esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamic Model of the Feed System
### Calculation of Restoring Force of Bearings
#### 2.2.2 Restoring Force of Deep Groove Ball Bearing  
According to the homogeneous coordinate transformation, the coordinates of the inner and outer raceway centers for the deep groove ball bearing in CS\({}_{\text{Db}}\) are expressed by:  
\[\left[\begin{array}{c}x_{ij}^{\text{D}}\\ y_{ij}^{\text{D}}\\ z_{ij}^{\text{D}}\\ 1\end{array}\right]=\text{Trans}\big{(}R_{\text{Dm}}\cos\theta_{\text{bj}}+ \delta_{Sx},R_{\text{Dm}}\sin\theta_{\text{bj}}+\delta_{Sy},\delta_{Sz}\big{)} \cdot\text{Rot}_{z}\Big{(}\theta_{\text{bj}}+\frac{\pi}{2}\Big{)}\cdot\left[ \begin{array}{c}0\\ -(R_{i}-R_{\text{bb}})\\ -(R_{i}-R_{\text{bb}})\\ 1\end{array}\right] \tag{47}\]  
\[\left[\begin{array}{c}x_{gi}^{\text{D}}\\ y_{gi}^{\text{D}}\\ 1\end{array}\right]=\text{Trans}\big{(}R_{\text{Dm}}\cos\theta_{\text{bj}},R_{ \text{Dm}}\sin\theta_{\text{bj}},0\big{)}\cdot\text{Rot}_{z}\Big{(}\theta_{ \text{bj}}+\frac{\pi}{2}\Big{)}\cdot\left[\begin{array}{c}0\\ (R_{\text{o}}-R_{\text{bb}})\\ (R_{\text{o}}-R_{\text{bb}})\\ 1\end{array}\right] \tag{48}\]  
where \(R_{\text{Dm}}\) is the pitch radius of deep groove ball bearing.  
Combining Equations (47) and (48), the radial distance \(\sigma_{\text{Dj}}\) of raceway centers can be expressed as:  
\[\sigma_{\text{Dj}}=\left(x_{ij}^{\text{D}}-x_{oj}^{\text{D}}\right)\cos\theta _{\text{bj}}+\left(y_{ij}^{\text{D}}-y_{oj}^{\text{D}}\right)\sin\theta_{ \text{bj}} \tag{49}\]  
Considering that the ball may lose contact with the raceway and according to the Hertz contact theory, the contact force \(Q_{\text{Dj}}\) of the deep groove ball bearing can be expressed as:  
\[Q_{\text{Dj}}=\begin{cases}\left(\frac{\sigma_{\text{Dj}}-B_{0}}{C_{\text{Dj} }+C_{\text{Dj}}}\right)^{2/3}&\sigma_{\text{Dj}}-B_{0}>0\\ 0&\sigma_{\text{Dj}}-B_{0}\leq 0\end{cases} \tag{50}\]  
The restoring forces along the \(x\) and \(y\) directions for the deep groove ball bearing can be expressed as:  
\[F_{\text{Dx}}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{Dj}}\cos\theta_{\text{bj}} \tag{51}\]\[F_{\text{Dy}}=-\sum_{j=1}^{N_{\text{bb}}}Q_{\text{Dj}}\sin\theta_{\text{bj}} \tag{52}\]
# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 2 Emotion Estimation Approaches and Environmental Factors
### Physiological Data  
Emotion estimation using physiological data primarily involves methods that utilize heart rate variability [19,20], electroencephalogram [12,21,22], and electrodermal activity [23,24,25]. These data are collected by using wearable devices that estimate emotions based on the analysis of autonomic nervous system activity and other factors. Because these devices are often relatively inexpensive and compact, users can perform tasks while wearing them, making them suitable for real-time estimation in workspaces. For example, in the healthcare, they may be used for stress management and mental health monitoring. However, when measuring data such as heart rate, users must ensure that the device is correctly worn at all times, which can lead to discomfort or stress, especially during prolonged use. In addition, long-term wear may cause skin issues, necessitating caution in actual usage. However, recent technological advancements have led to the development of new sensor technologies that provide more comfortable wear and higher measurement accuracy, raising expectations for future developments.
# Lattice Structure for Improving Cooling Uniformity in HPDC Mould Corners
## 5 Results and Discussions
### Lattice Structure Sleeves' Effect on Cooling
(**a**) Baseline cooling—no lattice structure; (**b**) cooling pipe with 1.5 mm lattice structure; (**c**) cooling pipes with 3 mm lattice structure; (**d**) two 1.5 mm lattice structures. Table 3 presents the mean and standard deviation of temperatures calculated at the cast interface surface, along with the difference between the maximum and minimum temperatures on this surface. The results indicate that increasing insulation with 3 mm lattice sleeves raises the average interface temperature, and it leads to more uniform temperature distribution. However, as previously discussed, this uniformity is achieved at the expense of an elevated corner temperature. The results for 1.5 mm thick sleeves across all three lattice structures are comparable to those obtained with a two-sleeve configuration. Nevertheless, the two-sleeve arrangement results in a less pronounced increase in corner temperature and a lower average temperature. As demonstrated in a previous study [1], conformal cooling can significantly enhance cooling efficiency and reduce cycle time by up to 70%, thereby increasing production rates. Additionally, it can extend the service life of 3D-printed tools by up to three times [18]. However, conformal cooling alone is not sufficient in complex geometries with small features that can be exposed to overcooling or overheating. The results of this study demonstrate that the lattice structure can limit the local heat transfer by increasing the thermal resistance in such regions. While this method has been applied to mould corners, it can be utilized in any local region within the mould. This study has introduced a new approach to controlling the cooling rate in specific regions by utilising lattice structures with thermal conductivity lower than that of solid tool steel. Future work will focus on validating the model after implementing the lattice structure and implementing lattice structures in multiple locations inside the mould to control heat transfer pathways. Figure 6: Temperature variation at the cast interface along the two sides of the corners for various lattice structure configurations. \begin{table}
\begin{tabular}{c c c c} \hline  & **Average** & **Temperature** & **Maximum and Minimum** \\  & **Temperature** & **Standard** & **Temperature** \\  & (°C) & **Deviation (°C)** & **Difference (°C)** \\ \hline Baseline design & 279 & 27 & 84 \\ \hline
1.5 mm—three & & & \\ lattice sleeves & 346 & 13.3 & 48 \\ \hline
3.0 mm—three & & & \\ lattice sleeves & 382 & 11 & 34 \\ \hline
1.5 mm—two & & & \\ lattice sleeves & 322 & 13.5 & 49 \\ \hline \end{tabular}
\end{table} 
Table 3: Temperature distribution statistics along the two sides of the cast interface.
## 3 Results and Discussion
### Percolation Column Test Results
#### 3.1.3 Cumulative As Release as a Function of L/S  
We analyzed the leaching of arsenic from contaminated soil or the stabilized soils for the cumulative mass release of As (see Figure 6). The maximum mass of the leached constituents under field conditions over long time periods can be estimated using the cumulative masses of the constituents released during a definite L/S range. The cumulative mass release of As per unit of solid material is calculated as follows [23]:  
\[\Sigma M_{i}=\sum_{i=1}^{n}[C_{i}\ \times\ (\Sigma L/S_{i}-\Sigma L/S_{i-1})] \tag{2}\]  
where \(\Sigma M_{i}\) is the cumulative mass released through the interval \(i\) (mg/kg-dry); \(C_{i}\) is the As concentration in the eluent collected during interval i (mg/L); \(\Sigma L/S_{i}\) is the cumulative L/S of eluate collected through interval i (L/kg-dry); \(\Sigma L/S_{i-1}\) is the cumulative L/S of eluate collected through interval i \(-\) 1 (L/kg-dry); and n is the total number of collected eluate fractions.  
The slope of the cumulative As released from the control soil was steeper than the stabilized soils (see Figure 6). The mass concentration (mg/kg) of the cumulative As released reached 3.62 mg/kg (at 50 L/S or 56.8 years). The stabilizers AMDS, steel slag, CMDS, and cement had a reduced cumulative mass release of As when compared to the contaminated soil (control). The cumulative As released from the stabilized soils after 50 L/S was 0.49 mg/kg (CMDS stabilized), 0.6 mg/kg (cement stabilized), 0.87 mg/kg (AMDS stabilized), and 1.46 mg/kg (steel slag stabilized). Therefore, the As is gradually released in the eluate of the stabilized soils.
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 3 Results: Performance of the Attention U\({}^{2}\)-Net Model  
Figure 5 shows the overall performance of the attention U\({}^{2}\)-Net model in mangrove classification based on the test dataset. We also carried out comparative experiments to compare our model with other machine-learning methods, including RF and U-Net. Examples of mangrove extraction results from four models are shown in Figure 6.  
The results showed that the attention U\({}^{2}\)-Net model had the best performance among the four models, as it achieved the highest overall accuracy, precision, and F1-score. The values of overall accuracy, precision, and F1-score for the attention U\({}^{2}\)-Net model were 96.5%, 92.0%, and 91.5%, respectively, which were much higher than those obtained from the RF model, which had the lowest performance. The RF model tended to misclassify the spatial patterns that appeared smooth and uniformly colored on remote sensing images, such as water or bare land, as mangroves. Therefore, it exhibited a low precision of 52.2% and also a low overall accuracy and F1-score. Compared with the RF and U-Net models, the attention U\({}^{2}\)-Net model could almost completely extract mangroves from satellite images, as it has a deep learning architecture that could capture more features and details of mangroves. The U\({}^{2}\)-Net model had a high precision of 91.3%, which was significantly increased compared to the U-Net model. The U-Net model may have overestimated the area of mangroves, as it may have classified some small patches of mangroves as a few large patches, resulting in a coarse and inaccurate output (Figure 6d). After considering  
Figure 5: Mangrove classification results obtained from four models.  
the attention gate, although the overall performance of the attention U\({}^{2}\)-Net model was only slightly improved, more detailed information about mangroves could be extracted from this model (Figure 6f). The attention gate could help the model focus on the regions of interest and ignore the irrelevant regions, thus enhancing the quality of the output. The attention U\({}^{2}\)-Net model could extract more detailed information about mangroves from satellite images, such as their boundaries and shapes (Figure 6f). This indicates that our model performed quite well in effectively and accurately mapping mangroves and can provide useful information for mangrove conservation and management.
## 3 Experiments and Results
### Parameter Settings  
In this work, parameter tuning is extremely important, which significantly affects classification performance. To identify the optimal parameter settings for our proposed model, we conducted parameter tuning experiments using four-category classification tasks on five subjects, respectively. The experiments aimed to investigate the impact of several key parameters on the model's performance, including the number of local 1D convolution layers, the number of global 1D convolution layers, and the numbers of layers and neurons in the BiRAU. Additionally, we also varied the ratio of random slices according to a previous work [18] and chose a range of 70-90% for the length of the random segments.  
Table 2 summarizes the results of the parameter tuning experiments. It could be observed that consistent results were achieved across experiments for different subjects. The optimal parameter settings for all five subjects consisted of 32 local convolution layers and 256 global convolution layers, along with a two-layer BiRAU with 32 neurons, which attained the best average accuracy of 90.25%.  
In summary, a 32-layer local 1D CNN could capture the feature information for each task. However, we needed a larger global 1D CNN to identify the differences between tasks. A two-layer BiRAU with 32 neurons could handle the temporal dimension well, as shown by its high correlation coefficient with the EEG signals over time. Notably, the following experiments were performed based on this model's parameter setting.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 3 Forward Error Correction
### BCH Codes  
A BCH code is a kind of binary cyclic code discovered by [PERSON], [PERSON], and [PERSON] [9]. This code has been studied intensively due to the strict algebraic structure introduced in the codes. In BCH codes, the codewords are created by dividing a polynomial \(m_{i}(x)\) by a generator polynomial \(g(x)\) and taking the remainder which will be presented as parity check bits \(r(x)\). The encoded data \(C(x)\) will be constituted as:  
\[C(x)=m_{i}(x)+r(x) \tag{1}\]  
The characteristics of the code are determined by the selected generator polynomial \(g(x)\). For integers \(m_{1}\) and \(t\), the BCH can correct up to independent errors, and its possible codes are [1]:  
\[\begin{array}{ll}\text{Block length}:&N=2^{m_{1}}-1\\ \text{Parity check bits}:&N-K\leq m_{1}\times t\\ \text{Minimum distance}:&d\geq 2t+1\end{array}\]  
where \(m\geq 3\), \(t<2^{m-1}\) and the distance represents Hamming distance.  
The decoding procedure of this code is more complicated and is performed in three steps [10; 11]:  
* Calculate the syndrome from the received codeword. The syndrome vector is only a function of the error pattern;
* The error location polynomial is found by using a set of equations derived from the syndrome; and
* Erroneous bits are corrected by using the error location polynomial.  
One of the most significant advantages of this code is that it is quite simple to be encoded and decoded. The decoder can be simply implemented compared with modern coding technology, such as the turbo and low-density parity check (LDPC). Another advantage is the BCH code can detect and correct errors up to nearly 25% [12].
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 4 Antenna Implementation and Measurement  
The fabricated Bi-log hybrid antenna of the prototype is shown in Figure 8a. This antenna prototype was measured on an open area test site (OATS) in which the ground plane size is 50 m (L) by 40 m (W), as shown in Figure 8b. The measuring system is based on the standard site method and applies a three-antenna method for the test. The AF affects the magnitude of the received signal in the EMI test that offers the correlation between the calibrated values in different distance conditions. Its performance has been evaluated with and without the 5 dB attenuator on the terminal of the proposed antenna during the 10 m test range. The measured results of AF and antenna efficiency are displayed in Figure 9a,b, respectively. To ensure accurate and reliable EMI measurements, it has become a norm to assess the isolation between different polarizations in linear antennas. The measurement errors depend on the contribution of cross-polarization rejection curves. The cross-polarization rejection from 200 MHz to 2000 MHz is shown in Figure 10. The bandwidth selection was based on the requirements of ETS-Lindgren 3106B's high-precision double-ridged guide antenna for cross-polarization measurement techniques. The maximum cross-polarization rejection of the proposed antenna is 10 dB. In addition, based on the comparison of Figures 2 and 4, the achievements of antenna performance are a VSWR of <2.0, stable and linear AFs, and antenna symmetry of <\(\pm\)0.5 dB. The measured results are in good agreement with the simulated ones, thereby validating the design and confirming its performance. Furthermore, this proposed antenna is ideal for conducting EMI measurements due to its ability to cover a broad range of frequencies from 30 MHz to 6 GHz, while still being small and compact. The antenna can be used for EMI testing in various types of semi-anechoic chambers, including 1 m, 3 m, and 10 m configurations, which are required to conform to the ANSI C63.4 standard for test procedures and antenna specifications.  
Figure 8: Photographs of the proposed antenna: (**a**) the prototype and (**b**) the measurement setup in an OATS.  
Figure 7: Simulated result of the Balun impedance.
# 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data
## 5 Results of Parametric Study
### Removal of Potential Sources Inside the Model  
Wind tunnel tests studied in this paper aim at studying the external noise produced by the car model. In contrast with three-dimensional surface mapping, volumetric mapping poses the question of potential sources inside the model. In fact, the latter can be assumed to be a rigid body, and all potential sources inside it are meaningless, with their strength being zero. The car model has been simplified using a closed envelope of the car body. The point source grid with 50 mm step is trimmed removing all of them inside the model envelope. The effect of the completeness of the grid, i.e., the comparison of maps obtained on the full grid and on the trimmed one, are performed. Only the case with the double  
Figure 8: Source power ratio \(W_{s}\) obtained with single and dual array in case of 20 dB of attenuation. Ideal source power ratio is 0. (**a**) Single array. (**b**) Dual array.  
Figure 6: Source power ratio \(W_{s}\) obtained with single and dual array in case of 6 dB of attenuation. Ideal source power ratio is 0. (**a**) Single array. (**b**) Dual array.  
Figure 7: Source power ratio \(W_{s}\) obtained with single and dual array in case of 10 dB of attenuation. Ideal source power ratio is 0. (**a**) Single array. (**b**) Dual array.  
array is considered, which was demonstrated to be the best performing one in the previous paragraph. No background noise is present in these simulations, and the propagation of sound from acoustic sources to microphone locations is considered as free-field, without any masking effect. \(D_{s}\) and \(W_{\infty}\), depicted in Figure 9, do not show a meaningful difference between the full and trimmed conditions. Instead, Figure 10 shows that the trimming slightly enhances source strength underestimation. This effect is produced by the artifacts generated in the first IRLS iteration. As the starting point of IRLS is a \(L_{2}\)-norm minimization, the energy of a generic source near the discontinuity/edge of the grid cannot be spread as it would happen with a continuous grid; thus, much energy is concentrated in potential sources near the discontinuity. The presence of these unwanted peaks in the map produces the need for major regularization in the following IRLS iterations, thus leading to more underestimated source strengths.  
On the one hand, removing points inside the model has a negative, but limited, effect on source quantification. On the other hand, the car model occupies about half of the calculation volume; therefore, the number of calculation points can be dramatically reduced. However, from this comparison it is possible to conclude that the completeness of the point grid does not significantly affect the final result. In order to make a balance of pro and contra, the next step hereafter is to understand how refinement of the grid can impact the quality of the results.
# Exploitation of Three Non-Conventional Yeast Species in the Brewing Process
## 1 Introduction  
In the brewing industry, _Saccharomyces cerevisiae_ and _Saccharomyces pastoris_ are the most used yeast species in starter cultures ensuring certain advantages in the fermentation process and in the standard product quality. In the past few years, brewers have payed attention to yeast selection not only for their fermentation efficiency and technological advantages but also for the aromatic characters and the flavors they give to the final product. To this end, recent genetic investigations have focused on methods to enhance the fermentation efficiency and aromatic profile of selected _S. cerevisiae_ strains [1; 2; 3]. Other studies proposed the isolation of new starter yeasts (_S. cerevisiae_) from natural matrices [4; 5] and the selection of wine yeast strains [6]. In recent years, the rapid expansion in the number of microbreweries has led the brewers to differentiate their products through the use of alternative raw materials and the selection and use of non-conventional yeasts [7; 8; 9; 10]. Indeed, biofavor from metabolic pathways of yeasts may be a suitable strategy to obtain beer with a different aromatic taste [11; 12]. In this context, the use of non-conventional yeasts could be an alternative way to enhance the aroma profile of beers. Within non-conventional yeasts, different genera and species have been proposed. _Torulaspora delbrueckii_ is a yeast species widely studied in winemaking for its ability to produce fruitiness and positive aromatic flavor, and for this reason, it was also evaluated for beer production, both in pure and in mixed cultures with different _S. cerevisiae_ starter strains [7; 13]. Beersproduced with pure cultures of _T. delbrueckii_ were characterized by a low alcohol content (2.66% _v_/_v_) but at the same time with distinctive analytical and aromatic profile [7; 14]. _Lachancea termotholerans_, another non-conventional yeast species, was proposed by [PERSON] et al. [15] to produce sour beers without the use of lactic acid bacteria. Indeed, they investigated the use of a strain of _L. thermotolerans_ in pure culture able to decrease the pH value and increase the glycerol and lactic acid production. Other non-conventional yeasts belonging to the _Hansenispora vineae_, _Lachancea fermentati_, _Schizosaccharomyces japonicus_, and _Wickerhamomyces anomalus_ species showed promising fermentation aptitude and sensory features for the production of sour beer [8]. In addition, yeasts belonging to _Cyberlindnera fabianii_, _Pichia kudriavzevii_, and _Pichia kluyverii_ were evaluated to tailor the aroma production and the ethanol content in beer [16; 17].  
In the present study, after a preliminary screening of yeasts from the Collection of the Department of Life and Environmental Sciences (DiSVA), we evaluated the use of selected strains of _L. thermotolerans_, _W. anomalous_, and _Zygotorulspora florentina_ in pure and in mixed fermentation at different inoculation ratios with _S. cerevisiae_ in the brewing process. The yeast strains' population dynamic and their influence on the biofavor of beer were evaluated.
## 2 Related Work
### Embedding Algorithm
#### 2.1.3 Prediction Error Expansion  
Prediction-error expansion (PEE) is a data hiding method proposed by [PERSON] et al. in 2004 [5]. The concept of PEE involves expanding the difference, \(P_{e}\), between the cover image pixel \(\chi\) and the predicted pixel \(\chi_{p}\) for a given pixels block, \(\begin{pmatrix}x&b\\ a&c\end{pmatrix}\), and then hiding the secret data within that difference. The method of calculating the predicted pixel \(\chi_{p}\) using the cover image pixel \(\chi\) and surrounding pixels is defined by Equation (4):  
\[\chi_{p}=\begin{cases}\max(a,b)\text{ }if\text{ }c\leq\min(a,b)\\ \min(a,b)\text{ }if\text{ }c\geq\max(a,b)\\ a+b-c\text{ }if\text{ }otherwise\end{cases} \tag{4}\]  
Once \(\chi_{p}\) is calculated, the prediction error is calculated by determining the difference \(P_{e}\) between \(\chi\) and \(\chi_{p}\), as described in Equation (5):  
\[P_{e}=\text{ }\chi-\text{ }\chi_{p} \tag{5}\]  
Subsequently, the generated \(P_{e}\) is expanded and the binary secret bit-stream, \(b_{i}\), is added. As a result, \(\overline{P_{e}}\) is generated, and the entire process is described by Equation (6):  
\[\overline{P_{e}}=2\times P_{e}+b_{i} \tag{6}\]  
Finally, the conclusive pixel \(\overline{\chi}\) with hidden secret data is defined by adding \(\overline{P}_{e}\) to the prediction pixel as described in Equation (7):  
\[\overline{\chi}=\chi_{p}+\overline{P}_{e} \tag{7}\]  
By repeating the above process, it is possible to generate a stego-image with the inserted secret data.