esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
## 6 Results and Discussion
### Selecting a Base Classifier to Build Our Model
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Acc (\%)** & **Sn (\%)** & **Sp (\%)** & **MCC** & **AUC** & **F1-Score** \\ \hline SVM-RBF & **90.6** & **86.0** & **91.8** & **0.730** & **0.945** & 0.785 \\ \hline SVM-Linear & 90.4 & 85.5 & 91.6 & 0.724 & 0.936 & 0.781 \\ \hline RF-50 & 84.3 & 76.5 & 86.3 & 0.570 & 0.903 & 0.661 \\ \hline RF-100 & 84.9 & 78.5 & 86.5 & 0.589 & 0.904 & 0.675 \\ \hline RF-200 & 84.4 & 79.5 & 85.6 & 0.584 & 0.906 & 0.671 \\ \hline XGBoost & 86.5 & 82.5 & 87.5 & 0.634 & 0.923 & 0.710 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of our employed classifiers using the Physicochemical feature group on the independent dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Acc (\%)** & **Sn (\%)** & **Sp (\%)** & **MCC** & **AUC** & **F1-Score** \\ \hline SVM-RBF & **90.6** & **86.0** & **91.8** & **0.730** & **0.945** & 0.785 \\ \hline
**SVM-Linear** & **92.1** & **86.0** & **93.6** & **0.765** & **0.937** & **0.813** \\ \hline RF-50 & 89.4 & 84.0 & 90.8 & 0.698 & 0.933 & 0.760 \\ \hline RF-100 & 90.8 & 88.0 & 91.5 & 0.740 & 0.941 & 0.793 \\ \hline RF-200 & 90.5 & **86.5** & 91.5 & 0.729 & 0.944 & 0.784 \\ \hline XGBoost & 90.1 & 85.5 & 91.3 & 0.718 & 0.942 & 0.775 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of our employed classifiers using composition feature group on the independent dataset. higher dimension is better for maintaining the patterns in the data. In other words, the underlying pattern in the data can be expressed in a linear manner. As shown in Table 4, Bigram's results are lower than those of other feature groups. This demonstrates that, with respect to the number of generated features (400), Bigram does not provide significant discriminatory information compared to other feature groups. Comparing the results in Tables 1 and 2 shows that we can achieve slightly better results by using composition. As discussed in [45] and considering the similar nature of composition and occurrence feature groups, we just use the composition feature group for the next step, which is the combination of all feature groups.
# Labeling Peptides with Radioiodine: An Overview of Traditional and Emerging Techniques
## 4 Indirect Radioiodination Methods: The Labelling of the Prosthetic Groups
### Diazotisation  
Diazonium salts are employed in nucleophilic aromatic radioiodination reactions and can be used to radioiodinate aryl amines (Figure 12) [25]. Diazonium salts readily undergo nucleophilic aromatic radioiodination. A challenge with using diazonium salts is their instability. To circumvent this, storage-stable triazenes can be used as synthetic equivalents. However, this approach has limitations, including the need for modified precursors, using a large excess of reagents, and the potential for side reactions [25; 26; 27]. Diazotisation is typically performed using sodium nitrite at low temperatures in an aqueous solution of hydrochloric or sulfuric acid. The resulting diazonium salt solution is then treated with radioactive sodium iodide. A significant disadvantage of the diazotization method is the high reactivity of intermediate aryl cations or radicals, which can lead to the formation of by-products. An efficient methodology for radioiodinating aryl amines involves using a polymeric reagent to generate nitrite anions. This allows diazotization and the subsequent Sandmeyer reaction to occur under mild conditions and at low concentrations of diazonium salts. This method has been successfully applied to prepare SPECT diagnostics, including [125]Idomazenil, [125]CNS1261, and [125]IJBOX, with RCYs ranging from 47% to 75% [25]. Triazene derivatives are less reactive than diazonium salts, which can limit their use in radiolabeling applications (Figure 12).  
In summary, while diazonium salts offer a direct route for nucleophilic aromatic radioiodination, their instability and the potential for side reactions have led to the development of alternative strategies, such as using triazenes or polymeric reagents, to achieve milder and more controlled reaction conditions.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation  
The link budget simulation is crucial in network design, systematically evaluating performance under various conditions by adjusting transmission power and antenna height based on each antenna's location relative to its AP. These adjustments affect path loss, influenced by terrain and distance. In a flat desert with minimal obstructions, all antennas are set to a height of 5 m, considering earth's curvature and potential vehicle obstructions to maintain line-of-sight and signal propagation. This height aids Fresnel zone clearance, minimizing costs. The height increases only to counter first-zone obstructions, as taller structures are costlier. Otherwise, only TX power is adjusted to optimize RSSI.
## 3 Results and Discussions
### Evidence for Intermolecular Disulphide Bond Formation
#### 3.2.2 The Impact of Extraction pH on Hempesed Protein Subunits  
SDS-PAGE analysis was also used to investigate HPI samples extracted at different pH. In this study, samples extracted at pH 12 were inappropriate for SDS-PAGE analysis as they might contaminate other protein profiles on the gel. The undesirable profiles could be attributed to the hydrolysis of proteins. The peptides can be broken down into smaller peptides and amino acids under high pH, resulting in the smearing and decreased resolution of the protein bands [57]. As observed in the Figure 5, while all non-reducing profiles (lanes 2-4) showed the typical hempesed protein monomer bands (most predominant at ~50 kDa), the intensity of the high MW smearing (>50 kDa) was greater at a higher extraction pH. The increased abundance of edestin oligomers is consistent with the reduction in -SH (Figure 3B) and can be attributed to the more chemically reactive form S\({}^{-}\) existing at high pH. In the reducing protein profiles (Lanes 5-7), there was an absence of smearing at the high MW while the edestin subunits were present, again consistent with the hypothesis that the edestin conjugates are linked by intermolecular SS bonds.  
Hence, unlike other traditional plant protein sources, such as soy protein [58], pea protein [59], and lentil protein [60], we have shown that hempesed protein extraction involves two processes: protein solubilization and intermolecular disulphide bond formation. During hempesed protein extraction, pH was a major factor for both processes, as OH\({}^{-}\) acts as a reactant and trigger for protein solubilization and SS formation. Notably, disulphide bond formation appears to be an inevitable consequence of alkaline extraction and is the presumed cause of insolubility of the HPI products. Therefore, finding a balance between the protein yield and the intermolecular SS bonding is crucial.  
Figure 5: Non-reducing (NR) and reducing (R) SDS-PAGE gels display the effect of extraction pH on hempesed protein subunits. The extractions were carried out at a fixed temperature (37 \({}^{\circ}\)C) and time (2 h). Lane 1: Protein standard; Lane 2: pH 9, non-reducing; Lane 3: pH 10, non-reducing; Lane 4: pH 11, non-reducing; Lane 5: pH 9, reducing; Lane 6: pH 10, reducing; and Lane 7: pH 11, reducing.
# Development of Heavy Metal Potentiostat for
## 2 Methodology
### Accuracy and Noise Measurement of HMstat  
The HMstat performance test was conducted to evaluate: (1) the capability of the PCP to accurately apply the desired potential to WE, (2) the accuracy of the CMP to measure the operating current \(I_{w}\), and (3) the noise current measurement of the CMP under operating current \(I_{w}\). To assess the performance of the HMstat, a dummy cell was designed and connected to the CE, WE, and RE of the HMstat. The dummy cell (refer to Figure 2c) is an electronic circuit used to replicate the primary electrochemical cell with a known operating current \(I_{w}\).  
As mentioned earlier, three types of \(V_{ap}\) (constant, ramp, and square wave potential) were used for the HMstat performance test. Under a different kind of \(V_{ap}\), the accuracy of the PCP and CMP was measured based on the percentage error (PE), as given in the following equations:  
\[Accuracy\ of\ PCP=100-PE_{PCP} \tag{3}\]  
\[Accuracy\ of\ CMP=100-PE_{CMP} \tag{4}\]  
where \(PE_{PCP}\) and \(PE_{CMP}\) are the percentage error of the PCP and CMP, respectively, calculated using Equations (5) and (6).  
\[PE_{PCP}=\left|\frac{V_{in(m)}-V_{ap}}{V_{ap}}\right|\times 100, \tag{5}\]  
\[PE_{CMP}=\left|\frac{I_{out}-I_{w}}{I_{w}}\right|\times 100, \tag{6}\]  
where \(V_{in(m)}\) is the measured \(V_{in}\) taken through AI0, \(I_{out}\) is the measured output current taken through AI1, and \(I_{w}\) is the current flow through \(R_{wr}\).  
Here, the noise current measurement of the CMP was taken as the standard deviation of the data [22; 30; 31].  
The performance of the HMstat was then compared to another available potentiostat in the online market, which is the Rodeostat (Rstat). The availability of the Rstat, which was tested under different types of \(V_{ap}\), was shown in Table 1.
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 3 AFSFusion Network Model  
### Basic Idea  
The proposed AFSFusion network model, as depicted in Figure 2, is engineered to process both IR and visible images. It trains a considerable number of parameters in the deep convolutional neural network and generates the output image, commonly referred to as the fused image. To construct the total loss function, the model adopts three renowned types of loss functions, namely, MSE, SSIM, and TV. An AWA strategy is devised to enable each loss function to automatically set the weight values allocated to the IR and visible images. The strategy utilizes a VGG16 network model to extract features at various network levels for both the IR and visible images. The feature response values are then computed, normalized, and employed to generate the corresponding weight values, denoted by \(\omega_{ir}\) for the IR images and \(\omega_{vis}\) for the visible images. This facilitates the preservation of the most significant texture details in the IR or visible light in the final fused image, rendering the generated images more consistent with human eye perception. During the development process, the model constantly evaluates its performance on a validation set, and the architecture and parameters are adjusted as needed.
## 2 Materials and Methods
### Determination of Dipiclinic acid (DPA) Release from Spores  
DPA levels were monitored for sporicidal treatment 1 in SDW and BSA to demonstrate the protective effect of organic matter. DPA is associated with stress resistance in bacterial spores [37]. The release of DPA from the spore core and the subsequent formation of a Terbium (Tb)-DPA complex is indicative of sporicidal activity. This study used the method published by [38]. Briefly, solutions of DPA (499-83-2, Sigma Aldrich, Arklow, Co., Wicklow, Ireland) at a concentration of 0.5, 0.75, 1, 1.25, 2.5, 3, 4.5, and 5 \(\upmu\)M in water (dH\({}_{2}\)O) and 200 nM terbium (III) chloride (TbCl\({}_{3}\); 451304, Sigma Aldrich, Arklow, Co., Wicklow, Ireland) in Tris-HCl buffer (20 mM, pH 7.5) was sterilised by membrane filtration (Merck Millipore, Ireland). In a 96 well microplate (Costar 96 well, black microplates; Corning Incorporated, Product Ref 3916), 150 \(\upmu\)L of TbCl\({}_{3}\) was mixed with 50 \(\upmu\)L of each concentration of DPA solution. A microplate reader (Tecan, Spark Control V. 30) was used to record the fluorescence of the Tb-DPA complex with excitation set at 280 nm, emission at 545 nm, a delay time of 50 \(\upmu\)s, an interval of 1200 \(\upmu\)s and gain value set at 1279. The relative fluorescence units (RFU) was plotted against the concentrations of DPA to generate a standard curve. The RFU values and standard curve were used to derive a regression equation to calculate DPA released from each sample. After treatment at the different contact times, the treatment suspensions were centrifuged at 7500\(\times\)\(g\) for 10 min and the supernatant was retained. Exactly, 50 \(\upmu\)L of the collected supernatants were mixed with 150 \(\upmu\)L TbCl\({}_{3}\) solution in a 96 well microplate (Costar 96 well, black microplates; Corning Incorporated, Ref 3916). A microplate reader (Tecan, Spark Control V. 30) was used to determine the fluorescence of DPA-Tb complex. Corrected RFU values were obtained by subtracting the RFU of treated spores from the untreated control. Total DPA content was estimated by using corrected RFU and line equation derived from the standard curve above.
# Near-Nyquist-Limit Optical Communication and Ranging Method Based on Waveform Matched PPM
## 6 Conclusions  
A method of integrated optical communication and ranging based on PPM was presented. The ranging accuracy can be improved with a higher slot rate at the same sampling rate. In particular, a novel PPM symbol decision method based on waveform matching was proposed to solve the problem of slot energy leakage near the Nyquist sampling limit. A gain of around 1.1 dB in bit error performance is achieved by the proposed method compared with the traditional method. Reliable optical communication and high accuracy of range measurements are obtained at the same time by the experimental hardware platform. Real-time video transmission was demonstrated, and the ranging accuracy was higher than 0.4 mm.  
**Author Contributions:** Formal analysis, C.H., Y.L., J.L. and X.B.; funding acquisition, J.A.; writing--original draft, C.H.; writing--review and editing, C.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Scientific Foundation of China under grant 62001030.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The integration of Internet of Things (IoT) technology into the agricultural sector enables the collection and analysis of large amounts of data, facilitating greater control over internal processes, resulting in cost reduction and improved quality of the final product. One of the main challenges in designing an IoT system is the need for interoperability among devices: different sensors collect information in non-homogeneous formats, which are often incompatible with each other. Therefore, the user of the system is forced to use different platforms and software to consult the data, making the analysis complex and cumbersome. The solution to this problem lies in the adoption of an IoT standard that standardizes the output of the data. This paper first provides an overview of the standards and protocols used in precision farming and then presents a system architecture designed to collect measurements from sensors and translate them into a standard. The standard is selected based on an analysis of the state of the art and tailored to meet the specific needs of precision agriculture. With the introduction of a connector device, the system can accommodate any number of different sensors while maintaining the output data in a uniform format. Each type of sensor is associated with a specific connector that intercepts the data intended for the database and translates it into the standard format before forwarding it to the central server. Finally, examples with real sensors are presented to illustrate the operation of the connectors and their role in an interoperable architecture, aiming to combine flexibility and ease of use with low implementation costs.  
Internet of Things (IoT); wireless sensor networks; precision agriculture; IoT standards; IoT protocols; interoperability; sensor integration; connector devices; interoperable IoT architecture; OGC SensorThings +
Footnote †: journal: Network
## 3 Results
### Simultaneous Polymerization Reaction with Emulsification
#### 3.1.1 Styrene  
The formation of an O/W emulsion with styrene as a monomer was first attempted by using a capillary-type flow system. The O/W emulsion was then observed with cryogenic transmission electron microscopy (cryo-TEM). Figure 2a shows the successful formation of an O/W emulsion using styrene in the absence of AIBN. The contrast of a cryo-TEM image depends on the electron density of the chemical compounds [39]. Since styrene bears a benzene ring with rich electron density, the oil (styrene) phase gave high contrast to the water phase. Thus, the use of the capillary-type flow system under hydrothermal conditions made it possible to yield an O/W emulsion of styrene, as well as typical oil phases such as decane and octanoic acid [28; 29].  
The formation of polymer particles is considered to be driven by the hydrothermal emulsification of an O/W emulsion in the presence of the polymerization initiator AIBN. Typical TEM images are shown in Figure 2b. A polystyrene particle, dispersed in water, was observed. The diameter of polystyrene the particles was indicated to be 100-200 nm. The size dependency of polymer particles was next examined by altering the initial size of the O/W emulsion. The flow rate of Solution II was altered while keeping the flow rate of Solution I (0.30 mL/min) to control the size change of the O/W emulsion. A flow rate ratio of Solutions I and II = 1:2 yielded the minimum size of the emulsion without AIBN (58-120 nm). The larger flow rate ratio favored the large mean diameter of the O/W emulsion. Fixing the flow rate ratio of Solutions I and II (1:2), the polymerization reaction of O/W emulsions with different sizes were then monitored as a function of the polymerization time (Figure 2c). In the case of the O/W emulsion with 100 to 200 nm of diameter, the mean diameter of polystyrene (PS) particles slightly altered into 30 and 210 nm, respectively. Polystyrene particles of a mean diameter of r nm were termed PSr. The size distribution of PS30, PS210, and PS800 indicated a single peak (Figure 2d). The mean diameter of the O/W emulsion was roughly correlated with that of the polymer particles, as shown in Figure 2e. Therefore, the O/W emulsion was likely to act as the template for the formation of polymer particles. The final size of the polymer particles was likely to be controlled by the flow rate ratio of Solutions I and II.  
Figure 2: (**a**) Cryo-TEM images of O/W emulsion of styrene. (**b**) TEM image of nanoparticles made of PS. (**c**) Time course of mean diameter for PS particles. Samples at t = 0 and t > 0 represent O/W emulsion without AIBN and after polymerization reaction, respectively. (**d**) Diamter distribution of PS800, PS210, and PS30. (**e**) Comparison of diameter of O/W emulsion with that of polymer particles. PSxx indicates PS with xx nm of final mean diameter.
## Appendix B Simulation Setup for LTE  
Unlicensed LTE is based on the 3 GPP Release 12 LTE technology to be used in the unlicensed spectrum. LTE is used as a secondary cell within the LTE carrier aggregation framework anchored by a licensed primary cell. In our simulation, the downlink system for LTE is considered.
## 4 Results and Analysis  
In this article, one image with less cloud and better quality in each month has been selected to perform batch inversion of the long-term FPAR from the 2014 annual FY-3C MERSION data. Based on the parameterization of the PROSAIL model (Table 6), the parameter ranges of grassland, cropland, shrub, broadleaf forest, and needleleaf forest have been taken as the inputs into the PROSAIL model to simulate FPAR, and a look-up table has been constructed to invert FPAR. The inversion results are referred to as FYgrass-FPAR\(\backslash\)FYcropland-FPAR\(\backslash\)FYshrub-FPAR\(\backslash\)FYbroadleaf-FPAR\(\backslash\)FYneedleleaf-FPAR for different  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Grassland** & **Cropland** & **Shrub** & **Broadleaf** & **Needleleaf** \\  & & & & & **Forest** & **Forest** \\ \hline N & 1–1.8 (0.2)/ & 1–2 (0.2)/ & 1–1.8 (0.2) & 2.15 & 1–2 (0.2)/ \\  & 1.5–2.5 (0.2) & 1.5–2.5 (0.2) & & & 1.5–2.5 (0.2) \\ Cab & 10–50 (5)/ & 20–50 (5)/ & 10–30 (5) & 30–60 (5)/ & 30–50 (5)/ \\  & 30–70 (5) & 40–70 (5) & & 40–80 (5) & 40–70 (5) \\ Car & 8 & 8 & 8 & 8 & 8 \\ Cbrown & 0 & 0 & 0 & 0 & 0 \\ Cw & 0.0176 & 0.075 & 0.005 & 0.015 & 0.015 \\ Cm & 0.014 & 0.0075 & 0.00161 & 0.009 & 0.01 \\ LAI & 0–2 (0.1)/ & 0.2–2 (0.1)/ & 0–1 (0.05)/ & 1–4 (0.05)/ & 1–4 (0.1)/ \\  & 1.6–7 (0.4) & 1–7 (0.2) & 0–4 (0.1) & 2–7 (0.2) & 1.6–7 (0.2) \\ LIDF & 18/38 & 50/60 & 20 & 58 & 45/50 \\ Hspot & 0 & 0.1 & 0 & 0 & 0 \\ posil & 0–1 (0.2) & 0.2–2 (0.2) & 0–1 (0.2) & 0–1 (0.2) \\ \hline \hline \end{tabular}  
* \({}^{1}\) tts/tto/psi: Obtained from the angle data of FY-3C MERSION data. \({}^{2}\) References [33–35] for grassland; [21, 36, 27] for cropland; [38] for shrub; [39–42] for broadleaf forest; [43, 44] for needleleaf forest.  
\end{table}
Table 6: Inputs to the PROSAIL model for different vegetation types.  
vegetation types, and the date of the image is roughly regarded as the FPAR value of the current month. Finally, the MODIS/GEOV1/GLASS FPAR products were selected to analyze the accuracy of the FPAR inversion results using cross-validation.