esa-sceva/satcom-chunk-collection huggingface dataset:
# Microscopic Multiple Fatigue Crack Simulation and Macroscopic Damage Evolution of Concrete Beam
## 5 Conclusions  
The numerical model developed in this paper is directly based on microscopic mechanism of concrete fatigue. Thus it can simulate the fatigue growth of multiple microcracks in concrete, as well as the macroscopic damage evolution. The numerical method is applied to three-point bending beams. Results clearly show that, no matter from macroscopic or microscopic view, fatigue rupture of beams endures three stages: (1) Microcrack growth and coalescence, (2) the formulation of macroracks and stable fatigue damage evolution, and (3) unstable crack growth and the final fracture of concrete beam.  
It is also observed from the simulation that different stress levels of fatigue load can lead to different failure patterns. At low stress level, the rupture of beam is mainly caused by one (or several) macrocrack and damage is highly localized. For high fatigue stress situation, macrocracks appear in large quantities, mostly scattering over the entire area. The computed results are consistent with experimental observation in published literature.  
The numerical model could be used to calculate S-N curve. The macroscopic damage evolution of concrete beams is achieved. Based on the results of damage evolution, structural fatigue assessment is also made possible.  
Although the present work is about plain concrete, the method can also be applied to other types of concretes, such as high-performance concrete or fiber reinforce concrete, or even masonry materials and structures, as long as the fatigue failure is mainly caused by the growth of microcracks. Last but not least, it shall be pointed out that the numerical modeling is currently stipulated to 2D problems. Computational cost will be substantially added once it comes to 3D. Computational efficiency shall be further improved in our forthcoming work.  
Conceptualization, B.W., K.T. and Z.L.; methodology, code and software, B.W.; data analysis, K.W.; writing--original draft preparation, B.W.; writing--review and editing, K.T. and B.W.; supervision, Z.L.; funding acquisition, Z.L. and K.T.  
This research was funded by National Natural Science Foundation of China under grant No. 11872278 and 51878154, and by the National Program on Major Research Project under grant No. 2016 YFC0701301.  
The authors declare no conflict of interest.
# Feedback Devices for Cardiopulmonary Resuscitation: A Narrative Review
## 2 Classification of Feedback Devices for Cardiopulmonary Resuscitation
### Application Status and Effects of Cardiopulmonary Resuscitation Feedback Devices  
CPR feedback devices are widely utilized in CPR training. In studies investigating CPR training, it has been observed that training with a feedback device enhances CPR competency and overall quality more significantly than traditional CPR training methods [35]. Furthermore, the utilization of feedback devices can augment skill acquisition and overall performance in cardiopulmonary resuscitation [36]. In a comparative study evaluating the impact of device-based real-time visual feedback against traditional instructor-led chest compression skills training and self-efficacy, the group guided by the QPCR feedback device demonstrated enhanced accuracy in compression depth and frequency, surpassing the results achieved through traditional methods by 24.47% and 19.63%, respectively [37]. It has been shown that the TrueCPRTM device effectively enhances resuscitation skills among nursing students. Participants who used TrueCPRTM during training exhibited an 18% increase in adequate compression rate and a 23% increase in full chest release [18].  
From a practical standpoint, the combination of current CPR audio-visual feedback devices and smartphones offers a more accessible feedback tool for emergency situations. By utilizing the data collected through a smartphone's built-in accelerometer, real-time assessment of compression depth and frequency can be achieved, aligning closely with the actual values. This enables accurate real-time feedback on CPR quality through a dedicated CPR mobile application [38].
# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 3 Materials and Methods
### Network Architecture  
#### 3.1.1 EMTCNN Augmentation  
MTCNN is a cascaded structure composed of relatively light CNNs including a proposal network (P-Net), refinement network (R-Net), and output network (O-Net). Since MTCNN has a pyramid structure, it extracts face candidate regions from images at various scales and uses them as input images. This allows the model to learn different image scales effectively. The facial landmark extraction process is divided into three steps; each step is performed on a different network. In the first step, the facial region candidates are extracted through the P-Net. Then, non-maximum suppression (NMS) [45] is used to remove highly overlapped candidates. In the second step, the filtered candidates are refined through the R-Net and NMS. In the last step, one facial region and its five facial landmark points are produced through the O-Net.  
To extract the 68 most popular facial landmark points, instead of 5, we extend the O-Net of the MTCNN model by using two \(3\times 3\) convolution layers and increasing the number of convolution filters. This allows the second \(3\times 3\) convolution layer to see 5 x 5 regions with fewer parameters than using the \(5\times 5\) convolution layer [46]. Nevertheless, as the network expands, the number of parameters naturally increases, so there is a limit to expanding the network. To avoid this, we reduce the number of facial region candidates generated by the P-Net by ignoring small facial regions. Even though there is a trade-off between processing time and detection accuracy when manipulating the O-Net, there is still a limit to the extraction performance that can be achieved just by changing the parameter numbers while maintaining the real-time processing property. Hence, to improve the accuracy of real-time facial landmark point extraction without significantly increasing the number of parameters, we augment the EMTCNN model with two convolution techniques--dilated convolution and CoordConv. Figure 1 shows the structure of our augmented EMTCNN.  
Figure 1: Augmented EMTCNN with modified O-Net.  
Table 1 shows the number of parameters of major CNN models used for facial landmark extraction [40; 41; 42]. Although the numbers of parameters are increased a lot by expanding and augmenting the existing MTCNN, they are still small compared to those for other networks.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method
### Amplitude Synthesis of Antenna Array Radiation Pattern under Constraints
#### 3.1.1 Ten-Element Antenna Array
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elements** & **1** & **2** & **3** & **4** & **5** \\ \hline
**Level 1** & \(-19.02\) & \(-19.63\) & \(-19.92\) & \(-20.83\) & \(-21.18\) \\
**Level 2** & \(-20.62\) & \(-20.17\) & \(-20.95\) & \(-21.03\) & \(-20.82\) \\
**Level 3** & \(-21.61\) & \(-21.46\) & \(-20.38\) & \(-19.39\) & \(-19.24\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Response table (in decibels) after the first iteration, i.e., element levels (dB). By comparing the results obtained with those of PSO (Particle Swarm Optimization) (see Figure 4), we observe the following:  
* A gain of 0.6 dB in terms of secondary lobe minimization. * A convergence speed of 80 iterations for the Taguchi method. * The actual time required for our digital optimization tool is approximately 10 s.  
With this method, we aim to minimize the error criterion; this amounts to minimizing the difference between the desired pattern (template) and the pattern obtained through synthesis.
## 5 Performance Evaluation
### Experimental Results
The first one made use of NetBricks to implement a specific VNF, and aimed to not only quantify the memory and storage footprint of a NetBricks application, but also to evaluate the boot time of the relevant microVM. All these tests were carried out by deploying the NetBricks VNF within a Kata container, and by using QEMU [48] and Firecracker [37; 40] hypervisors to evaluate differences. We stress that this is an original contribution of our work. To the best \begin{table}
\begin{tabular}{l l l} \hline \hline
**Configuration** & **Server \#1** & **Server \#2** \\ \hline CPU & Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40 GHz & Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30 GHz \\ \hline RAM & 128 GB @ 2133 MT/s & 64 GB @ 2133 MT/s \\ \hline Disk & 280 GB (145 MB/s write speed) & 280 GB (145 MB/s write speed) \\ \hline Network interfaces & 1 \(\times\) 10 Gbps, 4 \(\times\) 1 Gbps & 1 \(\times\) 10 Gbps, 4 \(\times\) 1 Gbps \\ \hline VNF packages & - _Kafka_ broker [45] and _ZooKeeper_ & _Kafka_ publisher, based on _Sangrenel_[46] \\  & - NetBricks packet generator: _packet-test_ & \\ \hline Virtualization & - _Kata Containers_[36; 47] as serverless container & \\ software & runtime with _Docker_ & \\  & - _Firecracker_[37; 40] or _QEMU_[48] as microVM & \\  & hypervisors & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Configuration of servers used in the testbed. of our knowledge, the experimental deployment of NetBricks in Kata containers is a novel solution. The second experiment focused on using a VNF implemented with a standard package, but again deployed within a Kata container. Firecracker and the classic QEMU hypervisors were used to evaluate differences. This test is of paramount importance, since it is not realistic to think of substituting all VNFs with specialized NetBricks applications, and thus it is important to see if deploying a standard VNF (in our case, a Kafka broker) in a Kata + microVM environment suffers from significant limitations, with respect to the requirements reported in Table 2.  
Although these experiments were executed on single instances of sample functions and did not include integration with an orchestration platform, they worked well for our purposes. In fact, our aim was to show that the serverless functions realized with the proposed framework had minimal overheads, in terms of resource footprint. Thus, they are suitable candidates to be instantiated in many replicas in the same edge server, with a startup time compliant with mMTC requirements for the analyzed IoT use cases. In addition, we also showed that data plane KPIs of a single instance are compliant with most service requirements, also beyond those of plain mMTC services.
# Analysis of Salt Mixture Contamination on Insulators via Laser-Induced Breakdown Spectroscopy
## 2 Experiments  
The experimental setup is depicted in Figure 1. The LIBS system consisted of a Q-switched laser (Beamtech Nimma-900), where the laser pulse duration was 10 ns, shot-to-shot energy variation (RMS) \(\leq\)1%. We used an Avantes optical fiber spectrometer (190 nm to 650 nm) with 6 channels, the spectral resolution was 0.05 nm, and a Stanford Research System Delay Generator SRS DG645. The laser was focused on the surface of the sample by a lens with a focal length of 100 mm. It was ablated operating at a wavelength of 1024 nm. The laser energy of the Nimma-900 could be adjusted from 1 J to 900 J. Laser fluence was determined at 1.9 J/cm\({}^{2}\) for 38 mJ, 3.8 J/cm\({}^{2}\) for 76 mJ, and 6.4 J/cm\({}^{2}\) for 128 mJ.  
All chemicals used in the experiments were acquired from Aladdin and were of analytical reagent grade. Mixed samples of Na\({}_{2}\)CO\({}_{3}\) and NaHCO\({}_{3}\) were prepared with a series of Na concentration gradients. The total mass of each sample remained constant, and the mass of Na\({}_{2}\)CO\({}_{3}\) and NaHCO\({}_{3}\) to be mixed was calculated separately according to the total concentration of Na set by the concentration gradient and the various mixing ratios of Na in Na\({}_{2}\)CO\({}_{3}\) and NaHCO\({}_{3}\). The total concentration of Na for each sample varied from 27.4% to 43.4% because the mass percentage of Na in Na\({}_{2}\)CO\({}_{3}\) was 43.4%, and that in NaHCO\({}_{3}\) was 27.4%. Similarly, the quantity of CaSO\({}_{4}\) and CaCO\({}_{3}\) to be mixed in each sample was obtained using the above method, and the Ca mass fraction of the mixtures ranged from 29.4% to 40%, corresponding to the mass percentages of Ca in CaSO\({}_{4}\) and CaCO\({}_{3}\), respectively. Varying ratios of Na\({}_{2}\)CO\({}_{3}\), NaHCO\({}_{3}\) and CaSO\({}_{4}\), CaCO\({}_{3}\) were prepared and mixed, as shown in Table 1. The ratios of the Na and Ca mass fractions of Na\({}_{2}\)CO\({}_{3}\):NaHCO\({}_{3}\) and CaSO\({}_{4}\):CaCO\({}_{3}\) are also shown in Tables 1 and 2. Pellets were formed from each sample by using a circle pellet forming press with a diameter of 8 mm under 5 tons of force, which improved the LIBS ablation of the sample.  
were selected randomly for each sample, and the spectral data for each point were obtained after 10 rounds of 10 Hz continuous ablation. The averaged background spectrum was subtracted from the processed spectral data, and the resulting spectra were averaged. The NIST database was used to determine spectral intensities for Ca, C, Na, etc. [19]. The above analyses were repeated for the two groups of samples, #1-1 through #1-6 and #2-1 through #2-6, at a laser fluence of 3.8 J/cm\({}^{2}\) and 6.4 J/cm\({}^{2}\), respectively. Due to the influence of Na matrix effects, LIBS analysis at 1.9 J/cm\({}^{2}\) was added for the analysis of samples #1-1 through #1-6. The elements were measured, as shown in Table 2.
## 4 Discussion  
Figure 3 shows the fuzzy mean and the final linguistic judgments for each dimension (lean, agile, resilient and green), divided by indicator (procurement, production, distribution and reverse logistic).  
KPIs in the lean dimension show very high performance in any activity: procurement, distribution and reverse logistics have fuzzy mean equal to 0.9, with a linguistic judgment equal to \"very high\"; while production has a fuzzy mean equal to 0.65 and a linguistic judgment equal to \"high\".  
The agile perspective shows a very heterogeneous distribution: procurement and distribution show low values (FM = 0.25, ratings \"low\" linguistic judgment), while Production reaches a FM of 0.65 (\"high\") and reverse logistics stands out with a FM of 0.77 (\"very high\"). As for Resilience, the results are more homogeneous: Procurement and Production reach FM values equal to 0.90 (\"very high\"), distribution has FM equal to 0.65 (\"high\"), while Reverse Logistics stands at 0.76 (\"very high\").  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Indicator** & **Truth Value** & **Xk** & **FM** & **Final Linguistic JudgmentI** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Test**  
Finally, the Green dimension highlights high values for production (around 0.68) and reverse logistics (0.75); while procurement and distribution have \"low\" values, around 0.25 and 0.31 respectively.
## 4 Scheduling Optimization Framework
### State Representation  
The mechanism conducts decision-making at epochs \(e_{p}\). These epochs are: (a) after a production activity, (b) after the maintenance of the system, (c) after the system repair, and (d) when the system is idle. In order to make a decision, the mechanism receives the present state of each manufacturing facility at \(\epsilon_{p}\). In this regard, the mathematical formulation of each manufacturing facility is given as follows:  
\[\phi(t)\leftarrow(\delta(t),\sigma(t),\pi_{a}(t),\pi_{e}(t)) \tag{1}\]  
Figure 1: The presented RL/ad hoc production and maintenance control mechanism.  
In the expression above, \(\delta(t)\in[0,1,2,\ldots,o]\) refers to the degradation of the manufacturing facility, \(\sigma(t)\in[downtime,working,idle,maintained]\) denotes the status of the manufacturing facilities, and \(\pi_{a}(t),\pi_{e}(t)\in[0,1,2,\ldots,S_{max}]\) represents the inventory of standard and faulty goods, respectively. Furthermore, it does not perform decision-making if any machine ceases operation, or \(S_{1}\) and \(S_{2}\) reach their maximum capacity.
## 4 Proposed CLCQ Protocol
### Selection of Forwarding Candidate Set  
This section will examine the process of selecting the forwarding candidate set in detail. By comprehensively considering factors such as node depth, energy, and buffer  
Figure 6: Protocol framework.  
state, we will further filter out eligible forwarding nodes from the neighboring nodes. Only nodes that meet the specified criteria can be selected as the next hop.  
In UACNs, the neighbor set of node \(n_{i}\) is defined as  
\[Neighbor_{i}\ =\ \{n_{i1},n_{i2},n_{i3},\ldots,n_{im}\} \tag{11}\]  
where \(m\) represents the number of neighbor nodes of node \(n_{i}\). If node \(n_{j}\) satisfies Equation (12), then \(n_{j}\) belongs to the forwarding candidate set of \(n_{i}\).  
\[candidateSet\ =\ Subset_{1}\cap Subset_{2}\cap Subset_{3} \tag{12}\]  
\[Subset_{1}\ =\ \{n_{j}\subseteq Neighbor_{i}\ |\ depth(n_{j})\leq depth(n_{i})\} \tag{13}\]  
\[Subset_{2}\ =\ \{n_{j}\subseteq Neighbor_{i}\ |\ E_{\text{res}}(n_{j})\geq 0.2E _{init}(n_{j})\} \tag{14}\]  
\[Subset_{3}\ =\ \Big{\{}n_{j}\subseteq Neighbor_{i}\ |\ Buffer^{j}_{now}\leq 0.8 Buffer^{j}_{ \text{max}}\Big{\}} \tag{15}\]  
To ensure that packets are transmitted towards a decreasing depth, nodes with a depth less than or equal to that of node \(n_{i}\) are first selected from the neighbor nodes according to Equation (13), forming a subset \(Subset_{1}\). Furthermore, in order to prevent certain nodes from depleting their energy prematurely and creating routing holes, neighbor nodes with less than 80% of the initial energy are filtered out according to Equation (14), which helps extend the lifespan of the network. Concurrently, to alleviate network congestion and enhance packet transmission efficacy, only nodes whose current buffer length is not more than 80% of the maximum buffer length are selected according to Equation (15). Finally, the intersection of the aforementioned subsets is selected as the final forwarding candidate set. However, if the forwarding candidate set is empty, this article proposes that the forwarding candidate set be assigned all neighbor nodes of the current node \(n_{i}\), denoted as \(Neighbor_{i}\). This approach ensures uninterrupted packet transmission.
## 4 Simulations
### The Impact of Pre-Exponential Factors  
Figure 14 shows the oil recovery results under different pre-exponential factors. As shown in Figure 14a-c, during the oxidation process, with an increase in the pre-exponential factor for light oil burning, the early daily oil production increased but later gradually decreased. In the case of heavy oil burning and coke oil burning, an increase in the pre-exponential factor led to a decrease in early daily oil production, but this reduction stopped at a certain point. The changes in the pre-exponential factors of these three reactions had a relatively small impact on the final oil recoveries.  
Figure 13: Oil recovery results under different activation energies: (**a**) light oil burning; (**b**) heavy oil burning; (**c**) heavy oil cracking; (**d**) coke oil burning.  
According to the results shown in Figure 14c, the variation in the pre-exponential factor for heavy oil cracking had a significant impact on ISC, with different effects on the recovery rates. When the activation energy was greater than 4.17 \(\times\) 10\({}^{9}\) (kPa-s)\({}^{-1}\), an increase in the pre-exponential factor for heavy oil cracking led to an increase in early daily oil production in simulations. However, as the reaction progressed, a declining trend was observed in the final oil recoveries.  
This is because, with a constant activation energy, a smaller pre-exponential factor resulted in a lower frequency of molecular collisions during the reaction. Although the early-stage reaction was slower, it allowed for a more complete reaction, leading to a higher final oil recovery. Conversely, an excessively small pre-exponential factor could cause insufficient fuel supply in the HTO phase, resulting in slow oil recovery. On the other hand, when the pre-exponential factor was large, the numerical simulation process accelerated the conversion of reservoir crude oil into coke, burning a significant amount of crude oil and ultimately reducing the oil recoveries.  
Therefore, when performing numerical simulations for heavy oil ISC, the pre-exponential factor for heavy oil cracking should not be excessively large or small. The optimal pre-exponential factor value was around 4.17 \(\times\) 10\({}^{9}\) (kPa-s)\({}^{-1}\). This value ensured a thorough reaction without burning too much crude oil, providing sufficient fuel for the HTO stage.
## 5 Implementation and Results
### IEEE 34 Bus Test System
Figure 4: IEEE 34 bus Routing Repair Crew (RRC) schedule. Figure 5: Sensitivity analysis on IEEE 34 bus RRC schedule. The optimal resource number for each crew is shown in Figure 6. The results show that the tasks of Crew 2 are very low. Hence, a lower number of resources is assigned to Crew 2. Regarding Section 2, the EDRP can effectively reduce the outage cost in the outage management problem. In this paper, the effects of the EDRP on the outage management problem were investigated, and the results are compared with each other. The results for the outage management cost with and without the EDRP are shown in Figure 7.  
Referring to Figure 7, the outage cost of the distribution system is affected by the EDRP, and the outage cost is significantly decreased. In this study, the outage load points that are directly affected by faults are not considered in the EDRP. Some damage components are repaired by crews, and the load points in that area are permitted to contribute to the EDRP. According to this fact, the comparison of with- and without-EDRP scenarios shows that the decrease in outage cost in Step Times 4 and 5 is much higher than that in Step Times 1, 2 and 3. An important goal of the outage management system is to return outage loads to the grid. The served load in each time step is shown in Figure 8. Referring to Figure 8, it can be concluded that the EDRP is a very effective program for increasing the served load. Figure 6: The optimal number of resources is assigned to each crew in the 34 bus case. Figure 7: The effect of EDRP on the outage cost in the 34 bus case. The optimal priority list of the damaged components for repair by crews is shown in Table 4.  
Referring the Table 4, crews can't repair any outage in the first step time (This is shown by \"*\" in the first row of Table 4). Also, M4 is the priority damaged component that is repaired, and, in the end, all the faults are cleared from the distribution power system.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 2 Materials and Methods  
An actual fan blade from the second fan stage of the _Pratt&Whitney J78D-9_ turbofan engine was used as a rotor for the experimental tests. Fourteen customized lead zirconate titanate (PZT) flexible actuators, based on PIC 255 material, were evenly distributed in seven pairs along the pressure surface (PS) of the blade, as depicted in Figure 1. The materials and geometric specifications of both the blade and PZT actuators are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PZT Material** & **Density (kg/m\({}^{3}\))** & \((\mathbf{d_{32}},\mathbf{d_{33}},\mathbf{d_{15}})\) (10\({}^{-12}\) C/N)** & **Poisson Ratio** \\ \hline PIC 255 & 7800 & \((-180,400,550)\) & 0.34 \\ \hline
**Blade Material** & **Density (kg/m\({}^{3}\))** & **Elastic Modulus (GPa)** & **Poisson Ratio** \\ \hline Ti-6 Al-4V & 4430 & \(113.8\) & 0.342 \\ \hline
**Dimension** & **Length (m)** & **Width (m)** & **Thickness (m)** \\ \hline PZT actuators & 0.035 & 0.025 & 0.0005 \\ \hline Blade & 0.245 & 0.075 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material and dimensions of the blade and PZT actuators.