esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Caveats  
Focusing on the actual implementation of a second-order DPLL, the step-invariant model for the NCO and loop filter is commonly used. This model is implemented on a GNSS software receiver. For a simulated binary phase shift keying with a chipping rate of 1.023 MHz pilot signal with a constant Doppler shift, the carrier phase tracking error is shown in Figure 10. The largest bandwidth (36 Hz) results in \(BT\) = 0.72, which is near the stability limit (\(BT_{\text{osc}}\) = 0.75) when \(t_{\text{D}}\) = 0. In this case, oscillations are visible, but carrier tracking remains stable.  
At first glance, the implementation of a DPLL seems straightforward; however, two important flaws should be taken care of. Therefore, they are considered and implemented in the receiver to see the effects on the phase tracking performance of the DPLL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Jerk Stress [g/s]**} & \multirow{2}{*}{**Oscillator Type**} & \multicolumn{4}{c}{\(BT\) Lower Limit} \\ \cline{3-6}  & & **T = 1 ms** & **T = 4 ms** & **T = 10 ms** & **T = 20 ms** \\ \hline \multirow{2}{*}{0} & TCXO & 0.004 & 0.013 & 0.032 & 0.064 \\  & OCXO & \textless{}0.001 & 0.003 & 0.007 & 0.014 \\ \hline \multirow{2}{*}{1} & TCXO & 0.007 & 0.028 & 0.069 & 0.137 \\  & OCXO & 0.006 & 0.024 & 0.060 & 0.120 \\ \hline \multirow{2}{*}{4} & TCXO & 0.011 & 0.041 & 0.102 & 0.204 \\  & OCXO & 0.010 & 0.038 & 0.095 & 0.190 \\ \hline \multirow{2}{*}{10} & TCXO & 0.014 & 0.055 & 0.136 & 0.271 \\  & OCXO & 0.013 & 0.052 & 0.130 & 0.259 \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(BT\) lower limits of third-order DPLL.  
Figure 9: \(BT\) lower limits of third-order DPLL for jerk dynamic stresses.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 2 Fundamentals of Finite Element Modeling
### Crack Growth Criteria and Models
However, implementing microstructure-sensitive models can be computationally intensive, especially when coupled with complex finite element analyses or multiscale modeling approaches [92]. 8. Fretting Fatigue Models: these models aim to predict the growth of fatigue cracks in materials subjected to cyclic loading and contact-induced fretting. These models typically incorporate finite element analysis (FEA) to describe the mechanical behavior of the material and the contact conditions. The criterion for fatigue crack growth in refetting fatigue models is based on various factors such as SIFs, strain energy density, or crack closure. Fretting fatigue models can simulate the interaction between the crack and the contact zone, considering the effects of normal and tangential loading, surface roughness, and slip [93; 94; 95; 96]. In finite element analysis of fretting fatigue, accurately modeling the crack-tip is crucial but often requires a refined mesh, which can be computationally expensive and complex [97; 98]. 9. Probabilistic Approaches: Probabilistic Approaches in crack growth simulation using finite element methods (FEM) involve incorporating uncertainties in material properties, loading conditions, and geometrical parameters to provide a more comprehensive understanding of crack propagation behavior. These approaches are valuable for assessing the reliability and safety of structures under variable conditions. These models use statistical distributions and stochastic methods to quantify the uncertainty in fatigue life predictions and reliability assessments [99; 100; 101]. However, developingprobabilistic models is inherently more complex than deterministic models, requiring expertise in statistical methods and uncertainty quantification [32; 102; 103].
## 1 Introduction
However, [PERSON] [10] did not provide an explanation about the underlying physics of why the two merged \"attached masses\" would merge and result in a reduced total \"attached mass\". [PERSON] [5] developed a mathematical model to describe the acoustic effect upon a single hole from the neighboring holes. He found that hole-hole interaction effect will increase the end correction magnitude. [PERSON] [12] stated that when small holes are in extreme proximity to each other, they will act as a big hole in terms of their impact on passing acoustic field. Therefore, in this situation, the overall acoustic radiation effect is not only generated by each single hole, but also the perforation region as a whole. [PERSON] [14; 15] proposed a \"geometrical tortuosity model\" to account for the acoustic radiation effect of the perforation region as a whole, which he called \"heterogeneity distribution effects\". However, the exact definition of parameters such as tortuosity in these models require external input and remains difficult for engineers when it comes to designing industrial distributed resonators. In this paper, the acoustic end correction effect for distributed Helmholtz resonators or PPA's, in which holes are in proximity with each other, is conveniently proposed to be a combination of two radiation effects--the acoustic radiation effect by each single orifice and the acoustic radiation effect by the overall perforation area. This paper will also propose an easily applicable model for the prediction of resonance frequencies of resonators. This resulting model will be of significant value in the design of practical distributed Helmholtz absorbers, especially those distributed Helmholtz resonators applied on gas turbine engines to control combustion instabilities.
## 1 Introduction  
Traditional fermented foods are a precious resource as they provide inexpensive, practical and convenient nutrients in the modern world under the threat of famine. Tarhana is a traditional fermented food that is prepared to consume as an instant soup by cooking a quantity of a dough powder in hot water [1,2]. It has nutritious and easy-to-digest properties, as well as having gained importance for infant nutrition in recent years [2,3]. Although there are slight differences in the production of tarhana, the tarhana dough is prepared by mixing wheat flour, yoghurt, sourdough and various vegetables and spices (tomato, red pepper, onion, mint, salt, etc.), and the dough is subsequently dried and ground after fermentation [1,4]. This fermented food is traditionally stored in a textile pouch, but plastic packages are preferred to avoid volatile loses [5]. However, it is difficult to keep the tarhana from moisture absorption, resulting in microbiological risks and pest formation problems during storage [6]. In fact, [PERSON] and [PERSON] [7] showed the survival of the molds and some pathogenic bacteria such as _E. coli_ O157:H7, _S. aureus, S. typhimurium_ and _B. cereus_ during the fermentation and storage period of tarhana. Additionally, aflat ranging from 0.7-16.8 ug/kg were determined at 23.2% of 138 collected tarhana samples, whereas 29 samples contained Aflatoxin B1 (AFB1) ranging from 0.2-13.2 ug/kg [8]. Irradiation may be one of the possible solutions to reduce the risk of microbiological and pest encroachment during the storage of tarhana [9].  
The use of irradiation [9; 10; 11], which involves the exposure of food to gamma and X rays to ensure quality and safety, has gained importance in recent years due to the large increase in foodborne diseases. Food irradiation is one of the prescribed methods for the control of the significant losses that can occur during the storage and distribution of products due to microorganisms, parasites and insects [9; 11]. The high-energy rays applied to food systems stop the vitality of microorganisms or larvae in food by causing damage to their DNA. This sterilization of the food without the use of heat has led to the technique being referred to as \"cold sterilization\" [9]. The International Atomic Energy Agency (IAEA) concluded that energy beams emitted from food irradiated with doses below 60 kGy, with gamma rays from cobalt-60 and cesium 137, were less than 5 MeV in strength and could be considered insignificant [12].  
The present study aims to determine the effect of irradiation on the quality and characteristics of tarhana to avoid microbiological risks and pest formation. Accordingly, the microbiological, chemical and physical effects of irradiation applied to packaged dried tarhana to prevent microbiological, physical and chemical losses during its storage is investigated.
## 2 Materials and Methods
### Programming Implementation of the Modular Model Composition (Edge Computer)  
After receiving the configuration, parametrisation, and measurements on the communication bus from the PLC, the concrete model is automatically composed in the EC, and the economic optimisation is computed as follows: The model composition and economic optimisation is implemented in Python [21] using the coin-or/pulp modelling language [22] and the coin-or/clp solver [23]. The parametrisation in the PLC is communicated via Modbus to the EC. The bus address of each device is composed of class-specific address-offsets together with sequential numbers instance_no for every additional component. The parametrisation is read by the EC. The system equations are then instantiated depending on the configured system architecture (number of devices) and the parameters of the corresponding devices. The system equations are composed by using classes for the device types. Figure 4 shows an UML class diagram for the five device types that are used in the modular model composition. The classes for the device types that can occur multiple times (BESS, PV, and EV) contain the class variable instances of type list. The constructor-method of each class registers all invoked instances in the instances variable. Using Pythons' list comprehension, iterating over instances (derived from the parametrisation) is used to modularly compose the equation system. The assembly of the system of equations is conducted before every computation of the LP-solution (every 15 min), such that the model can be reconfigured on the fly when parameters change or additional devices are commissioned. Again, read-and-write methods for parameters, measurements and optimal setpoints are implemented for each class. The predictors for load and PV are also configured by parametrisation. These prediction methods and their accuracy have already been described in a previous publication, on which this modular model is based [13].
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 2 Background
### Defense Strategies
The capacity to detect zero-day assaults and wholly unknown threats is a significant advantage of anomaly-based IDS [37]. However, the identified anomaly or suspicious behavior sometimes leads to false alarms. Occasionally, it may identify several factors, such as policy changes or offering a new service, as an intrusion. A hybrid detection approach [38, 39] considers the anomaly-based and the signature-based techniques discussed above and can be used to identify intrusions. In a hybrid system, the signature-based detection system is used to identify known types of intrusions and an anomaly detection system is used for unknown attacks [40]. In addition to these  
Figure 2: Flow chart of defense strategies in cybersecurity. methods, stateful protocol analysis, which is similar to the anomaly-based method but employs established standard profiles based on agreed definitions of benign activity, can be used to detect intrusions that identify protocol state deviations [36]. A self-aware automatic response system would be the most effective of these options since it eliminates the requirement for a human link between the detection and reaction systems. There is a recent concept called Advanced Anomaly-Based Detection, which works by observing the network traffic for a certain duration [24]. Reinforcement learning (RL) is one of the advancements of Artificial Intelligence that can extend the logical reasoning of intrusion scenarios and prevent inexperienced attacks. The lack of cybersecurity attack data makes this technique extremely valuable to prevent the system against future attacks. Based on the agent or attack type, RL can be grouped into model-based and model-free approaches [41]. The application of machine learning techniques is extended in each of the branches of IDS. In the early period, it was only applicable for anomalous network data [42]. Later on, machine learning techniques were proven to be highly effective for the deployment of other IDS techniques on both the host and network domain [42]. With this observation, an adaptive and evolving model was built to deal with evolving malware signatures. Figure 3 summarizes the types of IDS based on detection and deployment.
# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
## 6 Engineering Case Verification  
### Description of Wind Turbine  
An engineering case of generator bearing defect of wind turbine is applied for demonstrating the proposed method. The sketch of installed acceleration sensors of generator bearings is shown in Figure 15a. And the vibration signal was sampled at \(fs\) = 16,384 Hz. Figure 15b illustrates the structure chart of wind turbine, and the type of generator bearings is SKF6324, whose structure parameters are listed in Table 2. The speeds of the main shaft and the generator rotor were \(n_{1}\) = 21.63 rpm and \(n_{2}\) = 1519 rpm respectively. Then the rotating frequency of generator rotor was \(fr\) = 25.3 Hz. The monitoring system showed the vibration of generator front bearing was abnormal. Thus, the collected engineering signal with length of 8192 points is intercepted and analyzed here. The theoretical fault feature frequencies of inner ring \(fi\), outer ring \(fo\), roller \(fe\), and cage \(fc\) of the abnormal generator bearing can be respectively calculated as \(fi\) = 123.18 Hz, \(fo\) = 79.21 Hz, \(fe\) = 55.48 Hz and \(fc\) = 15.39 Hz.  
Figure 14: Comparison results of experimental signal by MCKD method: (**a**) The waveform of deconvolution signal; (**b**) The envelope spectrum of deconvolution signal.  
Figure 13: Comparison results of experimental signal by MED method: (**a**) The waveform of deconvolution signal; (**b**) The envelope spectrum of deconvolution signal.  
Figure 15: (**a**) The sketch of installed acceleration sensors; (**b**) The structure chart of wind turbine.  
Figure 16 describes the waveform and the spectra of collected engineering signal. The periodic impact components in the waveform aren't abundant. And the kurtosis index of this engineering signal, which is used to evaluate the impulsive phenomenon, is only 2.46. It is indicated that the SNR of engineering signal is very low. The frequency spectrum is very simple and there exist three energy concentration regions. In the envelope spectrum, only several peak spectral lines unrelated to any theoretical fault feature frequency of generator bearing can be found. And we can't make any conclusion based on these limited information.
## 5 Formulation of the LMI Stability Criterion
### Design of the LMI Condition
#### 5.2.4 LMI Approach with Unknown Parameters \(\epsilon\) and \(\bar{\delta}\)  
The two parameters \(\bar{\delta}\) and \(\epsilon\) are employed as decision variables to optimize in this fourth approach of the LMI criteria, where they are regarded as unknown. Recall that we need at the same time to minimize \(\epsilon\) and maximize \(\bar{\delta}\). The idea is based on previous results and starts from the matrix inequality (77). Thus, we adopt the same change in variables as previously described by taking \(\eta=\frac{\gamma}{\epsilon^{2}}\), \(\mathcal{M}_{3}=m_{3}\mathcal{I}\), and \(\lambda=\frac{m_{3}}{\epsilon^{2}}\). Then, the minimization of the parameter \(\epsilon\) lies in minimizing \(\theta_{1}\) subject to the LMI condition (82c). Additionally,the maximization of the parameter \(\delta\) lies in minimizing the variable \(\theta_{2}\) by considering the LMI condition (91c).  
As a result, we can derive the following theorem, which presents a minimization problem under LMI conditions.  
**Theorem 4**.: _Given that \(\zeta\geq 0\) and that matrices \(\mathcal{S}>0\), \(\mathcal{R}\), and \(\mathcal{M}_{1}>0\) exist, together with scalars \(m_{2}>0\), \(m_{3}>0\), \(\gamma>0\), \(\lambda>0\), \(\theta_{1}>0\), \(\theta_{2}>0\), and \(\eta>0\), the following LMI-based problem is appropriate._  
\[\min\ \theta_{1}+\theta_{2}\] _s.t._ (93a) \[\left[\begin{array}{ccccc}\Xi_{30}&\mathcal{S}&\mathcal{O}&\mu_{ 3}\mathcal{S}\mathcal{C}_{1}^{\mathrm{T}}&\mathcal{O}&\mathcal{O}\\ \star&-\gamma\mathcal{I}&\mathcal{O}&\mathcal{O}&\mathcal{O}&\mathcal{O}\\ \star&\star&\star&\eta\mathcal{D}_{1}\mathcal{D}_{1}^{\mathrm{T}}-\mathcal{M} _{1}&\eta\mathcal{D}_{1}\mathcal{B}^{\mathrm{T}}&\mathcal{O}\\ \star&\star&\star&\eta\mathcal{I}-\left(\sigma\big{(}\frac{1}{\alpha}-1\big{)} ^{2}+\frac{1}{\beta_{0}}\big{)}m_{2}&\eta\mathcal{B}^{\mathrm{T}}&\mathcal{O }\\ \star&\star&\star&\star&\eta\mathcal{B}\mathcal{B}^{\mathrm{T}}-\lambda\mathcal{ I}&\lambda-m_{3}\\ \star&\star&\star&\star&\star&\star&\frac{m_{3}}{\sigma}\end{array}\right]<0 \tag{93b}\] \[\left[\begin{array}{ccccc}\theta_{1}-\gamma&1\\ \star&\eta\end{array}\right]>0\] (93c) \[\left[\begin{array}{ccccc}\theta_{2}-\lambda&1\\ \star&m_{3}\end{array}\right]>0\]  
_Thus, the knee exoskeleton's nonlinear dynamics (5) are stabilized at the intended position by the affine state-feedback controller (25). Relation (78) is used to compute the controller's gain \(\mathcal{K}\). Moreover, expression (83) may be used to compute the lowest value of the parameter \(\epsilon\), and relation (92) can be used to establish the upper bound for the parameter \(\delta\)._  
Thus, we derive three LMI stability conditions, which are denoted by Formulas (93b), (93c), and (93d), involving the unknown variables \(\mathcal{S}\), \(\mathcal{R}\), \(\mathcal{M}_{1}\), \(m_{2}\), \(m_{3}\), \(\gamma\), \(\lambda\), \(\theta_{1}\), \(\theta_{2}\), and \(\eta\).
## 4 Result and Discussion
The results of the HS algorithm solutions are represented with the \"OR\" abbreviation, and the results of the finite element solutions are represented with the \"PR\" abbreviation in the tables. In Tables 6-8, the compatibility of OR and PR results ensures a satisfaction. The application of static limit equilibrium equations for the application of beams on elastic soil theory supplies the safety degree to be at the boundary situation (Factor of Safety: _FoS_: 1.0). The factor of safety values obtained from finite element analyses shows that the envisaged design dimensions are partially safe (_FoS_ > 1.0). However, the admissibility criteria of this safety degree can change in terms of the project type and requirements. A comparison of the values given in Tables 6 and 8 shows that the increase of the excavation depth causes an increase in the relative result difference between the suggested methods. This relative difference is greatly increased for the excavation depths bigger than 8 m, and stability conditions cannot be ensured after 9 m excavation depth in the analysis conducted with the software. It is a remarkable detail that the optimization analysis cannot also solve the stability problem for 10 m excavation depth when the shear strength angle is low. The integrated evaluation of both methods shows that the applicability of cantilever soldier piles is limited to a critical excavation depth. In this context, it can be said that the application of the HS algorithm gives more optimistic results.
## 1 Introduction
Such defects are usually eliminated by increasing the number of rolling cycles. In addition, the non-isomorphism of the composites' properties is also a problem. Sometimes, both powder metallurgy and rolling are combined for the synthesis of composites, which makes it possible to obtain Al-Al\({}_{2}\)O\({}_{3}\) composites with a better distribution of dispersing particles [23]. Direct reaction synthesis (DRS) refers to the process in which reactant powders or compacts of reactant powders are directly added to molten metal, and ceramic reinforcing particles are formed in situ through the exothermic reaction between reactants or between the reactant and the component of the melt. During the in situ process, the reinforcement phases formed in the matrix metal by direct chemical reactions are thermodynamically stable, free of surface contamination, and disperse more uniformly. These factors lead to the stronger particle-matrix bonding at the \"in-situ\" process. DRS in situ synthesis can be applied as a commercial method because of its simplicity, low cost, and near-net-shape-forming capability. The biggest problem of the traditional DRS method is the necessity of a long mixing time at a high temperature, which causes the agglomeration of small initial particles [24; 25]. The introduction of alumina to the molten aluminum matrix by the \"in situ\" process was proved by different independent physical-chemical methods such as XRD, Raman spectroscopy, and others [26]. Nowadays, synthesizing Al-Al\({}_{2}\)O\({}_{3}\) nanocomposites is an important practical and fundamental task in the field of inorganic chemistry. There is a very high need to create a high-tech method for the synthesis of non-porous aluminum-alumina nanocomposites with improved mechanical properties. The novelty of the present work is that the aluminum matrix was reinforced by \"in situ\" perfectly wetted and well-distributed Al\({}_{2}\)O\({}_{3}\) nanoparticles, formed via a direct chemical reaction between molten aluminum and rutile TiO\({}_{2}\) nanopowder under a molten salt layer at 700-750 \({}^{\circ}\)C in air atmosphere. The microstructure and mechanical properties of the composites were investigated by XRD, Raman spectroscopy, SEM, and tensile testing. This research aims to develop a method for the industrial large-scale \"in situ\" synthesis of aluminum-based matrix composites, reinforced with ceramic nanoparticle inclusions. These composites are formed via the reaction between molten aluminum and the salt electrolyte flux, which contains rutile TiO\({}_{2}\) nanopowder as the precursor. The effect of nano-alumina inclusions content on mechanical and thermal properties of the Al-nano-Al\({}_{2}\)O\({}_{3}\) composite is analyzed.