esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexural and Shear Performance of Prestressed Composite Slabs with Inverted Multi-Ribs
## 4 Analysis of Flexural Behavior and Shear Strength  
### Non-Linear Flexural Analysis  
In this study, a layered analysis [17; 18] shown in Figure 8a was performed to evaluate the flexural behavior of the JAS. Since no damage at the interface between the PC unit slab and cast-in-place concrete was observed in the CF specimen, the PC and the cast-in-place concrete were considered to be fully composite in the flexural analysis. On the assumption of strain in concrete at the compression edge (\(\varepsilon_{t}\)) and neural axis depth (\(c\)), the strain of compression reinforcement (\(\varepsilon_{s}^{\prime}\)), the strain of strands (\(\varepsilon_{ps}\)), and the concrete strain (\(\varepsilon_{c,i}\)) in each layer can be calculated through the compatibility conditions. Then the stresses corresponding to each strain can be obtained from the constitutive laws for steel and concrete materials shown in Figure 8b. In this study, the [PERSON] model [19], the elasto-perfectly  
Figure 7: Measured strains from test specimens; (**a**) Strain distribution in the section located at maximum moment region (UF specimen); (**b**) Strain distribution in the section located at the maximum moment region (CF specimen); and (**c**) Stirrup strains measured from the shear test specimens.  
plastic model [20; 21], and Ramberg-Osgood model [22; 23] were adopted as the constitutive equations of the concrete, reinforcing bars, and strands, respectively. The compression force of the PC (\(C_{PC}\)), of the cast-in-place concrete (\(C_{RC}\)), and of the reinforcement (\(C_{s}\)), and tension force of strands (\(T_{ps}\)) can be calculated by multiplying the stress and areas of each element, and the moment-curvature response can be obtained from iterative calculations performed until the conditions for equilibrium of forces (i.e., \(C_{PC}+C_{RC}+C_{s}+T_{ps}\approx 0\)) are satisfied. In addition, the load-deflection response can be derived from the numerical integration of the curvature (\(\phi_{i}\)) for each section, as shown in Figure 8c. A numerical calculation example can be found in Appendix A.  
Figure 8: Non-linear flexural analysis; (**a**) Layered analysis model; (**b**) Constitutive laws of materials; (**c**) Numerical integration for estimating mid-span deflection.
## 1 Introduction
Several studies have been carried out in which energy savings are sought, where methods such as genetic algorithms (GA) have been applied to optimize energy collection [51], also physical models [52; 53; 54], as well as computational fluid dynamics (CFD) techniques to predict the microclimate of solar greenhouses [55; 56; 57]. Prediction methods can be divided into two groups: physical methods based on mathematical theory, which requires a large number of parameters to be determined, as well as the difficulty of measuring those parameters; and black box methods based on modern computational technology (particle swarm optimization algorithm, least squares support vector machine model), which do not always guarantee convergence to an optimal solution and easily undergo partial optimization [58]. On the other hand, instead of being programmed, neural networks (NNs) learn to recognize patterns. These systems are highly appropriate to reflect knowledge that cannot be programmed or justified, as well as to represent non-linear phenomena [59]. Figure 1 presents the interest topics in greenhouses and the classification of the models used. Within the latter, as presented, several studies have been developed for different applications in greenhouse crop production. However, since greenhouses are non-linear, invariant over time and with a strong coupling [15], several investigations have opted to use artificial neural networks (ANNs) for the simulation, prediction, optimization, and control of these processes. Mathematical analysis methods have been developed for the optimization of the ANNs database. These models present a variables relationship, make the variables less trivial, and simplify the structure of the network. In this way improve greenhouse total yield [60]. This review explores different ANNs investigations and applications in greenhouse technology. Presents trends for future research in the development of this type of model will improve its application and integration with the 4.0 technologies that are currently applied in smart agriculture (SA) but are little used in greenhouse production such as the internet of things (IoT), machine learning (ML), image analysis, big data, among others. The structure of this document is: Section 2 gives an explanation of ANNs, different activation functions, types and different knowledge about ANNs. Section 3 presentsthe NNs application for the prediction of microclimates in greenhouses. Section 4 shows neural network applications in greenhouse energy optimization. Section 5 indicates other studies and ANNs in greenhouse applications. Finally, Section 6 addresses the challenges in the development of NNs in greenhouse agriculture.
# Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density
## 2 Preliminaries
### Impulsive Noise Models  
When an image is taken or transmitted, some noise may appear, mainly due to the sensors or to transmission channels. Different noise models were proposed in order to deal with these corrupted images. One of these models is the impulsive noise, in which some pixels are changed by random values. Thus, if we have an original image \(I\), we will refer the noisy image obtained from \(I\) as \(A\). The gray level values of \(A\) are given by  
\[A(i,j)=\left\{\begin{array}{ll}I(i,j),&\text{with probability }1-p,\\ \
u(i,j),&\text{with probability }p,\end{array}\right. \tag{1}\]  
where \((i,j)\in\{1,\ldots,M\}\times\{1,\ldots,N\}\), and \(\
u(i,j)\) is an identically distributed, independent random process with an arbitrary underlying probability density function [16], which is the intensity value of the noisy pixel. Two types of impulsive noise were deeply studied: the salt-and-pepper noise and the random-valued impulsive noise. In some impulsive noise models, noisy pixels are often replaced by some alternative values \(s_{\text{min}}\) and \(s_{\text{max}}\), where \([s_{\text{min}},s_{\text{max}}]\) is the image dynamic range. Then, the observed gray level is given by  
\[A(i,j)=\left\{\begin{array}{ll}s_{\text{min}},&\text{with probability }p,\\ s_{\text{max}},&\text{with probability }q,\\ I(i,j),&\text{with probability }1-(p+q),\end{array}\right.\]  
where \(r=p+q\) defines the noise level. The model (1) is more general since the noisy pixel is any value from \([s_{\text{min}},s_{\text{max}}]\). In our study we deal with \(s_{\text{min}}=0\) (pepper) and \(s_{\text{max}}=255\) (salt).
## 4 Conclusions  
This study aimed at verifying the possible distinction between grated PR with different rind percentages with an electronic nose, taking into account two other variables: seasoning degree and working processes of rind. In parallel, a consolidated technique, i.e., SPME GC-MS, has been used to understand which VOCs characterized analyzed samples. This combined analysis has produced promising results that pave the way to assess cheese quality and avoid frauds.  
First of all, with GC-MS, the VOCs that characterize grated cheeses have been individuated. The results concerning PR are compliant with those found in the literature. Indeed, fatty acids that describe the aroma and taste profile of PR have been found in greater quantity for 24-months seasoned samples as compared to 12-months ones. In addition, VOCs, whose amount is bigger in rind compared to pulp, were found and they are acquiescent with chemical reactions that take place in this product.  
The multivariate statistical analysis made with PLS indicated how to proceed during the classification stage. A hierarchical approach was used, both for PLS-DA and ANNs. ANNs classification rates are the highest, suggesting that in future they could be improved to increase their performances. These first results are encouraging, and further research is in progress to add more samples and to acquire greater statistical significance for the achieved results.  
Conceptualization, M.A. and V.S.; Methodology, M.A. and E.N.-C.; Software, M.A.; Validation, M.A., E.N.-C. and V.S.; Formal Analysis, M.A.; Investigation, M.A.; Resources, E.N.-C., D.Z., V.S., E.C. and G.S.; Data Curation, M.A.; Writing-Original Draft Preparation, M.A.; Writing-Review & Editing, V.S., E.C. and G.S.; Visualization, M.A.; Supervision, V.S. and E.C.; Project Administration, V.S.;  
**Acknowledgments: Authors are grateful to Parmigiano Reggiano Consortium (CFPR) for giving us samples. Conflicts of Interest: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{2}{c}{**First Step Ripening Stage**} & \multicolumn{1}{c}{**Second Step Working Processes**} & \multicolumn{1}{c}{**Third Step Rind Percentage**} \\ \hline \multirow{4}{*}{PLD-DA} & \multirow{4}{*}{94.7\%} & 12 months: 100\% & WR: 61.1\% \\  & & SR: 90.2\% \\ \cline{3-4}  & & 24 months: 79\% & WR:90.2\% \\  & & SR: 95\% \\ \hline \multirow{4}{*}{ANN} & \multirow{4}{*}{100\%} & 12 months: 100\% & WR: 63.8\% \\  & & SR: 96.1\% \\ \cline{3-4}  & & 24 months: 100\% & WR: 58.8\% \\ \cline{3-4}  & & SR: 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification rates of Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANNs) divided per steps.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
## 3 Planar Resonant Elements for Sensing
To gain further insight on this, we have designed an OCSRR-loaded line (Figure 5a), and we have inferred the relative sensitivity as defined in (1), from the simulated responses obtained by considering MUTs of different dielectric constant. The results, depicted in Figure 6, and compared to those reported in [90], confirm that the relative sensitivity of the OCSRR-loaded line is comparable to the one of the DB-DGS, as predicted by the theory. Note that one difference between the OCSRR- and the CSRR- or the DB-DGS-loaded line is the type of response, with a maximum at \(f_{0}\) for the OCSRR-based line, and notched at resonance for the other lines. Figure 5: Topology of the designed OCSRR-loaded microstrip line (**a**), frequency response of the structure inferred from circuit and electromagnetic simulation (**b**), and simulated frequency response for various MUTs with the indicated dielectric constants (**c**). Dimensions (in mm) are \(r_{cat}=4\), \(c=0.1\), \(d=1.8\) and \(s=1.4\). The considered substrate is the _Rogers RO4003C_ with dielectric constant \(\varepsilon_{r}=3.55\) and thickness \(h=1.524\) mm. Circuit parameters are \(C_{c}=1.23\) pF and \(L=5.1484\) nH. The ports in panel (a) can be separated (if needed for connector soldering) by merely adding 50 \(\Omega\) access lines.
## 5 Numerical Example and Comparative Analysis  
### Numerical Example  
In this section we shall present a numerical example to select green suppliers in green supply chain management with P2 TLNs in order to illustrate the method proposed in this paper. There are five possible green suppliers in the green supply chain management \(O_{i}(i=1,2,3,4,5)\) to select. The experts select four attributes by which to assess the five possible green suppliers: 1 \(\text{C}_{1}\) is the product quality factor; 2 \(\text{C}_{2}\) is environmental factors; 3 \(\text{C}_{3}\) is the delivery factor; and 4 \(\text{C}_{4}\) is the price factor. The five green suppliers \(O_{i}(i=1,2,3,4,5)\) are to be assessed with P2 TLNs according to the four attributes (with weighting vector \(\omega=(0.2,0.1,0.3,0.4)\)), as shown in Table 1.  
Then, we utilize the proposed algorithm to select green suppliers in GSCM.  
**Step 1.** According to P2 TLNs \(r_{ij}(i=1,2,3,4,5,j=1,2,3,4)\), we can aggregate all P2 TLNs \(r_{ij}\) by using the P2 TLWBM (P2 TLWGBM) operator to get the P2 TLNs \(A_{i}(i=1,2,3,4,5)\) of the green suppliers \(O_{i}\). Suppose that \(t=r=3\); then, the aggregating results are in Table 2.  
**Step 2.** The scores, derived according to the information in Table 2, are shown in Table 3.  
**Step 3.** The ordering of the suppliers, according to the information in Table 3, is listed in Table 4. The order of the green suppliers is slightly different, but the best green supplier is O\({}_{3}\) or O\({}_{4}\).  
From Table 4, we can easily see that these two operators may generate slightly different ranking results. The main reason causing this ranking result difference is that the P2 TLWBM operator emphasizes the group influences; however, the P2 TLWGBM operator emphasizes the individual influences.
## 1 Introduction
### Overview of Paper  
In this paper, the design choices are described in Section 2, together with details of the control electronics, where most of the original developments were made, and the data acquisition protocol and data processing steps, which are strongly based on precursor systems. A first seaborne test of the system on Acqua Alta Oceanographic Tower (AAOT) is described in Section 3, where data acquisition and processing are also described for two precursor systems used for comparison, a manually supervised three-sensor hyperspectral system and the autonomous multispectral AERONET-OC system. Results from the prototype tests are described in Section 4. General system performance is evaluated with description of lessons learned for system improvements. A comparison is made between data acquired with PANTHYR and data from the two precursor systems. Finally, conclusions from these prototype tests and future perspectives for system refinement are outlined in Section 5.
## 5 Effectiveness of ECTMD to Control a Submerged Pipeline
The pipeline model is submerged 10 cm beneath the surface of the seawater, which is a sufficient depth for the full submersion of the ECTMD during vibration tests. A laser displacement sensor is installed to measure the vertical displacement of the submerged pipeline in the mid-span location. Two different cases, namely structure without control and structure controlled by ECTMD, are tested both in the free vibration and forced vibration case. A motor with unbalanced mass is mounted on the pipeline structure as a harmonic force generator, which can apply harmonic loads in different frequencies by adjusting the input voltage. Figure 12 plots the vertical response of the pipeline model in free vibration and resonant response. It can be observed that the damping ratio of the vertical vibration is significantly increased from 2.4% to 5.5% when the ECTMD is installed on the pipeline model and the free vibration reduces to zero within 10 periods. The resonant response is reduced by 80.5% while the peak of the no control case is 6.20 mm and the peak with ECTMD control is 1.21 mm. Additionally, the steady-state response in a large frequency range is tested and the vertical response amplitudes is plotted in Figure 13. The results show that the ECTMD can suppress the large amplitude vibration of the submerged pipeline model in a wide frequency range. The purpose of the current work is aimed to reduce large amplitude vibrations occurred on the subsea pipeline structures. To apply the proposed ECTMD in vibration control of a real environmental setting, the modal parameters such as frequency and modal mass of the vibration mode should be obtained from the finite element model or field test in the design stage. According to the proposed optimization method, the optimal frequency tuning ratio and the optimal damping ratio of the ECTMD can be designed when the modal parameters of a subsea pipeline are already known. In the implementation stage, the configuration of the ECTMD used in the control experiment is worth to be considered. The
## 4 Experimental Verification
We can conclude from Figure 10 that different viewers have different 3D feelings for the same picture. Viewer 14 has the strongest ability to fuse two images into a 3D picture, while the fuse ability of viewer 9 and viewer 10 is weak. Though the fuse ability is different, the perceived SD of every viewer decreases suddenly when the viewing angle is approximately \(16^{\circ}\). In addition, there is almost no 3D effect when the viewing angle is larger than \(20^{\circ}\). Although the similar factor becomes larger when the viewing angle is larger than \(16^{\circ}\), the lenslet aberration makes the two spots less similar and have larger size, which disturbs the fusion in the brain, thus reducing the perceived SD value. The results of the objective and subjective experiments show that the threshold value of similar factor  
Figure 3:1 Perceived stereo depth of “lotus” on the prototype at different viewing angle by different viewers. Figure 3:2 (**a**) The original stereo anaglyph of “lotus”; the left (**b**) and right (**c**) stereo image displayed on the prototype. is around 0.9. The experiment results agree well with the simulation results, which both prove the validity of our binocular model to analyze the influence of lens aberration on the SD. Four elder viewers of 54 to 57-year-olds who claimed they were with presbyopia watched the 'lotus' pattern in the same condition as the young viewers. The results are shown in Figure 3:3 The average perceived SD of elder viewers is about 100 mm lower than the mean perceived SD by the young volunteers. The variation trends of perceived SD to viewing angle is almost the same, particularly the threshold value of similar factors for both the elder and young viewers is around 0.9. It can be concluded that, although the stereovision of elder viewers degrades, the fusion mechanism of elder people is still the same as that of young people.
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 3 Materials and Methods
### Improved YOLOv5 Structure  
YOLOv5 has strong recognition ability, so in this study we chose it as the baseline. Based on the characteristics of the SAR images, we integrated the four aspects described above into the model of YOLOv5. Firstly, the designed MFAF module replaced the original detection head to help extract the feature information of multiscale targets. Secondly, the SIOU loss function replaced the original CIOU to accelerate the convergence of the network and improve the accuracy of detection. Thirdly, the designed Ghost-Conv and SE-Ghost-C3 modules replaced the original Conv and C3 modules, which reduced the size of the model. Finally, the CA mechanism was integrated into the backbone of YOLOv5 to improve the accuracy of recognizing targets in a complex background. The improved YOLOv5 structure is shown in Figure 5.  
Figure 3: Structure of the CA mechanism.
## 4 Discussion
We gathered some usability results from our first two participants, who have already finished the 14-session CCSP intervention in an augmented reality (AR) format. Participant 1 (P1) was an 85-year-old embroiderer with three years of formal education and a formal diagnosis of Vascular Dementia, while participant 2 ([PERSON]) was a stay-at-home wife of 76 years old with three years of formal education and a diagnosis of Lewy Body Dementia. Even though P1 and P2 had low digital literacy, both could autonomously perform all tasks available on _Musiquence_ (P1 became autonomous on the sixth session, and [PERSON] on the eighth). [PERSON] usually sang during sessions and provided personal information related to the themes and songs used--for instance, on the typical recipes theme, she would explain to the researcher how to prepare the meals; also, [PERSON] mentioned enjoying this task in particular due to the use of multisensorial stimuli (e.g., during the \"Foods\" theme sessions, we combined fruit-based odors) and the set of songs played during sessions. As for [PERSON], she initially resisted participation in our study, since she had no experience with technology. Nevertheless, at the end of the CCSP, [PERSON] mentioned appreciating the themes and songs used, expressing her joy in recalling some of the stimuli we used, such as, for instance, the tool used by the milkman to carry the milk through the village. Overall, at the end of the intervention, we verified that both participants improved their ability to interact with _Musiquence_, meaning there has been a learning effect throughout sessions. Our usability results are consistent with the findings of [PERSON] et al. (2018). The authors performed a study with IwD to test the feasibility of Musiquence in an AR format and found that the participants enjoyed performing the different activities and were able to autonomously finish all of them [32]. Moreover, the same authors studied the usability of this tool with healthcare professionals, who reported that the platform was easy to use [33].
## 4 Test Results and Discussion
### Surface Roughness Degradation Anisotropy  
Taking the tests carried out under 2.5 MPa normal stress as an example, the statistical distribution of the roughness parameters in the 36 analyzed directions of the joint surface was illustrated with a box-and-whisker diagram (Figure 12). According to the statistical results, the maximum and mean of the roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\) both declined as the number of shear cycles increased. Moreover, the degradation rate was first fast and then slow, and it tended to smooth after five shear cycles.  
The interlocking asperities of joint surfaces may undergo tooth cutting, asperity wear, and debris backfilling during cyclic shearing. When the normal stress is larger, the degradation degree is greater, which leads to a rapid decrease in the micro-morphological parameters of the joint surface and a rapid degradation of the shear strength. In the subsequent cyclic shear process, the tooth-cutting, asperity wear, and debris-backfilling effects of the joint were obviously weakened, and the changes in the joint surface topography parameters tended to occur slowly.  
In summary, two analyzed directions, those that were the most and least affected by cyclic shearing (the 90\({}^{\circ}\) to 270\({}^{\circ}\) and 0\({}^{\circ}\) to 180\({}^{\circ}\) analyzed directions, respectively), were selected for roughness parameter degradation anisotropy.  
Figure 11: Direct coordinate system of the 3D roughness parameter, \(A_{0}\theta_{max}^{*}/(C+1)\), of the lower joint surface under different normal stresses: (**a**) 0.4 MPa, (**b**) 1.0 MPa, and (**c**) 2.5 MPa.