esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 4 Simulation Analysis
### Parameters Identification of ARX  
Further, to identify the parameters and order of the ARX model, the reluctance actuator system and Bouc-Wen model are excited by the sine sweep signal, respectively. It is added that the change frequency of the sine sweep signal is [0 100] Hz. Then, the dataset \((x(t),B(t))\), which is needed in the process of identifying linear systems, was acquired. As such, by using \(x(t)\) as input and \(B(t)\) as output and employing the AIC criterion and least square method, respectively, the ARX model is identified as follows:  
\[G(z)=\frac{0.1786z-0.1774}{z^{2}-1.801z+0.8032} \tag{15}\]  
Overall, the rate-dependent hysteresis model based on the Hammerstein structure is established. And it is described in (1), (13), and Table 2.  
The identified Hammerstein structure is used to simulate the rate-dependent hysteresis loop, and the simulation effect is shown in Figure 13.  
From the figure, we can draw a conclusion that the Hammerstein structure can better simulate the hysteresis loop of the reluctance motor.  
**Remark 3**.: _In practice, when the demand for model accuracy is higher, Hammerstein model accuracy, which is not only affected by the composition of each structure, that is, the structural form of nonlinear system and linear dynamics, but also affected by the identification accuracy, can be improved. In application, the nonlinear system of the Hammerstein model is not limited to the Bouc-wen model. Many models that describe the nonlinear relationship of hysteresis, such as the JA and P models, can be applied. Also, in the linear part, the ARX model is not the only choice, and some second-order linear systems that can represent the linear relationship are also used. Meanwhile, the identification method plays an important role in model accuracy. At present, the identification method, which can extract all parameters of the coupled nonlinear static block and the linear dynamic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(a_{0}\) & \(-4170.7\) \\ \(a_{1}\) & \(594.76\) \\ \(a_{2}\) & \(-3294.6\) \\ \(\alpha\) & \(0.1203\) \\ \(\beta\) & \(3.6779\) \\ \(\gamma\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identified parameters of the classical Bouc-Wen model.  
Figure 12: Bouc-Wen model identification results: (**a**) Hysteresis loop obtained by experiments and the identified Bouc-Wen model; (**b**) Bouc-Wen model error regarding experimental data.  
block simultaneously, is developed. For the Hammerstein model described previously, however, the more conventional parameter-extraction techniques described below have been found to be adequate. In particular, reluctance motors have rate-independent characteristics at low frequencies; that is, the width of the hysteresis loop does not change with a frequency below 0.1 Hz. In application, the identification method, which is proposed in the paper, is simple and effective._
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 4 The Proposed Scheme: Mutually-Shared Region-Based Location Verification
### Mutually Shared Region Token  
The MSRLV makes use of location evidence between the location claimant and verifier, which is called the MSR token. The MSR token is a value that can be derived from the logical or physical features of sensors, but it should not be easy for any third party to guess or calculate it. For example, coordinates, transmission power or packet count from sensors who are involved in location verification cannot be used directly to calculate the token since these are easy to guess. In this paper, the token is the summation of the random values that are coming from common neighbors between two nodes, claimant \(c\) and verifier \(v\), as shown in Figure 2. The token should meet the following properties to provide security of the verification:  
* Mutual symmetry: The token should be known to two participants, but unknown to the other sensors. That is, two sensors, verifier \(v\) and claimant \(c\), need to know the same token without explicit message exchange. This implies \(msrToken_{v}=msrToken_{c}\), where \(msrToken_{i}\) means the MSR token computed at sensor \(i\).
* Independent computation: Two participants should be able to compute the token independently. This helps the scheme eliminate the necessity of explicit message exchanges, which might cause vulnerability in the system according to the attack model. This implies \(msrToken_{v}=f(data_{vc}^{t})=f(data_{vc}^{t})=msrToken_{c}\), where \(f()\) is the function that is used for the calculation of the token, and \(data_{ij}^{t}\) means any kind of data observed at both sensors \(i\) and \(j\) and collected by sensor \(i\) at time \(t\).
* Anti-deduction: The token should be safe against any deduction, so it should be processed to prevent adversaries from creating it with a guess. Thus, the use of raw data, such as transmission power, packet count or neighbor count, that is easy to guess or estimate should be avoided. This implies \((msrToken_{v}=msrToken_{c})\
eq msrToken_{m}\), where \(m\) means a malicious sensor.
## 2 Verification of Homogenization Analysis for Unidirectional Composites
### Homogenization Analysis and Experimental Results  
The effective properties of the homogenization analysis are outlined in Table 2 based on the change in volume fraction. To verify the homogenization technique used in this study, the effective properties were calculated using the ROM [33; 34], then compared with the homogenization analysis results. Table 3 compares the results of the homogenization analysis and the simple ROM (SROM) for the \(E_{11}\) tow. From table, the homogenization analysis results are well conformed with the SROM results.  
The aforementioned analysis results should be compared with the experimental results to enable a more detailed evaluation of the homogenization technique. To compare the homogenization analysis results with the experimental results, tensile tests were performed in accordance with the method described in Section 2.1 for unidirectional carbon/epoxy composites (TR50S/Epoxy); the results are shown in Figure 5. Table 4 summarizes the homogenization analysis and experimental results. The error rates of \(E_{11,low}\) and \(v_{12,low}\) were 1.13 and 9.09%, respectively. Thus, the homogenization analysis result correlated well with the tensile test results. From these results, the homogenization analysis technique in this study was verified for the carbon/epoxy composites.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Fiber (Carbon)** & **Matrix (Epoxy)** \\ \hline Young’s modulus (GPa) & 240.00 & 2.45 \\ Shear modulus (GPa) & 93.39 & 0.91 \\ Poisson’s ratio & 0.285 & 0.350 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties of the fiber and matrix (TR50S/Epoxy).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **VF 10\%** & **VF 20\%** & **VF 30\%** & **VF 40\%** & **VF 50\%** & **VF 60\%** & **VF 70\%** \\ \hline \(E_{11,tow}\) (GPa) & 27.20 & 49.80 & 72.40 & 97.20 & 120.90 & 144.75 & 168.47 \\ \(E_{22,tow}\) (GPa) & 5.84 & 6.74 & 7.80 & 6.62 & 9.06 & 13.19 & 22.42 \\ \(E_{33,tow}\) (GPa) & 5.84 & 6.74 & 7.80 & 6.62 & 9.06 & 13.19 & 22.42 \\ \(G_{12,tow}\) (GPa) & 1.95 & 2.24 & 2.57 & 2.09 & 2.71 & 3.75 & 6.14 \\ \(G_{13,tow}\) (GPa) & 1.90 & 2.12 & 2.35 & 1.63 & 1.96 & 2.51 & 3.79 \\ \(v_{12,tow}\) & 0.342 & 0.324 & 0.307 & 0.320 & 0.313 & 0.306 & 0.298 \\ \(v_{13,tow}\) & 0.342 & 0.324 & 0.307 & 0.320 & 0.313 & 0.306 & 0.298 \\ \(v_{23,tow}\) & 0.504 & 0.496 & 0.472 & 0.392 & 0.326 & 0.252 & 0.170 \\ \hline \end{tabular}
\end{table}
Table 2: Homogenization analysis results of the micro-level.  
\begin{table}
\begin{tabular}{c c c} \hline
**VF (\%)** & **SROM (GPa)** & **Result of Homogenization Analysis (GPa)** & **Error (\%)** \\ \hline
10 & 26.21 & 27.20 & 3.78 \\
20 & 49.96 & 49.80 & 0.32 \\
30 & 73.71 & 72.40 & 1.78 \\
40 & 97.47 & 97.20 & 0.28 \\
50 & 121.23 & 120.90 & 0.27 \\
60 & 144.98 & 144.75 & 0.16 \\
70 & 168.74 & 168.47 & 0.16 \\ \hline \end{tabular}
\end{table}
Table 3: Normalized \(E_{11,tow}\) comparison for the rule of mixture and the homogenization analysis result.
## 1 Introduction
Despite the complexity of the underlying mechanisms, satellite-retrieved far-red SiF has been directly compared with GPP from data-driven and global dynamic vegetation models as well as tower-based flux estimates [34; 53; 54]. Significantly positive, biome-specific correlations were found between absolute SIF and absolute GPP values with the highest correlations reported for managed vegetation systems [53; 54]. Note that the satellites are in sun-synchronous orbits, which means that the local (solar) overpass time is nearly constant and lighting conditions are similar across the globe. The goal of this paper is twofold. Firstly, we further explore the applicability of the data-driven retrieval approaches developed for broad spectral windows by [PERSON] et al. [21] (see also [36]). We set up our own algorithm for retrieval of far-red SiF from GOME-2A and follow the general method of [PERSON] et al. [21], while the details of the implementation differ in a number of aspects. Rather than performing detailed retrieval simulations, we can now make a comparison between the SiF time series produced by the two algorithm implementations and provide scientific verification of the data-driven method. Secondly, we further investigate the relationship between satellite-retrieved far-red SiF and flux tower GPP. To this end, we produce a SiF time series retrieved from GOME-2A spanning more than eight years. This time series is then compared with that of GPP derived from flux tower measurements. Here, we focus on flux tower measurements from Australia (OzFlux). These sites have the advantage of being representative of relatively large areas due to the relative homogeneity of ecosystems in Australia. The paper is structured as follows. Section 2 describes the retrieval setup that we used for producing the SiF time series from GOME-2A measurements. Section 3 provides a brief description of the resulting time series. Section 4 then compares retrieved SiF values with GPP derived from Australian flux tower measurements. An anomaly in the SiF series for Australia in early 2011 is discussed. Section 5 compares the SiF time series from our implementation (\"SiF KNMI\") with the latest implementation of [PERSON] et al. (2011) (\"SiF NASA (v26)\"; [[http://avdc.gsfc.nasa.gov/](http://avdc.gsfc.nasa.gov/)]([http://avdc.gsfc.nasa.gov/](http://avdc.gsfc.nasa.gov/)))). Section 6 presents sensitivity experiments with real GOME-2A spectra. A final conclusion is provided in Section 7.
## 4 Experiments and Results
### Evaluation Metrics  
The evaluation of the segmentation performance on the NEU-seg dataset involves the use of pixel-based metrics. These metrics are primarily used to assess the integrity of segmented objects. To assess the segmentation performance of our network, we use various metrics, including the mean intersection over union (mIoU), Dice coefficient, mean average precision (mAP), and recall, where mIoU is considered as the primary indicator.  
The mIoU and Dice coefficient are employed as evaluation metrics to quantify the degree of similarity between predicted segmentations and their respective ground truth labels, while precision and recall are used to represent the number of targets correctly identified based on the prediction results and the ground truth, respectively. These metrics are described by  
\[\text{mIoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}, \tag{7}\]  
\[\text{Dice}=\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}}, \tag{8}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{9}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{10}\]  
where TP, FN, and FP represent the number of pixels that are correctly identified as defective, the number of unidentified defective pixels, and the number of false positive pixels identified as defective, respectively.
# Shell Waste Management and Utilization: Mitigating Organic Pollution and Enhancing Sustainability
## 3 Environmental Applications
### Heavy Metal Adsorbents/Soil Amendment  
Soil pollution by heavy metals might result from industrial activities, fertilizers, pesticides, livestock residues, and other wastes with high metal content. It can represent risks for ecosystems and for population, by introduction into the food chain, or by decreasing the quality of land, as metals do not undergo microbial or chemical degradation in the soil, and persist for a long time [47].  
The property of calcined shells to adsorb heavy metals in water can be translated to remediation of soils contaminated with heavy metals. Contaminated soil pH is increased by CaCO\({}_{3}\), which leads to the formation of calcium silicate hydrate and calcium aluminate hydrate, subsequently forming metal hydroxides and a relatively impermeable soil layer, thus effectively ameliorating soils [48]. For agricultural purposes, this holds many advantages. For example, calcined oyster shells applied to agricultural soil lowered Cd\({}^{2+}\) and As\({}^{2+}\) contents in the edible part of cabbage by 98% (Cd\({}^{2+}\)) and 73% (As\({}^{2+}\)), making it safe for consumption. However, the best results were attained by the calcination of oyster shells at up to 800 \({}^{\circ}\)C, not negligible in energy demand [49]. Lower temperatures for drying seashells (105 \({}^{\circ}\)C) seemed to have yielded a promising material for immobilizing PbS in the industrial arid soil. The decrease in soil toxicity varied relative to the dosage and time of application, however the soil remained a high environmental risk category even after the treatment [50]. For copper-rich vineyard and mine soils, the application of crushed mussel shell diminished Cu\({}^{2+}\) desorption rates up to 86% at pH 3. Such additions could thus contribute to reduce potential hazard of copper-enriched soils under acidification events [51]. Typically, the neutralization of acidity and metal contamination in soils by liming improves fertility and oxygen level of soils, and crushed mollusc shells may be a valuable replacement for mined limestone.
## 1 Introduction
Most scholars deemed the overall urban infrastructure system the research subject for comprehensive evaluation. Constructing an index system from three benefits of urban infrastructure [31] (i.e., economic benefits, social benefits, environmental effects) or composition characteristics of urban Infrastructure infrastructure [32; 33] assess its resilience levels. Besides, some scholars assessed resilience from the perspective of the interaction between infrastructure and external environment, such as infrastructure-environment [34], infrastructure-economy [35], infrastructure-economy-society-environment [36]. It can be found that most indexes in existing evaluation systems of urban infrastructure resilience commonly described the statefulness while ignoring other stages, namely pressure and response. Therefore, these indexes system hardly reflected the dynamic nature of urban infrastructure resilience. So, this paper introduced the adaptive PSR framework into the evaluation of urban infrastructure resilience. Pressure-State-Response (PSR) was a causal-oriented framework proposed by the Organization for Economic Cooperation and Development (OECD) [37]. The PSR model included three indicators (i.e., pressure, state, and response), as shown in Figure 1. Pressure describes the threats and disturbances caused by the internal and external environment to the UI, explaining why the system changed. State represented the state of the UI under pressure. The response is the self-regulation of UI to adapt to changes and the preventive measures taken by the government and residents [38]. It is not difficult to find that the PSR model connects the causes, impacts, and response to environmental change. The three indicator layers' mutual restriction and effect derived a cycle development network, which continuously adjusted the system to a balanced and stable state. Given its logic, flexibility and comprehensive-ness, the PSR model was widely applied for ecological security assessment [39; 40; 41; 42], urban carrying capacity assessment [43; 44], and resilience assessment under various disaster risks [45]. UI resilience is dynamic and procedural, and it will also experience the dynamic development process of pre-disaster, mid-disaster and post-disaster states after being impact-ed or disturbed by external. Although most scholars have focused on the dynamic and procedural of resilience, the existing infrastructure resilience assessment process still paid too much attention to the state indicators of UI, ignoring the resilience process and positive feedback process of disturbance and UI. The logic structure of PSR, \"cause-effect-response\", could make up for the deficiency of the existing UI assessment system to further highlight the dynamic and procedural of resilience. Therefore, the PSR model was introduced in this paper, and construct the UI resilience evaluation system from the input of pressure, the change of UI state under pressure, the autologous feedback of UI and human actions.
## 3 Numerical Simulations and Discussion
### Parameter Values
#### 3.1.3 Effect of Harvest Rate (\(\upmu\))
By increasing the harvest rate, fewer livestock remain, thereby reducing the strain on resources and preventing overgrazing. If the harvest rate is not adjusted, Kgagodi could experience losses of up to 1000 animals, resulting in a reduction in over 2000 livestock compared with other regions. Upon analyzing the plots for Kgagodi in Figures 10-12, we observe that the Kgagodi area is particularly sensitive to variations in rainfall timing, with both scenarios showing biomass depletion, especially during mid-year. This depletion strongly impacts livestock, especially when rains are delayed. Increasing the harvest rate here could help stabilize the livestock population by preventing overgrazing during this critical period. Conversely, early rainfall allows for a more consistent supply of biomass, thus better supporting livestock health and productivity. These regions may not require a harvest rate as high as in Kgagodi for livestock management since plant biomass remains more abundant and is able to sustain livestock. However, monitoring is essential to ensure livestock numbers do not exceed the ecosystem's carrying capacity. Figure 11: Figure showing effect of increasing harvest rate when rainfall comes later than expected in different regions of Botswana.
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 5 Attacker Model Security Assessment
### DDS Model Security Assessment  
From the state space report of the attacker-based model shown in Table 5. Among them, REY-ATK represents the replay attack model, TAR-ATK represents the tampering attack model, and SPF-ATK represents the spoofing attack model. As can be seen, the number of state space nodes, state space arcs, and strongly connected nodes, as well as strongly connected arcs are consistent, indicating that there are no loops in the model, which is in accordance with the modeling expectations. At the same time, the number of dead nodes changes from 1 to 6. By writing ML and using the ListDeadMarkings () function, the serial numbers of all dead nodes can be determined, we can find that two dead markings are generated due to the introduction of tampering attacks resulting in the reset operation of the message from the participant, and the other two dead markings are replay attacks caused by attackers stealing legitimate messages and faking them to prevent legitimate messages from the participant. The number of spoofing attacks is 1, indicating that the protocol is completed, and there is no spoofing attack in the protocol, which is in line with the actual situation of the protocol. At the same time, use the NodeDescriptor() function to print the 10,380 node data, the data indicate that the state is the protocol termination state, indicating that the identity authentication is over, as shown in Figure 13. By comparing and analyzing the original model with the attack model, it is demonstrated that this attack  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Number** & **Name** \\ \hline State Space Nodes & 420 & / \\ State Space Arcs & 1056 & / \\ Strongly Connected Nodes & 420 & / \\ Strongly Connected Arcs & 1056 & / \\ Dead Markings & 1 & [420] \\ Live transition instances & 0 & / \\ Dead transition instances & 0 & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: DDS protocol original model state space report.  
model can effectively attack the connection and transmission processes of the original request commands, the original protocol has vulnerabilities that could be exploited by a man-in-the-middle attack, including replay and tampering.
## 4 Experiments and Analysis
### Analysis of Dynamic Experiment Results
In order to obtain the fluctuation trend of the CCS under different working conditions, through setting parameters, a specified number of frequency values were sampled in a certain time using the instrument. In addition, the standard deviation [24; 25] of all frequency numbers from each flow rate and water cut point was obtained. The standard deviation of each flow rate and water cut point was interpolated to obtain the 3D map, as shown in Figure 23.  
As can be seen from the figure, when the flow rate is 10-60 m\({}^{3}\)/d, the fluctuation of CCS responses is very small, and the flow rate and water cut have little effect on it. When the flow rate is 60 m\({}^{3}\)/d and the water cut is 0.3-0.4, the standard deviation tends to increase. It can be deduced that in this case, the oil and water emulsifies, a large number of continuous water flows into the instrument, and  
Figure 22: Calibration results of the CCS with a flow rate and water cut in the horizontal oil–water two-phase. the conduction current in the fluid increases sharply. Thus, the random fluctuation also increases, and the response volatility becomes larger. In addition, when the maximum standard deviation of the sensor appears at a low flow rate, it is considered that the oil bubble is large in volume and unevenly distributed in the water phase at this time. At the same time, due to the influence of the hydrophilicity of the insulating dielectric layer on the electrode surface, the oil-covered electrode or the departure electrode is accidental, so the response standard deviation is large.