esa-sceva/satcom-chunk-collection huggingface dataset:
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 2 All-in-Focused Image Combination
### Sum of Updated Modified Laplacian (SUML)  
In the proposed method, we improve the original SML and use it as a part of the focus measurement since the SML gives better efficiency than other focus measurement criteria [9]. When we consider the DCT coefficients, the higher energy property of the AC coefficients implies meaningful information in the in-focused region. Because the AC coefficients \(D(4,5)\), \(D(5,4)\), and \(D(4,4)\) are more important than other coefficients [12], we choose the AC coefficient \(D(4,4)\) for focus value calculation. The original modified Laplacian (ML) only considers variations in the x and y directions [13]. Thus, we modify the original ML and utilizes \(D(4,4)\). This value is small for both in-focused and out-focused parts in the homogeneous region. Thus, we propose the updated modified Laplacian (UML) for the block \(B(x,y)\) to include the diagonal directions and combine all the information of its neighborhood including sharp in-focused parts around the block. UML is defined by  
\[\begin{array}{rl}\
abla_{UML}^{2}B(x,y)=&\left|2D(4,4)-D(4-step,4)-D(4+step,4 )\right|\\ &+\left|2D(4,\,4)-D(4,\,4-step)-D(4,\,4+step)\right|\\ &+\left|2D(4,\,4)-D(4-step,4+step)-D(4+step,4-step)\right|\\ &+\left|2D(4,\,4)-D(4-step,4-step)-D(4+step,4+step)\right|\end{array} \tag{2}\]  
Figure 2: Input images: (**a**–**h**) of different focal lengths.  
where (\(x\),\(y\)) represents the block position, \(D\)(\(u\),\(v\)) is the AC coefficient at the position (\(u\),\(v\)) of the block \(B\)(\(x\),\(y\)). In (2), '_step_' is a fixed value, set as 1 in this paper. The focus measure at block \(B\)(\(x\),\(y\)) is computed as the _SUML_ value in the window around \(B\)(\(x\),\(y\)). SUML is expressed by  
\[\begin{array}{l}\textit{SUML}(x,y)\ =\ \sum_{i=x-N}^{i=x+N}\sum_{j=y-N}^{j=y+N} \delta(i,j)\ \ \text{where}\ \ \ \ \ \delta(i,j)\ \ =\ \ \left\{\begin{array}{cc}\
abla_{\textit{UML}}^{2}B(x,y),\ \
abla_{\textit{UML}}^{2}B(x,y)\geq T_{\textit{SUML}}\\ 0,\ \text{otherwise},\end{array}\right.\end{array} \tag{3}\]  
where \(\delta(i,j)\) represents the UML value that follows the threshold \(T_{\textit{SUML}}\) condition. The window size around \(B\)(\(x\),\(y\)) is \(N\times N\).
## 4 Distance-Based Energy Aware Routing (DEAR) Algorithm
### DEAR Algorithm
#### 4.2.4 Route Maintenance Phase  
As can be seen from Figure 5, link failure will be detected and route maintenance phase will be initiated if an ACK message is not received for certain TTL (time-to-live) time. Link failure is usually caused by reasons like node dies, interference, _etc._  
If the source node detects a link failure, it will restart the route setup phase by choosing another next hop in a similar way above. If an intermediate node detects a link failure, it will first attempt a local link repair process. In other words, the intermediate node will first try to choose another proper neighbor in order to fix the route to BS. In this way, the end to end latency can get reduced and the energy consumption as well as overhead can be reduced. This local repair process will last for certain time until an ACK message is received or when TTL is expired.  
If the local link repair process fails, a RERR message will be sent from intermediate node to source node in a reverse way based on the information stored in RREQ. Finally, this route will be deleted from source node and intermediate nodes and a new route setup phase will be initiated.
## 6 Discussion
For individual target variables S, Sr, and g02, before the introduction of the new input attributes (i.e., without the measurement date and the satellite-derived ones), the correlation of their estimated values with the true ones was 0.23, 0.23, and 0.12, respectively. After adding the satellite-derived attributes, these correlations rose to 0.50, 0.33, and 0.46, respectively. The maximum correlations were achieved after the measurement date (Day of Year, number of the middle day) was also added; the correlations then rose to 0.54, 0.40, and 0.64, respectively. Among the three modeled target variables, the highest accuracy was achieved for g02, where the relative absolute error was 0.762 (down from original 1.04) and the correlation coefficient was 0.64 (up from original 0.12). This striking difference can also be observed in Figure 5: on the left are the results of our earlier attempts to estimate the long-term upper bounds of g02 purely from the SHMU input attributes [15], and on the right, the results of our present attempt to directly estimate the field-measured values of g02 from satellite-derived attributes and the measurement date. In both cases, the ideal situation in which the estimated values equal the predicted ones is represented by the dashed diagonal line, while the solid green line represents the actual linear regression function. Although it is evident that more work needs to be done before these models can actually be used in practice, we consider these results a success and an important milestone. Finally, we would like to point out that we observed significant seasonality for all target variables, which was clearly shown by their high correlations with the measurement date. In fact, the effect of the measurement date alone nearly equaled the combined effect of all our satellite-derived input attributes. This means that the local levels of environmental pollution undergo relatively stable seasonal variations of a very general character, at least as represented by the three target variables S, Sr, and g02.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow  
A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data is presented here. The workflow was created in order to be used within the Sentinel application platform (SNAP), a common architecture for all Sentinel satellite toolboxes. The processing graph in 'xml' format allows the processing of Sentinel-1 GRD using the command line graph processing framework, which allows for batch processing of large datasets.  
The preprocessing workflow consists of seven processing steps, designed to best reduce error propagation in subsequent processes, described hereafter in separate subsections. The code to perform the preprocessing workflow is available on the GitHub repository [1] and in the Supplementary Materials as Computer Code 1.
## 3 Results
### Variable Importance  
The importance of each variable was assessed using two indicators provided by the RF model: mean decrease accuracy (%IncMSE) and mean decrease in node impurity (IncNodePurity) [56; 57]. The first indicator measures the decrease in accuracy and, consequently, the percentage increase in MSE when a variable is excluded from the model. A variable with high percentage increases in MSE has more predictive power than others, because when it is removed from the model the accuracy in estimating 1-h fuel load decreases. The second indicator measures the decrease in Gini impurity when a variable is chosen to split a node. The higher the value of mean increase in node purity, the greater the importance of the variable in the model (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE** & **MSE** & \(r\) & \(\mathbf{R^{2}}\) \\ \hline
**Subsample 1** & 0.10 & 0.01 & 0.70 & 0.50 \\
**Subsample 2** & 0.13 & 0.02 & 0.66 & 0.44 \\
**Subsample 3** & 0.10 & 0.01 & 0.68 & 0.47 \\
**Subsample 4** & 0.11 & 0.01 & 0.60 & 0.36 \\
**Subsample 5** & 0.10 & 0.01 & 0.68 & 0.46 \\
**Subsample 6** & 0.10 & 0.01 & 0.67 & 0.45 \\
**Subsample 7** & 0.10 & 0.01 & 0.65 & 0.42 \\
**Subsample 8** & 0.11 & 0.01 & 0.63 & 0.39 \\
**Subsample 9** & 0.10 & 0.01 & 0.71 & 0.51 \\
**Subsample 10** & 0.09 & 0.01 & 0.71 & 0.57 \\ \hline \hline \end{tabular} RMSE = Root Mean Squared Error; MSE = Mean Squared Error; \(r\) = [PERSON]’s correlation coefficient; R\({}^{2}\) = coefficient of determination.  
\end{table}
Table 4: Cross-validation: performance metrics of the Random Forest model evaluated in 10 training and testing subsamples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE** & **MSE** & \(r\) & \(\mathbf{R^{2}}\) \\ \hline
**MLR** & 0.11 & 0.01 & 0.63 & 0.40 \\
**SVM** & 0.10 & 0.01 & 0.63 & 0.39 \\
**RF** & 0.09 & 0.01 & 0.71 & 0.50 \\ \hline \hline \end{tabular} RMSE = Root Mean Squared Error; MSE = Mean Squared Error; \(r\) = [PERSON]’s correlation coefficient; R\({}^{2}\) = coefficient of determination; MLR = Multiple Linear Regression; SVM = Support Vector Machine; RF = Random Forest.  
\end{table}
Table 3: Performance metrics of the machine learning models used in the study.  
The most important variables influencing 1-h fuel load estimation were the 95 th percentile of CHM (CHM_maxq) and canopy cover (Canopy_cover) values, increasing the MSE by more than 10% (15.71% and 11.35%, respectively); both variables were randomly permuted (Figure 5A). NDVI and the other variables related to geomorphological data (Slope and Elevation) showed low importance values (<5%), while VH and Easternness were considered less important for the RF model. The importance of the CHM_maxq and Canopy_cover variables, followed by Vegetation_cover, was also confirmed by the mean decrease in Gini impurity, which indicated that these variables have the highest values of node purity and the highest degree of importance (Figure 5B).
## 2 Background and Related Work  
In this section, we first provide a brief account of IAB technology focusing on the main features introduced by 3 GPP. Then, we overview the recent studies in the field and provide their exhaustive classifications.
## 2 Methods
### Kernel Partial Least Squares Algorithm (KPLS)
Three typical kernel functions with local characteristics are as follows:  
\[\text{Gaussian kernel function}:\ \mathbf{K}_{\text{Gau}}(x,y)=\exp \left(-\frac{\|x-y\|^{2}}{2\sigma^{2}}\right) \tag{8}\] \[\text{Cauchy kernel function}:\ \mathbf{K}_{\text{Gauchy}}(x,y)= \left(-\frac{\|x-y\|^{2}}{\delta^{2}}+1\right)^{-1}\] (9) \[\text{Laplacian kernel function}:\ \mathbf{K}_{\text{ Laplace}}(x,y)= \exp\left(-\frac{\|x-y\|}{\gamma}\right) \tag{10}\]  
where \(\sigma\), \(\delta\), and \(\gamma\) are the width parameters of the three kernel functions, respectively. At \(x=0.2\), Figure 2 shows the characteristic curves of three kernel functions with local characteristics under the same width parameter. Figure 1: The characteristic curve of the polynomial kernel function. All three kernel functions have strong local characteristics, as shown by the fact that the values of the kernel functions are relatively large near the test point, while the values of the kernel functions become progressively smaller for data far away from the test point. However, the sensitivity of the three kernel functions to the width parameter has some differences. Based on the above analysis, it can be seen that for the same original low-dimensional nonlinear input variable space, using different types of kernel function types and kernel function parameters can characterize the inherent properties of the original variable space from different perspectives and map it to a diverse LVS.
# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
## 5 Case Study
Inconsistencies--the presence of multiple, isolated data sources managed by various individual organizations along the supply chain can result in discrepancies, the reconciliation of which can be resource-consuming (e.g., time, financial, human). * SC3. Quality--product quality can be affected by the lack of real-time tracking capabilities of the organizations involved in the supply chain (e.g., scaling, monitoring, and easy setup). * SC4. Data accuracy and integrity (Immutability)--unauthorized data tampering is not allowed. Any alterations must be detected. * SC5. Data privacy and security--open-source solutions that can be peer-reviewed are often trusted, as compared to closed sources solutions. * SC6. Decentralization--the transfer of authority from the organization, as the owner of the data, to the stakeholders. * SC7. Audits--the use of different data sources can lead to difficult, time- consuming audits. STP is a tool for tracking and tracing assets across different value chains. It is designed to be used by different types of organizations, such as SMEs, large companies, or non-profit entities, from different industries. One of the core features of STP is the ability to transfer traceability and value chain data to a decentralized or centralized ledger by providing the end-users with the possibility of making specific data publicly and securely available. The system architecture of the solution is described in the following section, while Table 7 presents the manner in which our solution addresses the above specific challenges. \begin{table}
\begin{tabular}{c c} \hline
**BigchainDB** & **QLDB** \\ \hline Decentralized, single or multiple nodes (SC6) & Cryptographically Verifiable (SC1, SC4, SC5) \\ \hline Immutability (SC4) & Serverless architecture (SC3) \\ \hline Open source (SC5, SC7) & Centralized (SC2) \\ \hline Public or private ledger (SC1) & Private or public ledger (SC1) \\ \hline \end{tabular}
\end{table} 
Table 7: BigchainDB and QLDB features against the specific challenges.
## 3 Results and Discussion
If one tries preparing highly selective RO membranes using porous nanomaterials with larger pores than hydrated ions, the interior surface functionalization of the nanomaterials will need to be carried out as demonstrated in the previous simulation [40]. A few porous nanomaterials are listed in Table 1 as potential candidates that could be used to prepare highly selective RO membranes without compromising water permeability. However, this does not necessarily mean that porous nanomaterials with the pore size corresponding to the above ranges always meet the requirement to make highly selective RO membranes due to some performance influence factors. First, porous nanomaterials need to be individually incorporated in an active layer during interfacial polymerization to ensure their intrinsic properties, in terms of selective water transport, are clearly exhibited in an active layer during filtration. If porous nanomaterials were incorporated in an active layer in the form of aggregates (e.g., zeolite) or used after being embedded in much larger materials (e.g., aquaporin Z), it could be hard to demonstrate their intrinsic material effects regarding selective water transport due to the interstitial space between individual nanoparticles or the gap between the vesicle and the polymer matrix of an active layer. It is also essential that interfacial polymerization should not be significantly affected by nano-additives because there is concern that polyamide chains' changes (e.g., variable  
Figure 3: The specific energy consumption (SEC) as a function of membrane permeability. The SEC was plotted based on the data for BWRO summarized from [PERSON] et al. [39]. BWRO is modeled with a 2000 ppm NaCl feed solution at 65% recovery. crosslinking degree), possibly caused by the additives, may cancel out the addition effect of the porous nanomaterials. Lastly, the pore size of an active layer needs to be controlled as low as those of the used porous nanomaterials. If the polymer matrix in an active layer has even larger pores than the nano-additives, diffusion species will pass mainly through the pores of the loose polymer matrix instead of the pores of the nano-additives. This too will preclude us from observing the intrinsic characteristics regarding selective water transport of porous nanomaterials.
## 3 Results and Discussion
### Effect of Working Conditions on Penetration Distance
Figure 13: Effect of viscosity on lateral penetration distance. As shown in Figures 15 and 16, the impact of back pressure on penetration distance is similar to viscosity. This is because the increases in back pressure and viscosity both increase flow resistance. With the increase in back pressure, both the lateral and vertical penetration distances of the tow grouting methods exhibit an exponential decrease. The percentage increase in the lateral distance gradually increases with the increase in back pressure, while the percentage increase in the vertical distance first increases and then decreases. Figures 17 and 18 present the impact of grouting pipe length on penetration distance. With the increase in the length of the grouting pipe, the lateral penetration distance for both grouting methods decreases exponentially. As the grouting depth increased from 2 m to 8 m, the lateral dispersion distance for steady pressure grouting decreased from 259 mm to 238 mm. The lateral dispersion distance for pulsatile grouting decreased from 321 mm to 291 mm. The increase in proportion falls within the range of 23.9% to 22.2%. The results also show that vertical penetration distance remains relatively unchanged. This is because, although the length of the grouting pipe has increased, the pressure in the grouting pipe  
Figure 14: Effect of viscosity on vertical penetration distance. Figure 15: Effect of back pressure on lateral penetration distance. remains unchanged, which has a relatively small impact on vertical penetration. However, as the slurry spreads upward along the annular space, there is a gradual loss of pressure, which weakens the pulsation effect. It can be observed that there is a connection between different parameters and the dispersion behavior of the slurry. Regarding the lateral distance, the greater the flow resistance, the better the performance of pulsating grouting. However, for vertical dispersion distance, there is a critical value, and when the flow resistance is too high, the performance of the pulsating slurry decreases. Figure 16: Effect of back pressure on vertical penetration distance. Figure 8:0 Effect of length of grouting pipe on lateral penetration distance.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
###### Abstract  
Message Queuing Telemetry Transport (MQTT) enables asynchronous confirmation of message reception by brokers but lacks a way for publishers to know when subscribers receive their messages without adding additional communication overhead. This paper addresses this problem by improving MQTT to establish end-to-end communication between a publisher and subscribers, reducing message exchanges, using what is called End-to-End MQTT (E-MQTT). In E-MQTT, a publisher sets the number of responses that it will wait for when it sends a message. After the broker collects the response messages from subscribers, it sends one aggregated response back to the publisher. The publisher also can receive the response message synchronously or asynchronously. Experimental results consistently show that E-MQTT outperforms traditional MQTT in terms of delay, especially when the publisher needs to monitor when its query message is received by subscribers. Although E-MQTT packets are slightly larger due to additional fields, the difference in packet size compared to MQTT is not significant.  
Keywords:asynchronous communication; end-to-end communication; MQTT; publish-subscribe model; synchronous communication +
Footnote †: journal: Applied
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Evaluation Metrics  
To compare the performance of our proposed model to those from other studies, we used the evaluation metrics proposed in [2], which have been widely applied to evaluate the performance of monocular depth prediction methods. The evaluation metrics are shown below:  
\[\mathbf{AbsRel}=\frac{1}{|T|}{\sum_{i\in T}\frac{\left|d_{i}-d_{i}^{*}\right|}{d _{i}^{*}}} \tag{6}\]  
\[\mathbf{SqRel}=\frac{1}{|T|}{\sum_{i\in T}\frac{\left\|d_{i}-d_{i}^{*}\right\| ^{2}}{d_{i}^{*}}} \tag{7}\]  
\[\mathbf{RMSE}=\sqrt{\frac{1}{|T|}{\sum_{i\in T}\left\|d_{i}-d_{i}^{*}\right\| ^{2}}} \tag{8}\]  
\[\mathbf{RMSElog}=\sqrt{\frac{1}{|T|}{\sum_{i\in T}\left\|log(d_{i})-log(d_{i}^ {*})\right\|^{2}}} \tag{9}\]  
\[\mathbf{Accuracy}=\%\;of\;d_{i}\;s.t.\;max(\frac{d_{i}}{d_{i}^{*}},\frac{d_{ i}^{*}}{d_{i}})\;=\;\delta<\textit{threshold} \tag{10}\]  
where \(d_{i}\) and \(d_{i}^{*}\) are the predicted depth value of pixel \(i\) and the ground truth depth value of pixel \(i\), respectively, and \(T\) is the total number of valid ground truth pixels.
## 2 Materials and Methods
### Statistical Analysis  
Continuous variables (age, diameter, area, circumference, volume) are presented as means \(\pm\) standard deviation in cases of normal distribution or median + interquartile range in cases of not normal distribution. Categorical variables (sex) are presented as frequencies and percentages. Pearson or Spearman correlation was used to assess association between MV dimensions and LA volume, and linear regression was used to obtain the formula that predicts LA volume based on MV area. Bland-Altman test of agreement was used to establish limits of agreement and the mean bias between measured volume and estimated LA volume using the formula. All tests were performed using SPSS statistics program version 25, and \(p\) values < 0.05 were considered statistically significant.
## 2 Materials and Methods
### Phytochemical Research  
#### 2.3.1 Analysis of Phenolic Compounds by UPLC-MS/MS  
Phenolic compounds in _Filipendula_ extracts were analysed both quantitatively and qualitatively using a UPLC-MS/MS system. Chromatographic separation was performed on an Acquity H-class UPLC chromatograph (Waters, Milford, MA, USA) fitted with a YMC Tiart C18 column (dimensions: 100 \(\times\) 2.0 mm, 1.9 \(\upmu\)m). A 1 \(\upmu\)L sample of the extracts was injected. The column temperature was consistently kept at 40 \({}^{\circ}\)C, and the mobile phase flowed at a rate of 0.5 mL/min. For solvent A, an aqueous solution of 0.1% formic acid was utilised, while solvent B consisted of pure acetonitrile. A gradient elution program was applied under the following conditions: solvent B at 5% from 0 to 1 min, increasing to 30% between 1 and 5 min, followed by a linear rise to 50% from 5 to 7 min. Between 7.5 and 8 min, the column was rinsed using solvent B, and from 8.1 to 10 min, the column was re-equilibrated back to its starting conditions of 5% solvent B. Chemical structure analysis of phenolic compounds was conducted using a Xevo triple quadrupole tandem mass spectrometer (Waters, Milford, MA, USA). Negative electrospray ionisation (ESI) mode was utilised to produce ions for MS/MS analysis. The parameters for MS/MS included a capillary voltage set to \(-\)2 kV, nitrogen gas for desolvation heated to 400 \({}^{\circ}\)C at a flow rate of 700 L/h, a gas flow rate of 20 L/h, and an ion source temperature maintained at 150 \({}^{\circ}\)C. Phenolic compounds were identified qualitatively by comparing their MS/MS spectral data and retention times with those of analytical-grade standards. Quantitative determination was performed using standard dilution methods and linear regression fit models [27,28].
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 3 Numerical Preparations
### Mechanical Analyses  
The temperature history received from the previous step was used as an input loading for mechanical analysis. The identical finite element models with meshing sizes equal to those in the thermal analysis were employed for the entire mechanical analysis, except for the analysis of the boundary condition and element type. The total strain at any node in the welded plate is defined as follows:  
\[\varepsilon^{total}=\varepsilon^{e}+\varepsilon^{p}+\varepsilon^{ther} \tag{10}\]  
where \(\varepsilon^{total}\), \(\varepsilon^{e}\), \(\varepsilon^{p}\), and \(\varepsilon^{ther}\) are the total, elastic, plastic, and thermal strains, respectively. In this study, [PERSON]'s law with the temperature-dependent Young's modulus and Poison's ratio were employed to model the elastic strain. In addition to this, for the plastic strain part, the von Mises yield surface and temperature-dependent material properties were used to model plastic strain.