esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
After treatment with Se-enriched DB-yeast, the Se sedimentation content of the edible part of GIFT muscle reached \(1.34\pm 0.04\) mg/kg, which was 4.3 times and 1.8 times higher than the no Se-additive and Na\({}_{2}\)SeO\({}_{3}\) groups, respectively, and also higher than most recommended food sources of Se such as tuna (approximately 1.08 mg/kg), yellowfin (approximately 1.08 mg/kg), halibut (approximately 0.55 mg/kg), and sardines (approximately 0.53 mg/kg) [5]. Therefore, GIFT treated with Se-enriched DB-yeast can be further used as an effective food source of Se in the human diet. A previous study revealed that the minimum lethal dose of Se in the form of Na\({}_{2}\)SeO\({}_{3}\) varies from 1.5 to 8.0 mg/kg body weight in farm animals [12]. Here, the LD\({}_{50}\) of Se-enriched DB-yeast (18.97 mg/kg of Se content) was considerably higher than that of Na\({}_{2}\)SeO\({}_{3}\) (5.08 mg/kg of Se content) against mouse, indicating that organic Se (Se-enriched DB-yeast) is less toxic (non-toxic) than inorganic Se (Na\({}_{2}\)SeO\({}_{3}\)), similar to the results reported by [PERSON] et al. [11]. Therefore, Se-enriched DB-yeast has the potential to be a safe and effective Se-enriched fodder additive. In the future, Se-enriched DB-yeast can also be applied as a fodder additive for pig, chicken, cattle, etc. in an effort to produce more types of Se-enriched foods as a way to combat Se deficiency. Moreover, Se-enriched beer prepared by adding organic Se during the beer fermentation process may also be a good source of Se-enriched food and warrants further study.
# The Odyssey of Entropy: Cryptography
## 3 Entropy and Encryption
### Analysis and Evaluation
#### 3.2.1 Entropy as a Security Measure
The LDP (large deviation principle) was used by [PERSON] and [PERSON] [147] to derive the moments of the guesswork for a source of information determined by a Markov chain. It was shown by [PERSON] [148] how entropy and guesswork can be simultaneously used to evaluate the security of selectively encrypted information. * **Confusion and Diffusion:** Confusion and diffusion are two properties suggested by [PERSON] [1] in order to make the statistical analysis of a cryptosystem as difficult as possible. Confusion states that the ciphertext is a complex function of several portions of the key, and this function cannot be simplified to an easily analyzable function. On the other hand, diffusion requires that each plaintext symbol affects several symbols in the ciphertext and each ciphertext symbol is a function of several symbols in the plaintext. This property diffuses the statistical structures of the plaintext over the symbols of ciphertext. The relation between entropy and the mentioned two properties were studied in several research works. For example, entropy was used to evaluate the security of chaotic confusion-diffusion image encryption schemes [149; 150]. Moreover, [PERSON] et al. [151] used entropy improvement techniques in combination with confusion and diffusion mechanisms in their proposed cryptographic schemes. * **Indistinguishability:** Indistinguishability states that given the ciphertext corresponding to a plaintext randomly chosen from a plaintext space with only two elements (determined by the adversary), the adversary will not be able to identify the encrypted message with a probability significantly greater than that of random guessing (\(\frac{1}{2}\)). Indistinguishability plays a significant role in provable security. Some research works have investigated the relation between indistinguishability and entropy. As an example, one may refer the research reported by [PERSON] [152]. In this research, smoothed Renyi entropy and min entropy were used to evaluate the indistinguishability of universal hash functions. Universal hash functions have many important applications in QKD (quantum key distribution), cryptography, privacy amplification (leftover hash lemma), error-correcting codes, parallel computing, complexity theory, pseudorandomness, randomness extractors, randomized algorithms, data structures, etc. (see [153; 154; 155; 156; 157; 158] and the references therein).
## 2 Structure and Mathematical Model of CTEHS
### Mathematical Model of CTEHS  
#### 2.2.1 Magnetism Model  
In order to study the output performance of CTEHS, the nonlinear magnetism model of the system must be established first. In this research, the magnetism between magnets is calculated and modeled according to the point magnetic charge dipole theory. Figure 2 shows the geometric positions and relations of magnets 1, 2, and 3.  
Figure 1: Overall schematic diagram of CTEHS.  
In Figure 2, \(M_{1},\ M_{2}\), and \(M_{3}\) are the magnetization of the three magnets, \(P_{1}(i=1,2,3,4,5,6)\) is the dipole point of the magnet, \(r_{ij}(i=3,4,5,6,\ j=1,2)\) is the orientation vector of the point dipole, \(w\) is the transverse length of the three magnets, \(u(L,t)\) is the response displacement of the end magnet 1 in the z-direction, and \(\alpha\) is the rotation angle of the end magnet 1. Based on the point magnetic charge dipole method, the magnetism between magnet 1 and magnet 2 can be obtained as  
\[F_{12}=\frac{Q_{12}}{4\pi}\left(-\frac{\delta_{1}}{\left(\left(d_{\text{h}}+w \right)^{2}+\delta_{1}^{2}\right)^{\frac{3}{2}}}+\frac{\delta_{1}}{\left(\left( d_{\text{h}}+2w\right)^{2}+\delta_{1}^{2}\right)^{\frac{3}{2}}}+\frac{\delta_{2}}{ \left(d_{\text{h}}^{2}+\delta_{2}^{2}\right)^{\frac{3}{2}}}-\frac{\delta_{2}}{ \left(\left(d_{\text{h}}+w\right)^{2}+\delta_{2}^{2}\right)^{\frac{3}{2}}}\right) \tag{1}\]  
where \(Q_{12}=\mu_{0}M_{1}S_{1}M_{2}S_{2}\), \(\delta_{1}=u(L,t)-d_{\text{v}}/2\), \(\delta_{2}=u(L,t)+\alpha w-d_{\text{v}}/2\). \(S_{1},S_{2}\) are the surface areas of magnets 1,2 in the z-direction, and \(\mu_{0}\) is the magnetic permeability under vacuum conditions.  
Similarly, the magnitude of the magnetism between magnet 1 and magnet 3 is  
\[F_{13}=\frac{Q_{13}}{4\pi}\left(-\frac{\delta_{3}}{\left(\left(d_{\text{h}}+w \right)^{2}+\delta_{3}^{2}\right)^{\frac{3}{2}}}+\frac{\delta_{3}}{\left(\left( d_{\text{h}}+2w\right)^{2}+\delta_{3}^{2}\right)^{\frac{3}{2}}}+\frac{\delta_{4}}{ \left(\left(d_{\text{h}}^{2}+\delta_{4}^{2}\right)^{\frac{3}{2}}}-\frac{\delta _{4}}{\left(\left(d_{\text{h}}+w\right)^{2}+\delta_{4}^{2}\right)^{\frac{3}{2} }}\right)\right. \tag{2}\]  
where \(Q_{13}=\mu_{0}M_{1}S_{1}M_{3}S_{3}\), \(\delta_{3}=u(L,t)+d_{\text{v}}/2\), \(\delta_{4}=u(L,t)+\alpha w+d_{\text{v}}/2\).  
Therefore, the total magnetism between the three magnets is  
\[F_{m}=F_{12}+F_{13} \tag{3}\]
# Day and Night: Locating the General Practitioner's Panel after Hours
## 2 Materials and Methods
### Multiple Time Setting p-Median Problem
Objective function \[\min\sum_{i\in I}\sum_{j\in I}d_{j}x_{ij}+Wd_{j}^{\prime}c_{ij}x_{ij}^{\prime}\] (1) Subject to: \[\sum_{i\in I}x_{ij} =1 \forall j\in J\] (2) \[\sum_{i\in I}x_{ij}^{\prime} =1 \forall j\in J\] (3) \[\sum_{i\in I}y_{i} =p\] (4) \[\sum_{i\in I}y_{i}^{\prime} =p^{\prime}\] (5) \[x_{ij}-y_{i} \leq 0 \forall i\in I;j\in J\] (6) \[x_{ij}^{\prime}-y_{i}^{\prime} \leq 0 \forall i\in I;j\in J\] (7) \[y_{i}^{\prime}-y_{i} \leq 0 \forall i\in I\] (8) \[p-p^{\prime} \geq 0 \forall i\in I\] (9) \[\min\sum_{i\in I}\sum_{j\in I}d_{j}x_{ij}+Wd_{j}^{\prime}c_{ij}x_{ij}^{\prime}\]0 (10) \[\min\sum_{i\in I}\sum_{j\in I}d_{j}x_{ij}+Wd_{j}^{\prime}c_{ij}x_{ij}^{\prime}\]1 (11) \[\min\sum_{i\in I}\sum_{j\in I}d_{j}x_{ij}+Wd_{j}^{\prime}c_{ij}x_{ij}^{\prime}\]2 (12) \[\min\sum_{i\in I}\sum_{j\in I}d_{j}x_{ij}+Wd_{j}^{\prime}c_{ij}x_{ij}^{\prime}\]3 (13)  
In the multiple time setting p-Median notation, an apostrophe (\({}^{\prime}\)) is used to denote variables relating to the secondary setting. Note that (2), (4), (6), (10) and (12) are identical to constraints from the p-Median problem. Additionally, each of these constraints have been duplicated ((3), (5), (7), (11) and (13), respectively) to constrain the secondary facilities identically to the primary facilities. Constraint (8) has also been added to restrict the sites of secondary facilities to be shared with primary facilities, and Constraint (9) has been added so that the number of secondary facilities must be no greater than the number of primary facilities. A weight variable, \(W\), has also been added to the after-hours term in the objective function. This variable allows for a linear discount rate to be applied to adjust the relative cost of travel between time settings. For example, if policy dictates that after-hours travel is half as costly for patients, \(W=0.5\) incorporates this preference. If travel in either time setting is considered equally costly, \(W=1\) assigns equal weight to travel in both settings. Increasing \(W\) is expected to bias facility locations toward after-hours demand locations if demand differs between the regular and after-hours settings. This variable is proposed for the purpose of adjusting relative cost between time settings, but a sensitivity analysis of this variable is not within the scope of this paper.
## 3 Theoretical Part  
The introduction of new technologies into the educational system brings many advantages but also challenges. Understanding the theoretical foundations behind the application of gamification, immersive technologies, and the metaverse in education is crucial for the development of effective educational tools and methods. This theoretical framework provides an overview of key concepts and theories that support the use of gamification, XR, AR, VR technologies, and the metaverse in education, with a particular focus on their role in the preservation and promotion of cultural heritage.  
First, the history and development of gamification will be considered, starting from its early roots in education to its modern applications in digital environments. Gamification has become a significant tool for motivating and engaging students, and its use in educational contexts offers opportunities to enhance the learning experience through interactive and entertaining elements [1].  
Next, the psychological and pedagogical aspects of gamification will be explored, explaining how and why games can be effective in education. Special attention will be given to self-determination theory and flow theory, which provide insights into the motivational mechanisms that gamification activates in students. These aspects are key to understanding how gamification can improve learning and why it is increasingly integrated into educational programs [2].  
Furthermore, the chapter will address XR technologies and the metaverse, their foundations, and their potential applications in education. Technologies like VR, AR, and the metaverse enable the creation of immersive educational experiences that go beyond traditional teaching methods. These technologies are particularly useful in the context of cultural heritage preservation, allowing users to explore and learn about historical sites and artifacts interactively and participate in globally accessible virtual environments where they can collectively explore and share knowledge about cultural heritage [3].  
Finally, the theoretical framework will cover the application of 3D modeling in education and the preservation of cultural heritage. The digitalization of artifacts and historical sites through 3D modeling, especially within the metaverse, offers new opportunities for documentation, research, and education, making cultural heritage accessible to a broader audience, including those unable to physically visit certain sites [4].  
This theoretical framework provides a solid foundation for the further development of the work, enabling a deeper understanding of the key concepts and technologies applied in the project. Based on this overview, the following chapters will delve deeper into the specific aspects of gamification, XR technologies, and the metaverse, and their integration into educational programs aimed at preserving and promoting cultural heritage.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Pond Objects Filtering (Post-Processing)  
In a final step, we focused on the evaluation of multiple criteria in order to improve the aquaculture mapping result and minimize the false positive rate. For this purpose, we added geometrical parameters and overlap analysis based on water features derived from the OSM dataset. All these processes are part of the post-processing workflow and have been integrated into a customized model with QGIS for automatic post-processing of the extracted aquaculture pond datasets.
# Universally Composable Oblivious Transfer with Low Communication
## 4 UC-Secure 1-out-of-2 OT Protocol
### Random Oblivious Transfer
#### 4.1.3 The Privacy of the Sender
Suppose there exists \(\overline{b^{\prime}}\), which satisfies \(\overline{b^{\prime}}=bits\big{(}a^{T}\overline{s^{\prime}}+h,\in_{q},\in_{p} \big{)}\) and \(\overline{b^{\prime}}\approx B-\sigma T\), then, for the R\({}^{*}\), \((B-\sigma T)^{T}bits\big{(}s,\in_{p},\in_{p}\big{)}+h_{1}\) and \(\overline{b^{\prime}}\overline{b}its\big{(}s,\in_{p},\in_{p}\big{)}+h_{1}\) are distinguishable. Besides, we know that \(c\) and \(\sigma\) are independent of each other from Theorem 1. In addition, \(bits\Big{(}v_{1-\sigma}-2^{\varepsilon_{p}-\varepsilon_{t}-1}(bits\big{(} \big{(}A^{T}bits\big{(}s^{\prime},\in_{p},\in_{p}\big{)}+h_{1}\big{)},\in_{p}-1,\in_{t}\big{)}\big{)}+h_{2},\in_{p},1\big{)}\) and \(bits\Big{(}v_{1-\sigma}-2^{\varepsilon_{p}-\varepsilon_{t}-1}(bits\big{(} \big{(}\big{(}A^{T}\mathcal{U}\big{(}R_{p}\big{)}+h_{1}\big{)},\in_{p}-1,\in_ {t}\big{)})+h_{2},\in_{p},1\big{)}\) are distinguishable. In summary, \(sk\) and \(sk^{\prime}\) are indistinguishable for R\({}^{*}\), and \(Pr(X_{1})=1/2\). Therefore, the protocol guarantees the sender's privacy. \(\square\)
## Materials and Methods
### Chlorophyll and Carotenoids Concentration  
Chlorophyll (Chl) (a and b) and carotenoid (Car) content were determined spectrophotometrically as described by [PERSON] et al. [41]. Briefly, 0.3 g of each sample was added to 1.5 mL acetone 80% (v/v) (Scharlab Co.) and centrifuged at 7000 \(g\) for 10 min. The supernatant was used for the analysis. The absorption of the solution was measured at 663 nm, 648 nm, and 470 nm in a spectrophotometer (Ultrospec 2100 Pro). Chlorophyll (a and b) and carotenoid content of the extracts was calculated by the following equations:  
\[\text{Chl }a=12.25\times\text{Abs}_{663}-2.55\times\text{Abs}_{648}\ (\upmu\text{g mL}^{-1}), \tag{1}\]  
\[\text{Chl }b=20.31\times\text{Abs}_{648}-4.91\times\text{Abs}_{663}\ (\upmu\text{g mL}^{-1}), \tag{2}\]  
\[\text{Car}=[(1000\times\text{Abs}_{470}-1.82\times\text{Chl }a)\ -(85.02\times\text{Chl }b)]/198\ (\upmu\text{g mL}^{-1}). \tag{3}\]  
Chlorophylls and carotenoids were expressed as \(\upmu\)g g\({}^{-1}\) FW.
# Noise Prediction Using Machine Learning with Measurements Analysis
## 3 Results and Discussion
Therefore, the results showed that the full dimensional feature performed the best, indicating that the historical features may have more information than the time features. Thus, the more feature dimensions are input in the GBM enables efficient identification of suitable training features in response to reliable prediction results. Moreover, this study indicates that the loudest location near the working environment was sensor 8, and 125 Hz was the most serious harmful frequency. For the practical issues, we could pre-improve the low frequency pump surrounding sensor 8 by using sound insulators or remind workers to prevent long-term exposure in that area. We counted the prediction results of all sensors with a R\({}^{2}\) greater than 0.7 at all frequencies, as shown in Table 2. Taking sensor 2 as an example, the R\({}^{2}\) of noise with a frequency of 500 Hz and with a frequency of 1 kHz were both greater than 0.7 (marked with an asterisk). Other sensors can be deduced by analogy from the prediction results at different frequencies, as shown in Figure 16.  
Figure 15: (**a**) Performance of input features set with 21 dimensions. (**b**) Performance of input features set with 16 dimensions. (**c**) Performance of input features set with five dimensions. (**d**) Performance of input features set with three combined kinds of different dimensions. Here, we focused on the noise value of 125 Hz as its dBA values reached levels that are harmful to the human body [15; 16; 17; 18]. Moreover, we found that sensors 3, 8, and 10 achieved favorable prediction performance, as shown in Table 2. Among them, the prediction performance of sensor 3 from 125 Hz to 4 kHz showed a R\({}^{2}\) greater than 0.7, and these noise frequencies were coherent with one another. Likewise, the sensor 7 noise frequencies between 2 kHz and 16 kHz were coherent with one another. As a result, we found that while the sensor prediction index R\({}^{2}\) of this frequency was above 0.7, the values near this frequency could also produce excellent prediction results (for example, 500 Hz and 1000 Hz of sensor 2, 125 Hz to 4 kHz of sensor 3, 500 Hz and 1000 Hz of sensor 4, and 500 Hz and 1000 Hz of sensor 6). Thus, we found mutual influence between similar frequencies and hypothesized that the noise sources of similar frequencies were likely to have very similar occurrence conditions. The coefficient of determination (R\({}^{2}\)) of the sensor was higher than 0.7, and the root-mean-square-error (RMSE) was less than 1 dBA. This indicates that the proposed model could accurately predict the trends of future Eq levels with an average error margin within 1 dBA. Therefore, we successfully completed predictions for all sensors at other noise frequencies, and derived an effective reference value for improving future prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Frequency** & **125** & **250** & **500** & **1k** & **2k** & **4k** & **
## 3 Results and Discussion
### Crack Propagation Life Analysis
#### 3.4.3 Initial Crack Size  
Initial crack size \(a_{0}\) is determined by the larger of the engineering-detectable crack size and the size \(a_{0\mathit{f}}\) calculated from crack propagation threshold value \(\Delta K_{th}\).  
The maximum allowable non-propagating crack size \(a_{0\mathit{f}}\) was calculated following Equation (8):  
\[a_{0\mathit{f}}=\Delta K_{th}^{2}/\pi(Y\Delta\sigma)^{2} \tag{8}\]  
where shape function \(Y\) is 1.5; \(\Delta\sigma\) stands for the nominal stress range at the far field, and the maximum stress range at the critical position was conservatively taken for the calculation.  
When \(a_{0\mathit{f}}\) is compared with detectable crack size, the possibility of crack detection can be evaluated. Currently, the detectable crack size of most non-destructive testing technology in engineering applications is 0.5 mm [7]. If \(a_{0\mathit{f}}\) determined by \(\Delta K_{th}\) is larger than the detectable crack size, the maximum non-propagating crack size is very likely to be detected.  
The maximum allowable non-propagating crack size \(a_{0\mathit{f}}\) of the anchorage zone was calculated according to Equation (8) and the \(\Delta K_{th}\) value in Table 2. Crack size \(a_{0\mathit{f}}\) and initial crack size \(a_{0}\) are listed in the last two columns of Table 3. For the anchorage structure studied, the \(a_{0\mathit{f}}\) for operating cases I, II, and III is 0.85 mm, 0.60 mm, and 0.94 mm, respectively.  
According to Table 3 for operating cases I and II, when the temperature effect is taken into account for the cable force range, the stress range increases by 19% and \(a_{0\mathit{f}}\) decreases by 29%. For operating cases II and III in Table 3, as the \(\Delta K_{th}\) value increases by 25% at low temperatures, \(a_{0\mathit{f}}\) increases by 56% accordingly.  
Considering the effect of low temperature on both the load response and fatigue resistance, \(a_{0\mathit{f}}\) at low temperature is slightly larger than the room temperature counterpart. Therefore, the non-propagating fatigue crack performance of the anchorage structure at low temperatures is equivalent to that in normal operating conditions.
# SVPWM Method for Multilevel Indirect Matrix Converter with Eliminate Common Mode Voltage
## 2 Multilevel Indirect Matrix Converter and the Proposed SVPWM Method
### The SVPWM Method for Elimination the CMV  
SVPWM techniques have been widely used for the power converter as a common industrial practice due to it has flexibility. The SVPWM is the general solution to the modulation problem of the IMC drive. This modulation can provide the possibility to obtain the highest voltage transfer ratio and to optimize the switching pattern by coordinate the switching state in the rectifier and inverter stages. The space vector approach for the multilevel IMC is based on the instantaneous space vector representation input current vector and output voltage vectors. The SVM produces a combination of vectors to synthesize the input current and output voltage vectors in the rectifier and inverter stages, respectively. Once the vectors and their duty cycles of each stage are determined, the switching pattern of the converter is obtained by combining the switching states from two stages in order to keep balance input and output currents. In this section, the novel SVPWM method is proposed to eliminate the CMV and to cancel the neutral-point current within the sampling period control.
# Measurements of Acoustical Parameters in the Ancient Open-Air Theatre of Tymdaris (Sicily, Italy) +
###### Abstract  
The emerging field of archaeoacoustics is attracting increasing research attention from scholars of different disciplines: the investigation of the acoustic features of ancient open-air theatres is possibly one of its main themes. In this paper, the outcomes of a measurement campaign of acoustical parameters in accordance with ISO 3382-1 in the ancient theatre of Tymdaris (Sicily) are presented and compared with datasets from other sites. Two sound sources were used (firecrackers and dodecahedron) and their differences were analysed. A very good reproducibility has been shown between the two measurement chains, with differences on average of 0.01 s for reverberation time \(T_{20}\), and less than 0.3 dB for Clarity C\({}_{50}\) and C\({}_{80}\) and for sound strength. In general, results show that the reverberation time and strength of sound values are relatively low when compared with other theatres because of the lack of the original architectural element of the scaenae froms. When combining this effect with the obvious condition of an unroofed space, issues emerge in terms of applicability of the protocols recommended in the ISO standard. This raises the question of whether different room acoustics parameters should be used to characterise open-air ancient theatres.  
open-air ancient theatres; acoustical parameters; ISO 3382-1; firecrackers Article