esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Samples and Methods  
All materials and samples were obtained from the Tianjin Branch of the China National Offshore Oil Corporation (Tianjin, China). In total, 155 source rock samples from 22 wells were included in this study: 34 samples from the E3s1 formation (13 wells), 32 samples from the E3s2 formation (12 wells), 83 samples from the E2s3 formation (17 wells), and 6 samples from the E2s4 formation (2 wells). The spatial distribution of each well is shown in Figure 1. A comprehensive overview of the data ranges and sample quantities for total organic carbon (TOC), vitrinite reflectance (Ro), and pyrolysis measurements conducted on the rock samples from the Bohai Bay Basin is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Well** & **Depth (m)** & **Shahejie** & **TOC (\%)** & **Ro (\%)** & **T\({}_{\text{max}}\) (\({}^{\circ}\)C)** & **I\({}_{\text{H}}\) (mg HC/g-TOC)** & **S\({}_{\text{1}}\) + S\({}_{\text{2}}\) (mg/g)**  
The source rock samples were pretreated with deionised water and ethanol to eliminate surface contaminants. Subsequently, the samples were pulverised to a particle size of 100 mesh, weighed for various experiments, and analysed for TOC using a American Leco CS344 Analyser designed specifically for determining carbon and sulphur contents. Subsequently, approximately 100-mesh powder samples were subjected to rock pyrolysis for a semi-process test, resulting in the acquisition of unbound hydrocarbons (S\({}_{1}\)), prospective hydrocarbons (S\({}_{2}\)), and maximum generation (T\({}_{\text{max}}\)). A German Leica MV-SP microphotometer equipped with an oil-immersed objective lens was used to measure Ro. The values were calibrated using the dual-Ro standard dual-calibration method. If the measurement deviation of the standard sample exceeded \(\pm\)0.02%, the sample was re-measured. The aliphatic hydrocarbon fractions in the hydrocarbon source rocks were analysed with gas chromatography-mass spectrometry (GC-MS) using an American Agilent 7890A gas chromatograph equipped with a DB5 ms fused silica capillary column (length: 60 m, inner diameter: 0.25 mm, film thickness: 0.25 \(\upmu\)m). Additionally, an American Agilent 5975C mass-selective detector with an ionisation energy of 70 eV was employed. The vaporisation temperature of the inlet was adjusted to 300 \({}^{\circ}\)C, and the sample was introduced without any division at a consistent flow rate of 1.5 mL/min, utilising helium as the carrier gas. The initial temperature of the oven was set at 50 \({}^{\circ}\)C and maintained for 1 min. Subsequently, it underwent a programmed increment of 20 \({}^{\circ}\)C/min until it reached a maximum temperature of 120 \({}^{\circ}\)C. There was subsequently a gradual elevation over 30 min to attain a final temperature of 300 \({}^{\circ}\)C. The samples were subjected to testing in full-scan mode. The main elements were determined using X-ray fluorescence spectrometry and achieved an analytical accuracy of over 98%, while inductively coupled plasma mass spectrometry was used to determine trace and rare earth elements, ensuring an analytical precision exceeding 95%.
## 4 Experiment and Analysis
### Transfer Learning with Small Sample Datasets PlantDoc++
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**IBSA\_Net** & **Precision** & **Recall** & **F1-Score** & **AlexNet** & **Precision** & **Recall** & **F1-Score** \\ \hline Yellow Leaf Curl & 0.943 & 0.746 & 0.833 & & 0.538 & 0.424 & 0.474 \\ Virus & 0.577 & 0.698 & 0.631 & & 0.481 & 0.619 & 0.541 \\ Mosaic virus & 0.853 & 0.861 & 0.856 & & 1 & 0.61 & 0.757 \\ healthy & 0.764 & 0.733 & 0.728 & & 0.624 & 0.563 & 0.562 \\ \hline
**VGG** & **precision** & **recall** & **F1-score** & **GoogLeNet** & **precision** & **recall** & **F1-score** \\ \hline Bacterial spot & 0.577 & 0.161 & 0.252 & & 0.188 & 0.548 & 0.280 \\ Early blight & 0.609 & 0.194 & 0.294 & & 0.2 & 0.014 & 0.026 \\ Late blight & 0.345 & 0.58 & 0.433 & & 0.338 & 0.27 & 0.300 \\ Leaf Mold & 0.333 & 0.049 & 0.085 & & 1.0 & 0.024 & 0.047 \\ Septora leaf\_spot & 0.366 & 0.364 & 0.365 & & 0.345 & 0.227 & 0.274 \\ Spider mites & 0.649 & 0.638 & 0.643 & & 0.818 & 0.078 & 0.142 \\ Target Spot & 0.35 & 0.449 & 0.393 & & 0.5 & 0.065 & 0.115 \\ Yellow Leaf Curl & 0.295 & 0.576 & 0.390 & & 0.278 & 0.682 & 0.395 \\ Virus & 0.318 & 0.5 & 0.389 & & 0.182 & 0.095 & 0.125 \\ healthy & 0.74 & 0.77 & 0.755 & & 0.312 & 0.8 & 0.449 \\ avg & 0.458 & 0.428 & 0.399 & & 0.416 & 0.280 & 0.215 \\ \hline
**MobileNetV3** & **precision** & **recall** & **F1-score** & **EfficientNet** & **precision** & **recall** & **F1-score** \\ \hline Bacterial spot & 0.5 & 0.125 & 0.200 & & 0.434 & 0.344 & 0.384 \\ Early blight & 0.507 & 0.521 & 0.514 & & 0.679 & 0.726 & 0.702 \\ Late blight & 0.579 & 0.693 & 0.631 & & 0.875 & 0.554 & 0.678 \\ Leaf Mold & 0.5 & 0.506 & 0.503 & & 0.583 & 0.675 & 0.626 \\ Septora leaf spot & 0.489 & 0.699 & 0.575 & & 0.821 & 0.346 & 0.487 \\ Spider mites & 0.675 & 0.675 & 0.675 & & 0.651 & 0.974 & 0.780 \\ Target Spot & 0.66 & 0.287 & 0.400 & & 0.672 & 0.796 & 0.729 \\ Yellow Leaf Curl & 0.52 & 0.955 & 0.673 & & 0.646 & 0.791 & 0.711 \\ Mosaic virus & 0.5 & 0.233 & 0.318 & & 0.4 & 0.605 & 0.482 \\ healthy & 0.736 & 0.881 & 0.802 & & 0.833 & 0.842 & 0.837 \\ avg & 0.566 & 0.557 & 0.529 & & 0.659 & 0.6653 & 0.641 \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._Figure 11: Confusion matrix for PlantVillage model cluster tested on PlantDoc++, (**a**) IBSA_Net. (**b**) AlexNet (**c**) VGG (**d**) GoogGet (**e**) MobileNetV3 (**f**) EfficientNet. Figure 12: Confusion matrix for PDDA model clusters tested on PlantDoc++, (**a**) IBSA_Net (**b**) AlexNet (**c**) VGG (**d**) GoogLeNet (**e**) MobileNetV3 (**f**) EfficientNet.
# Comparative Analysis of Derivatization Reagents for Catecholamines and Amino Acids
## 4 Discussion  
Generally, the S/N values of TMPy were greater than the other derivatized targets. In particular, the S/N ratios were improved more than 10 times for catecholamines such as DA and L-DOPA. For the amino acids, the S/N of GABA was slightly improved (three times) and the S/N of glycine was marginally improved (two times). The modest effect of TMPy on glycine may be due to steric hinderance related to the small structure of the glycine molecule. DPP derivatization resulted in the efficient ionization of DA and GABA, whereas L-DOPA and glycine were only marginally ionized. FMP-10 derivatization also produced a general increase in the ionization of all targets. TPP derivatization resulted in the most efficient ionization of DA and GABA. On the other hand, other targets were not detected following TPP derivatization which may be due to failure of the derivatization reaction. Overall, TMPy can be utilized to derive target molecules such as catecholamines and amino acids. Notably, the S/N value of TMPy-labeled L-DOPA was exceptionally improved relative to the other reagents. In L-DOPA, the position of the amino group adjoins a carboxy group, which renders it unable to react with polyaromatic derivatization reagents. The structures of the derivatization reagents used herein contain polyaromatic moieties, except for TMPy, which is considerably smaller than the other derivatization reagents (Figure 2). Thus, TMPy is not sterically hindered and easily reacts with the primary amine group of L-DOPA. Glycine could not be derivatized effectively with any of the reagents. Glycine is constructed by an amino group and carboxy group through a single carbon. Thus, the degree of freedom of the amino group in glycine is too small to react with derivatization reagents. In addition, originally, the reaction mechanism was different between pyrylium salts (TMP, DPP, and TPP) an FMP-10. TMPy, DPP, and TPP react with the primary amino group. On the other hand, FMP-10 reacts with the hydroxy group. As a result, comparatively, TMPy improved the S/N ratio of glycine more than the other reagents. TMPy is suitable in terms of reproducibility and cost in practical use.
## 3 Experiment and Calibration
The calibrated micro-parameters of the PFC3D model are presented in Table 2.  
Results of PFC simulations and uniaxial compression test are presented in Table 3. As shown in Figure 1b, the stress-strain characteristics of the DEM model were compared with the experimental data obtained from sandy mudstone under uniaxial compression. The simulation results were basically consistent with the test data. The difference in stress-strain curve was caused by the loading mechanism of numerical simulation [42]. In the process of sample formation, the particle had achieved self-balance under the effect of its gravity acceleration, and the particle had reached an ideal state through effective contact, while the microfracture of the actual rock sample led to initial compression phase. \begin{table}
\begin{tabular}{c c c} \hline
**Micro-Parameter** & **Description** & **Calibrated Value** \\ \hline \(R_{\text{min}}/\text{mm}\) & Minimum particle radius & 1 \\ \(R_{\text{max}}/R_{\text{min}}\) & Particle radius ratio & 1.66 \\ \(E_{c}/\text{GPa}\) & Effective modulus & 3 \\ \(k^{*}\) & Normal-to-shear stiffness ratio & 3.3 \\ \(\mu\) & Particle friction coefficient & 0.5 \\ \(\overline{E}_{c}/\text{MPa}\) & Bond effective modulus & 3 \\ \(\overline{k}^{*}\) & Bond normal-to-shear stiffness ratio & 3.3 \\ \(\overline{\sigma}_{n}/\text{MPa}\) & Normal bond strength (Mean \(\pm\) standard deviation\({}^{*}\)) & \(9\pm 3\) \\ \(\overline{\tau}_{n}/\text{MPa}\) & Shear bond strength (Mean \(\pm\) standard deviation) & \(15\pm 3\) \\ \(\lambda\) & Bond width multiplier & 1 \\ \hline \end{tabular} \({}^{*}\) According to the test results, it was set by PFC code  
\end{table} 
Table 2: Calibrated micro-parameters of the PFC3D model. Figure 1: Stress–strain responses of sandy mudstone and DEM model. (**a**) Uniaxial compression test, (**b**) Stress strain curve. To study the effects of \(\overline{\sigma}_{n}\), \(\overline{\tau}_{n}\), and \(\overrightarrow{k}\) on the macro-properties of the model, a total of 10 different bond normal-to-shear stress ratios, \(\overline{\sigma}_{n}/\overline{\tau}_{n}\) \(<\) 1 (0.1, 0.2, 0.4, 0.6, 0.8) and \(\overline{\sigma}_{n}/\overline{\tau}_{n}\) \(>\) 1 (1, 2, 3, 4, 6), were set based on the micro-parameters presented in Table 2. When simulating rock material, the reasonable range of values of \(\overrightarrow{k}\) was 1-11 [45,46]. Thus, each group \(\overline{\sigma}_{n}/\overline{\tau}_{n}\) corresponded to ten groups \(\overrightarrow{k}\). The value of \(\overrightarrow{k}\) was set to 1.11, 2, 3, 4, 5, 6, 7, 8, 9, 10.11. The influence on the rock macro-properties of the PFC3D model was monitored by varying \(\overline{\sigma}_{n}/\overline{\tau}_{n}\) and \(\overrightarrow{k}\). Additionally, the stiffness ratio factor (KK = \(k^{*}/\overrightarrow{k}\)) was taken as KK = 0.1, 1, 2, 4, 6, 8, or 10 to study the effects of \(k^{*}\) and \(\overrightarrow{k}\) on the model macro-properties under different conditions.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 7 Discussions and Findings
### Limitations of Research  
This research work focuses on the routing protocol security under Rank and Black-hole attacks in RPL-routing protocol. Though the results are promising, there are some limitations of the proposed secure routing protocol and experimentation that are worth mentioning for better understanding.  
The calculation of trust recommendation requires recommended trust values from neighbouring nodes. A considerable number of IoT nodes in an area means that a node will have many neighbours, in which case the star topology is preferred. From a topology perspective, a star topology consists of the central connecting devices, which are the relay nodes responsible for disseminating the information to the appropriate host or destination. In the RPL specification, the information forwarding is based on the selection of a parent node, and each sender node must forward the packet to its preferred parent node, which is selected by rules specified in objective function. Since our research work is based on RPL-routing protocol specification and implementation, which follows the P2P network model, therefore for the evaluation of this research work, the topology is constructed according to the RPL standard specified by IETF in RFC 6550, keeping in view the current developments and implementations of the standard.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods
### Hybrid Attention Methods  
#### 3.4.1 Supervised Training  
Adabins is a typical hybrid method that incorporates the advantages of Transformer and CNN architectures [73]. Adabins adopts a regression classification scheme, which consists of two stages. Although EfficientNet [74] is used in the first stage to generate decoded features, a simplified Transformer structure named mViT is utilized to adaptively predict which bin widths and pixel-wise probability belong to which bin center. The decoded features from the encoder-decoder structure are fed into the mViT module shown in Figure 18 to generate range attention map by pixel-wise dot product operations and predict bin widths by MLP. The highlight of AdaBins lies in the fact that it builds global dependency over the decoded features by pixel-wise dot product; meanwhile, the global dependency is beneficial to regress accurate depth map.  
Compared with DPT-base and DPT-large, DPT-hybrid exploits ReSNet50 instead of linear projection to create tokens [10]. Thus, the backbone of DPT-hybrid can be seen as a hybrid encoder consisting of CNN and Transformer. The benefit of the hybrid encoder is that it balances the depth accuracy and complexity by the convolutional operations.  
Figure 17: Overview of the pixel-wise attention module. The two inputs are the intermediate feature maps of the encoder and the decoder (image: courtesy to [PERSON] et al. [63]).  
Figure 18: Overview of attention module used in AdaBins, (image: courtesy to [PERSON] et al. [73]).  
[PERSON] et al. designed a supervised MDE method using 3D point clouds as supplementary information [75]. In their method, RGB images and point clouds are inputted into convolutional networks followed a simple AdaBins module to produce depth map. Unlike the serial mechanism of DPT-hybrid, DepthFormer [76] adopts a parallel scheme, which consists of two encoder branches based on Transformer and CNN, respectively. The features extracted by Transformer are firstly enhanced by spatial self-attention. Then, the features from CNN and Transformer are fed into a cross-attention module to model the dependency between the global and local information. Being similar to that of DepthFormer, [PERSON] et al. also exploited two separate encoders to extract global and local features [77]. The Transformer-based encoder uses images of 224 \(\times\) 224 as inputs, while the encoder based on DenseNet [78] uses images of 512 \(\times\) 512 as inputs. Notably, focal self-attention is employed to replace the regular self-attention to reduce the computational complexity of DepthFormer. [PERSON] et al. argued that the tokens of Transformer should not be equally treated because most attentions focus on regions that are similar to the reference patches [79]. In the light of this evidence, they proposed the token attention mechanism shown in Figure 19 to reweight the tokens. Features are firstly extracted by convolutional operations, then token attention is conducted using the features as inputs, and finally, Transformer is utilized to enhance the features.
## 3 Results and Discussion
### Manganese Content in Bacterial Cells  
Metal accumulation analysis revealed a significant increase in Mn content in 2 De cells (Figure 5), which indicated that the strain was able to bind to Mn efficiently. Interestingly, in the duration of the experiment, the strain accumulated a higher level of Mn from the soil than from the manganese oxide. This result was confirmed when post-exposure Mn contents in cell walls and intracellular spaces of the isolate were determined. Likewise, when bacteria accumulated Mn from the soil, its level increased with incubation time opposite to the level of Mn adsorbed from MnO\({}_{2}\) (Figure 6). In addition, the results suggested that a majority of Mn was cell-surface bound, as a high amount of this metal was present in supernatants after EDTA washing (Figure 6a). EDTA has the ability to porate the outer but not the inner membrane of gram negative bacteria; hence, it can chelate various cations from the exterior as well as from the envelope layer of the cells [57]. The ability of 2 De strain to accumulate high concentrations of Mn, and the specificity of the accumulation process, suggest the existence of dedicated Mn storage mechanisms in cells of this strain.  
Moreover, levels of intracellular and cell surface-bound Mn accumulated from MnO\({}_{2}\) decreased with incubation time (Figure 6). It is believed that this difference resulted from the forms in which Mn occurred. When Mn was in the form of MnO\({}_{2}\), it attached easily to the cell wall via physical adsorption or ion exchange, which is biosorption. The biosorption equilibrium occurs very quickly from the beginning of contact between the metal and microorganism as a result of rapid physical adsorption stage, followed by desorption of this metal into solution (Figure 6a) [58]. Mn from the metallurgical waste was mostly on the second oxidation stage and was sorbed onto bacterial cell walls as well but less rapidly than MnO\({}_{2}\), presumably due to the presence of other metals (Pb, K, Zn, Si, Au, Sr, and S), which probably influences Mn sorption to bacterial cells. The change of metal concentration in 2 De strain, that we observed in our experiment (Figures 5 and 6), suggests the biosorption phenomenon, which involves mechanisms of binding metals to the cell surface, and not bioaccumulation, during which ion transport to the cell takes place. These results indicate that 2 De strain might be a useful tool for bioremediation of Mn-contaminated soil, particularly when applying metal biosorption as a preliminary step to decrease metal concentrations prior to biological sulfate reduction to prevent microbial inhibition due to metal toxicity [59].  
Figure 5: Mn accumulation in cells of the 2 De strain during accumulation studies with 0.5 M MnO\({}_{2}\) (light grey column) and 4% soil from the metallurgical waste heap (dark grey column). Error bars are standard deviation (\(n=3\)).
## 4 Simulation-Based Performance Evaluation
### Analysis of FTC Properties  
As we mentioned in Section 3.1, FTC is affected by two parameters (\(Prob_{0}\) and \(K\)) outside the fuzzy-logic controller. The simulator provides average node degree calculated by the sum of node degree for all nodes divided by the number of nodes in the network. Figure 2 shows the impact of \(K\) on network settling time, accuracy and stability. Firstly, the average node degree quickly converges to its steady state value (after about 10 rounds). Secondly, the average node degree converges to the reference node degree \(k=3\). A lower \(K\) results in a more stable system, but longer convergence time. For instance, \(K=0.02\) is more stable than \(K=0.1\), but \(K=0.02\) takes longer time to reach steady state. Thirdly, the system is stable, but the system demonstrates heavier oscillation behavior when \(K\) is bigger.  
Figure 2: Average node degree with different K (\(Prob_{0}=0.8\), n = 60, k = 3).  
Figure 3: Average node degree with different \(Prob_{0}\) (K = 0.02, n = 60, k = 3).  
Figure 3 illustrates different features at the first several rounds. If \(Prob_{0}\) is higher at the beginning, the nodes are more likely to be connected than \(Prob_{0}\) is lower. Nevertheless, the average node degree goes to stable state after 10 rounds as well. As a result, \(Prob_{0}\) has an impact on the initial status of network.  
In short, the simulation results illustrate that \(K\) has an important influence on network settling time, accuracy and stability. In the following simulations, we choose \(K=0.02\), \(Prob_{0}=0.8\) and \(rounds=15\) to configure the parameters described in FTC protocol.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Quality Check  
Reliable estimates of F are only possible when quality checks of the data are performed before analysis. Particular attention needs to be paid to unattended systems that are measuring in the presence  
Figure 8: Simulated upward atmospheric transmittance components for a 10 m sensor height. Simulations were performed with the MODTRAN5.02 RTM using the mid-latitude summer atmospheric model and the default rural aerosol model with 23 km of visibility and at a spectral resolution of 1 cm\({}^{-1}\), i.e., around 0.05 nm at the O\({}_{2}\)-B and 0.06 nm at the O\({}_{2}\)-A (figure modified from [PERSON] et al. [117]).  
of a large variety of environmental conditions. [PERSON] et al. (2010) introduced a set of data quality indices, along with suggested thresholds, that aim to relate instrument properties and illumination conditions. Instrument-related quality checks include checking that the digital numbers recorded are within the specified range for good measurements, i.e., below instrument saturation and within the lower and higher limits of optimization. Illumination-related quality checks have to take into account that the solar zenith angle (SZA) affects the intensity of the incident light and the canopy interception and thus affects F (Kal
## 6 Conclusions  
The hardware system for the data collection and analysis of the antenna radiation measurement equipment has been developed, implemented, and verified. It uses a spectrum analyzer, an embedded computer, a data transmission module, and a ground station computer. A new algorithm for collecting and analyzing antenna radiation data has been created. Its implementation greatly improves data analysis performance and gives users 3D visualization for different distances and altitudes practically in real time with data filtering and grouping functionality. A radiation measurement system based on a hexacopter and fixed-wing unmanned aircraft has been designed and successfully experimentally verified. Selected antenna measurement for experimental investigation were performed from 190- to 330-m distance from the telecommunication base tower by changing altitude for measurements by 5 m, and the results were obtained and processed. From the collected data results, we could see if the telecommunication base station was designed correctly by electromagnetic field strength radiation in the distance from the antenna. From the results which we presented, we could see that the directional behavior indicated variations in radiation intensity around the antenna and that a more uniform or expected shape suggested good antenna performance. Such results are most suitable for use by the communications regulatory authority authorities.  
Conceptualization, D.P. and R.P.; methodology, D.B.; software, R.P.; validation, D.B. and T.J.; formal analysis, D.P.; investigation, D.P. and R.P.; resources, D.B.; data curation, T.J.; writing--original draft preparation, D.P.; writing--review and editing, R.P.; visualization, D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the Editors and Reviewers for many constructive suggestions and comments, which helped to improve the quality of the paper.  
The authors declare no conflicts of interest.