esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stress Analysis  
To clearly understand the deformative characteristics of the timber frame subjected to lateral cyclic loading, the deformed models and the minimum principal stress at the middle plane are plotted in Figure 10. Figure 10a,b show the compressive state of the frame under the displacement amplitude of +180 mm and -180 mm, respectively. It can be seen that since the frame is symmetrical, the two deformed models are also symmetrical.  
Figure 8: Results of the experiment and the simulation: (**a**) Hysteresis curves; (**b**) Skeleton curves.  
Figure 9: Results from the experiment and the simulation: (**a**) Stiffness; (**b**) dissipated energy.  
The detailed deformed state of the mortise-tenon joints in Figure 10a is given in Figure 11. It can be seen that when the top of the columns are moving right at the displacement of +180 mm, the columns and the beam are in contact at the intersection points of 2, 3, 6 and 7. The largest value of compressive principal stress is 15.4 MPa at intersection point 2. When the displacement amplitude is \(-\)180 mm, the columns and the beam compress each other at the intersection points of 1, 4, 5 and 8, inversely, as shown in Figure 10b.  
The vertical compressive stress in the beam, denoted as S33 in Figure 12, is vertical to the direction of the timber grain and is caused by the contact between the columns and the beam. Larger value of S33 in the beam means greater contact force derived from the column. If the contact forces exerted around the two intersection points of a mortise-tenon joint (for example intersection points 2 and 3) get larger, the bending moment caused by the two forces will also be greater, which then leads to more demand for the lateral loadings on the top of the columns.  
Figure 11: The minimum principal stress of the joints when the displacement = +180 mm.  
Figure 10: Minimum principal stress at the middle plane: (**a**) +180 mm; (**b**) -180 mm.  
Figure 13 shows the vertical compressive stresses S33 at the intersection points 2 and 3 with respect to the lateral loading displacement. It can be seen from the figure that when the timber frame moves left, the vertical compressive stresses are close to zero, which means the contact at these intersection points makes almost no contribution to the stiffness and strength of the timber frame. This is consistent with the phenomenon seen in Figure 10b. However, when the displacement amplitude increases from 0 mm to +180 mm, the envelope curve of S33 is similar to that of the reaction force of the timber frame, which confirms that larger values of S33 in the intersection points will demand greater lateral loadings on the top of the columns. Therefore, the envelope curves of S33 at all the eight intersection points are plotted in Figure 14. The symmetrical intersection points, for example 1 and 6, in the beam have the symmetrical envelope curves. At different displacement magnitudes, the vertical compressive stresses of intersection points 4 and 7 have larger values than those of other points.
## 3 Results and Discussion  
### Rheological Properties of the Emulsions  
Proteins are natural ingredients commonly used as emulsifying agents in the food industry [21]. Protein-based emulsions were used to reduce the fat content of the muffin formulations. Three different protein sources (soy, lupin, and spent brewery yeast) were used to stabilize the emulsions by forming a viscoelastic layer on the oil droplets [21]. Emulsions were prepared by mixing the 6% protein suspensions with sunflower oil in a 1:1 ratio (\(w/v\)), decided such as not to exceed the emulsifying capacity of the protein sources used in the study. The influence of the protein source on the rheological behavior of the emulsions was assessed by running low oscillatory and stepped flow tests.  
The viscoelastic behavior of the emulsions was characterized through dynamic oscillation measurements within the LVR. The results of the frequency sweep test are presented in Figure 2. For all tested emulsions, both storage and loss moduli progressively increased with frequency. In agreement with the observations of [PERSON] [22] and [PERSON] [23], these phenomena may be attributed to the solid-like behavior of the samples, exhibiting permanent interactions. Over the entire frequency domain considered in the experiment, the samples exhibited slightly higher G' compared to G\" values, suggesting the formation of weak elastic gel-like emulsions.  
The rheological behavior of the protein-based emulsions under flow conditions is presented in Figure 3. as apparent viscosity vs. shear rate rheograms. Regardless of the protein source, the emulsion samples presented shear-thinning behavior, which has been previously reported for protein-stabilized emulsions [22; 23]. [PERSON] and [PERSON] [23] explained the shear-thinning behavior through the flocculation of the emulsion droplets. In addition, [PERSON] et al. [24] suggested that the high shear-induced gradual disruption of the droplet aggregates might contribute to the shear-thinning behavior of the emulsions. The disruption of the droplets usually occurs at high flow rates when the hydrodynamic forces overcome the interfacial forces responsible for droplets aggregation [25].  
Figure 2: Evolution of the storage (G’—full symbol) and loss (G”—empty symbol) moduli with frequency for the emulsions prepared with soy proteins (circle), lupin proteins (triangle), and yeast proteins (diamonds).  
In the case of all tested samples, the shear stress increased, and the apparent viscosity decreased in the whole shear rate range of 0.1-100 s\({}^{-1}\). The highest values of apparent viscosity were registered for the lupin protein-based emulsion (Figure 3). At low shear rates, the soy protein-based emulsion had the lowest shear stress and viscosities values. For shear rates over 1 s\({}^{-1}\), the yeast protein-based emulsion was overtaken by the soy protein-based one (Figure 3). The slightly different shape of the yeast protein-based emulsion, with a more evident plateau of apparent viscosity values, might be due to the rather high content of minerals in the sample, which might weaken the attractive interaction between droplets [24].
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Experiment Design
#### 5.1.5 Measurement of Effectiveness  
To investigate the effectiveness of the proposed method, three measures of effectiveness (MOE) were adopted: carbon emission, stop frequency, and delay. These measures are used to quantify the performance of the proposed strategies in terms of carbon reduction and transportation efficiency.  
Carbon emission and stop frequency are used to assess the degree of ecological improvement. Carbon emissions are calculated using the VSP model. Stop frequency is defined as the average number of stops made by all vehicles and is also used to illustrate the principles behind the control mechanism. The proposed controller achieves carbon reduction by reducing the number of stops.  
Delay is used to measure the overall efficiency improvement of intersections. It is defined as the average delay experienced by vehicles passing through the intersection over the entire control period.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Ablation Studies  
Ablation studies were performed on the BSDS500 test dataset to analyze the validity of each component of the proposed model. The relevant experimental results are shown in Table 5, where \(L_{0}\) RR indicates whether the \(L_{0}\) regularizer is used, SAR indicates the use of the spatial attention mechanism, CAR indicates the use of the channel attention mechanism, and Iters denotes the total number of iterations in the deblurring process.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 4 Methodology and System Architecture
### Dynamic Hopping Rate Implementation
Implementing this functionality would involve configuring the monitoring frequency \(f_{\text{monitor}}\), defining appropriate thresholds for the metrics, and determining suitable values for \(\Delta R\) based on the system's performance requirements and operational environment.
## 3 Results
### Effects of Treatments on the Quality of Olive Oil over a 12-Month Storage Period
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{3}{c}{**Storage Period**} & \multicolumn{3}{c}{**Treatments**} \\ \hline  & **Harvest** & **6 Months** & **12 Months** & **CF** & **CD** & **AF** & **AD** \\ \hline C16:0 & 14.81 b & 16.52 a & 15.23 b & 14.93 b & 16.30 a & 15.00 b & 15.85 a \\ C16:1 & 1.21 a & 1.08 b & 0.96 c & 1.09 ab & 1.08 ab & 1.05 b & 1.10 a \\ C18:0 & 2.53 a & 2.36 b & 2.26 c & 2.48 a & 2.31 c & 2.40 b & 2.35 bc \\ C18:1 & 69.89 c & 71.77 b & 73.25 a & 70.88 c & 70.35 c & 73.19 a & 72.12 b \\ C18:2 & 7.09 a & 6.95 a & 7.04 a & 8.32 a & 7.54 b & 6.12 c & 6.12 c \\ C20:0 & 0.42 a & 0.33 b & 0.29 b & 0.35 ab & 0.35 ab & 0.32 b & 0.37 a \\ C18:3 & 0.90 a & 0.85 a & 0.78 b & 0.82 b & 0.91 a & 0.76 c & 0.88 a \\ C20:1 & 0.27 a & 0.18 b & 0.20 b & 0.21 a & 0.22 a & 0.22 a & 0.21 a \\ SFAs & 17.92 b & 19.21 a & 17.78 b & 17.79 b & 19.02 a & 17.76 b & 18.64 a \\ MUFAs & 71.43 c & 73.02 b & 74.40 a & 72.19 c & 71.67 c & 74.48 a & 73.46 b \\ PUFAs & 7.98 a & 7.80 b & 7.82 b & 9.14 a & 8.45 b & 6.88 c & 7.00 c \\ UFAs & 79.41 c & 80.82 b & 82.22 a & 81.34 a & 80.11 b & 81.35 a & 80.45 b \\ SFAs/ & 0.23 b & 0.24 a & 0.22 c & 0.22 b & 0.24 a & 0.22 b & 0.23 a \\ UFAs & & 9.13 b & 9.50 a & 9.69 a & 7.90 d & 8.50 c & 10.84 a & 10.50 b \\ CUFAs & & & & & & & \\ C18:1/ & 0.04 a & 0.03 b & 0.03 c & 0.04 a & 0.03 b & 0.03 b & 0.03 b \\ C18:2 & & & & & & & \\ Sualene (mg/100 g oil) & 740.86 a & 200.38 b & 199.48 b & 286.31 a & 396.32 a & 403.93 a & 434.39 a \\ \hline \end{tabular} The definitions of the letters are shown as follows: CF: control treatment and full irrigation; CD: control treatment and deficit irrigation; AF. _Allanthus_ presence and full irrigation; AD: _Allanthus_ presence and deficit irrigation. The factor ”storage period” refers to storage period irrespective of the treatment employed and the factor ”treatment” to each specific treatment irrespective of the storage period. Means within the same row followed by the same letter are not significantly different (separately for storage period and treatments) according to [PERSON]’s multiple range test at a significance level of \(\alpha\) = 0.05.  
\end{table} 
Table 9: Effects of storage period and treatments on fatty acid profile (%) and squalene concentration in olive oil over time.
## 7 Results
### Computing Time and Efficiency of the Adaptive Approach  
We conclude the results sections with some consideration of the computing times needed to execute the steps of the proposed approach and the time efficiency of the adaptive approach. Without using the adaptive approach, an exhaustive optimization across the 4 selected MWs would require the execution of the CNN classification (model training and prediction) four times to identify the optimal accuracy.  
Using the computing environment described in Section 4.3, the calculated CNN model training time is 1165 s, and the prediction time is 4.11 s for CWT-G at SNR = 25 dB with the DIFFNSR method, and the other MWs have similar computation times as the size of the input data is the same across the MWs, and the CNN architecture is quite similar (the only differences are the values of some hyper-parameters). The adaptive approach requires the calculation of the three features (i.e., variance, skewness, and kurtosis) on the entire data set and the execution of the'shallow' machine learning algorithm. This time to calculate the three features and create the feature matrix is, on average, 20.6 s across the 4 different MWs. Taking as an example the DT algorithm, which provided the optimal performance across the different values of SNR (see Figure 12), the time to generate the DT training model is 0.89 s, and the testing (prediction) is 0.024 s. Then, the overall computing time to implement the adaptive approach across the four considered MWs is 86.056 s, which is negligible (ratio of 0.01675) in comparison to the repetition of the CNN classification time for each MW. Then, the proposed approach is able to provide a significant gain in computing time.
## 4 Learning Scenarios Using Sensors
### Heart Rate  
Heart rate monitors have been used for several years for monitoring optimal heart rate during and after exercise. The devices have typically been a watch with a heart rate belt. The heart rate has been monitored from the wristwatch in real-time. Optionally, the belt or the watch saved the data, which could later be transferred to a computer and analyzed there. Nokia SportsTracker and Nokia N79 mobile phone enabled users to upload their heart rate and GPS data online to the SportsTracker service [37]. Using the service, exercise sessions can be studied by plotting the data on a map and making use of several graphical summaries. Similar services are available for almost all smartphones.  
Heart rate monitors can surely be used in physical education (PE) classes, as such. In addition, using services, such as SportsTracker, students can compare their exercise sessions with others and learn from the analysis.  
These kinds of data are important not only in running or bicycling exercises, but also in team sports, such as soccer and football. During the games, the coach could monitor in real-time, for example, who has run the longest distance. This information could then be used to select the best players and planning changes on the field. In this case, sensor data is augmenting the sports experience, and the data give opportunities to reflect one's activities _versus_ the measured heart rate during the game.  
Monitoring collective heart rate makes it possible for us to create new types of educational games. We define _collective heart rate_ to be a mathematical mean of measured heart rates in a group at a certain time. For example, a group of students wear heart rate monitors that send the data in real-time to the game server. The group should keep their collective heart rate at a certain level, for example, at 120 beats/min for a certain period, for example, one minute. After that, they have to raise their collective heart rate to the level of 130 beats/min and sustain it for a minute. The group can then decide how they gain the target heart rate and how they can sustain it (_i.e._, by running, jumping, _etc._). Groups compete against each other to see how well they can succeed in meeting the aims of the mission. A game involving a collective heart rate is an example of a collective sensor data augmented experience.
## 3 Results
### Passive Experiments  
In order to ascertain the health status of the NSCs, we performed experiments using 8-well Petri dishes from Kiwi (Airbus). We have called these passive experiments, because the cells remained within the same culture medium for 45 days (counting from handover to handover), having spent a total of 39.3 days in space from launch to splashdown of the dragon vehicle and the rest of the time in transit. Upon return, the cells were harvested from the hardware and seeded on poly-d-lysine. After the recovery period, the NSCs were seeded in Seahorse Bioscience XFplates (Agilent) to assess their cellular bioenergetic status. The oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR, a measure of glycolysis) were both higher in the NSCs exposed to the space environment as compared to the ground control cells, indicating a higher metabolic state (Figure 3). These measurements were performed one week after the NSCs returned to Earth. Therefore, one can infer that NSCs \"remember\" having been in microgravity. Alternatively, re-adaptation to 1 G may have prompted higher energetic needs.  
Figure 2: **Human NSCs proliferate in space**. Triple immunofluorescence performed on cells adhered to the mesh-cell carrier. A to D show NSCs that proliferated while in space. E to H show views of ground control NSCs. (**A**) The PSA-NCAM antibody labeled most but not all cells. (**B**) Cells expressing nestin as seen by the intensity of the marker in all NSCs. (**C**) Different intensities of BrdU-labeled cells, some organized in rows (arrowheads) or as dividing cells (arrows) as shown in the insets. (**D**) Most cells expressed BrdU and nestin and a subpopulation colocalized with PSA-NCAM. (**E**) PSA-NCAM was not expressed by the NSCs grown in 1 G (ground control). (**F**) Some cells expressed nestin at different intensities and others were negative. (**G**) Only some cells intensely expressed BrdU (arrowheads), while most had a faint expression (open arrowheads) or were negative. (**H**) The merged image shows that there were less cells in 1 G (ground controls) with respect to the cells sent to space. The BrdU incubation was for 72 h for all cells in automated units. (Bar = 50 \(\upmu\)m).
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 1 Introduction
### Haptics  
The simulation of the sense of touch falls under the field of Haptics. As [PERSON] et al. [18] describe, the word \"haptics\" (from the Greek work haptikos or to be \"able to come into contact with\") refers to the capability to sense a natural or synthetic mechanical environment through touch. In other words, Haptics can be described as the means whereby information is conveyed through touch [19]. Haptics is comprised of both kinesthetic and cutaneous (tactile) feedback. Kinesthetic feedback refers to the feeling of motion and relates to sensations originating in muscles, tendons, and joints [20], and cutaneous feedback to the stimuli detected by low-threshold mechanoreceptors under the skin within the contact area [21]. In other words, given the diverse mechanical receptors on human skin, the haptic system allows us to distinguish the shape, texture, and other surface properties when we interact with the real world [22]. In contrast to the visual and auditory systems, capable of providing highly precise spatial and temporal information, respectively, the touch system is particularly effective at processing material characteristics of surfaces and objects [23]. Haptics encompasses the sensory feedback received through tactile sensations, proprioception (awareness of body position and movement), and kinesthetic feedback (sensations of motion or strain in muscles, tendons, and joints) during physical interactions with objects or environments [24; 25; 26]. In educational and skill acquisition contexts, understanding Haptics is crucial for learning tasks that involve physical manipulation, such as fine motor skills, hand-eye coordination, and spatial awareness [27]. A complete discussion regarding the perception of touch is beyond the scope of this paper. However, [PERSON] and [PERSON] [28] provide a thorough tutorial on touch within the context of a fully active human observer that is written specifically for those outside the discipline interested in an introduction to the field.  
Accurately simulating touch within virtual environments is difficult, as it requires a realistic perception of pressure, temperature, position, movement, texture, and vibration through actuators embedded in high-end haptic devices that are cost-prohibitive for many applications. Moreover, in contrast to the other senses, touch is not localized to a specific body part(s) (e.g., the eyes or the nose), but is rather distributed across the entire body through the skin, and in the joints, muscles, and tendons [29]. Given the many difficulties associated with simulating the sense of touch in the virtual domain and the limited availability of cost-effective devices to do so, haptics is often ignored, particularly when considering iVLEs that predominantly focus on the cognitive and affective learning domains, often ignoring psychomotor skills development altogether as a result.
# Study of the Photo-Response of Doped GaAs with Aging
###### Abstract  
The aging of semiconductor materials is a topic of current interest. We studied the photo-response of epitaxial samples of GaAs doped with Ge and Sn up to 1 \(\times\) 10\({}^{19}\) atoms cm\({}^{-3}\). These samples were stored in a dry and dark environment for 26 years. We realized photoluminescence measurements at different temperatures and photoreflectance spectra at 300 K in three periods: 1995, 2001 and 2021. We found that environmental oxygen formed defects in GaAs, leaving lattice vacancies that provoked changes in the optical photo-response. In addition, we found that the vacancy concentrations could be as large as 5 \(\times\) 10\({}^{17}\) atoms cm\({}^{-3}\) over the 26 years. In this work, we demonstrate that the aging of semiconductor materials occurs even when they are not used within a functioning circuit, with the changes being greater when the material is not doped. Knowing about the aging of materials is important for the industry, particularly for the semiconductor industry, because aging-induced deterioration influences prices and guarantees.  
aging; optical response; doped GaAs; defects +
Footnote †: journal: Applied
## 2 Preliminaries
### DDPG Algorithm  
The DDPG algorithm [40] is a mature RL algorithm that can be used to deal with continuous action control. The idea of experience replays and random sampling are incorporated into the DDPG algorithm, and the process of establishing the target network is, also, considered. The DDPG is an actor-critic algorithm, where the basic framework includes actor networks and critic networks. The actor networks are used for policy search, while the critic networks are used for value function approximation. As shown in Figure 3, the DDPG algorithm includes two actor networks and two critic networks.  
The online actor network, \(u\), is used to output an action \(a=u(s\mid\theta^{u})\), according to the current state \(s\). The online critic network, \(Q\), is used to evaluate the action and obtain a \(Q\)-value \(Q(s,a\mid\theta^{Q})\). In the above, \(\theta^{u}\) and \(\theta^{Q}\) represent the parameters of the online actor network and online critic network, respectively. In addition, to follow the network update process, the DDPG also includes a target actor network \(u^{\prime}\) and a target critic network \(Q^{\prime}\).  
Figure 3: Framework of the DDPG algorithm.  
Figure 2: Wheel rotational motion.  
The critic network is updated, by minimizing the loss function between the target and online networks. The loss function is given as follows:  
\[L\Big{(}\theta^{Q}\Big{)}=\frac{1}{N}\sum_{t}^{N}\Big{(}y_{i}-Q\Big{(}s_{i},a_{i} \mid\theta^{Q}\Big{)}\Big{)}^{2}, \tag{6}\]  
where \(N\) is the number of mini-batches, and \(y_{i}\) is the target value, defined as  
\[y_{i}=r_{i}+\tau Q^{\prime}\Big{(}s_{i+1},u^{\prime}\Big{(}s_{i+1}\mid\theta^{ u^{\prime}}\Big{)}\mid\theta^{Q^{\prime}}\Big{)}, \tag{7}\]  
where \(\tau\) is the discount factor, and \(\theta^{u^{\prime}}\) and \(\theta^{Q^{\prime}}\) are the parameters of the target actor network and target critic network, respectively. The actor networks are updated according to the following formula:  
\[\
abla_{\theta^{u}}u\mid_{s_{t}}\approx\frac{1}{N}\sum_{t}^{N}\Big{[}\
abla_{a _{t}}Q\Big{(}s_{t},a_{t}\mid\theta^{Q}\Big{)}\
abla_{\theta^{u}}u(s_{t}\mid \theta^{u})\Big{]}. \tag{8}\]  
Further, the target networks are updated as follows:  
\[\begin{split}\theta^{Q^{\prime}}\leftarrow\lambda\theta^{Q}+(1- \lambda)\theta^{Q^{\prime}},\\ \theta^{u^{\prime}}\leftarrow\lambda\theta^{u}+(1-\lambda)\theta^ {u^{\prime}},\end{split} \tag{9}\]  
where \(\lambda\) is a configurable constant coefficient, such that \(\lambda\ll 1\).  
During the learning process of DDPG, the agent can only select actions based on the randomly generated policy, then optimize the policy through trial and error. This action may lead to sub-optimal policy actions in the overdrive system. Therefore, the agent requires the help of knowledge-assisted learning methods, to guide the learning direction in the learning process.