esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present work discusses the microstructure and mechanical properties of the as-cast and as-extruded metal matrix composites interpenetrated by stainless steel (Fe-18 Cr-9 Ni), titanium alloy (Ti-6 Al-4V), and aluminum alloy (Al-5 Mg-3 Zn) three-dimensional network reinforcement materials. The results show that the different reinforcement materials have different degrees of improvement on the microstructures and mechanical properties of the magnesium matrix composites. Among them, magnesium matrix composites interpenetrated by stainless steel reinforcement have maximum tensile strength, yield strength, and elongation, which are 355 MPa, 241 MPa, and 13%, respectively. Compared with the matrix, it increases by 47.9%, 60.7% and 85.7%, respectively. Moreover, compared with the as-cast state, the as-extruded sample has a relatively small grain size and a uniform size distribution. The grain size of the as-cast magnesium matrix composites is mainly concentrated at 200-300 \(\upmu\)m, whereas the extruded state is mainly concentrated at 10-30 \(\upmu\)m. The reason is that the coordination deformation of reinforcement and matrix, and the occurrence of dynamic recrystallization, cause grain refinement of magnesium matrix composite during the extrusion process, thereby improving its mechanical properties. Moreover, the improvement is attributed to the effect of the reinforcement itself, and the degree of grain refinement of the metal matrix composites.  
microstructure; mechanical properties; interpenetrating composites; magnesium alloy; metal reinforcement; metal matrix composites Article
## 2 Collected Data and Research Method
### Modeling of Complex Systems Using Artificial Neural Networks
Proceeding in the same way for the output layer, the output of the ANN for the \(j\)-th data sample is a scalar given by:  
\[z_{out}^{(j)}=h\Big{(}\mathbf{w}_{2}\cdot\mathbf{z}_{1}^{(j)}+x_{02}\Big{)}. \tag{7}\]  
Therefore, combining Equations (3) and (4), the output variables can be written in terms of the input variable as:  
\[z_{out}^{(j)}=h\Big{[}\mathbf{w}_{2}\cdot h\Big{(}\mathbf{W}_{1}\cdot\mathbf{ x}^{(j)}+\mathbf{x}_{01}\Big{)}+x_{02}\Big{]}=H_{\mathbf{w}_{2}\mathbf{W}_{1} \mathbf{x}_{01},x_{02}}\Big{(}x_{i}^{(j)}\Big{)}. \tag{8}\]  
Since our ANN has a single output, the weight matrix becomes a row vector, and the bias term is a scalar one. Optimizing the values for the weights \(\mathbf{W}_{1}\) and \(\mathbf{w}_{2}\), as well as the bias terms \(\mathbf{x}_{01}\) and \(x_{02}\), in order to reduce the prediction/modeling error, is equivalent to minimizing the following cost function, which depends on both the predicted \(z_{out}^{(j)}\) and the desired value \(y^{(j)}\):  
\[J(\mathbf{W}_{1},\mathbf{w}_{2},\mathbf{x}_{01},x_{02})=\frac{1}{2N}\sum_{j= 1}^{N}\Big{(}z_{out}^{(j)}-y^{(j)}\Big{)}^{2}+\lambda\Bigg{(}\frac{1}{N_{i}N_ {h}}\sum_{i=1}^{N_{i}}\sum_{k=1}^{N_{h}}w_{1,i,k}^{2}+\frac{1}{N_{o}}\sum_{i=1 }^{N_{o}}w_{2,i}^{2}\Bigg{)}, \tag{9}\]  
where \(N\) is the number of elements in the training data set and \(\lambda\) is the so-called regularization parameter, whereas \(N_{i}\), \(N_{h}\), and \(N_{o}\) are the number of neurons in the input, hidden, and output layers, respectively. \(w_{1,j,k}\) is the weight of the link between the \(i\)-th input neuron and the \(k\)-th neuron in the hidden layer, and \(w_{2,i,k}\) is the weight of the link connecting the \(k\)-th neuron in the hidden layer to the output neuron. The cost function can be minimized, for example, by error back-propagation, in which the error at the output is propagated toward the input of the ANN, and the weights are updated accordingly. For instance, a detailed mathematical description of the optimization process can be found in [24]. As can be seen, in the calculation of the cost function, all the training data are considered in a single batch. The cost function then depends on the selection of the training data set and, in particular, on the presence of outliers in the training data that may cause the parameters to converge to local minima or to slow down their convergence. This is especially critical when the training set is not as large, as in our case. In order to reduce these effects, for each configuration corresponding to the combination of training set size and the number of neurons, the training was applied to 50 training sets randomly selected from the whole set of data. The rest of the data were used for cross-validation of the model.
## 4 Structure Optimization under DoS Attacks
### Stackelberg Game Formulation  
As shown in Equation (20), the iteration searches a system structure and control scheme in simulation, and then a DoS attacker observes the system and takes attack actions. It is a leader-follower sequence, therefore a Stackelberg game model is proposed to obtain the optimal solution of control structure optimization and DoS attacks.  
There are two actors in the proposed model. One is a defender that is the system designer, and the other one is an attacker that is a DoS attacker. The defender's action is \(N_{c}=\{N_{c}(1),N_{c}(2),...,N_{c}(n)\}\). The reward function of defender is defined as  
\[r\left(N_{c},\delta\right)=\sum_{i}^{n}\left.Q_{i}\right|_{N_{c}(i),\delta}. \tag{22}\]  
The attacker's action is \(\delta{=}\left\{\begin{array}{cc}\delta_{N_{c}(1)1}&\delta_{N_{c}(2)2}& \cdots&\delta_{N_{c}(n)n}\end{array}\right\}\). The reward of attacker is \(-r\left(N_{c},\delta\right)\). Therefore, it is a zero-sum game. The structure optimization becomes a min-max optimization problem as follows:  
\[\left({N_{c}}^{*},\delta^{*}\right)=\arg\min_{\delta}\max_{N_{c}}r(N_{c}, \delta), \tag{23}\]  
where \({N_{c}}^{*}\) and \(\delta^{*}\) are the equilibrium of game model, which can also be treated as the optimal solution for both defender and attacker. Thus, it can obtain \(r(N_{c},\delta^{*})\leq r(N_{c}^{*},\delta^{*})\leq r(N_{c}^{*},\delta)\).
## 7 Conclusions
### Nomenclature  
_Symbols_:  
A area, m\({}^{2}\)  
A\({}_{\rm each}\)  
exchange surface, m\({}^{2}\)  
C heat capacity, J/K  
c specific heat capacity, J/kg K  
c\({}_{\rm f}\)  
specific heat capacity of fluid, J/kg K  
c\({}_{\rm solid}\)  
specific heat capacity when PCM is in the solid state, J/kg K  
c\({}_{\rm liquid}\)  
specific heat capacity when PCM is in the liquid state, J/kg K  
E thermal energy, J  
e thickness, m  
Gr Grashof number  
H height, m  
h specific enthalpy, J/kg  
h\({}_{\rm c}\)  
convective heat transfer coefficient, W/m\({}^{2}\) K  
h\({}_{\rm c1}\)  
convective heat transfer coefficient in non-ventilated air layer, W/m\({}^{2}\) K  
h\({}_{\rm c2}\)  
convective heat transfer coefficient in ventilated air layer, W/m\({}^{2}\) K  
h\({}_{\rm r}\)  
radiative heat transfer coefficient, W/m\({}^{2}\) K  
h\({}_{\rm r1}\)  
radiant heat transfer coefficient between glazing and wall, W/m\({}^{2}\) K  
h\({}_{\rm r2}\)  
radiant heat transfer coefficient between wall and insulating panel, W/m\({}^{2}\) K  
L\({}_{\rm A}\)  
latent heat, J/kg  
\(\dot{m}\)  
air mass flow rate, kg/s  
Nu Nusselt number  
P power supplied by air layer, W  
Pr and Pr andr number  
Q\({}_{\rm sol}\)  
solar radiation intensity, W/m\({}^{2}\)  
Ra Rayleigh number  
Re Reynolds number  
T temperature, \({}^{\circ}\)C  
t time, s  
V velocity of wind, m/s  
W width, m  
_Greek symbols_  
\(\alpha\)  
absorptivity  
\(\beta\)  
dilatation coefficient at constant pressure, K\({}^{-1}\)  
\(\delta_{1}\)  
non ventilated air gap width, m  
\(\delta_{2}\)  
ventilated air gap width, m  
\(\varepsilon\)  
emissivity  
\(\lambda\)  
thermal conductivity, W/m K  
\(\mu\)  
dynamic viscosity of air, kg/m s  
\(v\)  
kinematic viscosity of air, m\({}^{2}\)/s  
\(\xi_{\rm g}\)  
absorptivity of multi-reflection radiation intensity to glazing  
\(\xi_{\rm w}\)  
global absorptivity of wall (including multi-reflection)  
\(\rho\)  
density, kg/m\({}^{3}\)  
\(\sigma\)  
Stefan-Boltzmann constant, 5.67 \(\times\) 10\({}^{-8}\) W/m\({}^{2}\) K\({}^{4}\)  
transmissivity  
\(\phi\)  
heat flux, W
## 4 Discussion
### The Influence of Pressure in the Windchest on the Frequency Spectrum  
The slight decrease in the fundamental frequency of the pipe back wall was recorded at thicknesses 2 mm, 1 mm, and 0.5 mm. So, the question was whether it is possible and appropriate to compensate for this decrease by increasing the pressure in the windchest. The measurements showed that the fundamental frequency of the tone increases slightly with increasing air pressure for a given thickness of the back wall made of spruce. The fundamental frequency increased by 4-5 Hz when the pressure increased from 588 Pa to 716 Pa. The same tendency was noted when maple wood was used for the back wall of the pipe. When the pressure was further increased, the fundamental frequency remained the same for almost all monitored thicknesses of the back wall of the pipe.  
The sound pressure level of the higher harmonics of the sound spectrum of the pipe (with both spruce and maple back wall) gradually increased with increasing air pressure, except for the second harmonic, when the sound pressure level was almost the same for each back wall thickness and air pressure in the windchest.  
A slight decrease in intensity was found only at the fifth harmonic and an air pressure of 941 Pa, independent of the thickness of the back wall of the pipe from spruce wood. A wider representation of the higher harmonic frequencies of the wooden organ pipe was recorded when the back wall of the pipe was made from maple wood at all wall thicknesses.  
It can therefore be concluded that a change in the sound pressure level of the individual higher harmonics of the sound spectrum leads to a change in the timbre of the sound.
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 5 Simulation and Analysis
### Simulation of Parafoil System  
Figures 4 and 5 show the flight trajectories of the parafoil system without and with winds, respectively (the numbers of trajectories are consistent with the numbers in Table 2). We observe that when the deflections on the parafoil are different, the corresponding trajectories are different. The greater the unilateral deflection is, the smaller the turning radius is and the faster the turning rate is, and vice versa. According to the comparison of trajectories between Figures 4 and 5, the trajectories of the parafoil system drift due to the influence of wind fields. For example, the no.1 trajectory in Figure 4 drifts to the right when adding the lateral wind (2.0 m/s, 4.0 m/s) at 25 s (shown as the no.1 trajectory in Figure 5), and the drifting velocity and direction are related to the wind field.  
After adding the wind field \((V_{w,x},V_{w,y})\) = (2.0 m/s, 4.0 m/s) and applying 40% unilateral deflection, the simulation results are shown in Figure 6. We observe that the ground speed of the parafoil system changes periodically. When flying against the wind, resistance generates due to the relative motion of the wind, the ground speed of the parafoil system reaches to the minimum. On the other hand, its ground reaches the maximum value in a downwind situation.  
As the flight characteristics shown in Figures 4-6, the flight path of the parafoil system contains wind field information. Taken together, these results suggest that it is feasible to identify the wind field by extracting its trajectory changing characteristics.  
Figure 4: Trajectories of the parafoil system without wind.  
Figure 5: Trajectories of the parafoil system with wind fields.  
Figure 6: Speeds of the parafoil system before and after adding crosswind. (**a**) Turn right. (**b**) Turn left.
## 2 Materials and Methods
### Gilder Deployment and Data Collection
#### 2.2.1 Study Site  
The study site was the shelf-slope-oceanic ecosystem of LoVe (Figure 2). The area is considered rich in biological resources and was characterized as \"particularly vulnerable\" by the Norwegian authorities [20]. In this area, the phytoplankton spring bloom starts in the fjords by the end of March, progresses to the shelf by April-May, and extends to the basin by May-June. The increased primary production enhances biological activity from the seabed and across the entire water column. The increase in chlorophyll concentration prompts polyp activity in corals [23] and is followed by the ascension to the surface of the overwintering copep _Calanus finmarchicus_, the most abundant zooplankton in the region. _C. finmarchicus_ feeds on the phytoplankton production and reproduces during spring and summer [19; 39; 40]. The marine zooplankton community provides important trophic linking between the microalgae community and higher trophic level species, sustaining major fish stocks, including the highly abundant stocks of cod _Gadus morhua_ (i.e., the Northeast Arctic cod), herring _Clupea harengus_ (i.e., the Norwegian spring spawning herring), and blue whiting _Micromesistius poutassou_ [18; 41; 42; 43]. The LoVe region also provides important spawning grounds and larval areas for the commercial fish stocks of the Barents Sea and adjacent waters, including the Northeast Arctic haddock _Melanogrammus aeglefinus_ stock, the Norwegian spring-spawning herring _Clupea harengus_ stock, and the Northeast Arctic cod _Gadus morhua_ stock [43]. Cetaceans were also reported in the area [11; 44], with both humpback whale _Megaptera wroueanglie_ and killer whale _Orcinus oxca_ feeding on herring aggregations [44]. The Lotfoten Basin is the most eddy-rich region in the Norwegian Sea [45]. Mesoscale eddies transport phytoplankton and key ecosystem species, such as _C. finmarchicus_ and mesopelagic fish, across the shelf. Near the shore, tidal currents play a role in transporting cod eggs northward past the Lofoten Peninsula [46], while the Norwegian Coastal Current (NCC) and the Norwegian Atlantic Current (NwAC) play the main role in transporting eggs into the Barents Sea [19; 47]. For example, cod spawned in the LoVe region was found later residing in the Svalbard jfords [48].
###### Abstract  
This paper documents the retrieval of significant ocean surface wave heights in the Arctic Ocean from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011-2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement.  
CryoSat2; waves; oceanography +
Footnote †: journal: Remote Sensing  
1
Footnote 1: [[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
## 4 Experiment and Result Analysis
### Ablation Experiments
The results are shown in Figure 10. Through the heatmap, it can be intuitively seen that the model proposed in this paper can better focus on blueberry targets of different sizes at close and far distances while effectively suppressing environmental background noise. On the other hand, the YOLOv8n model shows insufficient attention to certain sizes of blueberries in the detection process, and some environmental background noise is present.
## 2 Noise Characteristics and Source Identification
### Noise Sources Identification  
The high-speed centrifugal air compressor for vehicle fuel cells is a compact multi-noise source device. The electromagnetic, aerodynamic, and structural vibration noises have their own characteristic frequencies. The acoustic characteristics transmitted to different measuring points are different and have relatively independent characteristics. The separation of acoustic signals by the ICA [13; 14] (independent component analysis) method based on negative entropy maximization can identify the sound source of the air compressor and clarify the direction for noise improvement.  
As shown in Figure 6, the independent component Y2 and the independent component Y3 have typical discrete characteristic frequencies. The signal peak of Y3 occurs at the fundamental frequency, the second frequency, and the third frequency of the rotation frequency, and reaches the maximum at the second frequency. According to the research of [PERSON] [15] and others, the radial electromagnetic force in the motor acts on the stator core, causing the stator and casing of the motor to vibrate periodically, which is the main reason for the electromagnetic noise generated by the motor. The maximum amplitude of the radial electromagnetic force pulsation of the 2-pole 24-cogged PMSM (permanent magnet synchronous motor) used in the high-speed centrifugal air compressor in this paper appears at the second harmonic frequency of the rotation frequency [16]. Therefore, the radiated acoustic signal is prominent at the second harmonic frequency of the rotation frequency and attenuates sharply at the fundamental frequency and the third harmonic frequency. Therefore, Y3 is the electromagnetic noise source.  
The maximum amplitude of the independent component Y2 appears at the rotation fundamental frequency and, with the increase in frequency, the signal peak of the characteristic frequency gradually decreases. Therefore, Y2 is the \"buzz-saw\" noise caused by the periodic rotation of the impeller, including the aerodynamic noise and mechanical noise. The independent component Y1 is concentrated in the low-frequency band, especially the narrow-band noise near 3866 Hz, without obvious periodic characteristics. Therefore, the independent component Y1 signal is the wide-band noise caused by the secondary flow and flow separation process of gas in the blade tip clearance [11]. Based on the noise characteristics of the high-speed centrifugal air compressor of the vehicle fuel cell, the noise signal of the rotating fundamental frequency is mainly composed of the fundamental frequency signal of the \"buzz-saw\" noise. The second harmonic frequency is composed of the motor noise and the \"buzz-saw\" noise, forming the maximum amplitude of the noise. The sound pressure levels of the discrete single-tone noise and the wide-band noise are low.  
Figure 6: Frequency characteristics of independent components.
## 2 Results  
### Expression of SLC10A1/NTCP in Human Liver and Correlation with Clinical Data  
In the present study, we first performed a detailed analysis of the hepatic expression of NTCP on mRNA and protein levels in 143 liver tissues of Caucasian origin. _SLC10A1_ expression on mRNA level was measured using quantitative real-time PCR (qPCR), while the NTCP protein levels were determined using LC-MS/MS-based targeted proteomics. The _SLC10A1_ mRNA expression showed a 44-fold variation (coefficient of variation: 66%) (Figure 1A) and was significantly affected by age, inflammation (i.e., C-reactive protein [CRP] levels), cholestasis, smoking, and alcohol consumption (Supplementary Table S1). At protein level, the expression of NTCP varied 10.4-fold (coefficient of variation = 38%; Figure 1B) and was also significantly associated with inflammation, smoking, and alcohol consumption (Supplementary Table S1). Protein and mRNA expression levels were significantly correlated (r\({}_{\text{S}}\) = 0.44; \(p\) = 6 \(\times\) 10\({}^{-8}\); Supplementary Figure S1).  
Figure 1: _Cont._  
Figure 1: Expression and genetic variability of _SLC10A1_/NTCP in human liver. (**A**) Hepatic _SLC10A1_ mRNA expression in 143 individuals. Results are presented as histogram, including cumulative frequencies. mRNA levels were measured using qPCR. (**B**) Hepatic NTCP protein levels in 143 individuals, which was analyzed by targeted proteomics using mass spectrometry. Results are presented as histogram, including cumulative frequencies. (**C**) Schematic overview of the _SLC10A1_ locus (hg19: chr.14:70242135-70264007) with all analyzed (genotyped as well as imputed) SNPs in the 143 liver samples in relation to Genome Aggregation Database (gnomAD) data (gnomAD_v2_1_ENSG00000100652_2019_03_+UTR; data accessed in March 2019). On the top panel minor allele frequencies (MAF) greater than zero of variants detected in samples with European ancestry are  
shown. On the lower panel MAFs available in samples with other ethical backgrounds are shown on a reverse y-scale. Common variants (MAF > 5%) are highlighted using vertical gray lines and rs numbers. While yellow points are showing variants genotyped with NGS, MALDI-TOF MS, or Sanger sequencing, small black points are illustrating SNPs that have been imputed from global SNP array information on the same liver subjects (Infinium Global Screening Array GSA v2.0). **(D)** Pairwise linkage disequilibrium map of the _SLC10A1_ gene region. The plot is created using Haploview and D\({}^{\prime}\) values are shown.