esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Economic Assessment  
During the economical assessment, the focus was on the net present value (NPV), internal rate of return (IRR), and payback period. Economic viability is obtained when the NPV and IRR are above zero and the payback period is shorter than the operation period of the plant. These values were automatically calculated by SuperPro designer in an executive summary. Besides the NPV, IRR and payback period, SuperPro Designer gave data about the total investment, operating costs, revenue, unit production costs, and gross margin (Table 4). The total capital investment consists of the direct fixed capital, the working capital, and the start-up costs. The operating cost included the expenses of raw materials, labor, facility, laboratory, utilities, and transportation.  
Furthermore, any possible subsidies were not included. It was assumed that the capital needed will come from an investor. NPV is the sum of the present values of annual net income earned in the period of project exploitation [62] and is defined as:  
\[\mathrm{NPV}=\sum_{k=0}^{N}\frac{c_{k}}{(1+i)^{k}} \tag{7}\]  
where \(C_{k}\) = net income in the kth year of the period of project exploitation, \(i\) = the discount rate, and \(n\) = period of the project exploitation.  
IRR is the discount rate where the NPV is zero [63] and can be defined as:  
\[\mathrm{IRR}=\sum_{k=0}^{N}\frac{c_{k}}{(1+i)^{k}}-C_{0}=0 \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plant Summary**} & \multicolumn{2}{c}{**Sugar Beet Pulp (SBP)**} & \multicolumn{2}{c}{**Grass Straw (GS)**} \\ \cline{2-5}  & **Treated** & **Untreated** & **Treated** & **Untreated** \\ \hline Total Capital Investment (€) & 2,584,000 & 1,931,000 & 39,450,000 & 1,993,000 \\ Capital Investment Charged to This Project (€) & 2,584,000 & 1,931,000 & 39,450,000 & 1,993,000 \\ Operation Cost (€/year) & 1,075,000 & 456,000 & 2,202,000 & 420,000 \\ Revenues (€/year) & 1,117,000 & 964,000 & 1,593,603 & 859,000 \\ Batch Size (kg) & 13,073 & 30,198 & - \\ Cost Basis Annual Production Rate (kg/year) & 1,424,990 & 1,228,939 & 1,962,890 & 1,094,616 \\ Conversion (\%) & 14.25 & & 19.63 & - \\ Unit Production Cost (€/kg) & 0.75 & 0.37 & 1.12 & 0.38 \\ Payback Time (years) & - & 5.09 & - & 5.85 \\ IRR (After Taxes) (\%) & - & 23.05 & - & 19.30 \\ NPV (at 5\% Interest) (€) & -671,000 & 3,367,000 & -10,543,000 & 2,763,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Executive summary of SBP and GS as feedstock for the production of bioethanol in SuperPro Designer.  
Figure 4: Process flow diagram of ethanol recovery section modelled in SuperPro Designer.  
where \(C_{0}=\) total investment costs.  
The payback period is the time required to recover the investment [62; 63] and is defined as:  
\[\text{Payback period}=\frac{\text{Cost of Investment}}{\text{Annual Cash Inflow}} \tag{9}\]
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 4 Proposed Approach  
Our proposed FedCO algorithm foresees two distinctive phases: _initialization_ and _iteration_. These phases are described in what follows, along with cluster optimization algorithms. In addition, the algorithm pseudo-code is reported in Algorithms 1 and 2. Let \(W=\{w_{1},w_{2},\ldots,w_{n}\}\) be the set of all available workers, and \(W_{t}\) is a subset of \(W\) that contains the workers selected at round \(t\). The workers in \(W_{t}\) can be the representatives of the clusters \(C_{t}=\{C_{t1},C_{t2},\ldots,C_{tk_{t}}\}\) obtained by applying a clustering algorithm to \(W\), or a set of randomly selected workers, and \(\mid W_{t}\mid<n\). ```
1:procedureFedCO(\(M_{0}\), \(W_{t}\subseteq W\), \(k_{t}\), \(T\)) InitializationPhase
2:\(t\gets 0\)
3:\(\forall\ w_{i}\in W_{t}\), Send(\(w_{i}\), \(\mathcal{M}_{t}\))
4:for each worker \(w_{i}\in W_{t}\) in paralleldo
5:\(\mathcal{M}_{t+1}^{i}\leftarrow\textsc{WorkerUpdate}(i,\mathcal{M}_{t})\)
6:endfor
7:\(\mathcal{M}_{t+1}=\sum_{w_{i}\in W_{t}}\frac{n_{i}}{n}\mathcal{M}_{t+1}^{i}\) following (2)
8:\(\mathcal{C}_{t}\leftarrow\textsc{Kmedoids}(k_{t},\{\mathcal{M}_{t+1}^{i}\ |\ w_{i}\in W_{t}\},W_{t})\) IterationPhase
9:while\(t\leq T\)do
10:\(t\gets t+1\)
11:\(W_{t}\leftarrow\textsc{SelectTopRanked}(p,C_{t})\)
12:\(\forall\ w_{i}\in W_{t}\), Send(\(w_{i}\), \(\mathcal{M}_{t}\))
13:for each worker \(w_{i}\in W_{t}\) in paralleldo
14:\(\mathcal{M}_{t+1}^{i}\leftarrow\textsc{WorkerUpdate}(w_{i},\mathcal{M}_{t})\)
15:endfor
16:\(\mathcal{M}_{t+1}=\sum_{w_{i}\in W_{t}}\frac{n_{i}}{n}\mathcal{M}_{t+1}^{i}\)
17:\(\mathcal{C}_{t+1}\leftarrow\textsc{Silhouette}(k_{t},C_{t},W_{t})\)
18:while\(|\ C_{t+1}|<|C_{t}\ |\)do
19:\(\mathcal{C}_{t+1}\leftarrow\textsc{ClusteringOptimization}(k_{t+1},C_{t+1})\)
20:endwhile
21:endwhile
22:endprocedure
23:functionSilhouette(\((k_{t},C_{t},W_{t})\)) \(\triangleright\) Check whether each cluster representative still belongs to its cluster
24:for\(w_{i}\in W_{t}\)do
25:for\(j=1,2,\ldots,k\)do
26: compute \(s(w_{i})\)\(\triangleright\) According to Equation (6)
27:endfor
28:if\(s(w_{i})<0,\forall j\in\{1,2,\ldots,k\}\)then
29:\(k_{t}\gets k_{t}+1\)
30:\(C_{tk_{t}}\gets w_{i}\)
31:else
32: Assign \(w_{i}\) to the nearest cluster \(C_{tj}\)
33:endif
34:endfor
35:\(\forall\ C_{tj}\ (j=1,2,\ldots,k)\) recompute the cluster center
36:return\(C_{t+1}\)\(\triangleright\) The new set of clusters
37:endfunction
38:functionWorkerUpdate(\((w_{i},\mathcal{M}_{t})\))\(\triangleright\) Local update
39:whileTruedo
40: Receive(\(w_{i}\), \(\mathcal{M}_{t}\))
41: LocalTraining(\(w_{i}\), \(\mathcal{M}_{t}\))
42:\(\mathcal{M}_{t+1}^{i}\leftarrow\mathcal{M}_{t}^{i}-\eta_{St}^{i}\)\(\triangleright\) Local update, (1)
43: Send(\(i\), \(\mathcal{M}_{t+1}^{i}\))
44:endwhile
45:endfunction
```
0: updated \(k_{t}\) and \(C_{t}\)
1:procedureClusteringOptimization(\(k_{t}\), \(C_{t}\))
2:\(s(C_{t})\leftarrow\)SilhouetteScore(\(k_{t}\), \(C_{t}\), \(W_{t}\))
3:\(C_{t}^{\prime}\leftarrow\emptyset\)
4:for\(C_{tj}\in C_{t}\) s.t.
## 4 Discussion  
The proposed algorithm can be mainly in three aspects: (1) The algorithm segments both the mandible and maxilla; (2) The segmented jaw contains not only the cortical bone but also the trabecular bone structures; and (3) The algorithm works well whether the mouth is in an open or closed state. The previous approaches did not address all these aspects. The 2D image-based methods [9; 11; 15; 16; 17] did not segment the whole trabecular bone structures. The 3D volume-based methods [10; 18] did not segment the maxilla and could not work when the mouth was in a closed state.  
The trabecular bone structures are too complicated to segment manually. Therefore, an approximate method was designed to quantify the accuracy of the trabecular bone structure. First, erode the ground truth volume twice by the spherical structuring element  
Figure 11: Maximly model: (**a**) Segmented by the proposed algorithm, (**b**) ground Truth, and (**c**) superimposing of (**a,b**).  
Figure 12: Jaw model that was segmented by the proposed algorithm when the mouth is closed.  
Figure 10: Mandibular model: (**a**) Segmented by the proposed algorithm, (**b**) ground Truth, and (**c**) superimposing of (**a,b**).  
with a three pixel radius and obtain the trabecular ground truth (TGT). Second, remove voxels of the TGT whose intensity values in the original CBCT images are larger than a threshold. Third, calculate the accuracy of the trabecular bone as defined as follow.  
\[Accuracy=\frac{TGT\ \cap Segmented\ Jaw}{TGT}\]  
The average accuracy values of the mandible and maxilla are 0.9941 \(\pm\) 0.0053 and 0.9982 \(\pm\) 0.0044, where the threshold is same as T, as calculated in Step I. These data illustrate that the trabecular bone structures are preserved accurately.  
After jaw segmentation, the structures of the trabecular bones can be observed by thresholding. Figure 13 shows some sections of the segmented mandible thresholding by T that was calculated in Step I. Figure 13e-h are the enlarged red square portions of Figure 13a-d.  
To clinically use CBCT for dental diagnosis and treatment planning, the potential applications of the proposed algorithm includes the following: (1) The proposed algorithm can automatically segment the structures of the mandible and maxilla, which currently has to be done manually by experts (taking around 12 h); (2) The proposed algorithm is convenient for dentists to observe the 3D structures of the mandible and maxilla alone without muscle, cervical vertebra, and the airways. For example, if a patient needs implantation in the mandible, the dentist can only observe the 3D mandible by the proposed algorithm, rather than all the tissues from the CBCT images; and (3) The proposed algorithm can provide a 3D model of the mandible and maxilla which can be transferred to an STL file for 3D printing.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 5 FSS Measurements
### Multi-Layer Frequency Selective Surfaces  
Single-layer FSSs are often compact in volume and light in weight as compared to the multi-layer FSSs. Considering that in some applications of FSSs the main requirement is a wider bandwidth and higher selectivity, single-layer FSSs conventionally produce just one resonance and may not be capable of fulfilling the selectivity and bandwidth requirements [120; 121]. In order to achieve a wider bandwidth and higher selectivity, multi-layer FSS structures with more complicated geometries can be used. Assembling an FSS with two identical metallic layers stacked with an impedance inverter in between produces a filter with a wider bandwidth and higher selectivity. The selectivity can be improved by implementing higher-order FSSs with additional metallic layers and impedance inverters. In addition, transmission zeros can be inserted in the out-of-band response to improve selectivity and suppress unwanted harmonics and frequency components [122; 123].  
However, the implementation of multi-layer FSSs can be costly and complicated compared to the single-layer structures. In addition, the numerical analysis of multi-layer structures is complicated because of the coupling effects between the fixed FSSs. Therefore, a single-layer FSS with patches and strips of different sizes can be arranged in a fractal geometry to produce multiple resonances using a single layer. This efficiently reduces the overall profile and fabrication complexity [124]. However, the fractal FSS reveals multiple unwanted resonance features because of the multiple types and size of elements per unit cell [125].  
Multiband FSSs with close band spacing have been of great interest in recent research because of their application in military systems, i.e., missile, aircraft, etc. to communicate with satellites [126]. In these applications, multiple operating frequencies are used [127]. The construction of an FSS with three bands [S-band (2.2 GHz), Ku-band (14.9 GHz), and Ka-band (25.25 GHz)] is of great challenge. The reflection and transmission ratio for the center of each frequency band is 1.48 or less [128]. Consequently, to achieve closer reflection and transmission ratios, a gridded square FSS was proposed. The ratio ranges from 1.3 to 2.1 or \(>2.5\) for a single FSS [2], but such a design has a major drawback of fabrication complexity because of the multi-layer structures. Another way of achieving close band spacing is using the resonant frequencies of double square units with active components that have different loadings [129; 130]. Subsequently, this design (dual band loaded FSS) mitigates the above-mentioned issues and achieves close band spacing, and the resonant frequency is tailored with different loadings of lumped components.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 3 Speech Databases and the Extraction of Features
### Speech under Simulated and Actual Stress (SUSAS) Database  
A wide variety of emotions and stresses are part of the five domains present within the SUSAS database. Actual domain (actual speech when stressed) and simulated domain (simulated speech when stressed) are part of the database. There were close to 16,000 utterances by 32 speakers (from 22-76 years of age), of which 19 were men and 13 were women [22]. To conduct fair comparisons with other studies, the Lombard condition was removed. This research took into consideration 13,890 records uttered by eight speakers (training considered five, and testing was conducted on the others) two times (each word repeated twice), who talked in 6 stressful talking conditions, which included fast, soft, low, slow, angry, and neutral.
###### Abstract  
This study aimed to investigate the effect of limb-specific resistance training on arterial stiffness in young adults. Twenty-four participants were randomly assigned to three groups: upper-limb resistance training (\(n\) = 8 (URT)), lower-limb resistance training (\(n\) = 8 (LRT)), and control group (\(n\) = 8 (CON)). Both URT and LRT groups performed the limb-specific resistance training at 70-80% of one-repetition maximum twice a week for 8 weeks. The aortic pulse wave velocity and augmentation index (Alx) were measured by the SphymoCor XCEL to assess central artery stiffness. Peripheral artery stiffness was evaluated by brachial to radial artery pulse wave velocity (ArmPWV) and femoral to posterior tibial artery pulse wave velocity (LegPWV) using Doppler flowmeters. URT significantly reduced Alx (4.7 \(\pm\) 3.0 vs. 0.3 \(\pm\) 2.9%, pre vs. post, \(P\) = 0.01), and ArmPWV presented a tendency to decrease following URT (10.4 \(\pm\) 0.3 vs. 8.6 \(\pm\) 0.8 m/s, pre vs. post, \(P\) = 0.06). LRT showed no negative influence on central and peripheral artery stiffness. Changes in serum triglyceride and leg lean body mass after resistance training were significantly associated with changes in Alx and LegPWV, respectively. URT is beneficial in decreasing central artery wave reflection and may help to improve local peripheral artery stiffness even in healthy young adults.  
resistance training; arterial stiffness; pulse wave velocity; augmentation index +
Footnote †: journal: Applied Sciences  
1
## 5 Analysis of Simulation Results
### Velocity Distribution at Different Cross Sections  
In order to visualize the flow field distribution, the velocity profile with the maximum velocity 15 m/s was analyzed.  
By comparing the velocity distribution located at different cross-sections of the pipe from 5D to 25D downstream the header without FC, it showed that the velocity profile at 25D was nearly fully developed (Figure 14). Compared with it, the velocity profile changes obviously from 5D to 20D, and it gradually stabilized as the length of the straight pipe section increased. The larger the \(v_{y}\) was, the greater the distortion effect on measurement accuracy. It also showed the range of change in \(v_{y}\).  
Figure 13: The orientation of the velocity components in the pipe.  
One can see from Figure 15 that the velocity distributions on individual paths were of significant differences on different paths in the two sections at 10D without FC. Affected by the vortex, the velocity profile was obviously deformed, the velocities of \(v_{\text{B}}\) and \(v_{\text{C}}\) were decreased, and the velocities of \(v_{\text{A}}\) and \(v_{\text{D}}\) were increased. It reached a completely symmetrical distribution state at 25D.  
From Figure 16, it can be seen that after the FC was installed, the high velocity began to converge toward the pipeline axis, the fluctuation of the velocity profile had been improved, and the maximum \(v_{y}\) did not exceed \(\pm\)0.5 m/s even at 5D with FC.  
Figure 14: Contours of velocity profiles on the different cross-sections downstream of the header without FC.  
Figure 15: Velocity distribution on individual paths without FC. (**a**) 10D; (**b**) 25D.  
Figure 17 showed that the velocity profile at 10D had been improved to some extent, and the flow distribution reached a fully developed state at about 15D. Thus, it could be found that the installation of FC could significantly improve the velocity profile and effectively eliminate swirls.
## 2 Materials and Methods
### Preparation of Au-ZrO\({}_{2}\)  
For the synthesis of Au-ZrO\({}_{2}\), initially, ZrO\({}_{2}\) was prepared via solvothermal method according to our previously reported method [23]. Briefly, 387.67 mg (1 mmol) of zirconium isopropoxide was dissolved in 30 mL of benzyl alcohol in a beaker (25 mL) under constant stirring. The benzyl alcohol solution of zirconium isopropoxide was transferred into a Teflon cup which was placed in a 50 mL stainless steel autoclave and heated up to 210 \({}^{\circ}\)C. The vessel was cooled down after 72 hrs. A white powder was obtained, which was separated by centrifugation and washed repeatedly with tetrahydrofuran (THF) and dried in vacuo. The as-obtained ZrO\({}_{2}\) powder was used to prepare Au-ZrO\({}_{2}\) employing typical ultrasonic method as follows: initially, 196.91 mg (0.5 mmol) of tetrachloroauric (III) acid trihydrate (HAuCl4.3H2O, 99%) was dissolved in 30 mL of ethanol (bright yellow solution obtained) in a 50 mL beaker. To this solution, 246.56 mg (2 mmol) of ZrO\({}_{2}\) NPs was added (1:4 ratio with respect to gold precursor), the mixture was briefly stirred. Subsequently, the solution was kept in a probe sonicator (Biologics Inc, Model 3000 MP, 115 volts, Cary, NC, USA) with a supply of 25 kHz ultrasonic waves with an input power of 460 W. After 5 min, the mixture turned to light purple, the sonication was stopped, and the product was separated through centrifuge (9000 rpm). The product formed was confirmed with UV-Vis analysis. Notably, in the absence of ZrO\({}_{2}\) NPs, the gold precursor did not reduce to form Au NPs even under the same conditions. Therefore, for the comparison of UV results, pristine Au NPs are prepared under similar conditions using citric acid as a reducing agent.
## 3 Experiments and Discussions
### Ex Vivo Full-Range Images  
Figure 12 shows the full-range spectral domain optical coherence tomography images of the ex vivo anterior segment of a rat eyeball using the FIP and FVP methods. Figure 12a is the cross-sectional image of the anterior segment of the rat eye obtained using the FFT of the original interference signal without FIP or FVP method. Figure 12b is the cross-sectional image of the anterior segment of the rat eye obtained using the FIP method. Figure 12c is the cross-sectional image of the anterior segment of the rat eye obtained using FVP method. Figure 12d is the CCA suppression ratio of FIP and FVP methods at the dashed line in Figure 12b,c. From Figure 12b,c, we found that the rat eyeball's cornea,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bandwidth (nm)** & **FIP (dB)** & **FVP (dB)** & **D-Value (dB)** \\ \hline
55 & 40.7 & 41.8 & 1.1 \\
60 & 41.8 & 44.2 & 2.4 \\
65 & 45.2 & 48.9 & 3.7 \\
70 & 45.7 & 50.3 & 4.6 \\
75 & 46.5 & 51.2 & 4.7 \\ \hline \hline \end{tabular}  
* FVP: five-frame variable phase-shifting method, FIP: five-frame invariant phase-shifting method.  
\end{table}
Table 1: The complex conjugate artifacts (CCA) suppression of different light source bandwidth.  
Figure 11: CCA suppression: The solid line is for FIP method, and the dashed line is for FVP method.  
lens, and iris could be clearly imaged by using these two methods. However, the CCA suppression ratio of FVP method is slightly better than the FIP one by about 2 dB, as can be seen from Figure 12d. As we know, the wider the bandwidth of light source, the bigger the polychromatic error. Therefore, if the bandwidth is wider, the superiority of FVP method will be more obvious.  
Figure 12: Imaging of the ex vivo anterior segment of rat eyeball. (**a**) Cross-sectional image of the anterior segment of rat eye obtained using fast Fourier transform (FFT) of the original interference signal without FIP or FVP method. (**b**) Cross-sectional image of the anterior segment of rat eye obtained using FIP method. (**c**) Cross-sectional image of the anterior segment of rat eye obtained using FVP method. (**d**) CCA suppression ratio of FIP and FVP methods at the dashed line in (**b**,**c**).
# Hailstorm Detection by Satellite Microwave Radiometers
## 3 The Hail Detection Model  
The physical basis of the hail detection method is based on the relationship between the hail diameters as estimated by hail reports and the scattering signal captured by the MHS channels. Generally, large hail is associated with large TB reduction and the signal extinction increases with frequency (see Table 2). As already noted, the magnitude of the TB reduction is the net volume effect of a number of parameters such as ice in the satellite FOV, its size distribution, and its vertical extent.  
In this section, the datasets used to train the model for hail detection are presented. Then the model conceived to calculate the probability of hail occurrences is presented.
## 3 The International Stellarator Confinement Database and the ISS04 Scaling Law
Another significant limitation resides in the lack of confidence intervals, which cannot be quantified, because the uncertainties in \(f_{ren}\) are not available. This is a particularly relevant drawback for extrapolation, one of the fundamental reasons scaling laws are derived in the first place. Indeed, in general, one of the main objectives of empirical scaling laws consists of providing guidance to the planning of new experiments and the design of new devices. Another delicate issue affecting the DB relates to the rotational transform. Indeed, it proves quite problematic to extract the dependence of \(\tau_{E}\) on the rotational transform from the DB. This is mainly the unfortunate consequence of two factors: the design of certain devices and the experimental programme of others. The heliotron/torsatron family of devices, due to engineering constraints, presents a very strong collinearity between the rotational transform \(t_{2/3}\) and the aspect ratio. In their turn, W7-A and W7-AS machines do not scan a significant range of \(t_{2/3}\). Consequently, it is difficult to isolate the effect of the rotational transform on \(\tau_{E}\). To alleviate this deficiency, an approach, similar to the one devised in [22] to obtain ISS04, has also been implemented to derive the scalings reported in this work. TJ-II dependence of \(\tau_{E}\) on the rotational transform is used as first guess for symbolic regression. This approach works quite well for the devices with shear, whose scaling shows a power law dependence on \(t_{2/3}\) with an exponent not much different from the one of TJ-II. On the other hand, for the shearless configurations much weaker dependencies on \(t_{2/3}\) would be better supported by the International Stellarator Confinement Database, a subject discussed more extensively in the next section.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13  
3 GPP introduced the following techniques in NB-IoT Release 13 to enable cellular massive IoT deployment for diverse use cases with low power, low complexity, and hence low cost. The introduced features and their corresponding objectives are as follows.