esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations
### Orbit Determination Refinement
If we indicate with \(\hat{\mathbf{\mathcal{C}}}_{t_{0}}^{RSW}\) the covariance matrix expressed in the RSW reference frame, where  
\[\hat{\mathbf{\mathcal{C}}}_{t_{0}}^{RSW}=\text{diag}\bigg{(}\ \ \left(\sigma_{p}^{R} \right)^{2}\ \ \ \left(\sigma_{p}^{S}\right)^{2}\ \ \ \ \left(\sigma_{p}^{W}\right)^{2}\ \ \ \left(\sigma_{v}^{R}\right)^{2}\ \ \ \ \left(\sigma_{v}^{S}\right)^{2}\ \ \ \left(\sigma_{v}^{W}\right)^{2}\ \ \ \ \bigg{)} \tag{6}\]  
and \(\sigma_{p}^{R}=\sigma_{p}^{W}=0.1\) km, \(\sigma_{p}^{S}=0.2\) km, \(\sigma_{v}^{R}=\sigma_{v}^{S}=\sigma_{v}^{W}=\)1 m/, we can express the covariance matrix in the ECI reference frame as  
\[\hat{\mathbf{\mathcal{C}}}_{t_{0}}^{ECI}=\mathbf{A}^{ECI2 RSWT}\ \hat{\mathbf{\mathcal{C}}}_{t_{0}}^{ RSW}\mathbf{A}^{ECI2 RSW} \tag{7}\]  
The values used for the errors and the standard deviations are typical values that can be obtained with accurate initial orbit determination [4]. The described procedure is repeated for all the objects of the passage list, thus creating a database of state estimates at time epoch \(t_{0}\). The goal of the presented analysis is to understand how the initial database evolves in time assuming orbit refinement with BIRALET only. For all the objects of the selected passage list, the OD refinement consists of two phases: estimate propagation and estimate update. The estimate propagation phase consists in propagating the available state estimate of a given object to the epoch of its new passage  
\[\overline{\mathbf{\mathcal{X}}}_{t_{obs,1}}^{raw}=g\big{(}\hat{\mathbf{X}}_{t^{\text{ pair}}}\big{)}\sim\mathcal{N}\Big{(}\overline{\mu}_{t_{obs,1}}^{raw},\overline{ \mathbf{\mathcal{C}}}_{t_{obs,1}^{raw}}\Big{)} \tag{8}\]  
where \(g\) is the nonlinear function (i.e., the considered orbital dynamics) mapping the state estimate from the epoch \(t^{old}\) to the new observation epoch \(t_{obs,1}^{new}\), \(\hat{\mathbf{X}}_{t^{old}}\) is the last available estimate at time epoch \(t^{old}\), whereas \(\overline{\mathbf{\mathcal{X}}}_{t_{obs,1}^{new}}\) is the a priori estimate at the new observation epoch \(t_{obs,1}^{new}\) (here assumed still a Gaussian RV). The estimate propagation is done by means of the unscented transform (UT). The UT is a method for calculating the statistics of a random variable that undergoes a nonlinear transformation. The basic idea of the UT is to convert the nonlinear transformation of the RV into a series of nonlinear transformation of selected realizations of the RV, called \"sigma points\", and then retrieve the mean and covariance of the transformed RV by performing a weighted sample mean and covariance of the transformed sigma points. Following the description given in [23], consider propagating the state estimate \(\hat{\mathbf{X}}_{t^{old}}\) through the nonlinear function \(g\) from \(t^{old}\) to \(t_{obs,1}^{new}\), thus obtaining the estimate \(\overline{\mathbf{\mathcal{X}}}_{t_{obs,1}}^{raw}\). The initial state estimate is assumed to be a normal random variable, so it can be described by a mean \(\hat{\mu}_{t^{old}}\) and a covariance \(\hat{\mathbf{\mathcal{  
3.
## 1 Introduction
Compared with the asymmetrical system, the maximum temperature and energy consumption of the optimized symmetrical design were decreased by 43% and 33%, respectively. For cylindrical cell battery packs, [PERSON] et al. [10] adopted 18,650, 26,650, and 42,110 cylindrical cells in the rectangular designs with three types of inlet and outlet positions to compare the cooling performances. The cell gap spacing size was found not to be too small or too large. The optimal ratio between spacing distance and the cell diameter decreases with the increase of cell diameter. [PERSON] et al. [11] compared the thermal performances of aligned and staggered layouts of 10 \(\times\) 6 cylindrical cells under a consistent air flow direction. For the same air flow rate, the cooling efficiency is proportional to the longitudinal interval for the aligned arrangement and inversely proportional to the longitudinal interval for the staggered arrangement. The temperature rise is proportional to the transverse interval for both aligned and staggered arrangements. Generally, there are three types of LIB cells: prismatic cell, pouch cell, and cylindrical cell. Since the pouch and prismatic cells have similar shapes and sizes (usually rectangular flat boards), the BTMS designs for prismatic cell packs are presumably also applicable for the pouch ones. Comparing the quantity of relevant heat transfer research in recent years, there is an observable difference between the quantity of papers on different subjects, with more research conducted on prismatic cells than cylindrical cells. Moreover, the commercial applications of air-cooling BTMS for prismatic or pouch cells (such as Nissan, BYD, and Toyota, etc.) are more common than those for the cylindrical ones (Tesla Motors, Inc. is the most dominant OEM user of cylindrical cells and only uses liquid-cooling BTMS for their thermal management). So, the commercial application of air-cooling BTMS for the cylindrical cell battery pack is another gap that needs more energy and commitments to cross over. The successful commercialization of air-cooling BTMS for cylindrical cells will be a breakthrough for the thermal management of EV cylindrical LIB cells. To fill these gaps, this study focuses on the effects of the cell spacing optimization on the cooling performances of air-cooling BTMS with 21,700 cylindrical LIB cells, which still lacks research in the literature. The optimization works include finding the optimal vertical spacing value and the most effective gradient vertical spacing design. The theoretically proven optimal design could be further validated by experiments and explored for potential commercial EV applications.
# Determination and Profiling of Human Skin Odors Using Hair Samples
###### Abstract  
There is no gold standard method for human skin odor determination; several techniques can be applied to collect, extract, transfer, anddetect human skin odors. However, none of these methods are suitable for field sampling of a large number of individuals. **Objective.** The present study aimed to develop a simple, fast, non-invasive, andlow-cost method for such a purpose. **Methods.** Considering that hair from legs can act as a retention mesh of volatile organic compounds (VOCs), samples of leg hairs provided by healthy adult males were collected and solid-phase microextraction (SPME), inheadspace (HS) mode, coupled to gas chromatography (GC) and mass spectrometry (MS) analysis of the samples was carried out. A pilot test was applied to detect five quality markers that are frequently reported in human skin odors. Then, several steps were performed for method standardization. The method was applied to 36 different individuals (3 sampled under laboratory conditions and 33 under field conditions), aiming to evaluate its applicability in both environments. **Findings.** A total of 49 VOCs were identified, and/73.5% of these have been reported in previous studies. **Main Conclusions.** Hair from legs can be considered an efficient tool for human skin odor sampling and a suitable and practical matrix for human skin odor profile determination by using HS-SPME/GC-MS.  
Keywords:HS-SPME/GC-MS; human skin odors; volatile organic compounds +
Footnote †: journal:
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 3 Properties of Concrete after Curing
### Test Program
#### 3.1.4 Test Method  
To investigate the efficiency of induction heating with regard to the strength development of concrete, the compressive strengths of concretes cured at four different curing conditions were evaluated. The compressive strengths immediately after 15 h of curing were determined from three cylindrical specimens of dimensions \(\phi\)150 \(\times\) 300 mm for each curing condition in accordance with ASTM C 39 [19]. It is noted that the compressive strengths of the specimens SS4-5h-70, for which steel form of a smaller size was applied, was measured using three cylindrical specimens of dimensions \(\phi\)100 \(\times\) 200 mm. For the specimens LS4-5h-70 with a cross-section of \(\phi\)300 mm, three cylindrical cores of dimen  
Figure 8: Test setup for induction heat curing and compression test: (**a**) test chamber, specimens, and setup for IHC; (**b**) setup for compression test.  
sions \(\phi\)100 \(\times\) 200 mm were extracted from the specimens immediately after curing for compression tests. In order to compare compressive strengths of concrete with different dimensions, size effect should be considered [4]. However, current research, studied the size effect of concrete subjected to uniaxial compression, showed that the strengths of \(\phi\)150 \(\times\) 300 mm and \(\phi\)100 \(\times\) 200 mm cylinders with the same length-to-diameter ratio (_L/D_) are almost the same for lower strength concrete [20; 21; 22]. Therefore, the conversion factor that accounts for the different sizes of concrete specimens was not considered in this test program. The compression tests for all the cylinders were carried out using a universal testing machine (UTM) with a maximum capacity of 2000 kN and the setup for the tests is shown in Figure 8b. Three additional cylinders that were subjected to ATC were removed from the steel forms 15 h after concrete casting. Then, these were cured in a water storage tank (moisture curing) at a temperature range of 20 \(\pm\) 2 \({}^{\circ}\)C until 28 days. Compression tests were carried out on these additional specimens to examine the specified design concrete strength at 28 days and use the results as benchmarks to compare the strength of concretes under the different curing regimes.
# Unit Cell Optimization of Groove Gap Waveguide for High Bandwidth Microwave Applications
## 3 Results and Discussion
It can be observed that the bandwidth is 15.7% greater compared to the recommended bandwidth of 1.3 GHz (2.6-3.95 GHz) for the EIA standard W284 rectangular waveguide. Based on the analysis of S parameters, it is inferred that the proposed mushroom-unit-cell-based EBG periodic structure provides a wider bandwidth towards lower cut-off frequency regions, which are often points of interest for practical fabrications.
## 4 System Model
### SN Local Computation Model  
For the optimization layer SN i, we use \(f_{i}^{L}\) to denote its computing power; then, the energy consumption required by SN i to compute data task can be denoted by  
\[E_{i}^{C}=\varepsilon(f_{i})^{2}X_{i}, \tag{7}\]  
The corresponding computation delay can be expressed as  
\[T_{i}^{L}=\frac{X_{i}}{f_{i}}, \tag{8}\]  
where \(\varepsilon\) denotes the energy factor, whose value depends on the coefficient of the chip architecture used [28]. We denote the data size after the computation as \(D_{i}^{C}\).
## 4 Discussion
The present study showed a significant increase in some of the muscle strength parameters, but it did not show any increase in muscle mass on the DXA scans. Possibly, the intensity of the exercises that could be achieved by this frail patient group with several comorbidities during the period of the intervention was not sufficient to be measurable as a change in muscle mass. Furthermore, the study group was small, and possibly the precision of DXA was not sufficient to detect an eventual change. Yet, [PERSON] et al. and [PERSON] et al. found improvements in muscle strength and muscle mass in patients with hip osteoarthritis and patients treated for hip fractures, respectively [42,43]. Our study population was older, especially compared to the patients in the study by [PERSON] et al. [42], and both studies' interventions occurred closer to the participants' surgeries than ours. With increasing age, the ability to improve muscle mass declines, and the process takes longer than for younger individuals. This stresses the importance of immediate and long-lasting physical rehabilitation after hip fracture surgery in order to lower the risk of new fall and fracture events, and it may explain some of the reasoning for the absence of increased muscle mass seen in our study. Several previous studies have shown training potential among elderly hip fracture patients and shown that they can perform an exercise program without adverse events [5; 10; 44; 45]. Likewise, the participants in the present study showed progression in all four resistance exercises, and the participants also reported high satisfaction, high adherence to the training program, and improved self-reported ADL after the three-month training program. However, the increase in OHS did not meet the MCID. After an FNF, the mean time to recovery in ADL function has been reported as six months (4-11 months), and the same has been reported for regaining muscle strength [46; 47]. The participants in the present study were all a minimum of 12 months after their FNFs and, therefore, should have fully recovered from their fracture events. As such, the physical improvements found in the present study, we believe, are in fact a result of the training, rather than spontaneous improvements over time. However, considering the decline in activity at the 12-month follow-up, continued training seems important and could be completed at home with the training program described in the present study.
## 4 Discussion  
This paper proposed a crack localization technique of FRCCs by developing a multi-channel electrical impedance acquisition system. The proposed technique was experimentally validated using specially manufactured four FRCCs specimens under temperature change and three-point bending loading conditions. The test results reveal that the electrical impedance signal change was dominant in only cracked regions compared to non-cracked regions, while the signal change induced by temperature variation showed a similar pattern in all sensing regions. It can be concluded that the proposed technique can be effectively used for crack detection and localization even under temperature variation conditions. However, the quantitative crack analysis is difficult, because the electrical impedance signals are significantly affected by the distribution and orientation of conductive fibers inside FRCCs. Although the feasibility of the proposed technique is demonstrated in this paper, the standardization of the FRCCs manufacturing process is necessary before the real field SHM applications. As the follow-up work, the influence of the orientation and distribution of internal conductive fibers inside FRCCs is now being investigated. Also, the detachable electrodes such as copper tapes will be tested instead of embedded electrodes for convenient of real field SHM application. Then, the fiber mixing control technique based on electromagnetic actuators will be developed. Furthermore, the proposed technique will be applied to various real scale structures.  
[PERSON]-K.A., [PERSON] and D.K. conceived and designed this study. [PERSON] and [PERSON]-S.K. made test specimens and tested. [PERSON]-S.K. and [PERSON]-K.A. performed data analysis and wrote the entire manuscript.  
This research was supported by a grant (18 CTAP-C143065-01) from Infrastructure and transportation technology promotion research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government.  
The authors declare that they have no conflict of interest.  
Figure 11: Damage index of second specimen at (**a**) Region A\({}_{i}\); (**b**) Region B; (**c**) Region C and (**d**) Region D. Damage index of third specimen at (**e**) Region A\({}_{i}\); (**f**) Region B; (**g**) Region C and (**h**) Region D. Damage index of fourth specimen at (**i**) Region A; (**j**) Region B; (**k**) Region C and (**l**) Region D.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 3 Results
### Functional Test Results  
This experiment tested the key performances of the buoy, providing theoretical support for the use of drifting sonar buoys in Antarctic krill detection. The experiments successfully validated the buoy's waterproofing, stability, and corrosion resistance, which are essential for all operations. They confirmed that sonar devices, temperature sensors, flow sensors, and solar panels function properly in complex maritime conditions. The experiments also verified that the buoy collects data as intended; this point has been discussed previously in Section 2.1. It is worth noting that our setting of the sampling interval is based on considerations of energy consumption and the efficiency of solar power generation [38]. The buoy's data collection logic, established based on preliminary assessments, efficiently reduces energy consumption by scheduling data acquisition at set intervals, allowing the buoy to operate effectively over extended periods. Additionally, the buoy's satellite communication capabilities were validated, with data successfully transmitted from the Antarctic to the base station, which also issued multiple commands for data collection.  
Figure 7: Target intensity (dB) echogram. (**a**) No Antarctic krill. (**b**) Antarctic krill.
## 3 Numerical Simulation
### Validation of the Numerical Model  
In this subsection, a mesh sensitivity analysis is performed, and the reliability of the current numerical model is validated as well.  
Based on the authors' previous work, structural analysis of SCS can be roughly considered as an axisymmetric problem [8; 24; 25]. Meanwhile, considering the mechanical behavior of SCS, the mesh size of the SSC is the key dominant factor that decides whether the mesh can yield a proper response of the FEA. Therefore, a mesh sensitivity analysis is performed with an additional model created for comparison. The medium and fine meshes have element edge lengths no greater than 0.8 m and 0.4 m, respectively.  
Figure 9 compares the radial displacement, circumferential stress and stress in flow direction of a typical section with different meshes under IWP. The position of the representative points of the section is shown in Figure 4. The comparison shows that the FEA results are hardly distinguishable from each other at most points. Meanwhile, it should be noted that the difference of stress in flow direction at the two points near the stay ring is relatively larger. Nevertheless, the comparison gives a good indication that both the medium and fine meshes can yield accurate FEA results in terms of the displacement and stress of a SCS. In the following analysis, consequently, the current model with medium element size (<0.8 m) is employed.  
Figure 8: External load.  
Figure 9: _Cont._In the construction period, dial indicators were used to monitor the deformation of steel structures. The arrangement of dial indicators is shown in Figure 10. Twenty-four sets of dial indicators were applied at the stay ring to measure the radial displacement of the upper stay ring, as well as the vertical displacement of the upper stay ring and the upper flange. Compared with the displacement data obtained in the field, no substantial differences are found in the FEA-reproduced results, and most results are similar in terms of their displacement values and distributions. A more detailed comparison and analysis is presented in Section 4.1. Consequently, it is believed strongly that the current numerical model would lend itself well for use by this study.  
Figure 9: Mesh sensitivity analysis: (**a**) radial displacement; (**b**) circumferential stress; (**c**) stress in flow direction.
## 5 Machine Learning Model Designs
### Regression and Classification Problems
#### 5.1.1 Linear Regression  
Linear regression is a type of predictive analysis that is simple and often used. In this kind of regression analysis, the number of independent variables is kept to a minimum to focus on the existence of a linear correlation between the independent variables and the dependent variable, also known as the target variable.  
\[y(x)=mx+c \tag{5}\]  
where \(y\) is the target variable, \(m\) is the slope of the line, and \(c\) is the y-intercept.  
The most significant benefit of using linear regression is that it takes a very short time to train and is a very simple model to implement. It has been observed in the literature that in some cases, linear regression performs the same as another statistical model, such as a decision tree or a random forest, or with just slight inaccuracy. However, the training time of linear regression is significantly lower compared to other models. Therefore, the linear regression machine learning model is often considered a baseline model to compare the performance of other machine learning models. The least squared error loss function used in linear regression is represented as shown in Equation (6).  
\[LSE=\frac{1}{2}(f_{\theta}(x^{(i)}-y^{(i)})^{2} \tag{6}\]  
where \(f_{\theta}(x^{(i)})\) shows the predicted value for a given point ad \(y^{(i)}\) represents the actual value. The cost function is the sum of all the LSE for all the data points in the dataset and can be represented as in Equation (7), where \(n\) represents a number of data points.  
\[J=\frac{1}{2n}\sum_{i=1}^{n}(f_{\theta}(x^{(i)}-y^{(i)})^{2} \tag{7}\]  
A more complex linear regression variant is polynomial regression. It can be represented as shown in Equation (8). However, it is observed that with a higher degree of polynomial equation, the model tends to overfit. To combat the overfitting problem, lasso and ridge regression techniques are used.  
\[f(x)=c_{0}+c_{1}x+c_{2}x^{2}+c_{3}x^{3}.....c_{n}x^{n} \tag{8}\]
## 1 Introduction
To apply the Fourier-transform method, there is a limitation regarding the intervals of the background fringes. If the interval is too long, the fringe pattern caused by the object includes a closed-fringe pattern and the filtering process cannot be applied except in a special case [21]. In the case where the background fringe cannot be controlled, some interferograms may have closed-fringes and a complete set of the phase-shift images for CT cannot be obtained. As a result, the quality of the 3-D reconstructed distribution is reduced. In this study, we will demonstrate the applicability of deep learning to extracting the phase-shift image from the fringe pattern even when the closed-fringe is included through a comparison to the Fourier-transform method. The outline of this paper is as follows. In Section 2, the Fourier-transform method to extract the phase-shift image is reviewed. In Section 3, we show the phase extraction by the deep learning. The comparison of the numerical results and discussions is presented in Section 4. Finally, the summary is presented in Section 5.