esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Emphasis on Sensory Stimulation without Interactions  
Many tools discussed in this review have emphasized only certain characteristics of immersion and presence through sensory stimulation while neglecting to offer means of interaction to the subject. Indeed, in addition to the visual devices used, such as immersive headsets, we note the use of various other technological devices to reproduce the realistic ambience of the events occurring in the VEs involved in the therapy. These devices include headphones to transmit sounds related to the VE, scent palettes to distill scents dependently of events in the virtual world, and devices that simulate vibrations of the patient's seat [6; 34; 35; 53]. Thus, patients are immersed in an environment that they can visually explore and, in some cases, navigate while the VE is manually updated by the therapist. This emphasis on sensory immersion and stimulation is largely due to the inclination of current approaches to VRET to consider the therapy as \"similar to traditional PE (Prolonged Exposure), with the largest difference being that aspects of the trauma are illustrated for the patient within a VR (Virtual Reality) simulator while the patient narrates the trauma experience\" [50]. Scholars have argued that interactions constitute an essential element of VR. According to [PERSON] and [PERSON], for example: \"VR is a medium composed of interactive computer simulations that sense the participant's position and actions and replace or augment feedback to one or more senses, giving the feeling of being mentally immersed or present in the simulation (a virtual world)\" [18]. Nonetheless, interactions are presently lacking in VRET.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 5 Power-Optimized Cooperative MAC Protocol
### Channel Reservation Phase  
When the sender S has a data packet to send and senses the channel idle, it sends the cooperative RTS (CRTS) packet to its recipient D like IEEE 802.11 distributed coordination function (DCF) protocol. On receiving the CRTS packet correctly, the recipient replies with a cooperative CTS (CCTS) packet if the channel is available from its perspective. With the help of the CRTS/CCTS handshake, other nodes in the network know the data packet transmission between S and D and set their own network allocation vectors (NAVs) to avoid packet transmission collisions. Meanwhile, S and D can estimate the channel gain \(h_{\text{SD}}\) by the received SNR of the CRTS and CCTS. The node \(\text{R}_{\text{i}}\) receiving both CRTS and CCTS knows that it is the common neighbor node of S and D and estimates the channel gain \(h_{\text{SR}_{\text{i}}}\) and \(h_{\text{R}_{\text{i}}\text{D}}\). The CRTS packet contains the residual energy of the sender \(E_{\text{S}}\), and the CCTS packet contains the channel gain \(h_{\text{SD}}\). With the information in control packets, the common neighbor nodes \(\text{R}_{\text{i}}\) of S and D can determine whether its corresponding parameters satisfy Equations (9), (12), (13) and (16). If satisfied, the node \(\text{R}_{\text{i}}\) can take part in the process of cooperative node contention.
# A Low-Profile Dual-Polarized Transmitarray with Enhanced Gain and Beam Steering at Ku Band
## 5 Simulation and Experimental Results of the TA Antenna
Dynamic phase control across the aperture is necessary for electronic beam steering, which can be employed by incorporating electronically tunable components. Furthermore, this advancement will allow better control of polarization, adaptive beam shaping, and multi-beam operation in real-world applications.
## 4 Proposed Localization Method
### Algorithm Development
#### 4.1.1 Calibration Stage
\right] \tag{79}\]  
Invoking the WLS theorem again, one has the solution of target position, denoted by \(\mathbf{u}\), from (74) as  
\[\mathbf{u}=(\mathbf{G}_{2}^{\mathrm{T}}\mathbf{W}_{2}\mathbf{G}_{2})^{-1} \mathbf{G}_{2}^{\mathrm{T}}\mathbf{W}_{2}\mathbf{h}_{2} \tag{80}\]  
where \(\mathbf{W}_{2}\) is the weighting matrix and it is determined by  
\[ \begin{array}{c}\mathbf{W}_{2}=\left[E(\mathbf{\Delta h}_{2}\mathbf{\Delta h }_{2}^{\mathrm{T}})\right]^{-1}\\ \hskip 14.226378 pt=\left[\mathbf{B}_{2}\mathrm{cov}(\theta)\mathbf{B}_{2}^{ \mathrm{T}}+\mathbf{D}_{2}\mathrm{cov}(\mathbf{\Delta s}-\mathbf{\Delta\hat{ s}})\mathbf{D}_{2}^{\mathrm{T}}+\mathbf{B}_{2}(\mathbf{G}_{1}^{\mathrm{T}} \mathbf{W}_{1}\mathbf{G}_{1})^{-1}\mathbf{G}_{1}^{\mathrm{T}}\mathbf{W}_{1} \mathbf{D}_{1}\mathrm{cov}(\mathbf{\Delta s}-\mathbf{\Delta\hat{s}})\mathbf{ D}_{2}^{\mathrm{T}}\\ \hskip 14.226378 pt+\mathbf{D}_{2}\mathrm{cov}(\mathbf{\Delta s}-\mathbf{\Delta \hat{s}})\mathbf{D}_{1}^{\mathrm{T}}\mathbf{W}_{1}\mathbf{G}_{1}(\mathbf{G}_ {1}^{\mathrm{T}}\mathbf{W}_{1}\mathbf{G}_{1})^{-1}\mathbf{B}_{2}^{\mathrm{T}} \right]^{-1}\end{array}  \tag{81}\]  
But as presented in (81), the unknown target position is required in the computation of \(\mathbf{W}_{2}\). Herein, to circumvent this dilemma, we preliminarily exploit the target position estimate contained in \(\theta\) to form \(\mathbf{W}_{2}\) and use (80) to estimate target position. After that we can utilize the estimated target position to update \(\mathbf{W}_{2}\) for another repetition. From the WLS theorem, the covariance matrix of \(\mathbf{u}\) can be approximated, given sufficiently small BR measurement noise and transmitter/receiver position error, as  
\[\mathrm{cov}(\mathbf{u})=\left(\mathbf{G}_{2}^{\mathrm{T}}\mathbf{W}_{2} \mathbf{G}_{2}\right)^{-1} \tag{82}\]
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Interfacial Bond Strength Analysis
#### 3.2.3 Influence of Shear Key  
Figure 7 presents a comparison of the interfacial bond strengths of specimens with and without shear keys at different shear angles. The bond strength of the interface was greatly improved when the shear key was set for the same shear angle. Specifically, the interfacial bond strengths of the specimens with shear angles of 30\({}^{\circ}\) and 45\({}^{\circ}\) increased by 60.42% and 37.31%, respectively. Hence, setting shear keys can effectively improve the interface bond strength.
## 3 Flow Analysis
### Spectral Analysis  
The wake center in the lateral (\(y\)-)direction at a fixed downstream position was plotted against time and further analyzed in the frequency, \(f\), -domain by calculating the power spectral density (PSD). The location of interest is 8D, as it is a candidate for the placement of a new turbine in a wind farm.  
In order to reduce noise from wrongly calculated wake centers, all points outside \(3\sigma\) either side of the hub were removed, \(\sigma\) being the standard deviation. The power spectral density was calculated using the Welch method with a Hanning window. The results were visualized in a log plot that was used to find the cutoff frequency \(f_{c}\). This is the frequency at which the value of the PSD starts to taper off, shown for the neutral case without yaw in Figure 3. It represents the border between the frequencies containing the most energy in the signal, which are more relevant to the analysis.  
Using a low-pass Butterworth filter with this \(f_{c}\) and plotting the resulting PSD in a non-log plot, it is then possible to identify the single frequency with the highest power density \(f_{peak}\), as done to investigate the frequency of meandering in Section 6.1.  
Cutoff frequency is also calculated analytically using Equations (1) and (2) for comparison. The equations are similar, but one uses rotor diameter \(D\), and the other wake diameter \(D_{w}\). Finally, the Strouhal number \(St\) is calculated using \(f_{peak}\).  
\[f_{c,r}=\frac{U_{hub}}{2D} \tag{1}\]  
\[f_{c,w}=\frac{U_{hub}}{2D_{w}} \tag{2}\]  
\[St=\frac{f_{peak}D}{U_{hub}} \tag{3}\]  
Figure 2: Contour plot illustrating the predicted wake center using various tracking methods at 8D downstream of the V80 turbine in neutral atmosphere. The dashed and solid lines represent the contours of the constant velocity for each method. Note that the wake center predicted by the Bastankhah model overlaps with that of the ideal sigma.
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
###### Abstract
Some researchers put forward the new joint structure of steel truss webs or the application of composite girders with steel truss webs in the new bridge type. [PERSON] [19] proposed a new type of PPL-steel tube joint suitable for composite girder bridges with steel truss webs and conducted experimental research on the new type of joint, obtained the failure mode of the new type of PPL-steel tube joint, and proposed a calculation formula of the shear strength of the new type of PPL-steel tube joint. [PERSON] [20; 21] put forward the application of a steel truss web in the arch rib to form the composite arch with a steel truss web and concrete top and bottom plates and conducted an experiment study on this new type of composite arch bridge. The results show that this new type of arch bridge has good mechanical performance and can effectively reduce the self-weight of the main arch ring structure, which is helpful to reducing its axial force and shortening the construction period. [PERSON] [22] conducted an elastic seismic response analysis for a steel truss web-concrete arch bridge with a span of 160 m, and the results show that the stiffness of the steel truss web-concrete arch is lesser than that of a concrete box arch, which can improve the horizontal seismic performance of the arch bridge to a greater extent and also has a certain effect on the resistance to vertical ground vibration. In summary, the current research on composite girders with steel truss webs mainly focuses on the mechanical performance of the connecting parts between the steel truss web and concrete top and bottom plates, while there are few studies on the overall flexural per  
Figure 1: Composite Girders with Different Plate (Truss) Webs: (**a**) Composite girders with steel plate webs; (**b**) Composite girders with corrugated steel webs; (**c**) Composite girders with steel truss webs. formance of PC composite girder steel truss webs, and no relevant research has been found on the flexural design calculation method of PC composite girders with steel truss webs. Therefore, an experiment and FE analysis were conducted on a prestressed concrete (PC) composite girder bridge with steel truss webs to investigate its flexural performance. The evolution process of the bending performance, the strain and deflection variation process with an applied load, and the strain distribution along the section height are discussed. The influence of the height-to-span ratio and eccentric loading effect on the flexural behavior of the PC composite girder with steel truss webs is analyzed. The method for calculating the cracking bending moment, the bending moment at the rebar yield stage, and the ultimate bending moment of a PC composite girder with steel truss webs is proposed.
## 4 Discussion
### Antioxidant Capacity and Related Parameters  
Similarly to our apricot study, antioxidant capacity including total phenol contents significantly increased by MeJA in CS and/or SL treatments in strawberry, peach and pomegranate fruits compared to untreated fruits [22; 28; 61].  
In SA treatments, [PERSON] et al. [22] demonstrated that SA significantly increased the TAC and TSP content of pomegranate fruits compared to control fruit. Previous studies showed that phenolic compounds, as an antioxidant substance, usually accumulated or at least maintained their levels under cold stress [62], which was the case in the SA and the MeJA treatments but not in the control treatments of this apricot study. The applications of MeJA or SA were able to keep the total phenol contents or were able to increase the antioxidant capacity during the storage periods (Figure 2). Our results indicate that postharvest applications of both MeJA and SA compounds have the potential to improve fruit health by reducing not only CI and FD but increasing the TAC of fruit tissues.  
Ascorbic acid content of apricot fruit progressively increases during fruit maturity stages [63]. Our results demonstrate that SA- and/or MeJA-treated fruit had lower ascorbic acid contents at the beginning of storage. Reports on the effect of MeJA on AAC of fruits are not consistent as, for instance, [PERSON] et al. [21] demonstrated that MeJA treatments did not change AAC, while [PERSON] et al. [28] demonstrated that MeJA significantly influenced vitamin C levels of fruits after CS and SL treatments on peach compared to control treatments. In SA treatments, [PERSON] et al. [58] demonstrated that AAC reduced significantly in the control and SA treatments at 0.7 and 1.4 mmol L\({}^{-1}\), while the AAC of the fruits was not changed in the dose of 2 mmol L\({}^{-1}\). Similarly, ascorbic acid levels remained the same by the end of storage in the SA treatments at 1 mmol L\({}^{-1}\) in peaches and 2 mmol L\({}^{-1}\) in oranges [31; 64].  
Previous fruit studies showed significant positive correlations among parameters of antioxidant capacity and phenol contents under storage conditions, e.g., [31; 65]. One study [31] provided these correlations in postharvest application of SA and this study investigated these correlations for MeJA and SA jointly. For instance, the reduction in fruit decay was associated with increasing antioxidant capacity for both SA and MeJA treatments, which corroborated well with the significant correlation coefficients, the significant regression, and the significant PCA (in PC1) relationships between FD and TAC (Tables 2 and 3, Figures 3 and 4). In the case of CI vs. AAC pair-variable, the increasing ascorbic acid level in the fruit tissues was connected to a decreasing level of chilling injury, which was confirmed by the significant correlation coefficients and also by the significant regression relationship between CI and AAC for both MeJA and SA compounds (Tables 2 and 3, Figure 3).
## 3 Results and Discussion
### Evaluation of Button Sensor Antenna: Simulated Results
#### 3.1.3 Three-Layer and Single-Layer Body Phantom Models
We again observe that with a minimum air gap of 5 mm between the body and the BSA design, the effect of body tissues is minimum, and the performance of the BSA becomes similar to that of free space, as indicated in [52; 56; 61; 62]. The results reveal that the architecture behaves similarly in the presence of a variety of phantom body models. These results attest to the suggested design's robustness. By keeping the air gap at 5 mm or more, the design becomes less affected by the variation in the tissue properties. Figure 11: S\({}_{11}\) for BSA. (**b,c**) on the body chest (flat), with a thickness of 50 mm and 88 mm, respectively, and (**a,d**) arm cylindrical models, with a radius of 50 mm and 100 mm. Figure 12: S\({}_{11}\) for BSA. (**a,c**) Homogeneous model (Muslce \((\varepsilon_{t})\) = 56.6 and \(\sigma\) = 1.33 S/m, thickness 50 and 88 mm), and (**b,d**) simplified arm models for radius (r) of 50 mm and 100 mm for the small and large size bodies, respectively.
## 4 Discussion
### Characterization of the Lipids and Economical Aspects  
The extracted SCO was analyzed with regard to various quality parameters. The iodine value of 48 is comparable to tallow, which has an iodine value of 35-48, and palm oil, which has an iodine value of 50-55. The SCO would thus be suited to biodiesel production, where a maximum iodine value of 115 should not be exceeded. Furthermore, the comparably low iodine value of the SCO indicates a good oxidative stability [48]. The peroxide value of 7.3 is quite low, also indicating a high oxidative stability. A comparable vegetable oil would be high oleic sunflower oil [49]. The high acid value of 4.3 indicates a high amount of free fatty acids. Depending on the intended use, this can be problematic, since it may limit the available options for downstream processing. For instance, the efficient alkaline transesterification of the fatty acids requires an acid value below 2, since the free fatty acids and the alkaline catalyst can form soaps [50]. This problem can, however, be addressed by using alternative catalysts for transesterification [51].  
The use of waste to produce VFAs, which can subsequently be converted to lipids for the production of biofuels, is in itself a very appealing concept. Due to decarbonization measures, blending regulations, and governmental subsidies, biofuels are still a relevant market. There remains, however, some uncertainty regarding the economic viability of such a process, although it has recently been suggested that VFA-based SCO could potentially compete with vegetable oils [2]. Assuming this calculation proves to be correct, various uses for each FA in the olechemical industry are conceivable, from surfactants and detergents to lubricants and soaps [52]. Another aspect of potential economic interest is the unusual odd-numbered fatty acids present in the lipids produced. Odd-numbered fatty acids have attracted interest as precursors for fragrances and plasticizers, as plant protection agents, and as active ingredients in medical treatments [53]. Overall, the presented data indicate that the produced SCO is comparably stable to oxidation and suitable for a wide range of applications.
## 3 Methodology for Data Pre-Processing
### Proposed Model Architecture  
The input and output layers of the neural net were determined by the feature selection and sequence length, as shown in Figure 4. This approach used four features with a sequence length of 128 and, thus, the input of the first layer was a 128 \(\times\) 4 matrix for each time step. The output was simply the predicted voltage for the next step.  
Figure 5 demonstrates that the LSTM cells had better validation results, compared to those of the RNN and GRU. From Epoch 30 onwards, the LSTM had lower MSE and performed slightly better than the RNN, with regard to the mean max error. In addition, the training time per epoch was three times faster when using the LSTM, compared to the RNN, and similar to that of the GRU. The hidden layers were determined empirically as two LSTM layers with 128 neurons each and one attached dense layer with 128 neurons. An additional dropout layer with a dropout rate of 0.2 was also used, in order to deal with overfitting issues. The model hyperparameters shown in Table 3 were determined empirically for the proposed models using a grid search algorithm.  
Figure 3: Training progress over training (**a**) and validation (**b**) sets.
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 6 Experimental Evaluation
### Parameter Settings and Evaluation Metrics  
The Box-Muller method was used to generate a random number set for the initial water level values at Bonners Ferry station in Case 1 and Hongqiao, Lecheng, and Liushi stations in Case 2. The set mean value was given as the state value at the beginning moment of data assimilation. Based on the parameter analysis and optimization described in the parameter analysis section, the ensemble size m, the standard deviation \(\sigma\), and the observation noise level \(\delta\) were taken to be 100, 0.1 m, and 0.005 m, respectively. As the water depth values were between 6 and 9 m for Case 1 and between 3 and 4.5 m for Case 2, the corresponding relative observation noise level values and the relative standard deviation values were approximately less than 5% of the water depth values.  
The RMSE, calculated based on the water lever difference between the simulation and measurements from the start of the data assimilation process to the end of the water level simulation, was used as an evaluation metric. Furthermore, the algorithm calculation cost in seconds and the time for the algorithm to reach stability were used as the other two evaluation metrics. For Case 1, the simulation period covered 27 days, and the data assimilation process started on day 5 (hour 96). For Case 2, the simulation period was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ensemble Size m**} & \multicolumn{4}{c}{**\(\delta\)/m and \(\sigma\)/m**} \\ \cline{2-5}  & \(\delta\) = 0.0001, & \(\delta\) = 0.0001, & \(\delta\) = 0.05, & \(\delta\) = 0.05, \\  & \(\sigma\) = 0.0001 & \(\sigma\) = 0.1 & \(\sigma\) = 0.0001 & \(\sigma\) = 0.1 \\ \hline
50 & 43.42 & 44.05 & 44.61 & 43.73 \\
100 & 95.19 & 96.34 & 98.21 & 98.77 \\
150 & 151.92 & 153.06 & 153.84 & / \\
200 & 219.14 & 222.66 & 222.48 & / \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational cost in seconds with different ensemble sizes at fixed \(\delta\) and \(\sigma\) values.  
from 8:00 on 7 August 2019 to 8:00 on 12 August 2019 (5 days in total), assuming that data assimilation took place from 0:00 on 9 August 2019. The filtering ended when the elements on the diagonal of the water level correction error variance matrix were less than 0.001 m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Section** & \multirow{2}{*}{**River Length/m**} & \multirow{2}{*}{**River Width/m**} & \multirow{2}{*}{**Average Slope**} & **Manning’s** \\
**Number** & & & & **Roughness** \\  & & & & **Coefficient** \\ \hline
1 & 1595 & 265–432 & & 0.035 \\
2 & 4634 & 173–319 & & 0.030 \\
3 & 10,327 & 188–343 & & 0.035 \\
4 & 1567 & 151–289 & 0.0004 & 0.035 \\
5 & 1543 & 106–197 & & 0.035 \\
6 & 4627 & 155–299 & & 0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Basic information of the Kootenai river.  
Figure 4: Schematic diagram of the Kootenai river.  
Figure 5: Boundary conditions for the upstream entrance: (**a**) water flow at Tribal Hatchery and the downstream exit, (**b**) water level at Klockmann Ranch.