esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
studied the mechanical properties of PMMA thin films containing DR1 that are grafted as side-chains in the dark and under illumination with 532 nm-wavelength laser light by nanoindentation experiments. Three experiments were performed to measure and study the viscoelastic properties of the thin film before, during, and after illumination; the creep recovery before and during illumination and constant load; and the transitory behavior of the thin film during indentation under constant load and light modulation. They observed a slight decrease in the elastic modulus, the hardness, and the irreversible viscosity but an increase in the creep coefficient of films upon illumination. They concluded that the transient behavior is related to the population change of the trans and cis isomers in DR1 [33]. In our work, we study the effect of DR1 doping and annealing on the thermomechanical and photomechanical properties of PMMA fibers. Specifically, we measure the temperature, pump light intensity, and polarization dependence of Young's modulus and stress in both unannealed and annealed plain PMMA and DR1/PMMA fibers. Annealing causes stress and polymer chain alignment to relax and is accompanied by observable changes in the fiber's shape. As such, the annealed samples should be closer to an isotropic state than the unannealed ones. Neat PMMA fiber is used as a control that has no contributions from the presence of a dopant. This allows the contributions of photothermal heating and photoisomerization to the photomechanical response to be isolated. Theoretical models are developed to determine the heating contribution. Coupling the experimental results and the theoretical models isolates the dominant mechanism. Finally, the temperature-dependent photomechanical efficiency of the fibers are determined to assess their promise for device applications. Such work complements studies of the photomechanical response of materials due to photoisomerization and their applications [34; 35; 36; 37; 38].
## 2 Development of a Numerical Model
### Material Properties and Process Conditions  
It has been reported that a \(CO_{2}\) laser with a long wavelength (\(\lambda\) = 10.6 \(\upmu\)m) can reduce the pattern effect and produce a relatively uniform temperature distribution within a chip owing to its low photon energy and resultant absorption mechanism [24]. Due to this advantage of the \(CO_{2}\) laser, it is considered as an ideal light source for the annealing of semiconductor devices [25; 26; 27]. In this study, it was assumed that the laser beam for dopant activation annealing has a wavelength of 10.6 \(\upmu\)m.  
In the far-infrared (FIR) light source considered in this study, the absorption mechanism in silicon undergoes a type of intra-band excitation known as free carrier absorption [28]. In this absorption mechanism, the absorption rate increases with an increase in the population of the free carriers in the material. Therefore, increasing the population of free carriers is vital for the efficient use of the laser energy during the LTA process. The population of free carriers can be increased through doping [29] and preheating [30] of the wafer (i.e., by increasing the dopant concentration and temperature of the wafer). According to [PERSON] et al. [31], the absorption coefficient can be expressed as a function of the dopant concentration and temperature, as follows:  
\[\alpha(N,\ T)=1.9\times 10^{-20}(T)^{3/2}\times[N+n_{i}(T)]+2 \tag{7}\]  
where \(N\) is the dopant concentration (cm\({}^{-3}\)), \(T\) is the temperature (K), and \(n_{i}(T)\) is the temperature-dependent intrinsic carrier concentration (cm\({}^{-3}\)), which is given by:  
\[n_{i}(T)=3.87\times 10^{16}\left(\frac{T}{300}\right)^{3/2}\exp\frac{-7020}{T} \tag{8}\]In this simulation, the dopant concentration was assumed to be \(4.5\times 10^{19}\) cm\({}^{-3}\).  
The thermo-physical and optical properties used in the simulation are summarized in Table 1. Temperature-dependent values were adopted for the main thermo-physical properties, in the case the thermal conductivity and specific heat. The spot size of the beam was 400 \(\upmu\)m, and a fixed laser power (105 W) and scanning speed (400 mm/s) were used in the simulation. Several numerical tests were performed according to the process conditions. Details of each test condition are given in Table 2.
# Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
## 4 Performance Analysis
### Experimental Setup  
In our experiment, we set the epoch size to 60 and the batch size to 32. The epoch size indicates the number of passes of the entire training image dataset completed by the model, and the batch size indicates the number of images utilized in one epoch.  
For the developed system, we trained our dataset on two models of EfficientNet, which were EfficientNetB0 and EfficientNetB1, to see the system's performance with different scales. Each model used a different sized image as the input. Therefore, for every model, we resized the images to make them compatable with the model input size. We resized the images for EfficientNetB0 to 224 and EfficientNetB1 to 240. Moreover, the EfficientNet models were loaded with weights pretrained on ImageNet.  
The images that were used during the experiment were taken from the first and the second subsets of our image dataset; the first subset represented the date fruit from the excellent surface quality class, and the second subset represented date fruit from the poor surface quality class. Each subset consisted of 55 images, and each of the images contained 10 date fruits. We split each class into fifty images for training and five images for validation to make a total of one hundred images for training and ten for validation.  
Instead of entering the entire image into our models, we cropped all date fruit inside each image and then resized them to the same as the model input size; after that, we inserted each cropped image as a single training image. Cropping date fruit from images of the first and second classes resulted in 500 cropped images for training and 50 cropped images for validation for each class. The total number for the training folder was 1000 images, and for the validation folder, there were 100 images.  
For YOLOv5, we trained the dataset on two models--YOLOv5n and YOLOv5s. Moreover, these models were trained with the same image input size, which was 640. In addition, we loaded these models with pretrained weights on COCO. Moreover, for YOLOv5 models, in addition to the training and validation folders, we attached the annotation files. These files were constructed by using our annotation algorithm. They were constructed from the first and the second classes, where 1000 date fruits were annotated for training and 100 date fruits for validation.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 3 Practical Application of RF-CNN-LSTM Model
### Model Accuracy Verification  
In this paper, the model is validated by both the mean R\({}^{2}\) accuracy and adjusted R\({}^{2}\) accuracy using the same proportion of the training set and test set. R\({}^{2}\) can illustrate the degree of explanation of the independent variables on the dependent variable, but with the increase in independent variables, R\({}^{2}\) will increase; in order to ensure that the model validates the results, this paper also introduces the adjusted R\({}^{2}\) squared precision. The formula is as follows:  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}} \tag{18}\]  
\[Adjusted\ R^{2}=1-\left[\frac{SS_{res}/(n-k-1)}{SS_{tot}/(n-1)}\right] \tag{19}\]  
where \(SS_{res}\) is the Residual Sum of Squares (RSS), which represents the difference between the predicted and actual values of the model; \(SS_{tot}\) is the Total Sum of Squares (TSS), which represents the difference between the actual values and the mean; n is the number of samples; and k is the number of independent variables.  
Figure 10: LSTM model prediction results.  
Figure 9: Prediction results of MLP.  
The closer the R\({}^{2}\) and adjusted R\({}^{2}\) values are to 1, the better the model fits the data. The results of the training set and test set accuracy calculations are shown in Table 5.  
As can be seen from Table 5, in the training set, the RF-CNN-LSTM prediction model and the LSTM prediction model have a higher fit, and the RF-CNN-LSTM prediction model R\({}^{2}\) accuracy and adjusted R\({}^{2}\) accuracy reach 0.9978 and 0.9965, which is higher than that of the LSTM prediction model, whereas the ARIMA model and MLP have poorer fit; in the testing. In the test set, the accuracy of the RF-CNN-LSTM model and MLP is higher; the R\({}^{2}\) accuracy and adjusted R\({}^{2}\) accuracy of the RF-CNN-LSTM model reaches 0.9788 and 0.9610, which is higher than that of the MLP, while the accuracy of the ARIMA model and the LSTM prediction model is lower. Therefore, compared with the three prediction models of ARIMA, MLP and LSTM, the RF-CNN-LSTM model proposed in this paper has a better prediction performance for the EDCU failure rate of subway vehicles.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.4 Resisting Impersonation Attacks (Threat Model 1)  
A malicious attacker \([PERSON]^{*}\) has \(SC:\{NID_{P},Y_{P}\}\) and tries to impersonate \([PERSON]\). \([PERSON]^{*}\) cannot derive \(W_{P}\) from \(NID_{P}\), where \(NID_{P}=E_{r_{MCS}}(ID_{P}\|W_{P}\|R_{MCS}\|w)\), because of the security of the symmetric \(\text{er}/\text{decryption}\) algorithm. Moreover, \([PERSON]^{*}\) cannot derive the private key \(r_{P}\) from a previous message \(V_{1}\), where \(V_{1}=h(X_{P}\|P_{P}\|Cer_{P}\|R_{P})\) and \(Cer_{P}=h(T_{r_{P}MCS}(x)mod\ p\|TS_{1})\), because of the one-way hash property and ECM-DLP. Hence, \(Pat^{*}\) cannot compute the correct \(W_{P}\), \(Cer_{P}\), \(P_{P}\) and \(V_{1}\) without the correct \(PW_{P}\) and \(r_{P}\), where \(W_{P}=h(ID_{P}\|PW_{P})\), \(P_{P}=h(W_{P}\|ID_{P}\|D_{D}\|T_{a}(x))\), \(Cer_{P}=h(T_{r_{P}}(T_{r_{MCS}}(x)mod\ p)\|TS_{1})\), \(V_{1}=h(X_{P}\|P_{P}\|Cer_{P}\|R_{P})\), and cannot send out the correct \(\{NID_{PD},NID_{P},V_{1},V_{2},C_{P},T_{a}(x),TS_{1}\}\). Therefore, a login will be detected by \(Doc\) or \(MCS\).  
Similarly, a malicious attacker \(Doc^{*}\) tries to impersonate \(Doc\). \(Doc^{*}\) cannot derive the private key \(r_{D}\) from previous messages \(V_{2}\) and \(V_{3}\), where \(Cer_{PD}=h(T_{P}(T_{r_{D}}(x)mod\ p)\|TS_{1})\), \(V_{2}=h(ID_{P}\|Cer_{P})\), \(V_{3}=h(ID_{D}\|Cer_{P}\|PH\|R_{D}))\) and \(Cer_{P}=h(T_{r_{P}MCS}(x)mod\ p\|TS_{1})\), because of the one-way hash property and ECM-DLP. Then, \(Doc^{*}\) cannot compute \(SK_{PD}=h(T_{r_{D}}(T_{a}(x)mod\ p))\), \(Cer_{P}=h(T_{r_{D}}(T_{r_{MCS}}(x)mod\ p)\|TS_{2})\), \(D_{PHI}=Cer_{D}\oplus PHI\), and \(V_{3}=h(ID_{D}\|Cer_{D}\|PHI\|R_{D})\) without \(r_{D}\), and so cannot send out the correct \(\{NID_{D},NID_{P},V_{1},V_{3},C_{P},D_{PHI},T_{a}(x),TS_{1},TS_{2}\}\). Therefore, a login failure will be detected by \(MCS\).
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 3 Conclusions and outlook  
Precipitation polymerization is the most common method to prepare monodisperse polymer microspheres, which also allows further functionalization via the next process. Much effort has been made to precisely control the particle size of the internal pore structures to satisfy various application requirements. In addition to the approaches and applications described above, other new techniques have also been developed, making it an irreplaceable tool to prepare functional materials. For example, nitroxide-mediated PP employing photo-crosslinkable prepolymers [128]; catalyst-free aza-Michael addition PP method enabled polymer particles with abundant active groups for direct post-modification in mild conditions [129]; the aqueous free-radical PP method used to prepare thermo- or light-responsive responsive microgels [130; 131; 132]; water-based redox PP for synthesizing polypyrrole nanoparticles as a promising substance for photoacoustic imaging [133]. Novel particles have been creatively prepared, such as deoxyribonucleic acid (DNA) cross-linked polymeric nanofframework [134] or those supporting Pd or Au catalysts [135; 136], exhibiting great potential in both material science and bio-medical areas.  
Figure 8: Images of typical polyethylene particles synthesized within 10–30 min (Polymerization time: (**A**) 10 min, (**B**) 20 min, (**C**) 30 min). Reprinted with permission from Ref. [125]. Copyright 2020 Elsevier B.V.  
The improved PP method resulted in microspheres with controlled particle size and active groups on the surface; the amount of monomer input and the yield of the particles were increased with guaranteed microsphere monodispersity; thus, providing strong support for the next step of industrial scale-up production and fitting specific applications in various areas. In the future, the introduction of each advanced technique into the area of precipitation polymerization is expected to progress and achieve great product morphology control and precise application rapidly.  
Precipitation polymerization is a powerful tool for the preparation of uniform polymer particles with a clean surface and can also be easily combined with other techniques. How to further reduce the production cost, simplify the production process, and expand the production scale will also be the focus of much attention and research in the future.  
Literature review and writing, R.Z. and R.G.; resources and editing, R.Z.; funding acquisition, Q.G. and J.L.; supervision, R.G. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 2 Traditional Methods before Deep Learning
### Decision Trees  
The decision tree is common machine learning method that can be used in image segmentation tasks. In traditional image segmentation, decision trees are usually utilized for image object classification; that is, to label and classify different objects or regions in an image. Decision trees classify data sets by gradually dividing them on the basis of different features from the root node to a leaf node, achieving the classification purpose. This method is simple and easy to understand and interpret, and it can handle multiple output problems. However, decision trees perform poorly when dealing with high-dimensional data because the feature space of the data set is often huge, and the classification results of decision trees are easily affected by noise and data uncertainty, resulting in poor generalization ability of the model. In addition, the performance of decision trees is limited by the size and complexity of the data set. Decision trees often become too complex and difficult to maintain with increasingly complex and large data sets. By contrast, deep learning methods can handle high-dimensional data through deep neural networks, thus performing well in image segmentation tasks.
## 3 General Framework for the Multi-Time-Scale Coordinated Operation of Combined System
### Day-Ahead Schedule for Next 24 h  
The day-ahead schedule is executed with an interval of 1 h for the next 24 h, and its objective is to minimize the power generation cost of battery-thermal-hydro-pumped storage. The power output limits, hourly ramping-up and down limits, and minimum on-off time constraints of thermal power units and hydro-pumped storage units, etc., are comprehensively considered in the model. Based on the wind-photovoltaic power load forecast data in the next 24 h, a mixed integer linear programming (MILP) model was established and solved to obtain the on-off status and optimal hourly power output of battery-thermal-hydro-pumped storage in the next 24 h.
# Study on Share Rate of Support Structure for Super-Large Span Twin Tunnels with Small Interval
## 5 Conclusions  
Based on the theory of stratigraphic slip failure mode and comparison of the simulation and the measurement, the share rate of twin tunnels with super-large span and small interval are studied in this paper. Furthermore, the analysis was verified and supplemented by the outcome of monitoring and measuring. Finally, the conclusions are drawn as follows:  
1. The formulas of calculating surrounding rock pressure of normal tunnels (usually including two or three lanes) in Chinese standard could be applied to the super-large span twin tunnels (including four or more lanes). The method was verified by field measurement.
2. The method of researching the share rate of the primary support and the secondary lining of the tunnel was proposed. The result demonstrates that the share rate of the primary support and the secondary lining of Daling Tunnel are both 40%. Usually for twin tunnels with super-large span in cities, not only the primary support but also the secondary lining should be strong enough to ensure the design is safe and reliable. In addition, this conclusion is conservative when compared to the measurement, which is practical for application in engineering.
3. The result of the theoretical calculation and numerical simulation was compared with the measurement to evaluate the research methodology and achievements. These two results match properly, which verify the correctness of this study. Hence the conclusions and the method of this research can make some reference to the design, construction, and maintenance of super-large span twin tunnels. Since this research is specific for twin tunnels with four lanes (each hole) in cities, this paper can make great contributions to urbanization, infrastructure construction, and even standards drafting. Moreover, this research is more focused on the first hole and does not consider the influence of construction sequence. Therefore, the conclusions drawn in this paper are more appropriate for the first hole.  
Conceptualization: K.S.; data curation: Y.Z. and Y.H.; formal analysis: X.Z.; investigation: X.Z., Y.Z., Y.H. and H.Z.; methodology: K.S.; software: X.Z., Y.H. and H.Z.; writing--original draft: K.S. and H.Z.; writing--review and editing: X.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number: 52178396.  
Not applicable.  
The data of simulation and monitoring in the article is not freely available due to legal concerns and commercial confidentiality. Nevertheless, all the concepts and procedures are explained in the presented research and parts of the research may be available upon request.  
The authors of the present work feel grateful and would like to thank Shandong Expressway Jitai Intercity Highway Co., Ltd., Shandong Expressway Jitai Intercity Highway Co., Ltd., National Natural Science Foundation of China, Southwest Jiaotong University, for providing facilities, fund and supports in measurement and monitoring.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 5 Agent-Based VM Migration
### Allocating Heuristics of BA  
The allocating heuristic also serves to balance the load of SAs and match the security level of the VMs to that of the host.  
For load balancing, the hosts in cloud computing are regarded as a host resource pool, and four commonly used VM migration algorithms [61; 62; 63; 64] were implemented into BA to facilitate the selection of initial destination hosts for newly arriving VMs. These algorithms include:  
Round-robin--this algorithm evenly distributes newly acquired VMs by assigning each VM to each host in turn, and this continues in cycle. For an illustration, refer to [64].  
Greedy--this algorithm assigns a newly arrived VM to the host with the lowest sum of CPU and memory utilization percentages. For an illustration, refer to [61].  
First-fit--this algorithm assigns an incoming VM to the first host in the pool with sufficient idle resources to match the VM's specifications. For an illustration, refer to [62].  
Best-fit--this algorithm assigns an incoming VM to the host with the smallest number of idle cores. Given that virtualized hosts can allocate significantly more (virtual) memory than their available physical memory to VMs [35], the number of accessible cores was chosen over the quantity of free memory. For an example, see [63].  
For security, after the host is selected according to the migration algorithm, it is also checked whether the security levels of the host and the VM match. If there is a match, migrate to the selected host; otherwise skip the host and continue the selection. If there is no suitable host, BA reserves the VM request for a period of time and re-initiates the application.
## 1 Introduction
The method first applies SVD to denoise signals and proposes an adaptive order determination method to select the optimal order for SVD denoising. This enhances the denoising capability and has strong adaptability to different noise environments, thereby overcoming the Prony algorithm's sensitivity to noise. Next, the Prony algorithm is applied to detect the frequency, amplitude, phase and attenuation factor of harmonics and interharmonics in the denoised signal. Finally, the dominant factor algorithm is proposed to screen the non-false components in the results. Simulation results show that the proposed method possesses remarkable noise denoising capability and adaptability to different noise intensities. The Signal-to-Noise Ratio (SNR) of the signal can be increased by about 20 dB on average, which makes the false components in the fitting result of the Prony algorithm small in amplitude and fast in attenuation. Moreover, the dominant factor algorithm can quickly and accurately screen out the non-false components, and the detection accuracy of frequency, amplitude and phase is high, which verifies the effectiveness and accuracy of this method.