esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 3 Experimental Study  
### Design Principles  
The scale specimen was designed based on the actual stress distribution of the bridge deck under the service limit state, in that the stress distribution in the specimen was similar to that in the actual concrete deck. According to [PERSON]'s research [27], when the thickness of the UHPC layer is between 25 and 40 mm, UHPC can only play a protective role to protect the concrete layer from external conditions; meanwhile, when the thickness of the UHPC layer reaches more than 40 mm, the UHPC layer can not only play a protective role, but can also be stressed together with the structure, adopting a strengthening role for the structure. Therefore, when designing a UHPC layer for a composite deck, the layer thickness should be more than 40 mm to ensure that the UHPC layer can be stressed together with the structure. In addition, since the UHPC layer can participate in the structural force, the conventional concrete layer beneath the UHPC layer can be allowed to crack to a certain extent without affecting the normal use performance of the structure under the serviceability limit state.
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 2 Materials and Methods  
### Inflatable Solar Dryer (ISD)  
A commercialized version of the ISD (SBD 50, GrainPro Inc., Zambales, Philippines) was modified to make it suitable for drying vegetables. The dryer was 26 m long and 2 m wide and consisted of a 150 \(\upmu\)m thick UV-stabilized transparent polyethylene (PE) foil as top layer and a 520 \(\upmu\)m thick reinforced black polyvinyl chloride (PVC) foil as bottom layer. Both layers were connected by a zipper, forming a foil tunnel that could be opened for loading and unloading the drying area. An air deflector was placed inside the ISD to prevent blowing away the vegetable leaves from the trays. The deflector was made of a plywood board reinforced with a wood beam and held by a metal rod. The tilt angle was 60\({}^{\circ}\) from the ground. The deflector was located in the preheating area, 2665 mm from the fans and 1000 mm from the first tray. In order to prevent cuts in the film from sharp edges of the deflector, a 45\({}^{\circ}\) chamfer was made on the upper edges of the sheet (see Figure 1).  
Air was blown through the foil tunnel by two 12 V/2.60 A DC axial fans (RDF2589B12N18S, Runda Electronics CO., LTD, Shenzhen, China), which inflated the tunnel and provided an airflow for drying. The fans were connected to a sealed rechargeable 12 V/75 Ah battery (GT 12-75C, GasTon Battery, Kowloon, Hong Kong), which was continuously charged by two 100 W\({}_{\text{P}}\) photovoltaic (PV) modules (Model 100W, Guangxi Sacred New Energy Co. LTD, Shenzhen, China). Each module was 134 cm long and 110 cm wide, installed with a tilt angle of 15\({}^{\circ}\) from the ground, and was facing south. During the night, the fan PV modules and battery were brought into a nearby building for safety reasons.  
In order to place the drying product, 21 trays covered with 4 \(\times\) 4 mm wire mesh, each with 1065 \(\times\) 860 mm surface area were lined up inside the dryer. Three trays each were placed on a wooden stand, which was 2630 mm long and 1065 mm wide. In total, seven wooden stands were placed in the ISD. See Figure 2.  
Figure 1: Inflatable solar dryer (ISD) before being inflated (**left**, note the deflector board) and the inflated ISD during drying experiments (**right**).
## 1 Introduction  
Soot particles from combustion processes can have a severe impact on human health [1, 2] and contribute to global warming [3]. The soot emissions from aircraft engines are affected by a wide variety of parameters, including liquid fuel atomization, turbulent mixing, residence times, and many others. To understand the sooting behavior of jet fuels and develop models that can accurately predict soot emissions, soot characteristics are often studied in model flames created under well-defined boundary conditions. Although there are many studies in the literature on the sooting behavior of gaseous fuels in model flames (e.g., for premixed flames [4, 5, 6]), similar data for liquid fuels are sparse. For kerosene, there are some data of Jet A-1 and its distillation fractions doped into a premixed ethylene flame [7] and for laminar diffusion flames burning Jet A-1 and synthetic kerosene [8, 9]. In addition to the soot models, the correct calculation of species and temperatures profiles of such flames is of great importance and the chemical mechanism used to calculate the combustion processes can have a large impact on the results of soot modeling attempts [10].  
The scope of this study is to contribute to the available data on flame structure and soot volume fraction in premixed kerosene flames. The flame structure was assessed by determining the main species concentrations and temperature along the axis of a premixed flat flame burning either kerosene Jet A-1 or a n-decane/1,2,4 trimethylbenzene surrogate mixture. Experiments were conducted using a combination of tunable diode laser absorption spectroscopy (TDLAS) and gas chromatography (GC). The soot volume fraction was measured using laser extinction. The combination of these techniques resulted in a comprehensive dataset that can be used in the future to develop detailed reaction mechanisms for kerosene flames including soot precursors and soot formation. The experimental results were compared to numerical simulations using different detailed reaction mechanisms. Finally, the influence of different surrogate blends on the formation of soot precursors was numerically investigated.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment
### Experimental Analysis
Therefore, selecting different input methods for different data types can improve prediction accuracy, as shown in Figure 10 for periodic ocean current data and Figure 11 for discrete AUV trajectory data. These figures demonstrate that our improved models can effectively capture the characteristics of different data types and make accurate predictions. Furthermore, we can see from the figures that, even after fine-tuning our improved models, high accuracy can still be achieved when predicting the AUV dataset. However, our proposed QCNN-LSTM model still achieves the highest accuracy, indicating that our model has strong generalization ability and can maintain high prediction performance with less computation under limited conditions. Figure 10: Prediction Results of Ocean Current Data. Figure 9: Performance of QCNN-LSTM model.
## 7 Conclusions and Future Scope  
Wireless sensor networks have significant barriers to deployment due to the need to conserve battery energy in forest field monitoring. Protocols for network routing have been devised to maximize the efficiency with which sensors use energy while transmitting data. This article suggests enhancing the energy-efficient weighted clustering based routing protocol by using a clustering algorithm in conjunction with a strategy that considers node degree, residual energy, the number of neighbors' nodes, average flying speed, and trust value to choose sensors as cluster heads which are being deployed in the environment to detect forest fire in an optimized way. In order to minimize the amount of data sent back and forth between nodes inside a cluster and the base station, the cluster head is chosen using the optimal weighting factor in the area.  
The weighted clustering algorithm developed in this study provides an improved method for selecting CH in WSNs by picking and combining specified characteristics with additional restrictions. As a result, our proposed algorithm's essential contribution is ensuring that a genuine CH is selected with the best performance parameters, primarily in signal strength and residual energy. Our proposed scheme addresses both the concerns of extended network longevity and energy efficiency. The simulation results demonstrate, evaluate, and compare our proposed method to various routing algorithms on a static network. In addition, WSN difficulties can be easily tackled using our suggested method and may be used more effectively in monitoring and surveillance services. In the future, we can evaluate our proposed approach with several new metrics, such as the number of SNs increasing, each SN having variation in initial energy, and how many nodes per square meter are in a particular region.  
Our selection procedures limit the transmission of redundant data in areas, preserve sparse areas for as long as feasible, and lessen the load on the network. Simulations demonstrate that the new protocol is superior to the old one in terms of network longevity, including mean sensor lifetime, mean energy dissipation per round, and several dead sensors after each round. Future research will examine the protocol's potential for use in different settings and also try to implement our protocol to flying ad hoc networks so that wildfire monitoring does not stick to the limitation of fixed nodes.  
Conceptualization, P.K., K.K., K.S. and S.K.; Methodology, P.K., K.K., K.S. and S.K.; Validation, P.K.; formal analysis, P.K., K.K. and K.S.; investigation, P.K.; resources, P.K., K.K. and K.S.; data curation, P.K., K.K., S.K. and K.S.; writing--original draft preparation, P.K., K.K. and K.S.; writing--review and editing, P.K., K.S., S.K. and S.K.; visualization, P.K., K.K. and K.S.; supervision, K.K. and S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 3 Control Design Methodology
### Design of the Mid-Level Control
#### 3.3.3 Transparency: Gravity and Friction Compensation
```
if\(|q|\ <q_{s}\)then \(\beta_{fr}\leftarrow\beta_{s}\) else if\(|q|\ \geq q_{s}\) and \(|q|\ \leq q_{f}\)then \(\beta_{fr}\leftarrow\beta_{f}\) endif if\(|q|\ >q_{f}\)then \(\beta_{fr}\leftarrow\beta_{m}\cdot(q_{max}/|q|)\) endif endif
```  
**Algorithm 2** Friction compensation: the algorithm implemented in the dedicated state machine for friction compensation involves monitoring the rotational speed of the joint. Depending on the speed value, the compensation effect is determined, mirroring the behavior of a damper. Specifically, if the speed is below a minimum threshold, denoted as \(q_{s}\), it indicates that the joint is initiating movement. To overcome initial static friction, the compensation effects against friction are amplified. Within a predefined ideal speed range for device activities, the compensation contribution remains constant. However, if the speed exceeds a high threshold, denoted as \(q_{t}\), a gradual effect begins to compensate less against friction until minimal compensation is achieved at the predefined maximum speed, \(q_{\max}\).
## 2 Characteristics, Principles, and a Case Study of SPH
### Principles of SPH
#### 2.2.1 Construction of SPH Basic Equation
However, it is particularly noteworthy that the dissipation of excess pore water pressure within flowing debris, as shown in Figure 5, is likely due to porosity changes in the soil following large deformations, which increase permeability and thereby facilitate the dissipation process. \[f(x)=\int\
olimits_{\Omega}f\big{(}x^{\prime}\big{)}W\big{(}x-x^{\prime}\big{)} \mathrm{d}x^{\prime}\approx\sum\limits_{j=1}^{N}f\big{(}x_{j}\big{)}W\big{(}x -x_{j},h\big{)}\frac{1}{\rho_{j}}m_{j} \tag{12}\]  
The above formula can also be written as follows:  
\[f(x)=\  
The approximation of the spatial derivative of the field function using particle discretization can be written as follows:  
\[\left\langle\
abla\cdot f(x)\right\rangle=-\sum_{j=1}^{N}\frac{m_{j}}{\rho_{j}}f(x _{j})\cdot\
abla W\big{(}x-x_{j},h\big{)} \tag{16}\]  
In the aforementioned equation, the gradient \(\
abla W\) is dependent on particle \(j\); hence, the functional particle approximation at particle \(j\) can be succinctly expressed as  
\[\left\langle\
abla\cdot f(x_{i})\right\rangle=-\sum_{j=1}^{N}\frac{m_{j}}{ \rho_{j}}f\left(x_{j}\right)\cdot\
abla W_{\mathrm{ij}} \tag{17}\]  
\[\
abla_{i}W_{ij}=\frac{x_{i}-x_{j}}{r_{ij}}\frac{\partial W_{ij}}{\partial r_{ ij}}=\frac{x_{ij}}{r_{ij}}\frac{\partial W_{ij}}{\partial r_{ij}} \tag{18}\]  
In the formula, \(i\) and \(j\) are the number of particles, \(m_{j}\) and \(\rho_{j}\) are the mass and density of particles, and \(N\) is the total number of particles in the system. In the above formula, it is observed that the function value at particle \(i\) can be accurately approximated as the weighted average of the corresponding function values of all particles within the compactly supported domain, employing a smooth kernel function. This characteristic renders the numerical integration inherent to the SPH method independent of a background grid. It is essential to highlight that the particle approximation method inherently accounts for both the quality and density of particles. This approach is particularly effective for modeling density in fluid mechanics problems involving field variables. Figure 5: Stabilised SPH prediction of the pore water pressure induced by contraction in the retrogressive failure of sensitive clay slope with \(K_{0}=0.5\).
## 2 Materials and Methods
### Model Development and Training  
In order to evaluate the efficacy of the chosen features we acquired during the extraction, a random forest classifier was utilized. The random forest classifier is an algorithm commonly used in machine learning, which combines the output of multiple decision trees to reach a single result [38]. In order to be able to compare the performance of this subsequent model to that of the prior visual detection model and demonstrate further enhancement of the prior model's capability, the pipelines and methods used in the previous work were applied for training and testing [18].  
Test-train split was utilized in the dataset, with 80% of the data used for training and the other 20% of the data used for testing. For handling missing values in the developed model, we used the simple imputer for our mean imputation strategy. Quality control was achieved by implementing a standard scaler to standardize features and remove the mean and scaling to unit variance [39]. Next, cross-validation was performed for the three classification models, using five folds to evaluate the models' performances and choose the model with the best performance ratio to advance.
## 3 Results and Discussion
### VOC Analysis of Strawberry Juice and Wines
#### 3.5.2 Coordinate Analysis of VOCs in Strawberry Juice and Wines  
Unconstrained PCA and constrainted OPLS-DA were performed to more intuitively characterize the differences in the VOCs of different cultivars of strawberry juice and wines. As shown in Figure 5A, the combined interpretation rate of PC1 and PC2 reached 65.7%, which could effectively describe the differences in VOCs among samples. There was a significant difference in VOCs between strawberry juice and strawberry wine samples, with corresponding sample points located in the first and third quadrants, respectively. The strawberry wine sample points of cultivar BX were relatively far away from other cultivars, and the difference between the strawberry juice sample points of cultivar BX and those of other cultivars was greater, as the strawberry juice sample points of cultivar BX were located separately in the second quadrant.  
The differential VOCs between strawberry juice and wine samples were \(\alpha\)-bisabolol, hexanoic acid, 3-methyl-1-butanol, and 2-methyl-propanoic acid. The OPLS-DA analysis results were generally consistent with the PCA results (Figure 5B), with strawberry juice and wine samples located in the upper and lower quadrants, respectively. Moreover, the distance between the cultivar ZJ and TZ strawberry juice and wine samples was relatively close, indicating that their VOCs were relatively similar. VIP analysis identified 16 differential VOCs with a VIP value greater than 1 (Figure 5C); they were 3-methyl-1-butanol, linalool, trans-2-pinanol, hexanoic acid, hexanoic acid ethyl ester, and so on.
## 1 Introduction
LS-SVM is a supervised learning method for recognizing patterns and quantitative predictions [28]. These methods enable to reveal the characteristic bands that represent the different chemical components in plants and select the feature wavelengths to design a robust and simple multispectral imaging system [29]. In addition, online monitoring applications for food quality can also be achieved using PLS-DA and LS-SVM techniques [21]. Therefore, PLS-DA and LS-SVM were selected to predict heat injury of Chinese cabbage. Thus, PLS-DA and LS-SVM models for NDE of heat-stressed Chinese cabbage were verified in the present study, and their performance was evaluated by comparing vegetation indices (VIs) derived from SMI data. The VIs used were the normalized difference vegetation index (NDVI), red-edge ratio (RE/R), and photochemical reflectance index (PRI), because the productivity and vegetation vigor of heat-stressed leafy crops are directly related to the values of these indices [30; 31; 32]. Therefore, the major objective of this study was to investigate the potential of the Vis/NIR SMI system for heat stress screening during the growth of Chinese cabbage. The detailed objectives of this study were as follows:  
1. Establish a Vis/NIR SMI system for spectral data (including hyper cubes) of Chinese cabbage under normal and heat stress conditions. * Identify growth parameters of Chinese cabbage grown under different temperature levels and acquire their spectral and VIs (NDVI, RE/R, and PRI) information. * Develop PLS-DA and LS-SVM models for distinguishing heat-stressed areas from Chinese cabbage leaves and compare their discriminant performance with obtained VIs to verify the developed model performance. * Provide pixel-based chemical images of heat-stress distribution in Chinese cabbage leaves using the newly developed models with an increase in heat stress intensity.
## 5 Case Study: Cryptographic Hardware Coprocessor for the Secure Subsystem of Rhea GPP
### Comparison with Existing TEEs  
According to the results of the case study illustrated in Sections 5.1-5.3, we propose a brief comparison between the hardware-based security solutions analyzed so far and the proposed hardware coprocessor for the Rhea GPP. The isolation approach adopted in the Rhea chip is similar to the one proposed in the AMD PSP, Rambus CMRT family, and Synopsis tRoot products, that is the physical and complete isolation of the security functions in a dedicated zone of the chip that is separated from the main computational unit of the system. All the analyzed solutions include programmable units (e.g., microprocessors or microcontrollers), secure memories, and hardware accelerators for the most computationally intensive cryptographic algorithms. It is difficult to fully characterize the communication mechanism between the secure zone and the non-secure zone for each solution. The AMD PSP adopts a mailbox-based mechanism to exchange requests of security services from the main CPU (i.e., non-secure zone) to the secure zone, and a similar approach is adopted in the Rhea chip. Instead, this kind of information cannot be extracted for the Rambus CMRT family and the Synopsis tRoot. The solution proposed by ARM (i.e., the ARM TrustZone) and Intel (i.e., the Intel SGX) is opposite to the previous ones.  
Figure 10: Power trace of the ECC engine acquired during the execution of a signature generation, after the integration of countermeasures against power analysis attacks. Extracted from [16].  
Figure 9: Power trace of the ECC engine acquired during the execution of a signature generation, before the integration of countermeasures against power analysis attacks. Extracted from [16].  
The execution of the secure software is allotted to the main processing unit(s) together with the normal software, and the isolation between the Normal world (non-secure zone) and the Secure world (secure zone) relies on software assets aided by dedicated hardware extensions: for instance, such software assets take the name of Enclave in the Intel SGX. On the one hand, this latter solution lowers the cost in terms of physical resources; on the other hand, the former approach guarantees a stronger isolation between the two zones. Indeed, as an example, the work in [89] demonstrates that Hardware-assisted Isolated Execution Environments (HIEEs) like the ARM TrustZone and the Intel SGX are susceptible to Denial-of-Service (DoS) and spoofing attacks. By contrast, such attacks cannot be performed against the AMD PSP and the Security Subsystem of the Rhea GPP because the physical isolation between the secure zone and the non-secure zone makes them immune.
## 4 Enterprise Network Functionality
### Centralized Management with SDN  
In the current enterprise networks, SDN becomes crucial since it separates physical hardware completely from network management [64]. Hence, a centralized control plane that can dynamically regulate the data flows throughout the network is supported by SDN. In addition, SDN enables managers to rapidly alter network resources, set security guidelines, and manage services in response to immediate demands via a centralized console. Thus, a tight integration of AI/ML technique via network orchestration can further strengthen SDN self-adaptation capabilities to satisfy IoT service conditions [65]
###### Abstract  
The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier-Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.  
RANS; separation; transition; field inversion; machine learning; U-Net Article