esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Verification
### Comparison of Group Velocities  
Before using the ellipse methods, the group velocity of S0 mode was determined. A pair of PZT transducers apart from the same actuator with different distances were used to receive Lamb wave signals in the intact plate. The group velocity was calculated based on the respective distance and arrival time at a frequency. The experimental testing was conducted over a range of frequencies. Following the experimental testing, the group velocity dispersion curves based on dispersion equation were also calculated for Lamb waves in the aluminum plate from 0 to 1 MHz\(\cdot\)mm.  
As shown in Figure 4, the results of the experimental testing and dispersion curves do not exactly agree with each other, which may result from the inconsistency between the theoretical and actual material parameters. Nevertheless, the results still show that there is effective actuation and reception of Lamb wave signals in the aluminum plate. It was found that the group velocity of S0 mode at a frequency-thickness of 0.56 MHz\(\cdot\)mm was about 4978.6 m/s. Meanwhile, the group velocity at a frequency-thickness of 1.12 MHz\(\cdot\)mm was about 4688.6 m/s as determined by extrapolation.
# FE Analysis of Motorcycle Helmet Performance under Severe Accidents
## 2 Injury and Strength Assessments
### Head Injury Assessment  
The helmet testing regulation usually evaluates the head injury of the headform based on head injury criteria (HIC) [34], which can be used to rate the safety associated with crash accidents and other applications. The HIC can be recorded directly with a measuring device installed at the center of gravity (CG) of the headform during testing. Furthermore, calculation of the HIC can also be performed based on the maximum acceleration during the decided time interval (15 or 36 ms) using Equation (1), where \(a(t)\) is the resultant acceleration, while \(t_{2}\) and \(t_{1}\) are time intervals.  
\[\text{HIC}=(t_{2}-t_{1})\left(\frac{f_{t_{1}}^{t_{2}}a(t)dt}{t_{2}-t_{1}} \right)^{2.5} \tag{1}\]  
HIC are widely used in various applications, such as sports, to investigate severe head injuries. A high HIC value indicates a high risk of head damage, which can cause a fatality. Furthermore, the HIC has some correlations to classify and predict a level of injury by assessing the probability based on its score.  
The Abbreviated Injury Scale (AIS) is also used to describe the probability of accident severity, which uses a scale at levels from 0 (no injury) to 6 (lethal injury) [35; 36]. AIS level 2 for body injuries (AIS 2+), which classifies moderate injury for each part, is always defined as the minimum assessment to predict the probability of a fractured skull when an impact occurs. Equation (2) utilizes the results of the HIC value to calculate the probability of AIS 2+, where \(N\) is the cumulative normal distribution, \(\mu\) and is 6.96352, and \(\sigma\) is 0.84664.  
\[p(fracture)=N\left(\frac{ln(\text{HIC})-\mu}{\sigma}\right) \tag{2}\]  
The AIS score can vary from 0% to 100%, representing the probability of injuries under each level. However, there are no criteria to distinctly classify the safe condition of humans based on the AIS.
## 5 Experiments and Results
### Analyses of Parameters' Impact on Data Cleaning Operations  
Considering all the input data except for those data points associated with total number of slots \(\leq\)1, we evaluate the number of filtered stations based on the thresholds \(f\) and _varianceThreshold_. Table 5 lists the values considered for \(f\) and _varianceThreshold_.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 2 Observations and Data Processing
### SWH Estimation  
The sea surface waves cause the roughness of the sea surface, so the signal received by the receiver has a multipath effect. Because of the change of the sea level, the GNSS signals come from any directions including scattered signals which may reach the receivers. The interference between the direct, reflected and scattered signals will affect the GNSS observation and cause the oscillation of the observation which can be reflected in the SNR pattern. Therefore, the SWH can be estimated from SNR. Based on X-band radar, empirical analysis has shown that there is a linear relationship between SWH and the square root of SNR.  
Due to the multipath effect, the reflected signal has a phase delay compared to the direct signal, which is related to the antenna height h and sea level in Figure 3. Interference between the direct and reflected signals will affect the GNSS observations and cause oscillations in the observations, which can be reflected in the SNR. In the GNSS navigation and positioning process, the SNR estimation algorithm and the technique of using SNR to improve the quality of direct signal have been mature and widely used. In the observation of sea level, the oscillation of the SNR can reverse the change in sea level.  
The linear relationship between the SWH and the square root of the radar image SNR is extended to the L-band based on GNSS-R estimated SWH. The inversion model is as follows:  
\[\text{SWH}=\text{A}+\text{B}\sqrt{SNR_{r}} \tag{1}\]  
The SNR can be DDMSNR for the CYGNSS system, and the formula of the DDM SNR as follows [17]:  
\[\text{DDM}_{SNR}=\frac{DDM_{power}-\langle noise\rangle}{\langle noise\rangle}, \tag{2}\]  
where \(\langle noise\rangle\) is the mean of the noise floor, and \(\text{DDM}_{power}\) is the specular point maximum power mentioned in Section 2.1.  
Because the CYGNSS program is satellite-based, the receivers are in motion compared to the ground-based GNSS, which increases the difficulty in data processing. The IPT pattern needs to receive signals from the same point over a period of time which requires the receivers to be static, so the IPT pattern is not applicable to satellite-based GNSS. Because the data provided by CYGNSS does not completely contain the necessary parameters for solving the Derivative of Correlation Function (DCF), the DCF method is not applicable to the significant wave height estimation of CYGNSS. Compared with airborne GNSS, satellite-based GNSS has a certain regularity of motion and can detect SNR and other information globally. Therefore, the CYGNSS SNR method can significantly obtain global SWH data.  
Figure 3: Direct signal and sea surface reflection signal path received by CYGNSS satellite.
## 1 Introduction
[23] designed a spacecraft rendezvous and docking controller based on a coupled orbit-attitude dynamic model by combining adaptive methods with the backstepping method using switching functions. With similar methods, [PERSON] et al. [24] proposed a neural networks controller for spacecraft formation using only aerodynamic forces. Given that linearization errors and external disturbances do exist in the dynamics of underactuated formation reconfiguration in elliptic orbits, a switching neural network controller (SNNC) for the underactuated spacecraft formation reconfiguration problem is proposed, using adaptive neural network controllers to approximate the uncertainties in the neural active region and using adaptive sliding mode controllers to approximate uncertainties outside the active region. The proposed SNNC enhances the steady-state control accuracy and does not rely on the upper bound of the uncertainties, thus, could improve the robustness of the system against uncertainties. Compared with the existing related research, the main contributions of this article can be summarized as follows. 1. The nonlinear terms and perturbations in the dynamics model of underactuated SFF are estimated in real-time by a neural network to obtain higher control accuracy. 2. The adaptation laws of the radial-based neural networks derived by using [PERSON]'s method are provided to estimate the unknown parameters of the system so that it is not necessary to know the upper bound of the perturbation in advance. 3. The control scheme is able to complete the underactuated formation reconfiguration task in elliptic orbits with the loss of radial or in-track thrust, improving the reliability of the task completion. The paper is structured as follows: Section 2 develops a model for the dynamics of underactuated spacecraft formations in elliptical orbits; Section 3 details the closed-loop state feedback controller design method, and numerical simulations are given in Section 4 to verify the performance of the proposed controllers. Finally, Section 5 concludes the full text.
## 1 Introduction
Reference [21] proposed an modified variable step size LMS (MVSSLMS) algorithm, which assumes that the input power remains unchanged and makes use of the average estimation of the autocorrelation of the error signal function to the control iteration of the step factor, but MVSSLMS is incompatible with a full-duplex system with variable input power. Reference [22] proposed a scheme based on optimal dynamic power allocation, with the objective of maximizing the rate of convergence, but the capability of channel tracking was not well considered. In reference [23], a relationship between the step and the mean square instantaneous error was built. This algorithm has strong tracking ability and fast convergence speed, but a weak anti-jamming capability. An algorithm for tuning the parameters of a multiple-tap analog SI canceller by channel estimation is presented in reference [24], but a performance analysis is not provided. In [25; 26], a variable step size algorithm is proposed, which has a larger value in the initial stage of the algorithm, to improve the convergence speed, and a smaller value near the convergence time to reduce the SSM error. However, this method is easily affected by the related noise and other factors. Reference [27] adopts a self-mixing RF SI cancellation structure to achieve better SI cancellation for a gaussian minimum shift keying(GMSK) narrowband full duplex signal at a lower hardware complexity, but its application scope is limited, and the improvement of RF cancellation abilities based on this structure is not large. In order to solve the above problems of the existing SI cancellation methods in the RF domain, and to suppress SI signals more effectively, this paper proposes an optimized LMS algorithm based on the step-size attenuation of convergence parameters. This algorithm is called DTV-LMS. In this paper, Interference cancellation in wireless networks has been addressed through exact mathematical optimization methods [28] within optimal wireless network design [29]. Compared to the RF domain SI cancellation reference algorithms mentioned above, the proposed method in this paper makes use of an evolved arctangent function, with the help of the relationship between the time parameter and error signal correlation value to coordinately control the step-size. Thus, this algorithm can maintain ideal characteristics, such as fast convergence and a good interference cancellation ratio (ICR) at low computational complexity. This paper is structured as follows. Section 2 presents the system model and the mathematical model of the CCFD system separately. The DTV-LMS algorithm and an analysis of its related abilities as they apply to SI cancellation in the RF domain are introduced in Section 3. Simulation results and analyses are given in Section 4. Finally, Section 5 summarizes this whole paper and proposes the prospect of future work.
## Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: [EMAIL_ADDRESS] (K.Y.); [EMAIL_ADDRESS] (G.Z.)  
2 Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (H.T.)  
3 Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +81-11-706-4174.  
_Received: 29 October 2012; in revised form: 22 November 2012 / Accepted: 23 November 2012 / Published: 26 November 2012_  
**Abstract:** Carbon dioxide (CO\({}_{2}\)) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO\({}_{2}\) concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO\({}_{2}\) concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO\({}_{2}\) concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO\({}_{2}\) concentrations on a global scale. We assumed that CO\({}_{2}\) concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson's correlation coefficient (R\({}^{2}\)) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R\({}^{2}\) was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO\({}_{2}\) concentration (XCO\({}_{2}\)), we found that the accuracy throughout the World is between -2.56\(\sim\)3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.  
Keywords:MODIS; CO\({}_{2}\) concentration; GOSAT TANSO; LST; NDVI/EVI; LAI/FPAR; GPP/NPP
## 5 Example Analysis  
To reflect service differentiation, this article selects four service scenarios with significantly different communication needs for communication technology adaptation, namely precise load control, electricity information collection, load management terminal monitoring, and electricity market trading. Based on the data center management system service and communication technology adaptation evaluation system constructed in this article, MatlabR2023b software (version number: 23.2) is used to implement adaptation algorithms, and remote and local communication technology adaptation decisions are made for four differentiated services. Finally, the communication technology adaptation results of the proposed services are analyzed and summarized.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 2 Methodology
### Recognition of Ship Encounter Scenarios
According to the previous formula, the parameters that can be calculated from the encounter trajectory pair, which contains vessel speed, heading, position, etc., include the relative bearing \(A\), the relative distance \(S\), the combined speed \(V_{R}\), and the DCPA. For encounter scenario classification, the course difference \(C_{diff}\) and the bearing \(C_{t}\) are also required. The course difference is easily obtained by subtracting the headings of the two vessels, while the bearing \(C_{t}\) can be calculated using the formula shown. According to the encounter scenario classification standard based on the COLREGs, the encounter scenario between the first vessel and the target vessel is identified using the calculated course difference \(C_{diff}\) and the relative bearing \(C_{t}\). The relative bearing and the heading difference are calculated as shown in Equation (22). \[\left\{ \begin{array}{l}Cog^{\prime}_{os}=0\\ Cog^{\prime}_{ts}=Cog_{ts}-Cog^{\prime}_{os}\\ C_{t}=n-Cog^{\prime}_{os}\end{array} \right. \tag{22}\]  
Based on the collision avoidance turning points identified in Section 2.2, this section searches for ship encounter pairs and accurately classifies the encounter scenarios  
Figure 8: Diagram of the rules for dividing the encounter scenarios. using a dual criterion of relative bearing and course difference. It successfully extracts the collision avoidance information from the AIS trajectory data of vessels in different encounter scenarios.
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 4 Application Development with the EMSOnto Approach
### EMS-Ontology for Modelling BESS Applications
Figure 4: Graph representation of EMS structure. Figure 5: Graph representation of variables’ types. UCs embedded within an EMS that involves battery support are surrounded by common particularities. Thus, a pattern that represents a generic UC can be reached (i.e., HLUC or PUC). One of those particularities is based on the fact that a UC is bound to a minimal storage capacity. For instance, a PCR use case requires a minimum of 1 MW to allow a pool of small BESS to participate [2], hence the concept BessSize. In a multi-functional BESS context, the battery needs to share its full capacity among services. Thus, the capacity rented per HLUC is modelled by the UCAh concept. An example of this is presented in UC5, where three services rent the whole capacity of the BESS. Household, PCR, and voltage control rent 20%, 40% and 40%, respectively. Those values are modelled by UCAh. On the other hand, not all the UCs are enabled at the same time, thus a flag that represents the current operation of a UC is modeled by Ena. Moreover, a State-of-Charge (SoC) is calculated per UC and represented by the concept UCSoC. This value monitors the support given by a battery to a certain UC and guides the monetization of provided power. Furthermore, a way to manage conflicts when more than one UC operates is by setting up priorities, hence the concept Priority. The mentioned concepts are subclasses of the concept ParamUC (see Figure 7). Additionally, parameters of a battery are grouped and modelled within the concept ParamDevice. For instance, the concept Pmax represents maximum active power to be set into the battery and BattAh the capacity of a battery. States of an electrical device such as active power, reactive power, etc. are modelled within the concept State by P, Q, respectively. In addition to this, information regarding controller requirements such as error, settling time, available time, etc. are represented under the concept ParamCont. Concepts and roles of the \(TBox\) component were previously addressed. They enable the classification of EMS-knowledge by concepts and the modeling of relations between data according to roles specification. Besides this, axioms defined within the \(TBox\) and a set of rules guarantee the inference of implicit data from initial knowledge stated by domain experts. An extract of those rules and axioms are described by DL language and SWRL in Table 4 as follows. Figure 6: Graph representation of variables’ attributes. Figure 7: Graph representation of parameters and states.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 4 Discussion
### Summary  
(1) A MATLAB tool has been developed, which takes inputs from the end user and calculates the optimal propeller geometry. The code is both fast, reliable and easy to use.  
(2) Theoretically, the propeller design created will achieve higher efficiencies at a range of RPM values than commercially available propellers. From the MATLAB code, it was calculated that whilst operating at 6500 RPM, an efficiency of 0.87 can be expected.
## Case Study
### Discussions  
The deformation reliability approach in Equation (13) based on the crossing rate model takes into account the potential failures at the depths rather than the monitored ones, and thus yields a reliability method that moves from \"point control\" to \"surface control\". Using the monitored data for location QCX04 in Table 2, the dependence of deformation reliability on the displacement limit is presented in Figure 7. The maximum monitored displacement is 73.35 mm according to Table 2. Correspondingly, in Figure 7, the deformation reliability equals 0 if the displacement limit is smaller than 73.35 mm. On the other hand, when \(R(x)\geq 73.35\) mm, the deformation reliability increases from 95.6% to 1. This means that the conventional \"point control\" may overestimate the deformation reliability by simply focusing on the deformations at limited points.  
Furthermore, taking into account the impact of ME, the deformation reliability is also shown in Figure 7 for the purpose of comparison. Clearly, the dependence of \(\mathbb{L}_{\text{me}}\) in Equation (24) on the displacement threshold is smoother compared with \(\mathbb{L}_{\text{mme}}\). For cases where \(\mathbb{L}_{\text{mme}}>0\) (that is, \(\min(y_{\text{lim}}-y_{i})\geq 0\)), it can be seen that \(\mathbb{L}_{\text{me}}<\mathbb{L}_{\text{mme}}\). This can be explained by noting that in Equations (13) and (24), \(\left(2\min\limits_{i=1}^{n}\frac{y_{\text{lim}}-y_{i}}{Ad_{i}}\right)<\prod _{i=1}^{n}\mathbb{I}(y_{i}\geq y_{\text{lim}})=1\), which gives \(\mathbb{L}_{\text{me}}<\mathbb{L}_{\text{mme}}\). This comparison again suggests the important role of ME in the deformation reliability of retaining walls.  
Figure 6: Verification of the method in Equation (24).  
Figure 7: Cumulative density functions (CDFs) of the maximum displacement computed with Equations (13) and (24).
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 2 Literature Review
### Contribution of the Study
#### 2.1.1 Application Area  
The studies in the literature present valuable insights into the principles and practices of lean manufacturing, mainly focusing on the significance of waste reduction and process optimization [38; 39]. While the studies mostly concentrate on material handling within a lean system, the principles discussed can be extrapolated to selecting all types of machinery utilized in lean manufacturing environments.  
Deciding on the right machinery is critical in launching a successful lean manufacturing approach [35]. This decision should be taken seriously and needs a thorough evaluation procedure considering multiple criteria. Some factors, such as machine capabilities, integration with other systems, cost-effectiveness, and sustainability, must be carefully considered. For example, a machine's ability to meet required production volumes and quality standards is paramount. Additionally, seamless integration with existing production systems and data infrastructure is crucial for achieving the data-driven decision-making that characterizes modern lean systems.