esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Normalization
#### 3.2.5 Resnet101v2  
Resnet101 and Resnet101v2 are enhanced convolutional neural networks (CNNs) from the deep residual networks (Resnet) with 101 layers. These architectures were introduced to address the challenges of training very deep neural networks (DNNs), which were prone to the vanishing gradient problem. ResNet introduced the concept of residual learning, allowing for a more effective training of extremely deep networks. They were trained on the ImageNet database. The two models learned rich feature representations for a variety of images as a result. The two models accept images 224 \(\times\) 224 pixels in size with three RGB colors. The concept of skip connections, also known as fast-forward connections, was utilized by Resnet. Before applying the rectified linear unit (ReLU) activation, the idea of feeding deeper layers from earlier layers was considered. The risk of vanishing gradient issues in the network is reduced by this shortcut or skip connection. In Resnet, the identity or residual block has both a forward and a fast-forward connection. The fact that ResNet101v2 uses batch normalization before each weight layer is the primary distinction between ResNet101v2 and ResNet101 [29].  
The mathematical model for ResNet101 involves the specifications of the individual building blocks and the overall architecture of the network. Resnet is composed of residual blocks, and ResNet101, in particular, has 101 layers. The following is a simplified representation of the mathematical model for a basic residual block in ResNet:  
Let \(x\) be the input to the block, and \(F(x)\) represents the residual function to be learned. The output \(y\) of the residual block is given by the following:  
\[y=F(x)+x \tag{2}\]  
The residual function \(F(x)\) is typically implemented as a series of convolutional layers with batch normalization and activation functions. This can be written as follows:  
\[F(x)=W_{2\sigma}\left(W_{1x}\right) \tag{3}\]  
In ResNet101v2, improvements are often introduced, such as pre-activation, which means that batch normalization and activation are applied before the convolutional layer. This can be written as follows:  
\[F(x)=\sigma(W_{2}.BatchNorm(W_{1}.x)) \tag{4}\]where \(W_{1}\) and \(W_{2}\) are the learnable weights of the convolutional layers, and \(\sigma\) represents the activation function such as ReLU.
# A Kriging Model Based Finite Element Model Updating Method for Damage Detection
## 2 Kriging Based Model Updating Method
### Kriging Model in Model Updating
However, it is unwise to set the residual of FRF between the theoretical and experimental FRF models as the objective function, because there is easily a big difference in magnitude between the two FRFs at a specified frequency, and in this case, their residual may overwhelm the influence of the smaller residuals at other frequencies. Hence, Frequency Domain Assurance Criterion, proposed by [PERSON] [39; 40] to compare the correlation between two FRFs, is introduced to be the objective function:  
\[f=\sum\limits_{i=1}^{n}w_{i}(1-|FDAC(\omega_{i})|) \tag{12}\]  
where \(n\) is the number of selected frequencies, \(\omega_{i}\) is the \(i\)th frequency, and \(w_{i}\) is the weighting. The revised FDAC given in [41] is adopted:  
\[\text{FDAC}(\omega)=\frac{\left\{ \begin{array}{l}\mathbf{h}_{\mathbf{a}}^{ \text{R}}(\omega)\\ \mathbf{h}_{\mathbf{a}}^{\text{I}}(\omega)\end{array} \right\}^{\text{T}}\left\{ \begin{array}{l}\mathbf{h}_{\mathbf{x}}^{\text{R}}(\omega)\\ \mathbf{h}_{\mathbf{x}}^{\text{I}}(\omega)\end{array} \right\}}{\left|\left\{ \begin{array}{l}\mathbf{h}_{\mathbf{x}}^{\text{R}}(\omega)\\ \mathbf{h}_{\mathbf{x}}^{\text{I}}(\omega)\end{array} \right\}\right|\left| \left|\left\{ \begin{array}{l}\mathbf{h}_{\mathbf{x}}^{\text{R}}(\omega)\\ \mathbf{h}_{\mathbf{x}}^{\text{I}}(\omega)\end{array} \right\}\right|\right|} \tag{13}\]  
where \(\mathbf{h}\) is a specified column of the FRF matrix, \(\omega\) is the frequency, subscripts \"a\" and \"x\" correspond to the theoretical and experimental data respectively and superscripts \"R\" and \"I\" denote the real part and imaginary part of the FRF vector \(\mathbf{h}\), respectively. It follows from Equation (13) that FDAC values are limited to the interval [-1,1], regardless of the modulus value difference between two vectors. There are 2 more main advantages of the proposed objective function:  
1. It contains more physical meaning during the model updating procedure, which can improve the reliability of the updating results. 2. The shape of response surface becomes more gentle, which reduces the fitting difficulty of the Kriging model. Moreover, unlike the sensitivity based method, it is not necessary to use FRFs of all the DoFs to calculate the FDAC value. The consistency of several sets of FRF curves is sufficient to ensure correct updating results, which allows elimination of the measured data with low signal to noise ratio.
# Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping
## 2 Related Work
A methodology to derive reversible circuits for a function by a given BDD is proposed in this work. The algorithm used to synthesize the reversible circuits exhibits linear behavior in terms of run-time and space features. Moreover, the BDD size affects the size of the resulting reversible circuit as it transfers the theoretical results known from BDDs to reversible circuits. However, the conducted experimental tests exhibited in better results (circuit cost) and significantly better scalability in comparison to previous synthesis approaches. A set of works have also been conducted regarding the consideration of all possible subtrees of a binary tree. The work presented in [13] is related to the analysis of the total number of subtrees of a tree. The authors of the work studied the number of possible subtrees in a given tree and the characterized set of factors that maximize/minimize the number of subtrees. However, the authors of the work presented in [14] developed algorithms to mine frequent subtrees from a database of rooted trees. They evaluatedthe use of two popular sequential encodings of trees to systematically generate and evaluate the candidate patterns. Experimental evaluation showed that their algorithms can achieve up to several orders of magnitude of increased speed for real data-sets when compared to state-of-the-art tree mining algorithms. However, the effectiveness of this algorithm has not been confirmed in the field of reversible circuit synthesis. Although all of the previous works are related to minimizing the synthesis cost, the work of [15] is related to synthesizing exact minimal reversible circuits for various types of gates and costs. They used the algebraic software GAP to reduce reversible logic synthesis problems. Their group-related algorithm was applied to Peres gates and showed a better performance than for Toffoli gates in the process of increasing the speed of the synthesis of reversible circuits. However, Toffoli gates are simpler to use for manufacturing reversible circuits. This work is considered to be a continuation of a previous work published in [7]. In that work, we proposed an algorithm to minimize the synthesis time of reversible circuits based on the consideration of subtrees instead of nodal-based mapping between BDD and reversible circuits. In this work, the proposed algorithm is implemented and tested to demonstrate the performance of the proposed algorithm in achieving a significant reduction in the synthesis time while preserving the correctness of the synthesized circuit.
## 2 Research Background and Motivation
Additionally, within the dedicated network area, devices either connect directly via 5G or access through a protocol converter deployed on the base station side, from the IoT aggregation nodes. This preliminarily realizes the concept of a unified network for rescue missions.
## 4 Implementation Issues, Results  
### Simulation Parameters  
In all the considered interception problems, a single equation binding interception time has been obtained that needed to be solved, using numerical methods with sampling time \(T_{S}=0.3\,\mathrm{s}\), resulting from observation of time constants of the obtained plots and on the basis of multiple simulations, and observations of performance indices behaviour.  
The following parameters have been set as default values:  
* \(T=\frac{3.25}{60}\,\mathrm{h}\),
* \(k=0.25\),
* \(p=0.25\,\mathrm{h}\),
* \(q=100\,\mathrm{km}\),
* \(a=-10\),
* \(g=400\,\mathrm{kmph}\),
* \(h=40\,\mathrm{kmph}\),
* \(v_{\mathrm{max}}=2500\,\mathrm{kmph}\),
* \(s=0.00045\,\mathrm{h}\).  
It is to be stressed that parameter \(s\) has been chosen in such a way so as to obtain interception time in minutes, to avoid false results.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Design Considerations in Modular Construction
#### 3.2.3 Lifting, Transportation, and Installation  
Installation is a key aspect of modular technology. Design considerations for installation include the crane capacity at the precast yard and on-site, transport, and access to the site [48]. Modular units undergo significant dynamic forces during handling, transportation, and erection, necessitating the application of appropriate load factors to ensure structural integrity. Serviceability limit checks should be performed to prevent cracks in the concrete slab wall distortion during handling and transporting. The lifting points must be strategically positioned so that sufficient bond anchorage can be developed to hoist the entire module safely and that the load distribution to all lifting points is uniform [49]. A steel collar frame is used to hoist the module so that the module would not be subject to inclined forces from the lifting wires.  
Logistics for module transportation from the factory to the site determine each module design's maximum size and volume, affecting the number of modules to complete the layout design. The modules' size is finalised so they can be transported on public roads.  
The handling stresses caused by dynamic forces during handling, lifting, and erection are considered in the flexural design of the modules. Load factors are to be applied to the self-weight of the module to allow for the safe handling, transportation, and erection of modules (Table 3). During transportation, a higher load factor of 1.5 is applied due to increased vibrations and impact forces encountered on the road. In contrast, yard handling and erection require a load factor of 1.2 to account for smaller dynamic effects during lifting and positioning. These load factors are integral to design calculations, ensuring the structural integrity of the modules is preserved across all stages and safeguarding against damage during handling and assembly. This underscores the significance of accounting for dynamic effects in modular construction to mitigate risks associated with handling and installation.  
Figure 7: Typical layout of modules: (**a**) clustered layout; (**b**) corridor layout.
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 3 Materials and Methods
### VOCs Detection Using GC-MS
#### 3.4.3 GC-MS Parameters and Statistics Processing  
An Agilent 7890A gas chromatography coupled with an Agilent 5975C triple-axis mass selective detector (Agilent Technologies Ltd., Mulgrave, VIC, Australia) was used for MS detection with a DB-Waxert column (60 m \(\times\) 250 um inner diameter \(\times\) 0.25 um film thickness; Agilent Technologies Ltd., Mulgrave, VIC, Australia). The GC method was based on a published study for volatiles detection from fungal-infected grapes [9]. In brief, the helium gas flow rate was held at 3 \({}^{\circ}\)C/min, with an initial oven temperature of 40 \({}^{\circ}\)C, held for 3 min before heating at 5 \({}^{\circ}\)C/min until 115 \({}^{\circ}\)C; increased at 2 \({}^{\circ}\)C/min until 166 \({}^{\circ}\)C; and then increased at 8 \({}^{\circ}\)C/min until 240 \({}^{\circ}\)C was reached. The final temperature was held for 15 min, resulting in a total run time of 67.75 min. Source, quadrupole, and transfer line temperatures were set to 230 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively. After a solvent delay of 8 min, the mass detector recorded a range between mass ion ratios (\(m/z\)) of 35-550, using selective ion monitoring/scan mode, according to a previously reported study [5], at \(-70\) eV. The mass spectral data were acquired using the Enhanced MSD ChemStation software (version E.02.00.493, Agilent Technologies Ltd., Mulgrave, VIC, Australia) and assessed using NIST MS search (version 2.0).  
Raw data MS data were analysed using Agilent MassHunter Quantitative Analysis (MS, version 8.0, Agilent Technologies Ltd., Mulgrave, VIC, Australia). The limit of detection (LOD) and limit of quantification (LOQ) were calculated with concentrations for signal-to-noise ratios of three and ten, respectively. Peak areas, relative responses, and calculated concentrations of each analyte detected from grape samples were imported into MATLAB (version R2022a, The MathWorks Inc., Novi, MI, USA).
## 3 Results
### Fragmentation Analysis
The values of patch size and edge indices obtained in this study show typical mangrove forest fragmentation over a rapidly developing region, where the disturbance of a large mangrove area into smaller patches occurred due to the transformation of mangroves into urban, agriculture, and aquaculture lands. Generally, the decrease in any forest habitat area or MPS has been shown to create fragments of different sizes (large and small) and isolation among the fragmented patches. Hence, this can result in species extinction and biodiversity loss due to decreased colonization and population size [17; 54]. The Shape metrics (MSI, AWMSI, and MPAR) explain the changes of the mangrove areas shape from regular to more complex patches. An MSI of 1 refers to a circular shape, and higher values indicate more irregular shape patterns. The MSI calculated in this study changed from 2.79 in 2000 to 1.96 in 2019, meaning that the complexity of the shape of the patches was reduced. This phenomenon indicates that the mangrove patches change from natural shaped patches (complex in shape) to man-made shape (regular in shape), due to anthropogenic activities [46; 55]. The AWMSI refers to the area weighted MSI, which highly depends on the area of patches and the NP. Slightly lower AWMSI was found in 2019 compared to 2000 due to a decrease in the sum of patch area and a large increase of NP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Unit** & **Year 2000** & **Year 2019** \\ \hline Class Area (CA) & ha & 13,612 & 10,705 \\ Total Landscape Area (TLA) & ha & 13,612 & 10,705 \\ Number of Patches (NP) & - & 130 & 402 \\ Mean Patch Size (MPS) & ha & 104.71 & 26.63 \\ Total Edge (TE) & m & 1,200,480 & 1,364,970 \\ Edge Density (ED) & m/ha & 88.20 & 127.509 \\ Mean Shape Index (MSI) & - & 2.796 & 1.962 \\ Average Weighted MSI (AWMSI) & - & 8.802 & 8.761 \\ Mean Perimeter Area Ratio (MPAR) & m/ha & 245.95 & 1685.40 \\ Mean Patch Fractal Dimension (MPFD) & - & 1.366 & 1.444 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fragmentation values for the year 2000 and the year 2019. This indicates that the MSI was divided by a relatively larger value in 2019 than in 2000, meaning that there were more fragmented patches in the region in 2019. MPAR, which considers the mean perimeter-to-area ratio shows an increased value for 2019 compared to 2000. Smaller patches with decreasing areas and relatively longer perimeters are the main reason for the large increase in MPAR. Finally, the MPFD, which has values between 1 (simple shaped) to 2 (complex shaped) have increased between 2019 and 2000, showing that the fragmented patches of mangrove in 2019 were more irregular compared to larger mangrove patches in 2000.
## 2 Materials and Methods
### Fracture Generation  
All specimens were thawed at room temperature for 24 h before testing. The end plates of each vertebra were embedded in a common epoxy resin (Fastray, Bosworth, Skokie, IL, USA). Anterior vertebral body height (VBH) was measured at the anterior aspect of the vertebral body with a digital caliper with an accuracy of 0.01 mm (Model PB-1B, Mitutoyo, Tokyo, Japan). The measured VBH values were defined as the initial height of intact vertebra for the subsequent calculation of the VBH ratio. Following intact VBH measurement, a compressive preload of 89 N was applied for 2 min [23]. To create a wedge compression of the anterior half of the vertebral body, the main vector of the axial force was centered at the end plate of the anterior fourth of the vertebral body [24]. Each vertebra was compressed at a rate of 5 mm/min using a hinged-plated device mounted on a material testing machine (Bionix 858; MTS Systems Corp., Minneapolis, MN, USA) to create an anterior wedge fracture and reduce anterior height by 25% of the initial value. To protect the posterior wall from compression fracture, the specimen was completely contacted with the custom-made resin apparatus (Figure 1). Force (N) and displacement (mm) data were recorded at 10 Hz, and the ultimate strength and stiffness of the specimens were recorded as intact groups and compared with the two following augmented groups. Ultimate strength was defined as the highest load value with increasing compression, which is the turning point on the force versus displacement curve. Compression stiffness was arbitrarily defined as the slope of the force-displacement curve between 500 N and 1500 N [25; 26]. The specimens were returned to the plastic bags and floated in a bath maintained at 37 \({}^{\circ}\)C for at least 1 h before augmentation.  
Figure 1: Photograph showing the experimental setup of the compression test. The main vector of the axial force was centered at the end plate of the anterior fourth of the vertebral body to create wedge compression of the anterior vertebral body.  
After fractures were created by mechanical compression, all specimens from T12 to L3 were randomly allocated into two treatment groups: the EVA(r) group (\(n=4\)) and the OsseeFix group (\(n=4\)).
## 3 Testing Results and Analysis
### Strength Characteristics Testing
#### 3.1.3 CBR Testing  
The CBR strength test results of phosphogypsum-modified lime-ash stabilized soil are presented in Figure 7. The CBR values of the lime-ash-stabilized soil initially increase and then decrease as the phosphogypsum content increases. When the phosphogypsum content is 4%, the CBR value of the lime-ash stabilized soil reaches its peak (15.73%). This value aligns with the requirements of the 'Technical Specifications for Construction of Highway Subgrades' (JTG/T3610--2019) [23] for the minimum subgrade fill CBR (2-8%). However, when the phosphogypsum content exceeds 8%, the CBR values of the lime-ash stabilized soil are all lower than those of the unmodified lime-ash stabilized soil. Furthermore, during the 96 h soaking process, an increase in phosphogypsum content results in noticeable expansion of the lime-ash-stabilized soil samples. This expansion results in a reduction in their bearing capacity.  
Figure 6: Variation trend of improved soil splitting strength under different phosphogypsum content.  
Figure 7: Variation trend of improved soil CBR under different phosphorusgypsum contents.
## 3 Results
### Relations Between Variables  
As illustrated in Figure 5, the interrelationships between the trace elements under scrutiny in the soil have been elucidated through PCA. PC1 and PC2 explained 43.55% and 23.97% of the variance, respectively. PCA 1 was dominated by chromium, cobalt, nickel and iron, while PCA 2 was primarily composed of cadmium and zinc. The arrangement of vector variables was found to allow for the conclusion that there was a positive correlation between copper content and manganese, albeit a weaker correlation with cadmium. Meanwhile, zinc content demonstrated a slightly weaker correlation with lead and iron. Furthermore, positive correlations were identified between nickel and cobalt and chromium, as well as between manganese and cadmium. The soil exhibited negative correlations between cadmium and nickel, and chromium and cobalt, and very weak correlations between cobalt and copper and manganese.  
Cluster analysis using multivariate exploratory techniques (single bond and 1-r Pearson's) confirms the effect of diesel oil pollution and the application of materials on trace element content in soil. As shown in Figure 6, the dendrogram indicates that diesel oil, being at the edge of the diagram, had a relatively low correlation with most heavy metals (based on results from all test series). The materials used had a more uniform effect on the content than diesel oil did, which was particularly significant for cadmium, chromium and copper. Zinc and lead, which are the most distant from each other in the dendrogram, were the least affected by both factors. The distribution of points in Figure 7 indicates that bentonite had a slightly greater impact on the content of the analysed trace elements in the soil than calcium oxide and organic matter in the form of compost.  
The analysis of the percentage of observed variation confirmed the hypothesis that the type of used materials had a more significant effect than diesel oil pollution on the content of trace elements in the soil (Figure 8). The impact of these materials was dominant for the contents of cadmium (58.21%), manganese (60.13%), chromium (61.30%) and lead (74.40%). It was also significant for nickel (33.13%), copper (37.57%) and zinc (40.97%). The impact of soil pollution by diesel oil on the content of trace elements in the soil was found to be minimal, with its dominance being observed only for copper (47.38%) and cobalt (83.94%). However, it was found to be significant for nickel (28.63%) and chromium (32.57%). Furthermore, a significant proportion of the interaction between applied materials  
Figure 5: Relations between trace element contents in soil calculated with PCA method.  
and diesel oil pollution was found to influence the content of trace elements in the soil. The highest concentrations were observed in nickel (37.79%), zinc (51.87%) and iron (75.90%).  
Figure 6: Influence of diesel oil and different materials on trace element content in soil, presented as a tree diagram.  
Figure 7: Relative influence of diesel oil and different materials on trace element content in soil, calculated with PCA method.
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Evaluation Results  
#### 4.2.1 The Experiment  
In every trial, we varied the flight plan, using 2 to 10 drones and between 5 and 100 stations (specifically, 5, 10, 15, 20, 25, 45, 60, or 100). Each of these configurations was then tested across 10 different, randomly generated station deployment scenarios. Deliberately commencing with a starting number of drones at two, rather than one, is intentional, as this work primarily focuses on the mTSP rather than the TSP. Cases (70 in total, resulting from \(7\times 10\) scenarios) where the number of drones was equal to or greater than the number of stations were excluded from the analysis, as it makes no sense to deploy more drones than the available stations. Table 1 shows the excluded cases.  
This resulted in 650 unique scenarios (9 drones \(\times\) 8 station groupings \(\times\) 10 scenarios \(-\) 70).