esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Features Extraction Method
### The Adopted Instantaneous Frequency-Shift Operation  
As a powerful tool for analysis of the nonlinear signal, the wavelet synchrosqueezing technique has been successfully used in the signal analysis of faulted rotor systems [27; 28; 29]. Moreover, a general application of Hilbert transform (HT) and IF widely used in [30; 31; 32] is briefly presented in this subsection. The collected signal from Equation (40) can be instantly characterized by IF which can be extracted by Hilbert transform to obtain an analytic signal. A typical time-varying signal is expressed as a sum of N intrinsic mode functions and residual in the form  
\[x(t)=\sum\limits_{i=1}^{N}x_{n}(t)+r(t) \tag{44}\]  
where \(x_{n}(t)=A_{n}(t)e^{i\varphi_{n}(t)}\), the amplitude \(A_{n}(t)>0\), and phase \(\dot{\varphi}(t)>0\). The Hilbert transform of the analytical signal \(z_{n}(t)\) is given by:  
\[z_{n}(t)=x_{n}(t)+iy_{n}(t)=A_{n}(t)e^{i\varphi_{n}(t)} \tag{45}\]  
whereas the Hilbert transform of \(x_{n}(t)\) is defined as  
\[y_{n}(t)=P/\pi\int\limits_{-\infty}^{+\infty}x_{n}(\tau)/t-\tau d\tau \tag{46}\]  
Figure 3: (**a**) Breathing shaft stiffness for \(\epsilon_{1c}=0.3\), (**b**) Torsional stiffness variation with speed for two joint angles.  
in which \(P\) represents the Cauchy principal value. The obtained signal \(z_{n}(t)\) permits the amplitude \(A_{n}(t)\) and phase \(\varphi_{n}(t)\) to be uniquely defined. The original signal is recovered by  
\[z_{n}^{*}(t)=A_{n}(t)e^{i2\pi f_{0}(t)} \tag{47}\]  
In this step, \(z_{n}(t)\) is replaced with \(z_{n}^{*}(t)\) to perform the candidate IF calculation. The instantaneous angular frequency of the component \(x_{n}(t)\) is the first derivative of its phase  
\[\omega_{n}(t)=\{d\varphi_{n}/dt\} \tag{48}\]  
and the actual IF of the original signal s(t) can be calculated by the actual IF of the original signal \(z_{n}^{*}(t)\) can be calculated by \(\varGamma_{n}(t)=\dot{\varphi}_{n}(t)/2\pi\).  
Since the vibration signals generated by a cracked driveshaft system present strong nonlinear features, discretization of the frequency resolution for such signals with weakly fluctuating IFs estimation can become more complex. To resolve the issue, NWSST was proposed in [28; 29], which can provide both excellent time and frequency resolutions for signals with highly oscillating IFs in a partial frequency region where the shifted IFs are located. After the IF-shift operation, the NWSST consists of the partial zoom synchrosqueezing reconstruction.
## 5 Limitation of Vibrational Spectroscopy for Non-Invasive Food Authentication
The black-box nature of many AI models also raises concerns about interpretability, reliability, and regulatory acceptance, particularly in high-stakes food safety and traceability applications. In summary, while vibrational spectroscopy offers substantial advantages as a non-invasive, rapid, and cost-effective tool for food authentication [76, 113, 114, 115], its practical deployment requires overcoming several technical, analytical, and operational limitations. Future research should prioritize developing standardized protocols for sample handling, spectral acquisition, and model validation; creating shared spectral databases of authenticated food products; integrating real-time quality assurance systems into food supply chains; enhancing instrument robustness and portability without sacrificing analytical performance; and advancing hybrid AI-chemometric frameworks to improve model interpretability and deployment readiness.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### IntraRecv Time Slots Utilization  
As shown in Figure 10, compared to previous scheme IntraSend Scheduling the proposed scheme IntraSend scheduling efficiently utilized the IntraRecv time slots. Because in the proposed scheme the clusters that have only outgoing flows the IntraSend scheduling will utilized the IntraSend time slots and not utilizing the empty time slots of IntraRecv. Whereas in the proposed scheme that clusters that have only outgoing flows the IntraSend scheduling will utilized the IntraSend time slots as well as the empty time slots of IntraRecv and hence efficiently utilized both the IntraSend and IntraRecv time slots.
## 3 Current Obstacles of BEs in Microbes  
### Limitation of Editing Activity  
As illustrated in Table 2, the multiplex gene editing and off-target effects are still the major problems of BEs. Only a few BEs can simultaneously edit up to five targets in nonmodel microbes [45; 56]. Most BEs just achieve double or triple simultaneous editing with low efficiency. One reason might be the technical limitation of creating long arrays of gRNAs. The different efficiencies at various targets are also one of the reasons, because BEs are reported to perform almost 100% editing efficiency at some targets, although they are unable to work at some sites, probably due to the essentiality of genes [44; 48; 57]. The other serious reason is that as the number of gRNAs increases, they must compete for a dwindling pool of fusion protein, which in turn decreases the efficiency of every gRNA [107]. This problem widely exists in CRISPR technologies and can be possibly solved by using conditional gRNAs, which are selectively triggered as needed in vivo [107].  
The off-target effects of BEs also need to be considered and surmounted in the future. The gRNA-dependent off-targets, based on mismatch annealing of gRNAs, can be decreased to an undetectable level by replacing spCas9 with high-fidelity Cas proteins [45], modifications of gRNA structure [54], etc., which are the same strategies used to decrease the off-targets of CRISPR/Cas9 [108]. However, the gRNA-independent off-targets are major and specific problems for BEs. They are thought to be nonspecific and random deamination by the deaminase domain. The gRNA-independent off-targets commonly happen at the transcribed regions because the R-loops formed by the exposure of single-stranded DNA by RNA transcription is a preferred substrate for the deaminases [53]. The on-target activities of BEs still need to be improved in the future. Currently, the structural engineering of deaminases to improve their specificity is one of the solutions, illustrated in Table 1. The expression levels and duration of BEs can also be optimized to mitigate the gRNA-independent off-targets [32]. One-step multiplex editing is recommended, rather than iterative editing, to minimize the off-target effects as the number of targets does not have a large impact on the off-target mutations [56].
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 4 Network Assumptions and Model  
In this section, the performance of the proposed SASC architecture is evaluated and compared with secure and authentication protocols, i.e., SEER and SecLEACH. The proposed SASC offers an energy-efficient and data gathering for traditional applications, e.g., WSN, and also presents various kinds of intelligence and secure data routing through IoT sensors. In the simulation setup, we deploy randomly 100 to 500 sensors in the squared sized observing area. The number of malicious nodes is fixed to 10 and dispersed randomly. The malicious nodes broadcast the false route reactions and forwarded the data packets towards unauthorized nodes or can drop the data packets. Public keys are generally known to all sensors via a publicly accessible directory that is made on the cloud server, however, private keys do not need to distribute and therefore they cannot be compromised. All nodes except the BS are limited constraints in terms of memory, storage, processing, and battery power. The transmission power of all the nodes is fixed to 20 m. In the beginning, all nodes have a uniform energy resource of 5 J. The numerical results of the proposed SASC architecture are measured against other algorithms in terms of network lifetime, packet drop ratio, energy consumption, average end to end delay, and transmission overhead. Table 1 illustrates the simulation parameters that are used for the computation of numerical results.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Value**} \\ \hline Number of malicious nodes & 10 \\ Transport layer protocol & UDP \\ \(E_{\text{{det}}}\) & 50 n/j bit \\ \(E_{amp}\) & 10 n/bit/m2 \\ \(E_{fs}\) & 0.0013 p/bit/m4 \\ Packet size, k & 20 bits \\ Payload size & 512 bytes \\ Initial energy & 5 J \\ \(\alpha\), \(\beta\) & 0.5, 0.5 \\ Nodes transmission range & 20 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default network factors.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Definition of the Vehicle  
The proposed GNC approach is applied to an aerial vehicle which features a maneuvering system composed of four canard surfaces, which is roll-decoupled from the main body of the vehicle. The motivation for this aerodynamic configuration is deeply explained in [1]. Note a canard is a small winglike surface attached to an aircraft forward of the main wing to provide extra stability or control, usually replacing the tail. Here, canards are decoupled 2 by 2, to produce control force and its related torque (see [1] for more details on this).  
Table 1 shows some characteristic parameters of the vehicle, including thrust typical parameter values, vehicle and fuel mass, inertia, and aerodynamic parameters. These parameters are obtained from fluid dynamics numerical simulations, experimental measurements, and wind tunnel verification (see [1] for clarifications). Note that, to keep continuity and derivability on aerodynamic coefficients and thrust, a cubic splines based interpolation method has been employed at intermediate points. According to the shown moments of inertia, the vehicle features planes of symmetry.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 7 Conclusions  
In this study, aeolian sand at the southern edge of the Mu Us Desert was used as the research object. And the relationship between four physical and mechanical indicators and the collapsibility coefficient was studied through in situ water immersion load tests, standard penetration tests, and indoor geotechnical tests. Then, by combining these data with a support vector machine, a method for evaluating the collapsibility of aeolian sand was proposed. The results indicate the following:  
1. The collapsibility coefficient of the aeolian sand in the study area ranged from 0.001 to 0.050. Specially, one pit had a moderate collapsibility, nine pits had a slight collapsibility, and four pits had no collapsibility. The P-S curve obtained from the in situ immersion load test was a broken line. It was divided into three stages: the linear elastic deformation stage, the elastic-plastic deformation stage, and the collapsible deformation stage.
2. Based on in situ and indoor test results, we used the support vector regression machine to establish an evaluation method for the collapsibility of aeolian sand based on several basic physical indicators.
3. The fitting degree of this method was 0.974. This shows that the four evaluation indicators (moisture content, dry density, void ratio, and saturation) of the aeolian sand had strong indicative relationships with the collapsibility and could be used to predict the collapsibility coefficient.
4. Machine learning algorithms, such as SVMs, established a regression relationship between multiple indicators and collapsibility coefficients, which could solve problems despite no clear mathematical relationship between the multiple independent variables and the single dependent variable.  
The proposed method is suitable for use in engineering applications and can be used as a reference for the evaluation of the collapsibility of aeolian sand at similar sites. In addition, the research conclusions can provide a reference for studies on aeolian sand collapsibility, including studies on the collapsibility mechanism.  
Conceptualization, Z.L. and Y.D.; funding acquisition, H.M.; investigation, Z.L. and H.M.; supervision, Y.D. and Y.S.; validation, H.M. and Y.S.; methodology, software, and writing--original draft, Z.L.; writing--review and editing, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, CHD (No. 300102262505); the Key Research and Development Project of Shaanxi Province (No. 2022 SF-197); the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2022Q-289); and Changqing Engineering Design Co., Ltd. (CEDC-Z-2022-0103 and CEDC-Z-2022-2934). All the support is gratefully acknowledged.
## 3 Performance Evaluation Methods
### Spatial Distribution Diversion  
Usually, a three-dimensional topology is very difficult to characterize. Here we compare the system performance of the sensor network deployment with the spherical accumulation of truncated octahedrons, which is inspired by the comparison of two-dimensional spatial sensor network topology with a hexagonal structure. This evaluation criterion can be used to estimate the integrity of our deployment by mathematically calculating the similarity of sensor network deployment results.  
The topology evaluation of a 3D sensor network can be compared with one good 3D stacking structure, which is generally accepted in theoretical analysis or real applications, such as the spherical stacking composed of truncated octahedrons. The assessment of differences can be estimated based on the radial distribution function in molecular dynamics simulations. It is well known that the radial distribution function in 3D space, as the spatial distribution function (SDF), can be defined as the ratio of local number density to the mean density of the particle  
\[\Omega(x,y,z)=\frac{\rho(x,y,z)}{\bar{\rho}}. \tag{18}\]  
Herein we define the SDF of our network topology as the ratio of the density of the spherical shell to the average density, as we are studying the regular spherical distribution.  
\[S(r)=\frac{\rho(r)}{\bar{\rho}} \tag{19}\]  
where \(r\) is the radius from the center of the whole sensor network.  
Therefore, the similarity between the network topology of our algorithm and the spherical stacking composed of truncated octahedrons can be evaluated according to the difference of radial distribution function, namely Spatial Distribution Diversion (SDD), as follows  
\[SDD=\delta(\Omega,\Omega_{T})=\frac{\int_{0}^{r_{T}}||S_{\Omega}(r)-S_{\Omega _{T}}(r)||^{2}dr}{\int_{0}^{r_{T}}||S_{\Omega_{T}}(r)||^{2}dr} \tag{20}\]  
where \(\Omega\) is the network topology to be analyzed, \(\Omega_{T}\) is the topology of the standard spherical stack of truncated octahedrons, and \(r_{T}\) is the radial distance distributed to its center (which is also the range compared with truncated octahedron stack). It can be seen that, when the SDD value is smaller, the formed sensor network is more similar with the truncated octahedron stack. In such a case, the SDD can be used to evaluate the quality of the network formed by the 3D deployment algorithm. When the SDD value is large, the network is poor. On the other hand, while the SDD value is small, we think the network is good, which has the potential to achieve high coverage and stable state.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.3 Route Management  
After the preferred route is chosen, it must be maintained throughout the period of data transfer from hop to hop. This module must be attentive in case of a sudden PU arrival and should have adequate ways of seamlessly handing the link to the next spectrum or channel, as the case may be. At a glance, the possibilities of frequent interruption during the data transfer phase usually make the idea of accommodating CBR traffic in CR schemes appear vague. However, [80] shows the possibility of guaranteed real-time CBR traffic over a CRSN. Additionally, [72] shows that satisfactory real-time CBR and BE can both be guaranteed in the presence of PS and TS, although TS exhibited a better throughput than PS. However, the route maintenance module should be able to discover new paths in case of the failure of already established paths, especially when mobility is considered.  
Other factors to consider during route maintenance include path delay, energy and path reliability. Any protocol should incorporate these metrics to maximize network resources because route maintenance only occurs during data transfer, which is the most resource-demanding phase. In this respect, [81] presents a spectrum-aware routing scheme for CRSNs by estimating the spectrum usage of both PUs and SUs using Bayesian learning. The metrics used were reliability, energy consumption and path delay. These metrics were combined using a multiple attribute decision making (MADM) algorithm. Their results showed a better reliability than the scenarios strictly based on power and delay metrics alone. Although the report claimed better performance in power management with respect to WSN scenarios, this finding was not illustrated. The performance was also not compared to established WSN and CRAHN solutions. Hence, the claims to adequacy cannot be verified.
###### Abstract  
This paper presents an innovative thermally coupled system architecture with a parallel coolant-heated metal hydride tank (MHT) designed to satisfy the hydrogen supply requirements of proton exchange membrane fuel cell s(PEMFCs). This design solves a problem by revolutionising the cold start capability of PEMFCs at low temperatures. During the design process, LaNi\({}_{5}\) was selected as the hydrogen storage material, with thermodynamic and kinetic properties matching the PEMFC operating conditions. Afterwards, the MHT and thermal management subsystem were customised to integrate with the 70 kW PEMFC system to ensure optimal performance. Given the limitations of conventional high-pressure gaseous hydrogen storage for cold starting, this paper provides insights into the challenges faced by the PEMFC-MH system and proposes an innovative cold start methodology that combines internal self-heating and externally assisted preheating techniques, aiming to optimise cold start time, energy consumption, and hydrogen utilisation. The results show that the PEMFC-MH system utilises the heat generated during hydrogen absorption by the MHT to preheat the PEMFC stack, and the cold start time is only 101 s, which is 59.3% shorter compared to that of the conventional method. Meanwhile, the cold start energy consumption is reduced by 62.4%, achieving a significant improvement in energy efficiency. In conclusion, this paper presents a PEMFC-MH system design that achieves significant progress in terms of time saving, energy consumption, and hydrogen utilisation.  
solid-state hydrogen storage; metal hydride; PEMFC; coupling system design; cold start 4.0.
## 5 Simulation and Analysis of Positioning Strategy of Constellation Station
### Comparative Analysis of Constellation-Maintenance Strategy Simulation Results  
The evaluation of the absolute and relative control strategies of the constellation was based on their ability to satisfy the availability and serviceability requirements of the constellation, as well as the availability of mature and reliable control technology. The overall orbital transformation time, fuel consumption, and minimum use of the measurement and control resources of the constellation satellites were taken as measurement indicators.  
The simulation results of the absolute control strategy showed that in low-solar-activity years, satellite 1 achieved the maintenance task of a \(\pm\)0.1\({}^{\circ}\) phase angle deviation. The maintenance control period was 13.29 days, and the change in the semi-major axis of each orbit-control maneuver was 59.79 m. The simulation results of the relative control strategy showed that in low-solar-activity years, the Walker constellation with the constellation configuration of Walker \(18/3/1\) only needed to carry out four orbit-control tasks for satellites \(1-3\), \(2-2\), \(2-4\), and \(3-6\). The phase-angle-maintenance task within the threshold of \(\pm 5^{\circ}\) for all the satellites in the constellation was realized over 300 days. A comparison of the simulation results with the control strategies are shown in Table 6.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 2 Literature Review
### Forecasting in Supply Chain  
Demand planning is one of the most important tasks in the supply chain management. This process is integrated by decisions supported in mathematical and statistical tools.  
In companies, the logistics department is also responsible for monitoring the demand, distribution, and management processes of the necessary resources [36]. One of the tasks it performs is to seek to meet service levels and keep low the operational costs through effective strategies. In the literature it is found that mathematical and statistical techniques such as mobil averages, exponential smoothing, and regression are the most popular ones [3; 37]. These methods are feed with the sales, billing, and storage database. The data generated are analyzed and using in classic or hybrid forecasting tools [38].  
As mentioned before, demand forecast is an essential activity in supply chain management. One of the lacks at the time of applying the methods is to contemplate a single qualitative or quantitative environment. The proposed method seeks to integrate these variable types and reduce bias [39].
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
## 2 Materials and Methods
### Kinetic Analysis  
The MTBE biodegradation was quantitatively described by four kinetic models. The first-order model can be determined by the following equation [25]:  
\[S=S_{0}\ e^{-kt} \tag{2}\]  
where \(S\) represents the MTBE concentration (mg/L); \(S_{0}\) represents the initial concentration (mg/L); \(k\) is the degradation rate constant (mg/(L-h)); and t is the time (h).  
In 1999, the Quiroga-Sales-Romero model was proposed based on the matrix consumption process [26]. The model can be used to simulate the kinetic behaviour of MTBE degradation, expressed as:  
\[S=\frac{h(S_{0}-q)-q(S_{0}-h)e^{(\mu_{max}t)}}{(S_{0}-q)-(S_{0}-h)e^{(\mu_{max }t)}} \tag{3}\]  
where \(S\) represents the MTBE concentration (mg/L); \(S_{0}\) represents the initial concentration (mg/L); q represents the residual minimum concentration that cannot be degraded by microorganisms (mg/L); h represents the maximum substrate concentration that can be degraded by microorganisms (mg/L); t is the time (h); and \(\mu_{\max}\) is the maximum growth rate of microorganisms (h\({}^{-1}\)).  
The other two common models for the biodegradation of a single substrate, the Logistic model and Boltzmann model [25], are shown as:  
\[y=A_{2}\ +\frac{A_{1}-A_{2}}{1+\left(\frac{x}{x_{0}}\right)^{P}} \tag{4}\]  
where \(A_{1}\) represents the initial concentration (mg/L); \(A_{2}\) represents the yield concentration (mg/L); \(x\) is the time (h); p is the power coefficient; and \(x_{0}\) is the half-degradation time (h).  
\[y=A_{2}\ +\frac{A_{1}-A_{2}}{1+e^{(x-x_{0})/dx}} \tag{5}\]  
where \(A_{1}\) represents the initial concentration (mg/L); \(A_{2}\) represents the yield concentration (mg/L); \(x\) is the time (h); \(x_{0}\) is the half-degradation time (h); and dx is the time constant.  
The kinetic parameters in Equations (2)-(5) were determined numerically by fitting the experimental biodegradation data to the solution of the corresponding equation. By determining which of three models provide the most accurate fit to experimental data, the characteristic of MTBE degradation at different concentrations can be further determined. All of the calculations were performed by fitting the data with Origin v2018 software (OriginLab, Northampton, MA, USA).