esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Streaming Services
#### 3.3.1 Media Streaming  
For media streaming services, [16] proposes a dynamic streaming over CoAP (DASCo). The algorithm uses the formats of dynamic streaming over HTTP (DASH) and employs CoAP for delivering the media segments to consumers. Some modifications are also made in default CoAP for DASCo to make the streaming service more efficient. The consumer's application requests the media segments consecutively by using the GET method of CoAP. CoAP's stop-and-wait data transfer can also help in downloading several media segments in parallel by changing the NSTART parameter of CoAP. The block-wise transfer is also useful in determining the progress of download and knowing when to stop it. Finally, to achieve delay sensitive live streaming, MAX_RETRANSMIT parameter of CoAP can be enhanced to get more retransmissions.
# The Influence of Tomato and Pepper Processing Waste on Bread Quality
## 3 Results and Discussion
### Texture Profile Analysis (TPA) of Bread
On the other hand, the addition of PB did not have a significant effect on the chewiness of the bread. The statistical analysis did not identify any significant changes in the cohesiveness of the bread crumbs based on the type of supplementation and the interactions between the variables (supplementation type and amount). Previous studies [7; 34; 35; 38] have reported on the transformative effects adding tomato (seeds and residue) and paprika powder has on crumb texture parameters. [PERSON] et al. [34] observed that an increase in the addition of tomato seed pomace resulted in a significant increase in wheat bread firmness. [PERSON] et al. [35] found that supplementation with powdered tomato seeds increased the hardness and chewiness of wheat bread crumbs while negatively affecting its cohesiveness. Interestingly, [PERSON] et al. [38], who used whole tomato powder for bread fortification, demonstrated that the hardness of enriched bread crumbs was lower than in their control sample, especially at higher levels of tomato powder supplementation. [PERSON] et al. [7], in their analysis of wheat bread fortified with whole pepper powder, showed that hardness, springiness, cohesiveness, and chewiness increased with the addition of paprika powder. During storage (72 h versus 24 h), there was an increase in the hardness and chewiness of the bread crumbs, while the cohesiveness values decreased in all recipe variants. In terms of springiness, the changes were minimal. In both the TP-fortified bread and the PB-fortified bread, the largest changes observed pertained to hardness, with slightly greater changes being observed in the bread fortified with TP. However, the values of this parameter
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Observation Models for Combined BDS-3, GPS, and Galileo RTK
#### 4.1.4 Stochastic Model  
During data processing procedure of ISB estimation and RTK positioning with Equations (3)-(5), the observational model is resolved with least squares adjustment and the exponential elevation weighting function is adopted as stochastic model [37; 38]:  
\[\sigma(\theta)=\sigma_{0}\Big{(}1+10\exp\Big{(}\frac{-\theta}{10}\Big{)}\Big{)} \tag{6}\]  
where \(\sigma(\theta)\) is the standard deviation (STD) of the undifferenced observation; \(\theta\) is the satellite elevation in degrees; \(\sigma_{0}\) is the a-priori zenith-referenced code and phase STD, which are set as 3 mm and 0.3 m for phase and code respectively in this contribution.
## 3 Results and Discussion
### TPC, TFC, and Antioxidant Activities
#### 3.1.3 Analysis of Molecular Dynamics  
The molecular dynamics (MD) approach was used to apply structural evolution to the docked complex structures of _BCL-2_ interaction. This work ran three separate MD simulations on the apo form of BCL-2, the complexes of _BCL-2_ with the known inhibitor BDA-366, and _BCL-2_ with Grewin. The examination of MD data is displayed in Figures 3 and 4. The RMSD versus the skeleton of every complex (after each complex had reached equilibrium, stability persisted for the whole 10 ns simulation period). In addition, throughout the simulation versus C atoms, root means square fluctuation (RMSF) analysis of all the complexes was computed. The computed RMSF values for each complex are shown in Figure 3B. After that, the compactness of the protein-ligand complexes analyzed by recording the ROG values were also obtained. For every complex, the radius of gyration is determined, as shown in Figure 4A. Further, Figure 4B shows the number of hydrogen bonds that have been computed and calculated. According to an MD simulation research, Grewin can effectively interact with _BCL-2_ to create a stable complex that is comparable to the control medication BDA-366.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protein** & **Compound** & \begin{tabular}{c} **Binding Energy** \\ **(kcal/mol)** \\ \end{tabular} & \begin{tabular}{c} **Hydrogen Bond** \\ **Interactions** \\ \end{tabular} & \begin{tabular}{c} **Hydrophobic** \\ **Interactions** \\ \end{tabular} & \begin{tabular}{c} **No. of Hydrogen** \\ **Bonds** \\ \end{tabular} \\ \hline \multirow{4}{*}{**BCL-2**} & Grewin & \(-7.5\) & ASP108; ARG143 & \
\begin{tabular}{c} PHE109; MET112; \\ ALA146 \\ \end{tabular} & 2 \\ \cline{2-6}  & Gmelofuran & \(-7.4\) & - & ARG104; LEU198; \\  & BDA-366 & \(-6.3\) & LEU134; ARG143 &
\begin{tabular}{c} ALA146; GLU133; \\ TRY105 \\ \end{tabular} & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interaction of compounds from Agallocha with amino acid residue of BCL-2.  
Figure 2: **(A)** Two-dimensional and three-dimensional docking interactions of Grewin with _BCL-2_. (B) Grewin and BDA-366 in complex with _BCL-2_ (Blue—Grewin, Orange—BDA-366).
## 2 Results and Discussion
### Synthesis and Characterization of the Co@g-C\({}_{3}\)N\({}_{4}\)/G Heterostructure Composites
The color of the Li\({}_{2}\)S\({}_{6}\) solutions visibly changed from orange-yellow to slight yellow after standing for 6 h of adsorption in the bottle containing the Co@g-C\({}_{3}\)N\({}_{4}\)/G material. However, almost no color change in the bottle containing g-C\({}_{3}\)N\({}_{4}\)/G was observed compared to the blank Li\({}_{2}\)S\({}_{6}\) solution. Even the color of Li\({}_{2}\)S\({}_{6}\) solutions absorbed by the g-C\({}_{3}\)N\({}_{4}\)/G material looked rufous, which may be due to the mapping of black material at the bottom of the bottle. This phenomenon indicates that the Co@g-C\({}_{3}\)N\({}_{4}\)/G material has stronger adsorption ability toward polysulfides, further showing that Co@g-C\({}_{3}\)N\({}_{4}\)/G can effectively anchor intermediate polysulfide species by the synergistic effect of physical adsorption and chemical interaction [42; 58]. Considering the unique affinity of Co@g-C\({}_{3}\)N\({}_{4}\)/G toward polysulfides, the Co@g-C\({}_{3}\)N\({}_{4}\)/G material was then used to modify the conventional PP separator for the improvement of electrochemical performance in Li-S cells. The modified separators were obtained using the vacuum-filtering method. In the optical images of functional separators using different coated materials (Figure 4b), an integrated modification layer without any detectable defect was seen, indicating the modified materials adhered well to the surface of the fresh separator. Scanning electron microscopy (SEM) was performed to further characterize the surface and cross-section morphologies of the modification layer. Evidently, the surface of the conventional PP separator in Figure 4c showed a typical macroporous feature, which easily led to the free diffusion of polysulfides across the separator. After introducing the Co@g-C\({}_{3}\)N\({}_{4}\)/G modification layer on the pristine PP separator, the macroproces in the original PP separator were fully covered by the Co@g-C\({}_{3}\)N\({}_{4}\)/G material (Figure 4d). Moreover, this still maintained an obvious large-size sheet-like morphology of the predominate graphene in the coating layer, with some big voids between the interlaced lamellae, as shown in Figure 4e. This unique sheet-like structure can exactly act as a physical barrier to block the migration of polysulfides, and the interlaced voids are beneficial to electrolyte penetration and Li ion transport. In addition, the cross-sectional SEM image (Figure 4f) of the modified separator shows that the Co@g-C\({}_{3}\)N\({}_{4}\)/G interlayer attached well to the surface of the fresh PP separator, as well as having a thickness of \(\sim\)20 um. Figure 3: (**a**) FT\(-\)IR and (**b**) XPS survey spectra of the prepared samples. (**c**) Comparisons of N 1s high\(-\)resolution XPS spectra in Co@g-C\({}_{3}\)N\({}_{4}\)/G and g-C\({}_{3}\)N\({}_{4}\)/G samples. (**d**) Co 2p high\(-\)resolution XPS spectra in the Co@g-C\({}_{3}\)N\({}_{4}\)/G heterostructure.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 3 Proposed CATR Routing Protocol for OppIoT Networks  
The design of our CATR protocol relies on the utilization of the beta distribution and the use of the node's context information to calculate the overall trust value of any node (which involves the calculation of its reputation). These values then serve to determine suitable non-malicious relay nodes that participate in the message routing process from source to destination nodes.  
The operational flow of our proposed CATR protocol is illustrated in Figure 1, which consists of (a) the Direct Trust (DT) Computation Module--which calculates the DT value of any node, (b) the Indirect Trust (IT) Computation Module--which calculates the Indirect Trust value of any node, and (c) the Overall Trust Computation Module--which aggregates the Direct Trust and Indirect Trust values of any node. We can describe this more precisely as follows:* The Direct Trust (DT) Computation Module involves five main sub-modules, namely (1) the Computation of Beta Trust Degree sub-module, (2) the Computation of Encounter Frequency Degree sub-module, (3) the Computation of Packet Forwarding Degree sub-module, (4) the Computation of Recent Contact Degree sub-module, and (5) the Computation of Contact Durability Degree sub-module;
* The Indirect Trust (IT) Computation Module calculates the IT value of any node based on the computation of its reputation index (RI), considering the reputations of that node gathered from its neighbors;
* The Overall Trust Computation Module computes the total trust value of any node.  
In this work, the calculation of trust value for a given node relies on the beta distribution and the design of a trust mechanism by mimicking the ideas inherited from [12]. The beta distribution is a type of probability distribution for possible values of a probability, which can be involved in the computation of the trust and reputation of a node in a network environment [12]. Through it, one can describe the distribution of the nodes' credibility by monitoring the nodes' behavior as completed [7]. This paper makes use of it jointly with a method to calculate the Direct and Indirect Trust of any node in order to determine its trustworthiness. The trust values of nodes are then used in deciding on the selection of relay nodes that are suitable to carry the data packets from their source node to the intended destination node.  
Figure 1: Working flow of our proposed CATR protocol.
## 2 Materials and Methods
### Geometric Accuracy and Contouring Time Evaluation  
Three parameters, Dice similarity coefficient (DSC) for quantification of degree of overlapping between two contours (Contour_A and Contour_B), Hausdorff distance (HD) defined as pairwise 3D point distance from one contour to another contour, and HD 95 th-percentile (HD95) were employed to evaluate the geometric accuracy of the 16 OARs contoured by the atlas-based and DL auto-contouring tools with the manual contours as the reference. Equations (1)-(3) were used for DSC, HD and HD95 calculations, respectively [5; 6; 10; 27].  
\[\text{DSC}=\frac{2(\text{Contour}_{\text{A}}\cap\text{Contour}_{\text{B}})}{ \text{Contour}_{\text{A}}+\text{Contour}_{\text{B}}} \tag{1}\]  
\[\text{d}_{\text{HD}}=\text{max}\bigg{(}\min_{\text{a}\in\text{A}}\text{d}( \text{a}),\min_{\text{b}\in\text{B}}\text{d}(\text{b})\bigg{)} \tag{2}\]  
\[\overset{\rightarrow}{\text{d}}_{\text{HD95}}(\text{A},\text{B})=\text{k}_{95 }\bigg{(}\min_{\text{b}\in\text{B}}\text{d}(\text{a},\text{b})\bigg{)},\text{ d}_{\text{HD95}}(\text{A},\text{B}) \tag{3}\]  
where a and b represent the points on contours A and B.  
A free, open source image computing platform, 3D Slicer 5.0.3 (The Slicer Community, Boston, MA, USA) with SlicerRT extension was used to determine the DSC, HD and HD95 values [9; 30; 31]. The DSC values, <0.50, 0.50-0.59, 0.60-0.69, 0.70-0.79 and 0.80-1.00 indicated poor, intermediate-poor, intermediate, good-intermediate and good degree of overlapping between two contours. For HD95, <4.0 mm, 4.0-5.9 mm, 6.0-7.9 mm and \(\geq\)8.0 mm represented good, intermediate, poor and very poor geometric accuracy [27].  
For the contouring time evaluation, the time required by the atlas-based and DL auto-contouring tools for the auto-contouring processes was recorded. Additionally, the same radiation therapist involved in the previous manual contouring process reviewed and edited the 16 OARs contoured by the two auto-contouring tools as per the clinical protocol of the hospital. The review and editing time was recorded as well [27]. No dataset with patient personal information was taken from the hospital.
## 1 Introduction
This method effectively integrates 2D image slices into 3D images, with the added flexibility to accommodate varying numbers of slice images according to the target scenario [38]. Overall, the third solution achieves a balance between performance and computational costs, making it more suitable for 3D segmentation of soil CT images; therefore, we will also adopt the third solution. In this study, we present a novel architecture, termed the bidirectional U-Net with long short-term memory (BDULSTM), which leverages the strengths of both U-Net and convolutional LSTM (CLSTM) to improve image segmentation performance. U-Net's encoder-decoder structure is a formidable two-dimensional segmentation method, exhibiting notable efficiency [27]. The encoder captures a broad contextual representation through progressive downsampling of features, while the decoder reconstructs the features back to the original input resolution for detailed pixel-level segmentation. Additionally, U-Net integrates skip connections that bridge encoder and decoder outputs across several layers, facilitating the preservation of spatial details that could potentially be lost in the downsampling process. The CLSTM is utilized to process two-dimensional sequences of soil CT images. The CLSTM module is strategically situated at the nadir of the U-shaped network, interfacing the encoder and the decoder, to amalgamate the image features from adjacent slices. The BDULSTM architecture is adept at processing sequential data while retaining the robust feature extraction capabilities of U-Net. By combining CLSTM's proficiency in sequence analysis with U-Net's structural benefits, our approach facilitates three-dimensional segmentation of soil CT images, thereby eliminating the need for three-dimensional convolution. The primary aim of this study is to propose an innovative three-dimensional soil pore segmentation methodology, referred to as BDULSTM, and to conduct a comparative analysis of this method with existing pore identification techniques reported in the literature via both quantitative and qualitative evaluations. A secondary objective is to examine the effects of varying slice counts in CT images on segmentation accuracy and the complexity of the model. This study demonstrates that the BDULSTM-based method for three-dimensional soil pore segmentation offers critical technical support for endeavors such as digital soil characterization and multi-scale soil modeling.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming  
### Considerations of the WMO  
The WMO establishes that the principal parts of a climate data monitoring system are the AWSs and the centralized processing system. It also specifies that such scenarios as interruptions in the energy supply, security failures, adverse climatic conditions, and other factors that could compromise the continuous registration of data must be foreseen and managed.
## 3 Results
### Effects of Digestion Agents on PS-MPs: Student's t-Test  
The effects of the selected digestion agents (i.e., KOH 5 M at room temperature and 50 \({}^{\circ}\)C for 48 h) were investigated. Untreated PS-MPs (n = 116) exhibited an average diameter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Digestion Reagent** & **Temperature/Time** & **Concentration** & **Digestion Efficiency** \\ \hline
**KOH** & 50 \({}^{\circ}\)C for 48 h & 5 M & 97.5 \(\pm\) 0.8\% \\ \hline
**KOH** & RT for 48 h & 5 M & 96.7 \(\pm\) 2.6\% \\ \hline
**KOH** & 50 \({}^{\circ}\)C for 48 h & 1 M & 91.6 \(\pm\) 1.5\% \\ \hline
**KOH** & RT for 48 h & 1 M & 89.7 \(\pm\) 2.1\% \\ \hline
**Fenton’s Reagent** & \textless{}50 \({}^{\circ}\)C for 20–30 min & - & 87.3 \(\pm\) 3.4\% \\ \hline
**HNO\({}_{3}\)** & RT for 72 h & 65\% & 82.3 \(\pm\) 1.2\% \\ \hline
**H\({}_{2}\)O\({}_{2}\)** & 50\({}^{\circ}\)C for 48 h & 30\% & 67.3 \(\pm\) 5.3\% \\ \hline
**H\({}_{2}\)O\({}_{2}\)** & RT for 48 h & 30\% & 64.1 \(\pm\) 3.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: % DE of agents tested. Results of % DE are obtained from three experimental trials.  
Figure 2: % RR and standard deviation (SD) of PS-MPs spiked on cheese digested with KOH 5 M RT and KOH 5 M at 50 \({}^{\circ}\)C.  
of 11.9 \(\pm\) 1.1 \(\upmu\)m. PS-MPs treated with KOH 5 M at room temperature and 50 \({}^{\circ}\)C showed average diameters of 11.3 \(\pm\) 0.2 \(\upmu\)m and 11.1 \(\pm\) 0.7 \(\upmu\)m, respectively. A Student's \(t\)-test was conducted to determine if the size reduction was statistically significant. Student's \(t\)-test confirmed a statistically significant difference between treated and untreated PS-MPs (Table 2).  
In both cases, the calculated t-values (5.5 and 4.7 for KOH 5 M at 50 \({}^{\circ}\)C and room temperature (RT), respectively) exceed the critical two-tailed t-values (1.98 and 1.97 for KOH 5 M at 50 \({}^{\circ}\)C and RT, respectively), indicating a slight statistical difference between the tested PS-MPs. Furthermore, the \(p\)-values for both cases are lower than the predetermined significance level (\(\alpha\)) of 0.05. This suggests that the null hypothesis (i.e., no statistical difference) should be rejected.
## 2 Materials and Methods
#### 2.2.5 User Interface Prototype  
* Graphic layout for the encoding mode Figure 7 presents the proposed prototype of the user interface of the interactive Hamming model by the general method in the \"Encoder\" mode. It consists of four panels: Panel 1. Control panel. Panel 2. Information register. Panel 3. Hamming encoder--a simple (general) algorithm. It represents the actual part of the model. In it, the calculations of the control bits are performed. The main actions that will be performed in this panel are as follows:  
- Marking the bits of the code combination. Initially, the bits of the code combination are undefined and have no values. From a list of pre-generated labels (\(S_{i}\) and \(C_{i}\)), the label is selected and placed on the corresponding bit of the code combination by dragging. After correctly marked bits, the bits from the information register are loaded, using a special button, and the control bits have no values at this stage.  
- Selecting the control bit. The desired control bit is selected from the code combination by clicking on it.  
- Compiling an equation. By clicking on the corresponding bits of the code combination, an equation from the selected labels is automatically filled in a special field, and at the same time after the equation, the bit values are also substituted.  
- Calculating the check bit. By clicking on the field with the equation, the value of the expression is written with the substituted values from the equation and the corresponding value is inverted with each click.  
- Writing the control bit. Once calculated, writing the value from the equation is done by clicking on the same control bit of the code combination that was selected earlier.  
Panel 4. Algorithm. It is based on the algorithm of Figure 6a.
* Graphical layout for the decoding mode The prototype of the user interface in the decoding mode is presented in Figure 8. It consists of four panels, similar to the encoding mode interface.  
Figure 8: Prototype of the user interface of the Hamming code research model—the general method, in the Decoder mode.  
Figure 7: Prototype of the user interface (UI) of the Hamming code research model—the general method, in the Encoder mode.  
Panel 1. Control panel.  
Panel 2. Code combination register.  
Panel 3. Hamming decoder--the general algorithm. Checks of the control bits are performed in it. The main actions that will be performed in this panel are as follows:  
* Select the control bit. The desired control bit is selected from the code combination by clicking.
* Formulating an equation. By clicking on the corresponding bits of the code combination, the equation of the selected bit is automatically filled in a special field, while at the same time, the bit values are replaced after the equation.
* Check bit calculation. After the equation is created, clicking on its field displays the value of the control bit, inverting it with each click.  
Panel 4. Algorithm. It is based on the algorithm of Figure 6b.
###### Abstract  
Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m\({}^{3}\) ton\({}^{-1}\), followed closely by RF-SVR at 5.05 m\({}^{3}\) ton\({}^{-1}\), while the minimum MBE was 0.03 m\({}^{3}\) ton\({}^{-1}\) in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R\({}^{2}\) values throughout all scenarios. Notably, the R\({}^{2}\) values for dual hybrid models surpassed those of triple hybrid models. The highest Nash-Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.  
s 2024 16 3241 10.3390/w16223241 10.3390/w16223241 10.