esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Design of Aircraft Landing Gear System
## 3 Dynamic Verification Model
### Landing Gear Model
#### 3.2.1. Wheel Assembly
The braking torque has been obtained based on the assumption of \"constant wear\" on the braking surface of the discs ([PERSON], 2013), which makes it possible to find it from the following:  
\[\tau_{brake}=F_{brake,act}f_{b}\frac{r_{o}+r_{i}}{2}N_{b} \tag{41}\]  
where \(f_{b}\) is the friction coefficient of the discs, \(r_{o}=\frac{min(D_{r,act},D_{s,out})}{2}\) and \(r_{i}=\frac{max(D_{r,sim},D_{s,in})}{2}\) are, respectively, the outer and inner radii of the braking surface, and \(N_{b}\) is the number of braking surfaces (equal to double the number of rotor discs). The braking torque \(\tau_{brake}\) is then divided by the effective rolling radius of the tire, thus making it possible to find the braking force \(F_{brake}\) acting on one wheel. This equation is also implemented in the other \"Main wheel\" subsystems and, when all summed together, provides the total braking force of the aircraft, which is then used for the longitudinal equilibrium. The braking force is also multiplied by the longitudinal speed of the aircraft, thus obtaining the instantaneous braking power \(P_{brake}\), which is converted into thermal power by the brakes themselves. Therefore, if the thermal equilibrium of the brake discs is considered, their temperature evolution can be found as in Equation (42). For the current application,each brake disc is considered to be a uniform thermal mass affected by the natural air convection:  
\[\frac{\partial T_{\textit{brake}}}{\partial t}=\frac{\frac{P_{\textit{brake}}}{N_ {b}}-(T_{\textit{brake}}-T_{\textit{air}})2H_{\textit{air}}\pi r_{o}t_{b}}{ \rho_{b}t_{b}A_{b}C_{b}} \tag{42}\]  
where \(T_{\textit{air}}\) is the environmental temperature, \(H_{\textit{air}}\) is the natural air convection coefficient, \(t_{b}\) is the thickness of a brake disc (rotor and stator discs thickness are assumed to be the same), \(\rho_{b}\) is the disc density, \(A_{b}\) is the braking surface, and \(C_{b}\) is the specific heat capacity of the disc. By then integrating Equation (42), the instantaneous temperature of the brake is found.
## 3 Pressure Data Analysis
### Unsteady Pressure Analysis
The bubble-type vortex is visualized at the shutter area ratio of 70% corresponding to the inner diameter of the diaphragm of d = 0.088 m. This vortex shape is less harmful from the dynamic point of view than the helical shape presented in Figure 8b. [PERSON] et al. [37] visualized a similar helical structure in a draft tube cone of a Francis turbine model operating at part-load conditions. Extensive experimental and numerical investigations have given a comprehensive understanding of the flow structure with various details of the hydrodynamic field [38]. [PERSON] et al. [39] have shown that the velocity profiles with central recirculation or quasi-stagnation region capture well the velocity field associated to swirling flow in a cone. The helical vortex is wrapped on the central recirculation or quasi-stagnation region with a larger angle than the cone wall, inducing strong pressure fluctuations on the cone wall. Figure 8: Visualization of the self-induced instabilities in decelerate swirling flows: (**a**) helical type with no diaphragm; (**b**) bubble type of diaphragm shutter area ratio of Aa = 70% (d = 0.088 m). Figure 7: Global dimensionless equivalent amplitude (**a**) and Strouhal number (**b**) versus shutter area from the outlet test section. Moreover, [PERSON] et al. [36] identify aperiodic pressure pulses on the cone wall because of self-merging and reconnecting of the vortex helix with the formation of a vortex ring. [PERSON] [40], [PERSON] [41] and [PERSON] [42; 43; 44] showed that the vortex breakdown is a sudden change in the slender vortex core, evolving downstream into a bubble shape or a helical pattern. [PERSON] et al. [45] numerically shows this recirculation region captured inside the cone. As a result, the bubble type is an axisymmetric expansion of the recirculation zone with smaller eccentricity being less harmful than the helical type. It may be worthwhile to note that the diaphragm inducing an axisymmetric flow forces the transition from a helical type to a bubble shape. A compromise between the pressure pulsation mitigation and the hydraulic losses has to be performed when this method is used. For this reason, the shutter area ratio of a diaphragm between 40-60% is recommended to be operated at part-load conditions to maintain a good balance between the hydraulic losses and the pressure fluctuation mitigation [34]. As a result, the dimensionless equivalent amplitudes on the cone wall obtained for the diaphragm control method with the shutter area ratio of 50% are compared against swirling flow with vortex rope and the method of axial water jet method with 10% discharge, developed by [PERSON] et al. [26] (Figure 9). By comparison, when it is used the diaphragm, the amplitudes has lower values than the water jet injection method.
## 1 Introduction
WFDB tools, version 0.10.0 include beat-by-beat (bxb) [23], run-by-run (rxr), measurement-by-measurement (mxm), event processing interface comparator (epic), and annotation to RR interval (ann2 rr) [24; 25] comparators, all of which comply with the American National Standards Institute (ANSI)/ Association for the Advancement of Medical Instrumentation (AAMI) protocols for testing ECG-derived results [26]. Despite the availability of these standardized tools, our review identifies several gaps and inconsistencies in the literature as shown in Figure 1, regarding the validation of predicted R-peaks. Figure 1: Graphical representation of various discrepancies found in the literature. Many studies fail to specify tools or custom validation methods used, and they often lack detailed descriptions of their validation processes. While some studies mention the use of a WFDB comparator, they do not provide sufficient details about its integration or the parameters involved [24; 25; 27; 28; 29; 30]. Additionally, several studies listed in Table 1 overlooked the presence of annotated non-beats in the MIT-BIH arrhythmia dataset and failed to explain how these beats were filtered when using custom validation methods. Discrepancies are also found in the selection of validation window size, with some studies deviating from the AAMI-sanctioned standard [28; 31; 32; 33; 34]. Although several studies have reported low false positive (FP) and false negative (FN) metrics, they failed to specify the employed window size [35; 36; 37; 38; 39]. Similarly, inconsistencies in reporting allowable window size tolerance [30] and variations in reported total number of beats for individual or complete MIT-BIH arrhythmia records were also identified [36; 40; 41; 42; 43; 44]. Furthermore, many studies validate algorithms on selected segments of individual records without specifying timestamps or providing a rationale for it [45; 46; 47; 48]. These gaps introduce uncertainty and confusion regarding the reliability of the reported statistical outcomes, affecting methodological comparability due to apparently misleading assessments of the detection algorithm's performance. Additionally, ambiguity in usage and integration of WFDB validation tools is another issue when reviewing the PhysioNet database and available literature. For instance, installation process and backend framework of the WFDB toolbox was extensively discussed in ref. [49]. However, their manuscript lacks a practical guide for validation variants to simultaneously test the entire dataset. On the other hand, implementation details using bxb for individual record testing of the MIT-BIH database is mentioned in ref. [31]. However, they did not explain all the parameters involved in the R-peak validation process. This underscores the need for a narrative review that thoroughly details validation methodologies for R-peak detectors, addresses the aforementioned discrepancies, and explains their effect on R-peak validation statistics.
# Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information
## 2 Materials and Methods
### Proposed Deep Learning Two-Stream-Based Emotion Recognition Model  
Figure 6 illustrates the proposed architecture of the Bi-LSTM and CNN two-stream-based emotion recognition model using the Korean speech data. As shown in the figure,  
Figure 5: Bi-LSTM architecture.  
the softmax value is obtained by extracting 1D spectrum features using MFCCs and GTCCs and then training based on the Bi-LSTM model. In addition, two-dimensional spectrogram images such as Bark spectrogram, ERB spectrogram, and log-mel spectrogram are obtained on the basis of time-frequency conversion and then trained using CNN-based pretrained learning models, such as VGGish and YAMNet, to obtain the softmax value. The softmax probability values output from the two models are either added or multiplied using the late score fusion method to obtain the final classification value, and eight emotions are then classified based on the two-stream model.  
The Bi-LSTM network used for 1D-spectrum learning used four Bi-LSTM layers, and each layer had 100, 100, 50, and 30 hidden layers. In addition, the dropout layers (with probabilities of 0.5, 0.5, 0.3, and 0.3) were added in between to prevent overfitting during training. In the CNN-based transfer learning model, the number of classes was changed to eight in the fully connected layer for classifying eight emotions, and the existing classification layer was changed to a new classification layer.
## 4 Numerical Modeling: Mass-Spring Waveguides
### Coupled Three-chain Mass-Spring Waveguides
and \(e_{3}\) spatial eigen modes and the solid lines are a fit to these identifiable resonances using dispersion relations with cutoff frequencies 14.08 kHz and 24.43 kHz. The dashed line in the inset of **(b)** identifies a frequency (equal to 33.78 kHz ) at which the (1 0 –1) and (1 –2 1) resonances nearly overlap and that lies between two resonances of (1 1 1) eigen mode. System parameters: \(N_{m}=48\), \(\eta=6.08\)Ns/m, \(\omega_{i}\in(0,60\) kHz), and \(\left(F_{0}^{\mu},F_{0}^{\
u},F_{0}^{\mu},\phi_{0}^{\
u},\phi_{0}^{\mu}\right) =\left(1\,\text{N},1\,\text{N},0,0\right)\) for mode: 1 1 1 excitation, \(\left(F_{0}^{\mu},F_{0}^{\
u},F_{0}^{\
u},\phi_{0}^{\
u},\phi_{0}^{\mu}\right) =\left(1\,\text{N},0,1\,\text{N},0,\pi\right)\) for mode: 1 0 –1 excitation, and \(\left(F_{0}^{\mu},F_{0}^{\
u},F_{0}^{\
u},\phi_{0}^{\
u},\phi_{0}^{\mu}\right) =\left(1\,\text{N},2\,\text{N},1\,\text{N},\pi,0\right)\) for mode: 1 –2 1 excitation. waveguides at a frequency of 33.78 kHz. We excite the first masses of chain 1 and 2 at out of phase, and we do not apply any force on chain 3 as represented by \(\left(F_{0}^{u},-F_{0}^{v},0\right)\) with the excitation amplitude ratio \(r=\left|F_{0}^{u}/F_{0}^{v}\right|\). Figure 10a shows the maximum amplitudes of the last mass \((N_{m})\) of each chain and the corresponding phase differences \((\phi)\) between each pair of chains of mass \(N_{m}\) as a function of \(r\), where \(\phi(x,y)=\frac{180}{\pi}\cos^{-1}\!\left(\frac{x\cdot y}{|x|y|}\right)\). We do indeed see that manipulation of the excitation amplitude ratio can be used to tune the eigen mode superposition. Therefore, by only tuning a single input, the relative excitation amplitudes of chains 1 and 2, we can navigate a sizeable portion of the Hilbert space of nonseparable states. Similar to the experimental result of Figures 6 and 10 shows that for a particular excitation amplitude ratio \(r=1.2\) (dashed line in Figure 10), the transmitted amplitudes of chains 1 and 2 are almost equal and that of rod 3 is small. In addition, phase difference between chains 2 and 3, \(\phi(v_{N_{m}}(t),w_{N_{m}}(t))\), as well as 1 and 3, \(\phi(u_{N_{m}}(t),w_{N_{m}}(t))\), are almost \(\pi/2\) and phase difference between chains 1 and 2, \(\phi(u_{N_{m}}(t),v_{N_{m}}(t))\), is approximately \(\pi\). This relation signifies that though the driving amplitude of rods 1, 2, and 3 are \(F_{0}(1.2,-1,0)\), the transmitted amplitudes are \(A_{0}(1,-1,\epsilon)\), where \(\epsilon\ll 1\) and \(F_{0}\) and \(A_{0}\) are constants. As seen above in the experimental results for the three-rod system, these relations require that the Bell state coefficients be complex.
###### Abstract  
Convenient cuts of poultry products are of significant interest, but they are perishable products with a short shelf life. Modified atmosphere packaging (MAP) or the application of active packaging based on edible and biodegradable coatings could extend the shelf life of perishable foods. The aim of the present work was the kinetic modelling of the effect of MAP and active coatings with antimicrobial agents on the microbial growth and shelf life of chicken cuts. Broiler chicken thighs processed with pectin-based (2% \(w/w\) aqueous solution) edible coatings enriched with 1% extract of citrus bidiflavonoids (flavourix) or 0.5% glucono-\(\delta\)-lactone were stored under aerobic conditions at 0, 5 and 10 \({}^{\circ}\)C. Untreated thighs were also stored aerobically or in MAP (42.7% O\({}_{2}\), 18.5% CO\({}_{2}\)). Quality evaluation was based on the growth of spoilage bacteria (TVC, _Pseudomonas_ spp., _Brochlorrix thermosphatata_), pH, colour, and sensory scoring. The tested quality indices were kinetically modelled and the Arrhenius model was used for the definition of the temperature dependence of the quality loss rates. _Pseudomonas_ spp. dominated spoilage at all packaging and temperature conditions. Microbial growth correlated well with sensory degradation (E\({}_{\text{a}}\) = 80-100 kJ/mol). Glucono-\(\delta\)-lactone-enriched edible coatings resulted in 2 days of shelf life extension for chicken thighs at 5 \({}^{\circ}\)C. MAP and active, edible coatings with citrus extract showed a similar effect on the quality deterioration rate, and thus the shelf life of chicken cuts. Based on microbial growth, the shelf life was 6-7, 11, and 13 days at 5 \({}^{\circ}\)C for the control, EC-glu, MAP, and EC-flav samples, respectively (limit of acceptability = 10\({}^{7}\) cfu/g for TVC). The results of the study show the potential for using MAP or edible, active coatings to extend the shelf life and improve the commercial value of broiler chicken cuts.  
active packaging; edible coatings; modified atmosphere packaging; poultry; shelf life; sustainable packaging; spoilage Article
## 2 Materials and Methods  
### Cultivation Conditions  
The cyanobacterium _Arthrospira platensis_ SAG 21.99 used in the study was obtained from SAG (Sammlung von Algenkulturen der Universitat Gottingen). The cultivation of _A. platensis_ was performed in modified Zarrouk medium with the following composition (per L): 10 g NaHCO\({}_{3}\), 0.5 g K\({}_{2}\)HPO\({}_{4}\), 1.0 g K\({}_{2}\)SO\({}_{4}\), 1.0 g NaCl, 0.04 g CaCl\({}_{2}\), 0.08 g Na\({}_{2}\)EDTA, 0.2 g MgSO\({}_{4}\)-7H\({}_{2}\)O, 0.01 g FeSO\({}_{4}\)-7H\({}_{2}\)O and 1.0 mL of trace elements (per l): 2.86 g H\({}_{3}\)BO\({}_{3}\), 0.02 g (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\), 1.8 g MnCl\({}_{2}\)-4H\({}_{2}\)O, 0.08 g CuSO\({}_{4}\) 5H\({}_{2}\)O and 0.22 g ZnSO\({}_{4}\)-7H\({}_{2}\)O. NaHCO\({}_{3}\) was used in the cultivation medium in order to ensure that no carbon limitation will occur and to help evaluate whether glycerol was used as energy and not as carbon source for mixotrophic growth of _A. platensis_. Under carbon limited conditions the expected CO\({}_{2}\) generated through the degradation of glycerol by the grown bacteria would lead to false results about glycerol assimilation. An inoculum of 500 mg/L was used, while the initial pH of the medium was adjusted to 8 [12]. The cultivations were carried out in 370 mL photobioreactors (PBR) of 71 mm diameter with a working volume of 250 mL. Cultures were aerated with sterile air (filtrated through 0.2 \(\upmu\)m), using a membrane air pump and were conducted in temperature-controlled room with constant temperature at 30 (\(\pm\)2) \({}^{\circ}\)C. Light was provided on the one side of the PBR by two 14 W LED lamps (one cool white and one of plant grow color-mixture of red and blue). Cultures were conducted in semi-continuous mode and the feeding (and withdrawal) was performed once per day at a dilution rate equal to 20% of the working volume of the culture. The experimental cultures were kept for at least 3 weeks to reach semi-steady state.
# MAG Welding Tests of Modern High Strength Steels with Minimum Yield Strength of 700 MPa
## 3 Results and Discussion
### Extended Tensile Tests  
Extended tensile tests were conducted only for the modern S700 MC-1. The aim of these tests was to define how the properties change along the weld line and the results are established in Figure 7. Specimens with reinforcements were used. Figure 7 shows a clear decreasing trend in the yield strength as the distance from the start of the weld increases. The decrease is more pronounced at both ends of the plate and in the stationary zone only a small decrease can be observed. The difference in yield strength between the both ends is 31-49 MPa. This trend can be observed in all heat inputs and the magnitude of the differences is approximately the same regardless of the heat input. The main factor driving these results may be the different thermal field and boundary conditions along the weld. The 8 mm plate thickness plays an important role in the transport of the heat energy via thermal conduction, and providing relevant heat capacity. At the start of the weld, a large volume of the plates is cold and higher cooling rate exists. As the weld seam reaches near the end of the plate, there is a saturation of the heat capacity and thus cooling rates are reduced. These results emphasize that, even though \(t_{8/5}\) cooling time is commonly used to predict and control the properties of the HAZ in welds, this assumption is only valid for the stationary period of the weld cycle. Thus, the \(t_{8/5}\) cooling time is not a correct value for controlling the quality of the HAZ at the start and end zones of long welds, and for welds with short length.  
With DIC measurements, localization of the strain was analyzed. A summary of DIC results is presented in Figure 8. The specimens used in DIC measurements were removed from the stationary zone and at the same location along the weld line with each other. The images have been taken at two points for each heat input, namely at the ultimate tensile stress and at the point just before the fracture. By increasing heat input, the localization in the softest domain of the HAZ is clear. In addition, the total absorbed energy decreases by increasing heat input because of the higher strain localization associated with the higher strength underneath in the HAZ.
## 3 Results and Discussion
### Adsorption of Emergent Contaminants
#### 3.2.2 Adsorption Isotherm
Furthermore, the Langmuir isotherm model has a higher regression coefficient R2 than the Freundlich model (Table 5), indicating the Langmuir model provides a better description of AC (based on organ nutshells) adsorption process in Dic and Caf. Therefore, these results suggest monolayer adsorption of AC on the surface of the adsorbent. Table 1 shows a comparison of absorbance capacity of Dic and Caf on various adsorbents reported in the literature, since the absorbance capacity of contaminants adsorbed varies as a function of different parameters (Initial concentration, contact time, etc.). Nevertheless, AC from organ nutshells presented high capacities for Dic and Caf, comparable or even higher than the ones obtained with other activated carbons derived from agricultural waste (Table 1). To understand the mechanisms associated with the adsorption of Dic and Caf by the AC from organ nutshells, it is important to evaluate a potential practical application of adsorbents related to the removal of this type of contaminant. The results presented in Table 6 also highlight that the surface area is not always the important feature in the removal of these adsorbate molecules. As mentioned before, textural properties were not the main factors in the adsorption of Dic and Caf since the AC obtained from organ nutshells presented a higher surface area and pore volume did not perform better regarding adsorption capacity of Dic and Caf. The large micropores developed on AC from organ nutshells do not provide an optimum size for adsorbates adsorption, which can explain the minor impact of surface area (Table 6). In fact, the role of the microporous network in the interaction with pharmaceutical molecules was previously demonstrated: If the critical dimension of the adsorbate molecule is close to the width of the micropores there will be an enhanced interaction and packing of the molecules [93].
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 1 Introduction
In [23], [PERSON] proposed the biogeography-based optimization algorithm with advantages of simple operation, few parameters, and high search accuracy [24]. In [25], [PERSON] and others used the Biogeography-Based Optimization (BBO) algorithms to select cluster heads and cluster nodes, and obtained good energy efficiency. However, the authors only took the distance between cluster heads and the distance between nodes in the cluster into consideration, while neglecting the energy consumed by data transmission between nodes. Deploying wireless sensors is limited by cost and power consumption [26]. Therefore, the following two issues need to be considered during deployment: one is to achieve efficient data collection; the other is to use as few sensors as possible and minimize the communication distance between sensors to reduce the total energy consumption. Because of the existing problem of deploying fewer sensors in terms of the distance between nodes, and the deficiency of some popular algorithms in the field of sensor deployment, we shall proceed as follows in this paper. The mutual information is used to describe the correlation between the observed and the unobserved points. The communication distance is described by the connection of the graph and the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm is used to choose the optimized submodular model. By considering the distance between clusters, the distance between nodes in the cluster, and the energy consumption of data transmission by the nodes, we obtain an optimized routing protocol in which the BBO algorithm is used to transmit data with an improved cost performance. The structure of this article is as follows. In Section 2, wireless sensor deployment optimization is introduced. The routing protocol of the wireless sensor network based on the BBO algorithm is presented in Section 3. In Section 4, we introduce the experimental verification, discuss the deployment effects using different algorithms, and analyze the performance of the protocol for the BBO algorithm. Finally, this article is summarized in Section 5.