esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
###### Abstract  
A problem for wind turbine operators is decreasing prices for wind-generated electricity. Many turbines are approaching their rated 20-year lives. A more economically viable and sustainable solution that reduces Levelized Cost of Energy (LCOE) and avoids expensive turbine replacement is retrofitting new spar caps blades. A new cost model assesses the feasibility of retrofitting 35 to 75 m turbines with GFRP (glass fiber reinforced polymer composite) and longer length CFRP (carbon fiber reinforced composite) spar caps. Spar cap cost scales with features such as mass, volume fraction and complexity. Organizational learning is a cost factor. Material and direct labor increase as proportions of total cost while tooling, capital, utilities, and indirect labor decrease. There is good agreement between a manufacturer and the model. Twenty-year turbines were compared with retrofitted spar caps over 25 years for LCOE. Same length GFRP and longer length CFRP spar cap retrofits decrease LCOE. Longer length CFRP spar caps decrease LCOE compared with GFRP retrofits over 25 years. CFRP material cost impacts CFRP retrofit feasibility. Retrofitted turbines must meet engineering, operational performance, and planning requirements criteria. Software algorithms may improve human learning and enable automatic updates from varying design and cost inputs, thereby increasing cost prediction accuracy.  
Spar cap; complexity; features; cost model; data constraints; organizational learning Article
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 6 Simulation and Analysis  
To verify the effectiveness of the proposed DD-NOMA algorithm, MATLAB software is used to simulate the resource allocation of non orthogonal multiple access. Meanwhile, the throughput and outage probability performance are adopted as the performance metrics. In the experiments, the proposed DD-NOMA algorithm is compared with Channel Correlation coefficient-based NOMA (CC-NOMA) [29], cognitive Radio network-based OMA (CR-OMA) [47] and Dynamic Clustering-based NOMA (DC-NOMA) [19]. In specific, CC-NOMA selects two users with the largest channel correlation coefficient to pair and utilizes the alternating direction method of multipliers (ADMM) method to obtain the suboptimal power allocation solution. CR-OMA algorithm adopts orthogonal frequency division to access the satellite ground cognitive network and proposes a joint resource allocation optimization model based on efficiency and fairness. Under the constraint of maximum tolerable interference outage probability, the optimal resource allocation is solved by the closed form expression of power allocation and subchannel allocation. In DC-NOMA algorithm, a stagger and fold method is proposed for user pairing to avoid the situation that the difference of channel gain may be small. Then, the KKT optimization method is used to solve the optimal power allocation to maximize the total user capacity under the constraints of total transmission power and minimum user rate.
## 4 Discussion  
### Residues in Austrian Wines  
One aim of this work was to give an overview of pesticide residues found in Austrian wine, must and must in fermentation samples. For this purpose, 250 Austrian wines, 45 Austrian musts in fermentation and 33 Austrian grape musts were analyzed regarding the content of 23 common residues. Fifteen of these compounds were detected in individual samples, and 12 compounds were detected in quantifiable concentrations. With the exception of fluopyram (fungicide against oidium with side effect against botrytis), only botryticide residues were found in concentrations above 0.100 mg/L. The highest concentration was detected from basalt in a must sample at 0.562 mg/L. [20] summarized the observed concentrations of common pesticide residues in grapes, grape must and wine by many authors [15; 17; 18; 19; 46; 55; 56] in recent years. The results of the present study in respect to botryticide residues are consistent with the results of these studies. Residue concentrations above 0.1 mg/L were obtained from basalt, fenhexamid, mepanipyrim, and pyrimethanil. However, it is remarkable that the maximum documented residue levels in the studied samples are lower than observed in other studies (in some cases above 1 mg/L) [20]. It is also interesting that the residue metalaxyl was not monitored in larger amounts in the investigated Austrian samples, but in other studies levels above 1 mg/L were found [20]. Fluopyram is not considered in the review [20], while other compounds that were not analyzed in the course of the present study, such as other systemic compounds like iprovalicarb, dithiocarbates or organic substances like copper and sulfur were discussed in the review [20]. As already mentioned in [5], the Austrian wine control is limited to the monitoring of botryticides. While the results of this present study support this approach, there should be further scientific studies to determine whether the levels of other individual compounds also justify random monitoring in the context of wine control.  
Furthermore, these results support the usefulness of studies on the reduction of pesticide residues by means of fining agents.
## 2 Models
### Aerodynamic Force Calculation  
The magnitude of lift and drag is calculated by lift coefficient and drag coefficient, which are denoted by \(C_{L}\) and \(C_{D}\), respectively, as follows:  
\[\begin{array}{l}L=C_{L}\cdot\frac{1}{2}\rho(Ma\cdot c)^{2}\cdot S\\ D=C_{D}\cdot\frac{1}{2}\rho(Ma\cdot c)^{2}\cdot S\end{array} \tag{5}\]  
where \(\rho\) is the atmospheric density and \(S\) is the reference area of the aircraft. The lift coefficient and drag coefficient under different angles of attack, rudder angles, and incoming Mach numbers are obtained by CFD simulation. Then, in the process of trimming angle of attack, the rudder angle is adjusted to make the pitching moment equal to 0, and the lift coefficient and drag coefficient at this moment are regarded as the data under this angle of attack in steady state [32]. Subsequently, the lift coefficient and drag coefficient are parameterized by polynomial fitting. The fitting polynomials are as follows, and the values of coefficients are given in Tables 1 and 2.  
\[\begin{array}{l}C_{L}=\sum\limits_{i=0,j=0}^{i+j\leq 3}A_{ij}\cdot Ma^{i} \cdot\mathbf{a}^{j}\\ C_{D}=\sum\limits_  
The atmospheric parameters change a lot at different altitudes, which has a significant impact on the calculation of aerodynamic forces. In this paper, the American Standard Atmospheric Parameters Model is employed. Firstly, a gravity potential height is defined [33]:  
\[H=\frac{h}{1+h/R_{e}} \tag{7}\]  
where \(R_{e}\) denotes the radius of Earth. When the altitude is within 20 to 32 km, the atmospheric density and temperature are calculated by the following formula:  
\[\begin{array}{l}W=1+\frac{H-124.9021}{221.552}\\ \frac{\rho}{\rho_{0}}=2.5158\times 10^{-2}W^{-34.1629}\\ T=221.552W\ (K)\end{array} \tag{8}\]  
where \(\rho_{0}=1.225\) kg/m\({}^{3}\).  
The sound velocity calculation formula is:  
\[c=\sqrt{1.4\times 287\times T} \tag{9}\]  
According to the formulas above, the lift and drag of aircraft can be calculated.
## 3 Results
### Free-Space Calibration
Figure 6: (**a**) Comparison between Modelled (**—**) and measured (•) empty free-space complex reflection \(S_{11}^{E}\) as a function of the stand-off distance \(\Delta_{r}\) from the reference plane (L\({}_{r}\) = 24.04 mm) – f = 10 GHz. (**b**) Comparison between Modelled (**—**) and measured (•) calibrated empty free-space complex reflection \(S_{11}\) as a function of the stand-off distance \(\Delta_{r}\) from the reference plane (L\({}_{r}\) = 24.04 mm)—f = 10 GHz. The reference plane is illustrated in yellow color (at \(\Delta r\) = 0) and the calibration standards are illustrated with \(\blacksquare\).
## 3 Deep Learning Model for Making Predictions
### Bayesian Optimization  
The CNN mentioned earlier contained unknown hyperparameters, including the network structure, learning rate, number of training cycles, number of samples per batch, and size and number of convolutional kernels. Manually selecting and tuning these parameters would be very difficult and time-consuming, so a Bayesian optimization algorithm was used to automatically search for the best combination of hyperparameters. The selection process is shown in Equation (3).  
\[x^{*}=arg\inf(x)\ \ \ \ (x\in\chi) \tag{3}\]  
where \(x\) is a combination of parameters, \(f(x)\) is the objective function (i.e., mean relative error), \(x^{*}\) is the best combination of parameters, and \(\chi\) is the range of parameter values.  
A search range was established for each hyperparameter. The search ranges for the number of CNN layers, learning rate, epoch, and mini-batch were 1-4, 10\({}^{-4}\)-1, 0-900, and 0-100, respectively. The optimization workflow for the deep learning model is shown in Figure 5. First, a combination of hyperparameters \(x_{0}\) was randomly selected from within the range of the hyperparameter values, then \(x_{0}\) was fed into the network for training to give the corresponding objective function value \(f(x_{0})\). Next, the location of the next sampling point was determined using a Gaussian process, and the probability distribution \(f(x)\) corresponding to \(x\) was calculated, and \(f(x)\) corresponding to \(x\) in the probability distribution was predicted using all of the \((x,f(x))\) inputs. The x values that minimized the objective function were used for training, and the objective function \(f(x)\) was calculated. Until the maximum number of iterations was reached, \((x,f(x))\) was used as an input for the Gaussian process. According to historical evaluation records (i.e., the mean relative error), this provided a reference for subsequent attempts to decrease the time taken to identify the parameters and to continually update the probabilistic model to give a new \((x,f(x))\). Ultimately, the x values corresponding to the minimum objective function \(f(x)\) value were identified as the optimal hyperparameter combination \(x^{*}\).  
Figure 5: Bayesian optimization workflow.
## 1 Introduction
Various speaker embedding vectors, such as \(i\)-vector [10], \(d\)-vector [11], and \(x\)-vector [12], are adopted. Furthermore, the proposed method efficiently reuses the decoder from the conventional SAT-ASR (speaker-attributed training for automatic speech recognition) [6] to eliminate the need for fully training the entire model. Experimental results show that the proposed method improves existing multi-speaker speech recognition models based on the Transformer network [6] in terms of word error rate (WER) on the _Libriinix_ dataset [13]. The proposed SAT-ASR model demonstrates improved WERs in multi-speaker speech environments. Without a speaker profile matrix, we observed a 0.33% absolute improvement (3.9% relative improvement) in WER, and with a speaker profile matrix, a 0.95% absolute improvement (11.5% relative) was observed. Section 2 presents related work on speaker embeddings and multi-speaker speech recognition models. Section 3 introduces the proposed SAT multi-speaker ASR model and speaker  
Figure 1: Multi-speaker speech recognition problem illustration. The voices of two independent speakers were recorded by a single microphone, denoted by \(\mathbf{x}\). A multi-speaker speech recognition system generates two or more word sequences, denoted by \(\mathbf{y}^{(1)}\) and \(\mathbf{y}^{(2)}\), where the parenthesized superscripts are speaker indices from the given audio recordings of overlapped speakers. profile. Section 4 evaluates the performance of this model. Finally, Section 5 concludes with a summary.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 2 Development and Implementation of the 3D Model
### Generation of Random Mechanical Properties
#### 2.2.2 Fracture Energy Random Generation  
The lognormal distribution characterizes the heterogeneity within the material's fracture energy. Its probability density function, denoted as \(f_{L}(x,\mu_{L},\sigma_{L})\), is defined in Equation (10), where \(\mu_{L}\) represents the mean and \(\sigma_{L}\) denotes the standard deviation of the variable's natural logarithm. Furthermore, the expected value \(E_{L}(X)\) and variance \(Var_{L}(X)\) of this distribution can be calculated using Equations (8) and (11).  
\[f_{L}(x,\mu_{L},\sigma_{L})=\frac{1}{\mu_{L}\sigma_{L}\sqrt{2\pi}}e^{-\frac{( \ln(x)-\mu_{L})^{2}}{2\sigma_{L}^{2}}} \tag{10}\]  
\[E_{L}(X)=e^{\mu_{L}+\frac{\sigma_{L}^{2}}{2}} \tag{11}\]  
\[Var_{L}(x)=\left(e^{\sigma_{L}^{2}}-1\right)e^{2\mu_{L}+\sigma_{L}^{2}} \tag{12}\]  
The numerical generation of random fracture energy relies on the convolution method, with the fundamental steps summarized in Algorithm 2. The approach involves initially generating random numbers from \(U(0,1)\) in order to after obtain random variables conforming to the standard normal distribution \(N(0,1)\) (a normal distribution with \(\mu_{L}=0\) and \(\sigma_{L}=1\)). Subsequently, through a straightforward algebraic operation, lognormally distributed random variables are obtained. Additional details regarding these random number generation procedures can be found in the work by [PERSON] [16].  
```
1:for each finite element do
2: Generate a sequence of 12 random values \(u_{1},u_{2},...,u_{12}\) from \(U(0,1)\);
3: Evaluate \(X=\left(\sum\limits_{i=1}^{n}u_{i}\right)-6\) ; // X follows N(0, 1)
4: Evaluate \(G_{IC}=e^{u_{1}+\sigma_{IC}X}\);
5: Return \(G_{IC}\) ; // \(G_{IC}\) follows the lognormal distribution
6:endfor
```  
**Algorithm 2** Generation of random fracture energy by the convolution method
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion  
### Reflection Coefficient of the Antenna  
The prototype diagram of the antenna is shown in Figure 8, where a coaxial cable is used for antenna feeding. The s\({}_{11}\) parameter of the antenna is measured using a vector network analyzer. To minimize the impact of the surrounding environment on the antenna test, the pork is fixed on foam for experimentation. Foam, being a porous material that includes air, has a dielectric constant close to that of air. As depicted in Figure 9, the designed antenna is implanted into the pork, and the other end of the coaxial cable is connected to the vector network analyzer via an SMA interface.  
A comparison between the actual measurement and simulation results of the antenna's reflection coefficient is presented in Figure 10. The operating frequency band of the antenna in pork with s\({}_{11}\) less than \(-10\) dB is 2.3-3.03 GHz (703 MHz). Compared to the simulation results, the resonant frequency of the antenna has shifted, and the relative bandwidth of the antenna in pork has reached 28%, which is an improvement over the 23.8% relative bandwidth observed in the simulation. Therefore, the experiments confirm that the operating frequency band of the proposed antenna fully covers the required ISM band, and the antenna exhibits better impedance matching after being implanted into pork. The actual measurement results are consistent with the simulation results. Of course, the analysis of the experimental results indicates that there is a certain degree of error between the actual measurement and simulation of the antenna, mainly due to the higher fat content in pork compared to human muscle tissue, which affects the dielectric characteristics of the testing environment. Another reason is the gap between the antenna and the pork tissue, because the antenna cannot be completely fixed during the implantation process, making it difficult for the antenna to fully conform to the muscle tissue as in the simulation  
Figure 8: Prototype diagram of the designed antenna.  
Figure 9: Experimental setup for measuring the s\({}_{11}\) of the proposed antenna.  
model. Additionally, the antenna itself is very small in size, which may lead to certain errors during the antenna's processing and welding.
###### Abstract  
This paper proposes a pattern distortion coefficient as a new figure of merit to quantitatively evaluate both mutual coupling and pattern distortions in multi-antenna systems. The proposed coefficient is defined as a cross correlation between unaffected and affected far-field patterns of antennas under test, and the input patterns are weighted using a Gaussian function to consider the target operation angle. The feasibility of the proposed approach is validated using a two-antenna system composed of an inverted-F antenna and a microstrip patch antenna, and the amount of mutual coupling is adjusted by changing the distance between the two antennas. The evaluation is further extended to a single-antenna system with a conducting wall that produces strong platform effects with serious pattern distortions. The results demonstrate that the proposed figure of merit provides quantitative insight into the amplitude and phase distortions of far-field patterns that can be caused by both mutual coupling and platform effects.  
shared-aperture antenna; mutual coupling; platform effect; antenna figure of merit
## 4 Discussion
### Feature Variables Selection, Model Construction, and Variable Contribution to the Model
#### 4.3.3 Random Forest Performance Across Different Months  
RF demonstrated inconsistent performance between January and August, with an R\({}^{2}\) of 0.55 in January and only 0.36 in August. Despite the similar sizes of the training and validation sets across the two months, and the consistent use of all variables in feature selection, several factors may explain the differences:  
(1) Salinity and Spectral Correlation: In January, the correlation between salinity and spectral data was weaker than in August, but the collinearity issue was also less pronounced (as discussed in Section 3.1). This suggests that the information in the January datasetwas more useful than in August. The summer month of August is characterized by lush vegetation, which could interfere with the accuracy of salinity inversion.  
(2) Weather Conditions: The Yellow River Delta experiences dry conditions in January with little rainfall, whereas August is marked by higher humidity and rainfall. The increased moisture in the air during August can alter the way electromagnetic waves reflect and absorb, which may affect the accuracy of salinity inversion in the region.
# A Multidisciplinary Approach for Improving Resource Efficiency in the Indian Surimi Supply Chain
### Surimi Supply Chain  
Surimi derives from fish flesh proteins that are not typically sourced for human consumption. Once the proteins are transformed into a paste, they can be used to create various seafood products shipped worldwide [20]. Since the late 1970s, surimi has made its mark on globalisation in food staples such as crab sticks [21]. The surimi harvest lacks the infrastructure to support sustainable change where it is needed most. In many low- to middle-income countries, improving storage and infrastructure mechanisms are among the most valuable strategies for reducing food loss [22].  
In India, there are around 15 surimi processing plants located mainly in the western coastal area, in the state of Gujarat and Maharashtra. Surimi is mainly produced from Ribbon Fish, Rani Fish, and Lizard Fish (weight of the catch ranging from 20 to 100 g). The process includes various steps such as de-heading, gutting, fileting, deboning, washing, dewatering, mixing with cryo-protectants, and freezing. The surimi supply chain is a cold chain in which temperature control after harvesting is very important to maintain the protein, quality of meat, and its gel-forming capabilities. Most of the processes in the surimi supply chain are not organised including logistics activities of fish raw material and RRM and cold chain management. The waste problems in the Indian surimi supply chain are linked to both the upstream and downstream operations [23]. This includes wastage of ice at pre-processing centres with no reuse of water and ice, wastage of fish raw material, and surimi RRM due to quality deterioration caused by inadequate cold chain management and longer lead times during transportation and handling [24; 25].
## 3 Linear Damping Approximation  
The electrohydrodynamic movements of the ink in the capillary can be described by the equation of linear damping system [19]  
\[m\ddot{x}+c\dot{x}+kx=0 \tag{2}\]  
where \(m\) is the mass, \(c\) is the damping coefficient, \(k\) is the spring constant, and \(x\) is the ink displacement inside the capillary nozzle. The components of Equation (2), the natural oscillation frequency \(f_{c}\), and the damping ratio \(\Gamma\), of the EHD-jet system were modeled by the following equations [19]  
\[m=\frac{\pi}{4}{d_{n}}^{2}\rho H \tag{3}\]  
\[c=8\pi vl \tag{4}\]  
\[k=\frac{\pi\gamma{d_{n}}^{4}}{2\Big{(}\frac{{d_{n}}^{2}}{4}+{h_{m}}^{2}\Big{)}^ {2}}\Bigg{(}1-\frac{{2{h_{m}}^{2}}}{\frac{{d_{n}}^{2}}{4}+{h_{m}}^{2}}\Bigg{)} +\rho g\pi\frac{{d_{n}}^{4}}{16b^{2}} \tag{5}\]  
\[f_{c}=\frac{1}{2\pi}\sqrt{\frac{k}{m}} \tag{6}\]  
\[\Gamma=\frac{c}{2\sqrt{mk}} \tag{7}\]  
where \(H\) is the approximate ink column height, \(v\) is the viscosity of the ink, \(l\) is the total length of the thinnest part, \(h_{m}\) is the approximate meniscus height, which is approximated by 20% of the inner diameter of nozzle [15], \(b\) is the radius of the capillary far above the nozzle, as illustrated in Figure 3. All values are shown in Table 1. Table 2 shows the calculated natural frequency and the damping ratios of the four different nozzles. Regarding the dimensions of the nozzle and the property of the ink, the damping ratios of nozzle A and C are higher than 1, and that of nozzle B is smaller than 1; these values correspond to overdamped and underdamped systems, respectively.  
Figure 3: Schematics of nozzle: \(\rho\) = density of ink, \(\gamma\) = surface tension of ink, \(v\) = viscosity of ink, \(H\) = approximate ink column height from ink reservoir to free end of nozzle, \(b\) = radius of capillary far above nozzle, \(h\) = distance between nozzle and substrate, \(h_{m}\) = meniscus height, \(d_{n}\) = inner diameter of nozzle, and \(l\) = total length of thinnest part of nozzle.