esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fatty Acids Profile
Comparing the TAV01 fatty acid profile with that of _Dunaliella viridis_ described in the literature, PUFA concentrations were relatively higher (62.2%) [55] than those obtained by _D. viridis_ TAV01 (49.7%). Although the nutrient composition of the growth media used were different, a possible factor might have been the higher salinity at which TAV01 was grown (3 M NaCl) compared to the _D. viridis_ isolated from the Dead Sea, which was grown at 2 M NaCl [58]. It is thus possible that if TAV01 is grown in the future at lower salinities, higher PUFA contents will be attained. In comparison, _Dunaliella salina_[59] and _Dunaliella_ sp. [57], which were grown at significantly lower salinities (0.85 M and 1.02 M, respectively) also presented lower PUFA concentrations, suggesting that either the growth conditions or the strains used were not ideal for achieving higher PUFA contents. Indeed, other authors have reported that the fatty acids profile of _D. salina_ grown under stress conditions show increased PUFA contents at increased salinity. The total omega-3 fatty acids, mainly C18:3 and C16:4, represented 92% of the total fatty acids in cells in a higher salinity medium (2.6 M) with relatively low amounts of nitrogen (2.5 mM). Conversely, under standard conditions of growth (1.3 M NaCl, 5 mM N), total omega-3 fatty acids accounted for only about 50% of TFA [56]. The authors attributed these results to an increase in enzyme activity causing elongation and desaturation of fatty acids due to a regulatory response to NaCl levels. This observation agrees with other studies on _D. salina_, which suggests that high salinity can promote the aforementioned biochemical modifications [21; 60]. For _D. tertiolecta_, the total lipids increased by 26% at high salinities (2 M NaCl) accompanied by a 10% increase in ALA [18]. Hence, it can be concluded that the manipulation of the growth conditions can be used to change the FA profile of microalgal cells; however, the optimal conditions for obtaining target metabolites must be carefully researched [61].
## 2 Results and Discussion
### Analysis of the Volatiles in P. Nobby's Pacific Sunset Flowers  
The volatile compounds in \(P\). Nobby's Pacific Sunset flowers were analyzed by solid-phase microextraction coupled with GC and GC-MS. A total of 31 components were identified in \(P\). Nobby's Pacific Sunset, including 14 terpenes, five terpene alcohols, four aldehydes, three esters, two hydrocarbons, one ketone, and two other compounds (Table 1). The main constituents were geraniol (\(28.31\%\pm 8.97\%\)), linalool (\(28.30\%\pm 16.78\%\)) and \(\alpha\)-farnesene (\(11.10\%\pm 4.49\%\)).  
Among these main compounds, the odor of geraniol is rose-like and geranium-like as described by [PERSON] _et al._[14] and [PERSON] _et al._[15]. [PERSON] _et al._[16] also indicated that linalool has a floral  
\begin{table}
\begin{tabular}{c c c} \hline
**RI a ** & **Compound** & **Content (\%)** \\ \hline
641 & 2-methylbutanal & \(0.60\pm 0.15\) \\
882 & styrene & \(0.02\pm 0.00\) \\
933 & benzaldehyde & \(0.08\pm 0.02\) \\
964 & 6-methyl-5-hepten-2-one & \(0.69\pm 0.55\) \\
981 & \(\beta\)-myrcene & \(3.87\pm 0.18\) \\
1021 & limonene & \(1.30\pm 0.04\) \\
1023 & _cis_-\(\beta\)-ocimene & \(2.17\pm 0.29\) \\
1035 & _trans_-\(\beta\)-ocimene & \(0.07\pm 0.02\) \\
1050 & \(\alpha\)-ocimene & \(0.10\pm 0.01\) \\
1060 & 3-methylphenol & \(0.07\pm 0.02\) \\
1065 & 2-methylphenol & \(0.22\pm 0.10\) \\
1080 & linalool & \(28.30\pm 16.78\) \\
1099 & (_E_)-4,8-dimethyl-1,3,7-nonatriene & \(0.82\pm 0.08\) \\
1189 & methyl salicate & \(0.07\pm 0.02\) \\
1201 & nerol & \(4.54\pm 1.05\) \\
1231 & geraniol & \(28.31\pm 8.97\) \\
1233 & geranial & \(2.04\pm 0.17\) \\
1357 & neryl acetate & \(0.91\pm 0.57\) \\
1360 & geranyl acetate & \(0.52\pm 0.26\) \\
1444 & _trans_-\(\beta\)-farnesene & \(1.08\pm 0.87\) \\
1469 & \(\alpha\)-humulene & \(0.17\pm 0.13\) \\
1477 & \(\alpha\)-curcumene & \(0.29\pm 0.19\) \\
1481 & (_Z_,_E_)-\(\alpha\)-farnesene & \(0.47\pm 0.07\) \\
1489 & \(\alpha\)-zingiberene & \(0.20\pm 0.10\) \\
1490 & _cis_-\(\gamma\)-bisabolene & \(<\)0.01 \\
1495 & (_E_,_E_)-\(\alpha\)-farnesene & \(11.10\pm 4.49\) \\
1544 & nerolidol & \(0.31\pm 0.25\) \\
1556 & _trans_-\(\gamma\)-bisabolene & \(0.17\pm 0.04\) \\
1686 & _cis_-11-hexadecenal & \(0.10\pm 0.07\) \\
1705 & (_Z_,_E_)-farnesol & \(1.18\pm 0.82\) \\
1721 & (_E_,_E_)-farnesal & \(0.17\pm 0.15\) \\  & Total & \(89.91\pm 1.19\) \\ \hline \end{tabular}
\end{table}
Table 1: Percentages of volatile compounds of \(P\). [PERSON]’s Pacific Sunset.  
aroma, and is found in flowers such as _Michelia alba_, _Chimonanthus praecox_, _Momordica charantia_, Hop, _Origanum vulgare_ and _Camelia sinensis_. Linalool is a naturally-occurring terpene alcohol found in many flowers to attract pollinators, but with anti-ethylene and anti-inflammatory functions [17, 18, 19, 20, 21, 22, 23]. _a_-Famesene has a wood-like, floral and weak spicy aroma [24, 25]. The odor of nerol is sweet floral as described by [PERSON] _et al._[26]. [PERSON] [27] cited that myrcene has a warm grassland-like and green-mango-like aroma, and at concentrations below 10 ppm, it has a sweet herbal aroma with citrus notes [28].
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 4 Experimental Results and Analysis
### Experimental Setup
#### 4.2.2 OCSVM Model Training Setup  
At present, the type of kernel function is basically selected by experience. After selecting the kernel function, the relevant parameters are determined through experiments. Several kernel functions widely used in practice include linear kernel function, polynomial kernel function, radial basis function and sigmoid kernel function, among which the radial basis function is the most widely used [40]. It has a wide convergence domain and is an ideal kernel function for classification. The radial basis function is also selected as the kernel function in this paper.  
In the second stage, 60 (200 \(\times\) 30%) positive sample feature vectors are randomly selected as the training set. All the positive samples are used as the test set, and negative sample feature vectors are not used. The offset coefficient \(d\) of the modified OCSVM will directly affect the shape of the final decision hyperplane and the identification result. The value of \(d\) is selected by the parameter trying; the goal is to modify the decision boundary and make sure all the 60 training sample points fall into the modified decision boundary. Increasing the value of \(d\) corresponds to expanding the boundary so that positive samples can fall into the decision boundary as much as possible, and at the same time, excessive values of \(d\) will reduce the resolution of abnormal samples. Finally, \(d\) is set to 30%; the corresponding distribution of test sample points in the feature space and the decision boundary are shown in Figure 10. It can be seen that normal sample points all fall into the modified decision boundary.
# A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
## 3 Proposed System
### Data Collection and Preprocessing  
Our data consist of ten distinct classes, of which nine represent different tomato diseases, while one is for healthy tomatoes. The data are a collection of RGB images of tomato leaves of dimensions 256 \(\times\) 256 pixels. Distention between infected and healthy leaves is based on their image classes. While taking the images, it has been ensured that each image should have a single centroid leaf. Furthermore, lighting and photography conditions are also kept identical. The 10 categories of images described above are shown in Figure 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Ref.** & **Algorithm** & **Disease Classification** & **Accuracy** \\ \hline \multirow{3}{*}{[2]} & EfficientNet-B7 & Binary (healthy/unhealthy) & Binary classification 99.95\% \\  & EfficientNet-B4 & 6 disease classes & Six-class classification 99.12\% \\  & 10 disease classes & 10-class classification 99.89\% \\ \hline
[3] & Random Forest & 4 disease classes & RF: 97\% \\ \hline
[8] & ResNet-50 + SeNet & 10 disease classes & 99.24\% \\ \hline
[9] & Light weight CNN & 10 disease classes & 99.25\% \\ \hline \multirow{3}{*}{[10]} & LeNet & \multirow{3}{*}{10 disease classes} & \multirow{3}{*}{VGGNet: 99.25\%} \\  & VGGNet & ResNet50 & \\ \cline{1-1}  & Xception & & \\ \hline
[11] & Hybrid CNN & 10 disease classes & 97\% \\  & (Hy-CNN) & & \\ \hline
[12] & Segmentation-based & 10 disease classes & 98.49\% \\  & CNN & & \\ \hline
[13] & CNN model & 10 disease classes & 91.2\% \\ \hline \multirow{3}{*}{[14]} & MobileNet, Resnet50 & \multirow{3}{*}{10 disease classes} & \multirow{3}{*}{Densenet121 Xception: 97.10\%} \\  & Xception, ShuffleNet & & \\ \hline
[15] &
\begin{tabular}{l} Restructured residual \\ dense network \\ \end{tabular} & 10 disease classes & 95\% \\ \hline
[16] & VGG19, AlexNet & \multirow{3}{*}{10 disease classes} & VGG19: 98.9\% \\  & & AlexNet: 98.8\% \\ \hline
[17] & Deep learning, SVM & 19 classes of diseases & 94.14\% \\ \hline \end{tabular}
\end{table}
Table 1: A comparison between the recently developed crop classification systems.  
The images are a part of the PlantVillage dataset [18], which consists of many plants, but we chose tomato as the base of our research. The images were collected from the fields and each classified by their disease.
## 2 Study Area and Data Sources
### Data Source  
A series of Landsat 5 TM and Landsat 8 OLI images were utilized to construct the ecological index. Specifically, this study employed the surface reflectance data products of Landsat 5 TM images from 1990, 1996, 2002, 2006, and 2008, as well as Landsat 8 OLI images from 2013, 2018, and 2022. The details of the Landsat data are presented in Table 1. To mitigate the uncertainties associated with seasonal variability, this study focused on the timeframe from June to September, coinciding with the peak of vegetative growth [49]. These Landsat data were accessed from the public data archive of the Google Earth Engine (GEE) platform ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)), accessed on 3 October 2023).  
Figure 1: Geographical location of Jincheng City.  
In this study, the Landsat Ecosystem Perturbation Adaptive Processing System (LEDAPS) [50] and the Landsat Surface Reflectance Code (LaSRC) ([[https://eartheengine.google.com](https://eartheengine.google.com)]([https://eartheengine.google.com](https://eartheengine.google.com)) accessed on 3 November 2023) [51] were used to calibrate the at-sensor radiance of Landsat 5 and Landsat 8 images for surface reflectance. We also utilized the C implementation of the mask function (CFMASK) [52] to determine useful pixel data quality information such as clouds, shadows, water, and snow. The thermal infrared (TIR) band (band 6) of the Landsat 5 TM Collection 1 Tier 1 raw scene was initially acquired at a resolution of 120 m/pixel and resampled to 30 m using three convolutions. The two TIR bands (bands 10 and 11) of the Landsat 8 Collection 1 Tier 1 raw scene were initially acquired at a resolution of 100 m/pixel resolution and resampled to 30 m using three convolutions [53].  
In addition, we utilized the China Land Cover Dataset (CLCD), which has a spatial resolution of 30 m, to investigate the relationship between land use change and ecological quality transformation. This dataset, compiled from 335,709 Landsat data on the GEE platform and published by Dr. [PERSON] of Wuhan University, provides yearly land cover information for China (1985,1990-2022) [54] ([[https://zenodo.org/records/4417810](https://zenodo.org/records/4417810)]([https://zenodo.org/records/4417810](https://zenodo.org/records/4417810)), accessed on 3 October 2023).
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 3 Simulation Verification  
To verify the accuracy of the above parameter model analysis, a simplified chiplet 2.5D integration model is established by the FEM simulation. The steady state thermal module in Ansys used for the simulation, as shown in Figure 4, is the 3D thermal model of the simulation, and the number of FEM simulation grids is 1,026,200. The thermal conductivities of each layer in the simplified model are depicted in Table 1. In this FEM simulation, it is assumed that the thermal conductivity is independent of temperature, and the thermal conductivity is set to a constant value. The bottom convection coefficient of the substrate is specified as 2 W/m\({}^{2}\)K, whereas the convection coefficient of the heat sink surface is specified as 50,000 W/m\({}^{2}\)K [20; 21]. The ambient temperature is taken as 22 \({}^{\circ}\)C. The heat source is located between the chiplet and microbump layer, where the chiplet is a flip chip. The metal and underfill composite structure of the microbump and C4 bump layer are considered to have an equivalent thermal conductivity of 5 W/mk. The two chiplets are of various sizes; chiplet1 has a dimension of 4 mm \(\times\) 4 mm, while chiplet2 has a dimension of 5 mm \(\times\) 5 mm. The dimensions of the heat sink and substrate are both 25 mm \(\times\) 25 mm, while the interposer has a dimension of 20 mm \(\times\) 10 mm. Additionally, the thickness of the chiplet is 0.5 mm, the heat sink is 1 mm, the substrate is 0.5 mm, the TIM is 0.1 mm, the microbump layer is 0.06 mm, and the interposer is 0.2 mm.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 2 Materials and Methods
### Control Strategies
#### 2.2.2 Self-Modulation Mode (SMM)  
In order to increase patient engagement during therapy and consequently the overall positive outcome, an additional proposed control strategy involves real-time modulation of robot motion based on patient control. As a first implementation towards more complex scenarios, discussions with clinicians supported the use of trunk inclination to modulate gait trajectory speed.  
For that purpose, a wireless inertial sensor is attached to the patient's upper body. Measurements of upper body angular displacement on the sagittal plane are used to increase or decrease the cadence of gait trajectory employed at the FGTM. Considering an upper-body angle \(\theta\), we defined a maximum trunk flexion \(\theta_{max}\), which is used to compute the applied gait speed using Equation (2):  
\[g(\theta)=g_{\alpha}\frac{\theta}{\theta_{max}}+g_{min}, \tag{2}\]  
where \(g\) is the gait speed, \(g_{min}\) is the minimum gait speed (0 km/h in this case, but often 0.8 km/h may be used [4]), and \(g_{\alpha}\) is a gain defined by the user. In this work, \(g_{\alpha}\) is computed considering maximum trunk flexion \(\theta_{max}\) (30\({}^{\circ}\) in this work) leads to the maximum defined gait speed (4.8 km/h in this case).
## 3 Ultrasonic Measurement of Air Void Distribution in Early-Age Concrete
### The Correlation between Transport Property and Air Void Structure  
The back-calculation results demonstrate that the air void content can decrease with the curing age. The shrinkage of air voids can affect the transport ability of concrete. In this study, the transport property was evaluated with the electrical resistivity measurement; the measured results are shown in Table 2. It is clear that the electrical resistivity increases with the curing age for all three sample types. The raising of electrical resistivity can be generated from the decreasing of ion concentration in pore solution and the decline of concrete connectivity. Based on reference [40], the ion concentrations in concrete pore solution become relatively stable after Day 7. Hence the obvious increase in electrical  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Sample**} & \multicolumn{3}{c}{**Compressive Strength**} & \multicolumn{3}{c}{**Ultrasonic Transmission**} & \multicolumn{3}{c}{**Ultrasonic Transmission**} & \multicolumn{3}{c}{**Electrical Resistivity**} \\  & \multicolumn{3}{c}{**(MPa)**} & \multicolumn{3}{c}{**Speed (m/s)**} & \multicolumn{3}{c}{**Attenuation at 500 kHz**} & \multicolumn{3}{c}{**(\Omega-m)**} \\ \cline{2-13}  & **Day 3** & **Day 7** & **Day 28** & **Day 3** & **Day 7** & **Day 28** & **Day 3** & **Day 7** & **Day 28** & **Day 3** & **Day 7** & **Day 28** \\ \hline Type 1 & 35.8 & 42.4 & 48.7 & 4468.2 & 4507.8 & 4612.9 & 128.1 & 116.7 & 105.3 & 47.3 & 51.2 & 57.3 \\ Type 2 & 29.0 & 37.7 & 45.1 & 4243.9 & 4311.7 & 4488.4 & 134.7 & 120.8 & 112.1 & 49.1 & 54.6 & 75.1 \\ Type 3 & 20.1 & 25.1 & 30.0 & 3788.9 & 3824.1 & 4024.2 & 155.0 & 141.2 & 129.4 & 47.5 & 87.6 & 116.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of concrete samples during the early stages.  
Figure 9: The comparison between the fitted and measured compressive strength. (**a**) The fit between the compressive strength and ultrasonic speed; (**b**) The fit between compressive strength and ultrasonic attenuation.  
resistivity from Day 7-28 can be mainly generated from the declining connectivity. This result is in accordance with the air void shrinkage during the curing age, as shown in Figures 6-8. In addition, the electrical resistivity can also increase with the air entrainment agent dosage as shown in Table 2. This result demonstrates that the small air voids generated by the added air entrainment agent are mainly isolated in the cement matrix. The small air voids can serve as an insulator [24], and reduce the conductivity of concrete.
###### Abstract  
The velocity pulse contained in near-fault ground motions have a tremendous impact on dam safety. Previous studies have mainly focused on the response of dams under near-fault seismic records without considering the obliquely incident seismic waves. In this study, the structure-soil interaction (SSI) is taken into consideration, and the nonlinear behavior of a conventional concrete roller-compacted concrete (CC-RCC) gravity dam under near-fault pulse records and non-pulse records is investigated with consideration of the obliquely incident P waves. On the basis of the dam site conditions, three groups of near-fault pulse records are chosen, and three corresponding non-pulse records are fitted by their acceleration response spectra. Combining with the viscous-spring artificial boundary, the wave input method is proposed to transform the near-fault seismic records into the equivalent nodal forces at the boundary of the foundation. The concrete damaged plasticity model is used for the nonlinear analysis. The results show that the pulse ground motions are more destructive than the non-pulse motions. The nonlinear behavior of the dam varies with the incidence angle of P waves and generally reaches a maximum at 60\({}^{\circ}\) and 75\({}^{\circ}\), the worst damage occurs at the interface between different materials of the dam, and the spatial variation of its damage is very obvious under near-fault seismic records with various incidence angles. Therefore, the effect of the angle of obliquely incident seismic waves and near-fault pulse effect should be considered comprehensively in the seismic analysis of dams.  
CC-RCC gravity dam; near-fault ground motions; structure-soil interaction (SSI); obliquely incident P waves; viscous-spring artificial boundary +
Footnote †: journal: Applied Sciences
## 3 Discussion
To directly evaluate their function on astaxanthin biosynthesis, hetero-expression in _E. coli_ was performed using candidate genes together with other genes that are essential for astaxanthin biosynthesis. It is not surprised that two candidates, _HpGGPPS1-1_ and _HpGGPPS1-2_ that closely related to _GGPPS1_ (Genbank accession no. KX236181), could promote the formation of astaxanthin in _E. coli_ (Figure 4). Sequence similarity analysis showed that _HpGGPPS1-1_ and _HpGGPPS1-2_ shared 99% identity in nucleotides of coding region and only one amino acid differences. By contrast, big variation (90% identity) was found in the 5 UTR region of _HpGGPPS1-1_and _HpGGPPS1-2_, indicating there are two functional _HpGGPPS_ in _H. pluvialis_ rather than one as previously reported [35]. No astaxanthin or other visible pigments were observed in _E. coli_ carrying the rest of six _HpGGPPS_ candidates, indicating they might not directly associate with astaxanthin production. Similar findings were also found in other species, such as _Arabidopsis_ having _AtGGPPS5_ and _AtGGPPS12_ gene with GGPPS domain features but without the function of converting IPP and DMAPP to GGPP [30; 36]. Additional in vitro studies could be conducted to investigate the potential function of these non-astaxanthin-related _HpGGPPS_ genes. Furthermore, the yield of astaxanthin in _E. coli_ carrying _HpGGPPS1-1_ and _HpGGPPS1-2_ genes is about 271-296% increased than that carrying _CrtE_ gene (the _GGPPS_ gene driven from _Pantoea ananatis_ as the positive control). In general, codon optimization is considered as an essential step to produce heterologous protein in _E. coli_ to overcome the impairment of gene expression from the codon bias [37; 38]. Followed by this conventional strategy, we expected more GGPPS proteins in _E. coli_ transformants with codon-optimized genes, thereby leading to more astaxanthin production, theoretically. Nevertheless, _E. coli_ with codon optimized _HpGGPPS1-1_ and _HpGGPPS1-2_ produced significant lower amount of astaxanthin (Figure 5), comparing with non-codon optimized corresponding genes, but produced similar amount of astaxanthin as _CrtE_ (Figure 5). A possible explanation would be that excessive GGPPS might inhibit the astaxanthin biosynthesis in _E. coli_. This hypothesis is partly supported by the evidence that no change or downregulation of _GGPPS_ was found in the salicylic acid and jasmonic acid stressed transcriptome analysis in _H. pluvialis_ during astaxanthin formation [39]. Further research can be conducted to test the suppressive role of GGPPS during astaxanthin biosynthesis. In conclusion, this study found two _HpGGPPS_ gene having the synthase activity to participate in the astaxanthin biosynthesis. Non-codon optimized _H. pluvialis_-driven _GGPPS_ genes produced significant higher amount of astaxanthin in _E. coli_, compared with bacteria-driven _GGPPS_ gene. This study improves the understanding of the role of _GGPPS_ in astaxanthin biosynthesis and thereby contributes to the design of efficient pathway for astaxanthin production.
## 3 Our Approach
### Channel Space Enhanced Attention Feature Extraction  
The channel space enhanced attention feature extraction module (CSFE) consists of two depth-wise separable convolutions, four fully connected layers, a channel attention block, and a spatial attention block. First, the input features \(F_{in}\) undergo a 7 \(\times\) 7 depth-wise separable convolution to extract local features. Then, layer normalization is applied to obtain normalized features \(F_{i}\). The normalized features \(F_{i}\) are passed to three parallel fully connected layers. The output of the first fully connected layer \(FC_{1}\) is passed through an activation function _ReLU6_, while the output of the third fully connected layer \(FC_{3}\) is processed by the spatial attention block (ESA). The output of the second fully connected layer \(FC_{2}\) is multiplied by the output of \(FC_{1}\), and the result is added to the features processed by ESA to obtain the fused features. The fused features are then passed to the fourth fully connected layer \(FC_{4}\) for further processing. Afterward, the processed features \(FC_{4}\) undergo a second depth-wise separable convolution to extract additional features. Finally, the channel attention block (ECA) is applied to the features to obtain the final output features. A residual connection adds the input features \(F_{in}\) directly to the features before the second depth-wise separable convolution layer, which helps alleviate the gradient vanishing problem and enhances feature learning.