esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simplified Analytical-Based Method
### Case Study
#### 2.2.1 Description
Their length varies according to the function of their position and is equal to the average distance between the radial trusses supporting them (Figure 2). Joists are 8 cm high and 12 cm or 24 cm wide, depending on whether they are located in the midspan of predalles slab or straddling two consecutive predalles slabs, respectively. The spacing between the joists is equal to 60 cm. From the above description, it can be gathered that the dimensions of the lightening elements in the floor cross-section are 42 \(\times\) 8 cm\({}^{2}\). The top flanges of HEA200 profiles are fully embedded in the topping RC slab of the floor, and the transverse reinforcement of the slab passes over the flanges, as shown in Figure 2. Hence, the slab is continuous all over the roof surface and bounded by the perimeter beams and the circular-shaped RC beam. Due to the presence of a space between the adjacent bottom predalles slabs (Figure 4), they are not in contact along their longitudinal edges. As a consequence, bottom predalles slabs do not contribute to the transmission of compression or tensile stresses along the direction orthogonal to their longitudinal axis. Therefore, their contribution to axial in-plane stiffness of floors along the circumferential direction is null. Figure 4: Space between adjacent bottom predalles slabs. Figure 3: Diagonal ties connecting the nodes of the adjacent radial trusses. A steel-framed structure with a 16-edge pyramidal shape and cellular polycarbonate panels is anchored to the top side of the circular-shaped RC beam, serving as a covering of the central hole. Under the gravitational loads, the circular-shaped RC beam and the radial trusses undergo downward vertical displacement. Due to the radial configuration of the roof-bearing structure and the translations of the radial trusses ending at the connection with the perimeter beams orthogonal to the perimeter beams' axes, in the horizontal plane, the points at the bottom of the circular-shaped RC beam undergo radial horizontal displacement. In particular, these displacements are outward (from the center) for the beam portions connected to the shorter radial trusses and inward for those connected to the longer radial trusses. At the same time, the ends of the radial trusses connected to the perimeter beams move outward, orthogonally to the perimeter. Due to the kinematic behavior of the bearing structure, the floors are subject to in-plane stress states. Hence, floors, due to their in-plane stiffness, interact with the roof bearing structure, influencing its deformed shape and, thus, the forces arising in structural elements. In particular, the stress state arising in each floor cell is mainly composed of the axial stresses in both the main directions of the floor, while the in-plane shear stresses are negligible. As a consequence, the simplified method described in Section 2.1 can be adopted to determine the in-plane transverse stiffness of the equivalent shell elements in their main directions in order to effectively reproduce the actual in plane-stiffness of floor cells.
## 2 Materials and Methods  
### Polyphenols in Almond Skin  
#### 2.1.1 Extraction Procedures--UAE, Blanching  
Californian almond kernels (_Prunus dulcis_) were provided by the supply service of GREEN laboratory (Avignon University, France). They were stored in sealed plastic packages and sheltered from light. Almonds used in all experiments were the Mission variety. Two main extraction processes were carried out: Ultrasound-Assisted Extraction (UAE) and Almond Blanching as the conventional process. UAE parameters (Ultrasound Intensity UI and Temperature T\({}^{\circ}\)) were determined based on the results provided by an experimental design (central composite design CCD). UAE was performed at low frequencies (20 kHz) with a 1000 W ultrasonic processor (UIP1000 hd, Hielscher Ultrasonics, GmbH, Germany) equipped with an Ultrasound Generator and sealing to titanium sonotrode BS4d18 (length 125 mm, diameter 18 mm) (Figure 1).  
In order to consider the power fraction converted to heat dissipated to the medium, calorimetric measurements were carried out to evaluate the real ultrasound power. The values of power were then calculated using Equation (1) [16]:  
\[P=m\;Cp\;\frac{dT}{dt} \tag{1}\]  
where \(m\) is the mass of solvent (g), \(Cp\) is the heat capacity of the solvent at constant pressure (J\(\cdot\)g\({}^{-1}\) K\({}^{-1}\)), and \(dT/dt\) is the temperature variation according to time. The applied ultrasonic power was then determined using the calculated power as given in the Equation (2) [16]:  
\[U\!I=\frac{4\;P}{D2} \tag{2}\]  
where \(U\!I\) is the ultrasonic intensity (W\(\cdot\)cm\({}^{-2}\)), \(P\) is the ultrasound power (W) calculated by Equation (1), and \(D\) is the diameter (cm) of the ultrasound horn.  
Twenty grams of whole almond kernels were immersed in 100 mL of distilled water (1/5: _w/v_) and introduced in a double mantle reactor for twenty minutes. The desired temperature was maintained by a cooling system (Huber, Germany). For the blanching process, the same ratio 1/5 (_w/v_) was used. Following this, 100 mL of boiled distilled water and twenty grams of almonds were put into the reaction for five minutes. These two processes were compared to Non-Ultrasonicated Almonds (NUA) where the same ratio was used without stirring and without ultrasound and at the same temperature as for ultrasonicated almonds.  
After treatment, almonds were kept apart for other uses at room temperature sheltered from light. The aqueous extracts were transferred in shaded glass flasks, freeze-dried for polyphenol analysis using freeze dryer (COSMOS - 80 \({}^{\circ}\)C/6 L + EDWARDS RV5 PUMP) and stored at +4 \({}^{\circ}\)C.
# Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
###### Abstract
We first use our multimodal conditional GAN to generate new data, then augment the training set with the generated data, and finally train a DNN classifier with the augmented training set for the emotion classification task. To be specific, our multimodal conditional GAN builds generators and discriminators for audio and visual modalities. Additional category information is used as their shared input to generate fake data of different categories. It is shown in [17; 47; 48; 49] that in the real multimodal data, the audio modality and the visual modality are highly dependent, which is beneficial to emotion recognition. Inspired by this, we measure the correlation between the audio modality and the visual modality in the generated multimodal data based on Hirschfeld-Gebelein-Renyi (HGR) maximal correlation [50; 51; 52], which makes the generated multimodal data close enough to the real multimodal data to help improve the performance of audio-visual emotion recognition. In such a way, our multimodal conditional GAN can (1) effectively generate the multimodal data with audio and visual modalities, and (2) fully consider the correlation between the audio modality and the visual modality in the generated multimodal data. Then, the generated multimodal data are used to augment our training set. Finally, a DNN model is used as the classifier to perform the emotion classification task with the augmented training set. Three public multimodal datasets, including eNTERFACE'05 [53], RAVDESS [54], and CMEW [55; 56; 57], are used to conduct a series of experiments. The experiment results show that our multimodal conditional GAN can significantly enhance the performance of audio-visual emotion recognition. To summarize, the main contributions of this paper lie in the following aspects:  
* We design an efficient multimodal conditional GAN to augment the multimodal data with audio and visual modalities for emotion recognition. * We model the correlation between the audio modality and the visual modality in the generated multimodal data to approximate the real multimodal data. * We conduct experiments on three public multimodal datasets to show that our multimodal conditional GAN can be effectively used for data augmentation of audio-visual emotion recognition. Figure 1: The pipeline of data augmentation in our framework. To the best of our knowledge, this is the first work to propose an efficient data augmentation approach with a multimodal conditional GAN for audio-visual emotion recognition. The rest of this paper is organized as follows. In Section 2, we show related works, including multimodal learning, and GAN and its application for data augmentation. In Section 3, we present our methodology in detail. In Section 4, a series of experiments are conducted to show the effectiveness of our multimodal conditional GAN. Finally, in Section 5, we draw conclusions and point out some future works.
## 2 Materials and Methods
### Qualitative Analysis
#### 2.5.3 Evaluation of Antioxidant Compounds and Antioxidant Activity  
The methods utilized for the determination of ascorbic acid, carotenoids, total phenols (TP), total flavonoids, and antioxidant activity have already been comprehensively described in [PERSON] et al. [53]. The following is a brief description of the procedures used for the determination. Each was performed with three replications.  
For ascorbic acid analysis, an aliquot of sample homogenate (2 g) was extracted with 20 mL of a mixture consisting of 30 g L\({}^{-1}\) metaphosphoric acid (MPA), 80 mL L\({}^{-1}\) acetic acid, and 1 mmol L\({}^{-1}\) EDTA [54]. Folin-Ciocalteu phenol reagent was used for subsequent spectrophotometric quantization in accordance with the method described by [PERSON] and [PERSON] [55]. The results were expressed as mg ascorbic acid per 100 mg of fresh weight.  
The same alcoholic extract carried out according to [PERSON] et al. [56] with minor modifications was used for the analysis of total phenols, flavonoids, and antioxidant activity. Approximately 2 g of lettuce leaf was homogenized in 20 mL of 80:20 methanol/water and then placed in an ultrasonic bath (Elmasonic P, Elma Schmidbauer GmbH, Singen, Germany) at 40 \({}^{\circ}\)C for 1 h in the darkness. After centrifugation for 20 min at 10,000 at 4 \({}^{\circ}\)C the solution containing the extracted metabolites was recovered and stored at \(-20\)\({}^{\circ}\)C until analyses were carried out.  
Total phenols were determined utilizing the method of Folin-Ciocalteu [57]. Color development is carried out at t.a. for 120 min in dark glass vials where 1 mL H\({}_{2}\)O, 100 mL of extract, 100 mL of Folin-Ciocalteu reagent, and 800 mL of Na\({}_{2}\)CO\({}_{3}\) 75 g L\({}^{-1}\) are successively mixed. The reading wavelength was 765 nm. Analytical values were reported as mg of gallic acid equivalent (GAE) per 100 g of fresh weight.  
The total flavonoid content was determined by spectrophotometric reading at 510 nm of the colored complexes that this class of compounds forms with aluminum salts in the presence of nitrite in a basic environment. The procedure described by [PERSON] et al. [58], with modifications [59] was performed. The results were indicated as mg catechine equivalent (CE) per 100 g of fresh weight.  
The antioxidant activity was determined on lettuce extract using the DPPH radical scavenging method [60,61]. Lettuce extract (0.1 mL), properly diluted, was added to the DPPH solution in methanol (3.9 mL of 0.0634 mM), shaken vigorously, and allowed to stand at room temperature for 60 min, when the reaction kinetic reached a steady state. The decrease in absorbance at 515 nm, proportional to the antioxidant power of the sample, was recorded. The antioxidant activity was estimated as the equivalent concentration of Trolox used as a reference standard for constructing the calibration line (umol TE 100 g\({}^{-1}\) fw).
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 3 Threshold Estimation and Data Set Generation by Machine Learning
### Basic Parameters
#### 3.2.2 Instantaneous BER  
The average BER that reflects the performance of the system is the average value of the instantaneous BER over a time period. For the FSO link, the average BER (\(\overline{P}_{b_{1},modulation}\)) can be given by the instantaneous BER (\(P_{e,modulation}\)) and the PDF, and is denoted as  
\[\overline{P}_{b_{1},modulation}=\int_{0}^{\infty}P_{e,modulation}f_{\gamma_{FSO}}( \gamma_{FSO})d\gamma. \tag{13}\]  
Similarly, the instantaneous BER of the RF link can be expressed as  
\[\overline{P}_{b_{2},modulation}=\int_{0}^{\infty}P_{e,modulation}f_{\gamma_{RF}}( \gamma_{RF})d\gamma, \tag{14}\]  
to express \(\overline{P}_{b_{2},modulation}\) as the average BER of the RF link. Simultaneously, the instantaneous BER of modulation modes can be expressed as  
* BER of L-PPM: \[P_{e,LPPM}=\frac{1}{2}erfc\left(\frac{\sqrt{\gamma\frac{L}{2}\log_{2}L}}{2 \sqrt{2}}\right),\] (15) where L is the symbol order, \(L=2^{n}\).
* BER of M-PSK: \[P_{e,BPSK}=\frac{1}{2}erfc(\sqrt{\gamma}),M=2,\] (16) \[P_{e,MPSK}=\frac{1}{\log_{2}M}erfc\left(\sin\frac{\pi}{M}\sqrt{ \gamma}\right),M\geq 4,\] (17) where M stands for the modulation length of PSK.  
BER of M-QAM: the instantaneous BER of M-QAM is shown as [17]  
\[P_{e,MQAM}=\frac{2\Big{(}\sqrt{M}-1\Big{)}}{\sqrt{M}\log_{2}M}erfc\left(\sqrt{ \frac{3\log_{2}M}{2(M-1)}\gamma}\right). \tag{18}\]
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 2 Finite Element Modeling, Analysis and Verification
### FEM of Haroun Experimental Tank [43]  
The cylindrical tank tested by [PERSON] [43] was an open-top, anchored support tank filled with water. The shell was made of aluminum with a Young's modulus of E = 68,940 MPa and a density of 2600 kg/m\({}^{3}\). The tank had a radius of 1.18 m and the height of the aluminum shell was 4.57 m, while the height of the water's free surface was 3.96 m. The shell thickness varied with height: up to 3 m, the thickness was 2.3 mm, and above this, the thickness was reduced to 1.6 mm. The shake table test was conducted using a record from the 1994 El Centro earthquake. To account for the scaling of the model, the record was sped up by a factor of 1.73 and scaled by 1.43 to achieve a peak ground acceleration (PGA) of 0.5 g. The FEM was developed using the previously outlined approach. The tank was discretized with 1236 shell elements for the base plate and the wall, while the fluid was modeled using 6084 solid elements with the ALE formulation. The final model, depicted in Figure 3a, consists of 8009 nodes. The discretization was chosen to balance the accuracy of the results with computational efficiency.  
An explicit nonlinear transient analysis was performed to replicate the experimental test. The results from the LS-DYNA FE model were compared with the experimental data from [PERSON] [43]. The comparison of the results is presented in Table 1, and the deformed shape of the model at different times during the transient analysis is shown in Figure 3b. The maximum meridional compression, measured 20 cm above the tank's base plate, was found to be nearly identical in both the experimental test and the FE model with a difference of less than 2%. The maximum radial displacement and base shear were also closely matched with differences between the numerical and experimental results consistently below 3%. These findings suggest that the modeling approach used in this study is sufficiently reliable for simulating the seismic behavior of cylindrical tanks under similar conditions.  
Figure 2: Comparison of the pressure time histories for the experimental and numerical method results.
# Spam Classification Based on Signed Network Analysis
## 3 A Primer on Social Psychological Theories for Signed Networks
### Validation  
As mentioned before, to validate our hypothesis, we measure balance surprise and status surprise for spammers and non-spammers in a Twitter dataset. To do this, we randomly select 1000 spammers and 1000 non-spammers (i.e., 2000 nodes in total), and then construct an ego-network for each. After that, we measure the average balance and status surprise value for each network.  
Figure 6 shows the comparison of the structural balance surprise values of spammers' networks and non-spammers' networks. We can see that the characteristics of spammers and non-spammers are markedly different in terms of surprise. In particular, in the case of normal users, type \(T_{3}\) is overrepresented in the network, while type \(T_{2}\) is underrepresented. Recall that this result is consistent with [PERSON]'s claim about the structural balance theory [31]. According to the theory, type \(T_{1}\) and type \(T_{3}\) are more plausible than other types in real-world social networks, which is clearly observed in the case of normal users. On the other hand, this pattern is not clearly shown for spammers. In particular, the balance surprise gap between types \(T_{2}\) and \(T_{3}\) is quite large in the case of normal users, but not in the case of spammers. Rather, we find a case inconsistent with the structural balance theory in the case of spammers; the balance surprise of type \(T_{1}\) is negative, which cannot be easily explained by balance theory.  
Figure 7a,b shows the generative surprise and receptive surprise for spammers and non-spammers, respectively. First, for the generative surprise result, we can see that both normal users and spammers have results consistent with the social status theory, i.e., the same surprise signs for all of the triads, but with different degrees of consistency (in particular, types \(T_{2}\), \(T_{8}\), and \(T_{10}\)). This difference appears more clearly in the receptive surprise result in Figure 7b. For most types of triads, we can see a large surprise gap between normal users and spammers. From these observations, we verify that spammers and non-spammers have different apparent network properties in terms of balance and status surprise, which shows the feasibility of using them as a basis for spam classification. All of these surprise metrics show the influence of social relationships on the edge signs. Therefore, the distinctive surprise patterns that appear in normal user cases imply that social relationships greatly affect the formation of the edge signs, as stated in our hypothesis.  
Although these results indicate that the proposed graph-converting method is reasonable, for better verification, an in-depth investigation into the performance (e.g., accuracy) is required. We leave this as our future work.  
Figure 6: Balance surprise. The variance of the balance surprise values of non-spammers’ ego-networks is bigger than that of spammers’ ego-networks.
###### Abstract  
This study investigates occupant kinematic and injury responses in zero-gravity seats under rear impacts at 16 km/h, 40 km/h, and 56 km/h and evaluates the protective performance of a conventional three-point seat belt system and a four-point seat belt system. First, a THUMS (Total Human Model for Safety)-based finite element assembly consisting of a regular seat model and a conventional three-point seat belt system was verified by comparing the kinematic responses and time-history curves of head acceleration, head rotation, and the T1 acceleration of PMHS (Postmortem Human Subject) tests. Then, a THUMS-based finite element assembly in a zero-gravity seat with a three-point seat belt system was created, and computational biomechanical analyses revealed that at low-to-medium impact speeds (16 and 40 km/h), the occupant exhibited backward sliding in the zero-gravity seat along the seatback with lower limb rotation and did not experience head and neck injury. However, a 56 km/h impact induced an excessive seatback rotation and caused the head to become out of position. The neck collided with the upper part of the headrest and caused a surge in the contact force between the neck and the headrest. The head injury and neck injury were comprehensively analyzed via the head injury metrics and neck injury metrics, including cervical spine injury metrics and cervical ligament injury metrics. Further, a four-point seat belt system was adopted and demonstrated better and more balanced restraining effects by reducing the relative displacement between the occupant's head and chest in the x- and y-directions by 26% and 84%, respectively. Therefore, the occupant's head remains in position and the collision between the neck and the headrest can be avoided. Maximum reductions in the head and neck injury metrics reached 70% and 57%, respectively. The current study illustrates the disadvantages of the traditional three-point seat belt system in restraining the occupant in a zero-gravity seat under rear impact and shows the four-point seat belt to be a better alternative. This study sheds light on seat belt system design and optimization towards future zero-gravity seats under rear impact.  
binomechanics; zero-gravity seat; occupant kinematics; injury risk; seat belt systems +
Footnote †: journal: Applied Sciences
## 5 Experiments and Results
### Comparison Results  
The results of value-based algorithms are shown in Section 5.3.1, Figure 5, and Table 4. The results of policy-based algorithms are shown in Section 5.3.2, Figure 6, and Table 5. The figures show the _WR_ curves changing over time and the tables show the final _WR_ values followed by the standard deviations. The results are averaged across five individual tests.  
We criticize the performance of all algorithms from three aspects, and the analysis is given below. Generally, all comparison algorithms are not specifically modified to deal with the action semantic conflict under global parameter-sharing constraint in heterogeneous MARL problems; therefore, they fail to achieve high _WR_ with small variance.  
Figure 5: Results of the training curves of comparison of value-based algorithms.
## 4 Hyperspectral Image Destriping Method
### Discussion  
#### 5.4.1 Parameter Settings  
The parameters in the model are set according to the experiment result, and the weight coefficients of each energy term are adjusted appropriately according to the application requirements. Experimental results show that the parameters of the model in this paper are set as follows: In the adaptive moment matching algorithm, \(p_{b}=\) the mean gray value of water area for each band, and when the column belongs to a high-brightness region, \(W_{max}=\) cols / 3, otherwise, \(W_{max}=\) cols / 4. In the variational algorithm, \(\lambda_{1}=10\), \(\lambda_{2}=1\), \(\lambda_{3}=0.1\), \(\alpha=1000\), \(\beta=100\), \(\gamma=10\), and the maximum iteration number of multi-level decomposition = 10, and the maximum iteration number of internal unidirectional total variational = 20, and the termination condition of the iteration is \(\|u_{k+1}\)-\(u_{k}\|^{2}/\|u_{k}\|^{2}<1\times 10^{-4}\). In the piecewise linear least squares model, we set \(t_{1}=10\), \(t_{2}=20\) when the color depth is 10-bit.  
When we conduct destriping experiments on other satellite images, we need to make the following adjustments. Firstly, we do not need to modify parameters for the adaptive moment matching method. Secondly, in the variational model, if the stripe noise is more serious, we need to increase the \(\lambda_{2}\) parameter, and vice versa. Finally, in the piecewise linear least squares model, we need to adjust parameters according to the color depth of the satellite image. For example, if the image color depth is 8, set \(t_{1}=3\) and \(t_{2}=5\).