esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Full Device Fabrication
### Ge PD Integrated with SU8 ML  
The SU8 MLs are directly processed on an 8-inch Ge PD device wafer, which require no extra carrier wafer or molding. During the fabrication, the first step is to spin-coat the 1 st SU8 layer on the device wafer, followed by a soft bake. Then, the 2 nd SU8 layer is spin-coated on top of the 1 st SU8 layer, followed by another soft bake. The next step is to define the targeted ML dimensions using a contact aligner for the process of photolithography. Since SU8 works as a negative photoresist, exposed regions would crosslink through the exposure to UV light. With a proper device design and process tuning in UV dosage, SU8 volume, baking temperature, and so on, the exposed SU8 region can form a half-ball lens shape standing a pedestal. The unexposed SU8 region can be removed by the developer. After the shape of the SU8 ML were confirmed, we moved to the last step, i.e., a hard bake to solidify the SU8 ML. Note that since SU8 is removable before the final hard bake, the Ge PDs beneath the SU8 remain intact if the MLs are removed due to incorrect processing. This advantage, i.e., the SU8 ML process can be re-worked when a process goes wrong before the final hard bake, gives our ML a better competitive edge over MLs fabricated by other approaches. Figure 9a shows the SEM image taken at a 45-degree angle, showing an ML integrated on a Ge PD. Note that the abnormal pattern shown on top of the ML region is caused by the charging effect of the electrical beam, since we would like to derive the actual size of the ML and so the ML is not protected by Pt coverage. The side view of the SU8 ML can be seen in Figure 9b, with a diameter of 54 \(\upmu\)m enlarged aperture and a total height of about 65 \(\upmu\)m. Figure 9c shows the OM image of four-channel Ge PDs with integrated SU8 MLs. These results confirm that our integration process is promising for mass production on an 8-inch Si substrate.  
Figure 8: (a) Schematic plot of the Ge p-i-n PD. (b) Cross-sectional SEM image of the fabricated Ge p-i-n PD showing a via on top of Si and a via on top of Ge. (c) Top-view OM image of the fabricated four-channel Ge p-i-n PD array.
# Method for Volume of Irregular Shape Pellets Estimation Using 2D Imaging Measurement
## 3 Results
### Estimation of Pellet Volume by Dividing Them into Circular Layers
Respectively, the average of absolute error--0.153 cm\({}^{3}\) and \(\sigma=0.171\). To evaluate the results produced during the experiment, it can be said that applied method of pellet evaluation of analysis of two-dimensional image of a pellet by dividing it into limited areas of cross-section is reliable and assures low MRE (Mean Relative Error) Equation (6):  
\[\delta=\frac{|0.15|}{|13.8|}\times 100\%\approx 1.109\%. \tag{6}\]  
It was determined that the applied method of pellet shape evaluation is effective with regular geometric shape of a pellet, which rarely matches real irregularities limited by the area of an angled pellet. For evaluation of the shape, 50 samples of same material (monoammonium phosphate) were randomly taken at different point of time of production. Figure 11 provides two dimensional images of scanned irregularly shaped pellets together with statistical graphs of distribution of irregularly shaped pellets in a sample, according to pellet's roundness. After evaluating the samples taken in course of the production, it was determined that the statistically most common irregularities of pellet shape were convexity, flatness of shape and various notches. Convexity of shape can be evaluated using the Equation (5) volume calculation formula, where different layers of pellet are treated as circles. However, in such cases each irregularity limited by the region of the pellet silhouette should be positioned in an area limited by \(>\frac{3}{4}\) of the pellet cross-section (Figure 12). When the pellet is flat, evaluation of its volume according to circles, which the cross-section consists of, creates discrepancies of measurements. Figure 10: Measuring of volume of formed pellet model based on imaging method of processing of digital images: (**a**) ratio between results produced by approximation of ellipsoid volume method and water displacement method; (**b**) ratio between results produced by approximation of pellet volume by dividing pellet’s volume into layers of circles and water displacement method. The cross-section of flat pellets or pellets with notches with respect to longitudinal axis of pellet is similar to the area limited by ellipse. According to previously presented methods of pellet volume evaluation, where separate layers of pellets are evaluated as circles, it is likely that measurement discrepancy will appear between measurement results, when they are compared to data received using control equipment. In the case of evaluating irregularly shaped pellets, calculation of circle area in place of a notch in the pellet decreases the area of its cross-section and this directly affects the shrinking of pellet volume.
## 3 Results and Discussion  
### Research into the Process of Combustion of 63.5 wt.% NiB + 36.5 wt.% Ti Powder Mixture Samples  
The process of combustion taking place in the samples of a 63.5 wt.% NiB + 36.5 wt.% Ti mixture is shown in Figure 2. It is evident that the synthesis process in the samples proceeds via the spin combustion mechanism. It has been shown that on the side surface of the compacted sample, a combustion source emerges (area 1), which then travels via a spiral trajectory (area 2) and makes a single revolution on the sample's surface (area 3). As the combustion source travels, the adjacent layer of the samples is heated and a new combustion source is formed that continues moving via a spiral trajectory.  
Figure 1: (**a**) Image of the constant pressure reactor, (**b**) a schematic of sample placement in the reactor: 1, the sample; 2, the reactor rack; 3, VR–20/5 thermocouples (O–500 \(\upmu\)m); 4, the igniting molybdenum spiral; and 5, the igniting layer.  
Spin combustion is initiated due to (1) low heat dissipation in the course of an exothermic reaction of the initial components and (2) the presence of endothermic synthesis processes in the system [1,4]. In this case, the combustion reaction is not localised in the flat layer of the sample, but rather in the zone with the largest reaction surface of the initial components. A local combustion source is formed, and it moves around the sample via a spiral trajectory.  
Figure 3 shows a thermogram of combustion of the samples of the Ti-NiB powder mixture.  
The peak of the thermogram (area 1) indicates an exothermic reaction of the initial components taking place in the Ti-NiB mixture sample. The reaction is accompanied by the dissipation of a great amount of heat. The apex of the peak corresponds to the system combustion temperature, which is 1650 \({}^{\circ}\)C. After the peak, a small decrease in the combustion temperature is observed (area 2). This is caused by intense heat absorption in the adjacent areas of the initial mixture, which is apparently due to endothermic processes taking place in these zones. One of such processes is presumably the incongruent melting of nickel-boron. Heat absorption by the adjacent zones can be the cause of combustion front destabilisation and the formation of a spin combustion wave. Then, temperature levelling throughout the entire layer and further temperature decrease occurs, which is characteristic of the cooling of synthesis products (area 3). Here, in the time range of 8 to 27 s, the SHS materials cool more intensely compared to the time range of 27 to 78 s. This is due to the fact that impurities are emitted in the course of synthesis and they deflect the heat from the sample via convective transfer.
## 4 Discussion  
The purpose of this study was to determine the effects of low-intensity resistance training on a force-matching task and balance in young, healthy men. Two findings seem of particular interest. First, the applied low-intensity resistance training program improved the force accuracy expressed by AE and CE but did not affect the steadiness (variability) of the force reproductions (VE). Second, resistance training did not cause any changes in stabilographic parameters that could account for the improvement in body balance. The results are broadly consistent with the results of our previous research on the effects of high-intensity strength training on force-matching task performance [11]. In that study, we observed that 12 weeks of high-intensity strength training led to a significant reduction in all measured force errors (AE, CE, and VE). Although the present study found that AE and CE decreased after low-intensity resistance training, there was no change in VE. AE reflects the average error from the target and measures performance accuracy, but VE serves as an indicator of the variability of the performance and provides no context for the target [21]. It can therefore be concluded that low-intensity resistance training resulted in the participants being able to reproduce the desired force value more accurately, but it did not affect the repeatability of this task. Based on available knowledge, we are unable to identify potential causes of divergent changes in these capabilities. When analyzing changes in CE in the experimental group, the median CE for both limbs fluctuated between \(-\)40 N and \(-\)50 N before training, so the subjects were generally \"under the target\". After training, the median CEs were 3 N for the right limb and \(-\)17 N for the left, respectively, which were close to zero in both cases. Lower CE values may indicate that the subjects in individual trials were closer to the target, but theoretically they could be very much over or under the target, provided that the number of over- and under-shots was similar. However, the reduction in AE after training seems to confirm the first variant. Therefore, it can be assumed that a decrease in the CE value after training also indicates an improvement in accuracy during the force-matching task. The search for a link between resistance training and postural control yielded two important results. First, EXP did not reveal any effect of time (i.e., of training) on its results. It can therefore be argued that the applied training did not improve subjects' stability
## 6 Evaluation
### Comparison with Existing Automated Sample Construction Methods
#### 6.2.3 Comprehensive Comparison under Android  
In the Android environment, there is a public packet capture tool named Packet Capture. The tool utilizes user certificates to capture packets in man-in-the-middle attack mode but requires root permission after SDK24. During the experiment, we connected the Android device to the PC hotspot and enabled the target application and Wireshark on the device. Based on the traffic captured by Wireshark on the PC hotspot, we compared the two methods in environment S.  
Firstly, we conducted experiments on Packet Capture. Then we also conducted an experiment on A3C-Android, and one pair of typical experimental results is shown in Tables 4 and 5. It should be noted that to ensure the experiment's authenticity, we did not choose to experiment by replaying the traffic but directly using the real traffic. Since Packet Capture software and A3C-Android in Android devices cannot run simultaneously (there is interference), we conducted two groups of experiments under environment S and compared the two tools based on the collection results Wireshark on VPN Server.  
It should be noted that although Wireshark is inefficient and has some errors in sample construction, the A3C-Android VPN server is strictly controlled, so it is feasible to use Wireshark as a baseline for two separate experiments. In addition, although the data used in the two experiments are inconsistent, resulting in their duration being inconsistent, the comparison between Packet Capture and A3C-Android is not strictly quantitative but a semi-quantitative qualitative comparison. However, since this experiment aims to prove the unavailability of Packet Capture tools, the results in the two tables can support this goal.  
Figure 10: System resource occupation comparison of A3C-Linux to Wireshark.  
Packet Capture saves packets according to the session, decrypts the requests at the application layer, and saves the results without using the pcap format. A3C-Android captures raw traffic without the link layer, while Wireshark captures packets with UDP tunnel shells. According to analysis, the more packets captured by the A3C are the ones that failed to be sent, and they are discarded before they reach the PC and can be excluded. However, the decrypted application-layer data captured by Packet Capture is challenging to use. The experiment shows that A3C-Android is the optimal automated sample construction solution on Android.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 4 Experimental Results
### Training and Fine-Tuning 2D CNNs  
Our first task is to specialize the three pre-trained CNN architectures (VGG-11, VGG-16, and ResNet50) for emotion recognition by fine-tuning them with the RAF-DB dataset. These three architectures were pre-trained either on the VGG-Face or ImageNet datasets. For fine-tuning the pre-trained 2D CNNs on the RAF-DB dataset, video frames were resized to \(100\times 80\times 3\), which is the mean dimension of video frames of the target dataset (SEWA-DB). The learning rate was fixed to \(1\times 10^{-5}\) and batches of size 16. Optimization was performed with the Adam optimizer. Different weights were assigned to each class according to the number of samples to deal with the data imbalance found in the RAF-DB dataset. This allows classes with few samples to affect the model's weights to the same extent as classes with many more samples. Moreover, low-level data augmentation, such as rotation, flipping, and highlight variations, can also help improve performance. Although data augmentation cannot bring important information for emotion recognition, it can prevent overfitting on a single sample and improve the model distributions.  
Table 1 presents the performance achieved by the 2D CNNs after fine-tuning on the RAF-DB dataset, where the suffix BN refers to batch normalization layers added to the original architectures after each convolutional layer to improve model convergenceand reduce overfitting. Furthermore, it is indicated for each architecture the dataset used for pre-training and the convolution block (2_1 to 5_1) where fine-tuning starts. In general, most fine-tuned models achieved an accuracy higher than the baseline models [62]. [PERSON] et al. [62] developed two 2D CNNs to analyze action units' detection efficiency on three datasets, including the RAF-DB dataset. The first CNN was based on residual connections with densely connected blocks (ResNet), and the second architecture was a 2D CNN consisting of four convolution layers and three fully connected layers. Such baselines achieved 76.54% and 78.23% accuracies on the RAF-DB test set, respectively. On the other hand, the proposed VGG-16 CNN model pre-trained with VGG-Face achieved 79.90% accuracy on the same test set.  
Other recent works, which employed attention networks, achieved better performance [73,74]. However, the proposed approach did not consider two common problems in face analysis in real-world scenarios: occlusions and pose variations. On the contrary, [PERSON] et al. [74] and [PERSON] et al. [73] addressed these problems using region-based attention networks. Attention modules are used to extract compact face representations based on several regions cropped from the face, and they adaptively adjust the importance of facial parts. Therefore, these models learn to discriminate occluded and non-occluded faces while improving emotion detection in both cases.
## 3 Results
### Secondary Outcome of the Intervention on Clinical Attachment Gain (CAL)
In one study, the clinical attachment gain was slightly larger in the control group compared to the NSAID group (mean difference: \(-\)0.28, confidence interval \(-\)1.23, 0.67), while in another study the opposite was observed with a slightly larger gain in CAL in the NSAID group compared to the control (mean difference: 0.30, confidence interval \(-\)0.92, 1.52). Only one study reported a larger CAL gain in the NSAID group than in the control group (mean difference: 1.58, confidence interval \(-\)3.26, 6.42). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study and Groups**} & \multirow{2}{*}{**Non-NSAID (\(\mathbf{n}\))**} & \multirow{2}{*}{**NSAID (\(\mathbf{n}\))**} & **ACAL in mm** & **ACAL in mm** & **Dose (\%)** \\  & & & & **Non-NSAID (SD)** & **NSAID (SD)** & \\ \hline \multicolumn{6}{c}{\(10\) day-follow-up} \\ \hline [PERSON] et al., 2010 & 20 & 20 & \(-\)0.50 (1.98) & \(-\)1.20 (2.13) & 50 \\ [PERSON] et al.,2007 & & & & 33.3 \\ smokers & \(15\) & 14 & 0.29 (0.51) & 0.01 (1.50) & \\ non-smokers & \(15\) & 14 & 0.01 (1.16) & 0.01 (1.31) & \\ \hline \multicolumn{6}{c}{4 week/30 days-follow-up} \\ \hline [PERSON] et al., 2010 & 20 & 20 & \(-\)1.20 (1.91) & \(-\)1.50 (2.00) & 50 \\ [PERSON] et al., 2008 & 10 & 10 & \(-\)2.23 (1.36) & \(-\)1.95 (0.71) & 100 \\ [PERSON] et al., 2014 & 16 & 16 & \(-\)0.009 (1.02) & 0.00 (1.27) & 50 \\ \hline \multicolumn{6}{c}{12 week-follow-up} \\ \hline [PERSON] et al., 2003 & 10 & & \(-\)1.3 (1.62) & & \\ Nimesulide & & 10 & & \(-\)1.35 (1.59) & 50 \\ Naproxen & & 10 & & \(-\)1.15 (2.02) & 137.5 \\ [PERSON] et al., 2008 & 47 & 54 & & 50 \\ moderate pockets & & & \(-\)0.83 (7.54) & \(-\)1.06 (7.42) & \\ deep pockets & & & \(-\)1.46 (12.82) & \(-\)3.04 (11.83) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clinical attachment level in comparison of non-NSAID and NSAID groups at three different follow-up timepoints. Figure 3: Forest plot comparing clinical attachment (CAL) gain between non-NSAID and NSAID groups.
## 1 Introduction  
The rapid development of nanotechnologies has made it possible to create innovative materials and devices applicable in many new technologies [1; 2]. Considering devices at the micro-scale, some new original multi-D.o.F. (Degree of Freedom) multi-hinge devices appeared in the literature, such as, for example, multi-axis MEMS (Micro-Electro-Mechanical Systems) gyroscopes [3], micro-mechanisms [4], and 3 D.o.F. micro-robots [5; 6; 7; 8], which take advantage of the appearance of new micro-hinges [9; 10; 11] and microrippers [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22].  
However, further miniaturization to the nano-scale is still possible, although there are still several severe concerns [23; 24], and therefore, the study of the action of grasping a nanoparticle by means of a nano-gripper remains difficult under many points of view.  
Since, at the nanometric scale, materials behave quite differently from the bulk, understanding the size and shape dependence of physical properties in nanoparticles is a fundamental step towards the design, fabrication, and assembly of materials and devices with predictable behavior. In particular, the mechanical properties of nanoparticles may be of fundamental importance in controlling their performances in specific applications. Mechanical characterization of nanoparticles presents several challenges, it being difficult to prepare isolated nanoparticles on a substrate and to locate one individual nanoparticle. It is even more challenging to apply the required low loads on the nanoparticles, to measure the small deformations produced and to interpret the experimental data. Nano-indentation and Atomic Force Microscopy (AFM) are two common tools used for the mechanical characterization at the nanoscale level [25; 26; 27; 28].  
Actually, the contacts between nano-gripper jaws and nanoparticles, with possible mechanical characterization of the nanoparticles, is still a rather unexplored field. Some techniques have been applied by means of AFM [29; 30; 31] or nano-indenters [32].  
This investigation presents the feasibility study of a nano-gripper to be used for the mechanical characterization of isolated nanoparticles. Firstly, the synthesis procedure and the morphological characterization by Scanning Electron Microscope (SEM) and Brunauer-Emmett-Teller (BET) method of silica nanoparticles KCC-1 will be presented. Then, the design and fabrication of a nano-gripper will be described, together with some crucial issues that make the process rather difficult and complicated. The contact of the nano-gripper jaws and the KCC-1 nanoparticles is documented by means of SEM imaging. Finally, a series of simulations based on finite element analysis will assess the force-to-voltage relations that govern the comb drives, which provide actuation to the microsystem.
# Surrogate-Based Optimization of a Centrifugal Pump with
## 4 CFD Setup
This approach, unlike the frozen rotor interface, avoids the convection through the pump of non-physical wakes created by the impeller blades and remove the impact of the relative position between impeller blade and solute. Moreover, [PERSON] et al. (2016) demonstrated how this approach provides results in good agreement with unsteady simulations. High resolution schemes are used for the velocity and the turbulent quantities. A mesh convergence study has been performed for the baseline geometry, using three grids with the number of elements ranging from 0.83 M to 3.09 M. In particular, Figure 9 shows a detail of the coarse (top-left), medium (top-right) and fine (bottom) mesh, while Figure 10 shows details of the impeller and solute medium mesh for the baseline geometry. The grid convergence study is summarized in Table 4, where the predicted pressure rise and efficiency are reported. As suggested by the convergence study, the grid with 1.53 M elements (medium) ensures a good compromise between computing time and accuracy of the results, and, therefore, it is chosen for the optimization. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Mesh** & **Elements** & **Elements\({}_{imp}\)** & **Elements\({}_{pol}\)** & \(\boldsymbol{\Delta p}\) [bar] & \(\boldsymbol{\eta}\) \\ \hline Coarse & 0.83 M & 0.10 M & 0.73 M & 3.321 & 0.853 \\ Medium & 1.53 M & 0.26 M & 1.27 M & 3.453 & 0.875 \\ Fine & 0.83 M & 0.50 M & 2.59 M & 3.483 & 0.877 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grid convergence study for the baseline geometry. The quantities of interest are the static pressure rise, \(\Delta p\), and the pump hydraulic efficiency, \(\eta\). Figure 9: Detail of the coarse (**top**), medium (**middle**) and fine (**bottom**) mesh for the baseline design.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 4 Proposed Solution
### Overview of Deep Reinforcement Learning
**Delayed actor update:** Delay here means the time delay of the actor network updating, that is, the actor is updated after the critic is updated several times. This is because the actor network's goal is to achieve the maximum \(Q\) value, and the critic network generating the \(Q\) value is constantly updated. Therefore, it is difficult for the actor networks to find the optimal \(Q\) value, or even get stuck in the sub-optimal value. By introducing a delay, the actor network is updated when the \(Q\) value of the critic network is relatively determined. The actor network is updated by the following equation:  
\[\theta_{i}\leftarrow-\text{min}_{i=1,2}\sum\left(y_{t}-Q_{\theta_{i}}(s^{ \prime}_{t},a^{\prime})\right)^{2} \tag{21}\]  
where \((s_{n},a_{n})\) is the state-action pair from replay buffer and \(n=1,2,\cdots D\), \(D\) is the size of the replay buffer. The target critic network and the actor network are soft-updated by the following equations, respectively:  
\[\theta^{\prime}_{t}\leftarrow\tau\theta_{i}+(1-\tau)\theta^{\prime}_{t} \tag{22}\]  
\[\varphi^{\prime}_{i}\leftarrow\tau\varphi_{i}+(1-\tau)\varphi^{\prime}_{i} \tag{23}\]