), \(F\)), where \(T\) = 10 time steps and \(F\) = 20 selected features.
* GRU Encoder: Two stacked GRU layers:
* Layer 1: 64 hidden units; dropout = 0.2;
* Layer 2: 32 hidden units; dropout = 0.2.
* Latent Representation: The final hidden state of the second GRU, forming a 32-dimensional embedding.
* GRU Decoder: Symmetric structure to the encoder, with 32 and 64 GRU units, respectively, followed by a dense projection layer to match input shape.
* Output Layer: Reconstructed time-series window (\(T\), \(F\)).
* Loss Function: Mean squared error (MSE) between original and reconstructed sequences.  
During inference, the reconstruction error is computed per instance and compared to a threshold \(\theta\), empirically defined as follows:  
\[\theta=\mu_{\text{train}}+3\sigma_{\text{train}}\]  
where \(\mu_{\text{train}}\) and \(\sigma_{\text{train}}\) are the mean and standard deviation of the reconstruction error over training data.  
This empirical rule helps distinguish anomalous behavior by identifying data points whose reconstruction error significantly deviates from the norm, assuming a near-Gaussian distribution of errors.  
Samples with error \(>~{}\theta\) are flagged as anomalies. Listing 1 presents the pseudocode of the detection phase.  
```
1 Algorithm:AnomalyDetectionusingReconstructionError
2 Input:test_data\(\leftarrow\)listoftime-seriessequences
3\(\theta\leftarrow\)anomalydetectionthreshold(computedfromtrainingdata)
4 foreachinputsequencex_tintest_datado
5x_hat\(\leftarrow\)model.reconstruct(x_t)
6 error\(\leftarrow\)MSE(x_t,x_hat)
7 iferror>\(\theta\)then
8 label\(\leftarrow\)'anomaly'
9 else
10 label\(\leftarrow\)'normal'
11 endif
12 endfor
```  
The full model has approximately 160,000 parameters and requires <1.2 MB of memory, making it deployable on Raspberry Pi-class devices.
## 2 Materials and Methods
### Dataset  
#### 2.2.1 Meteorological Data  
The gridded meteorological data used in this study were sourced from the CN05 gridded observational dataset. The gridded daily dataset, with the resolution of 0.25 deg latitude by 0.25 deg longitude, was based on the interpolation from over 2400 observing stations in China and included 4 variables: daily mean, minimum and maximum temperatures, and daily precipitation [33]. However, the meteorological data required for the Biome-BGC model includes the daily maximum temperature, daily minimum temperature, mean temperature, daily precipitation, saturation vapor pressure deficit, solar radiation, and day length. So, we needed to use the MTCLIM model to expand the basic meteorological data.  
The MTCLIM model, a mountain microclimate simulation model, is based on the topographic conditions and climate theory and uses the basic observation data of nearby sites to estimate the local daily near-surface meteorological parameters. It is designed to handle the special problems encountered in mountainous terrain, but it is applicable in flat terrain as well [34]. Therefore, this study used the MTCLIM model to expand the weather data based on the latitude, elevation, slope, and aspect data of each grid.
## 4 Discussion
Table 5 provides a summary of the model results, showing that the proposed GMDH-featured ANN achieved the R\({}^{2}\) values of 0.96, 0.87, and 0.75, respectively, compared to the regular ANN with performance accuracies of 0.83, 0.78, and 0.69, respectively. These results indicated that the proposed GMDH-feature ANN had an enhanced performance compared to the regular ANN. However, more experiments need to be performed to examine the model's performance for other transfer/optimizers and hyperparameters. This will be studied in future work. Based on the GMDH and MVR, the mathematical model derived from Tests #1, #5, and #8 shown in Table 6 is summarized in Tables 7-9. The GMDH model given in Equation (11) is a function of input data (x\({}_{i}\) and x\({}_{j}\)) and has six coefficients a\({}_{0}\) to a\({}_{5}\). Similarly, the MVR model shown in Equation (4) has input data (x\({}_{i}\) and x\({}_{j}\)) with three coefficients \(\beta_{0}\) to \(\beta_{2}\). The regression coefficients obtained from the GMDH and MVR were rounded to decimal digits and presented in Tables 7-9. Moreover, during MVR modeling, the \(p\)-test values of the coefficients of all the experiments showed less than 5%. Hence, the MVR model was statistically significant.