esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Instrumentation and Methods
### Instruments and Measurements  
The EC system is composed of a 3-D sonic anemometer (Model CSAT3, Campbell Scientific Inc., Logan, UT, USA), an open-path CO\({}_{2}\)/H\({}_{2}\)O analyzer (Model LI-7500, LI-COR Inc., Lincoln, NE, USA), and a CR5000 datalogger (Campbell Scientific Inc., Logan, UT, USA). The EC system is installed at 23.6 and 39.6 m above ground to measure CO\({}_{2}\) flux in Qianyanzhou subtropical coniferous forest [69; 72], and the hourly observational data (NEE/NEP, Re, and GPP) at 23.6 m from 1 January 2003, to 31 December 2017, were used in this study. Radiation (upward and downward short wave solar radiation, and upward and downward longwave radiation) and PAR were measured using radiometers (a four-component net radiometer, model CNR1, Kipp and Zone, Delft, ZuidHolland, the Netherlands; LI-190 SB Quantum Sensor, LI-COR, Inc., Lincoln, NE, USA) and recorded  
Figure 1: A sketch map about the site of Qianyanzhou subtropical coniferous forest (red point), located in Taihe County, Jiangxi Province, China.  
by a data-collection system (41.6 m for shortwave and longwave solar radiation, and 23.6 m for PAR). Solar radiation (global radiation Q, direct radiation D, scattered radiation S = Q\(-\)D \(\times\) cos (Z), Z is the solar zenith angle, and PAR) and meteorological variables were also measured by another set of solar radiation systems ([PERSON]'s group) from 21 May 2013-31 December 2016, at the Qianyanzhou station [70]. Meteorological variables (air temperature (T, \({}^{\circ}\)C), relative humidity (RH, %), and water vapor pressure (E, hPa)) from 2003-2017 were obtained using a sensor (HMP45C, Vaisala, Helsinki, Finland) [72; 73]. Figure 2 displays CO\({}_{2}\) flux, solar radiation, and meteorological measurement instruments operated by the Qianyanzhou experimental station and [PERSON]'s group, respectively.
## 5 Conclusions  
In this work, the effectiveness of various configurations of a thermal energy storage (TES) system using a phase change material (PCM) placed inside a spherical capsule has been investigated.  
Experimental tests were performed to analyze the melting (TES charging) and solidification (TES discharging) of the PCM: it has been verified that by increasing the wall temperature by 15 \({}^{\circ}\)C (from 56 to 71 \({}^{\circ}\)C), the time to complete the charge (i.e., to reach thermal equilibrium) decreased by approximately 70%, while the temperature trend was independent of the initial temperature during solidification.  
The benefits of introducing a solid foam inside the PCM, to increase its thermal conductivity and thus to reduce the system's response time, was then investigated by numerical simulations.  
If the whole operation cycle is considered, i.e., both charging and discharging phases of the TES, and despite the difficulties encountered in reproducing the temperature trend of the PCM during melting, the following conclusions can be summarized:  
* The use of a SiC solid foam increases the conductivity of the material encapsulated in the sphere but hinders heat exchange due to convective motions;
* For small increases in thermal conductivity, relatively small reductions in the operation times are obtained (only 10 min during melting compared to the case of pure paraffin);
* Considering the practical difficulties to set up such systems and their costs, this solution does not bring substantial benefits, unless a significant increase in the thermal conductivity of the material is obtained (more than 50 fold that of the pure PCM).  
Finally, it was found that the use of highly conductive wall materials does not appreciably increase the system efficiency, due to the much larger influence of the insulating layer of the solid PCM.  
Conceptualization, C.M. and R.B.; methodology, C.M. and F.V.; software, C.M.; validation, C.M. and F.V.; investigation, C.M.; data curation, C.M. and A.N.M.; writing--original draft preparation, A.N.M. and R.B.; writing--review and editing, F.V., A.N.M. and R.B.; supervision, F.V. and R.B. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the MASE (Italian Ministry of Environment and Energy Security) and ENEA.  
Not applicable.  
The datasets presented in this article are not readily available due to technical limitations. Requests to access the datasets should be directed to corresponding authors.
# Self-Sovereign Identity-Based E-Portfolio Ecosystem
## 2 Background and Related Work
### Hyperledger Fabric (HLF)
#### 2.2.3 Offline Signing  
HLF facilitates the development of services through software development kits (SDKs) tailored for various programming languages. These SDKs empower service providers in constructing their offerings. While web services provide convenience with their user-friendly interface and minimal need for additional installations, the present SDK faces constraints when utilized in browsers due to compatibility challenges.  
Typically, service providers furnish an SDK environment for users to execute transactions and serve as identity managers. This involves storing the user's X.509 certificate and \(P_{r}k\), as depicted in Figure 1. In this situation, as a user triggers a request, the service provider employs the stored \(P_{r}k\) to sign transactions. While this method is convenient, it may fall short of meeting stringent privacy requirements. The storage of \(P_{r}k\)s in services introduces vulnerabilities, potentially leading to identity fraud and unauthorized transactions.  
Offline signing emerges as a solution to address user concerns about \(P_{r}k\) security. Illustrated in Figure 2, this approach keeps the \(P_{r}k\) securely with the user. When a user wants to create a transaction, the service provider generates a transaction draft based on the user's certificate and returns it. The user then signs the draft with their \(P_{r}k\) and submits the signed transaction to the blockchain network through the service provider.  
By adopting offline signing, \(P_{r}k\)s are not stored with external organizations, granting users a self-sovereign identity. This approach enhances security by preventing unauthorized access to \(P_{r}k\)s, giving users full authority over their transactions. In summary, offline signing is an effective solution for safeguarding the security and privacy of user transactions on the HLF.  
Figure 1: Service providers overseeing a user’s identity management.  
Figure 2: Offline signing flow.
###### Abstract  
(1) Background: Soil stability is essential for hydroseeding applications, particularly in erosion-prone areas. This study examines the effects of coir fiber reinforcement on soil properties and optimizes fiber length and content for improved performance. (2) Methods: Triaxial tests, soil physical measurements, and cracking experiments were conducted on sandy and silty soils using five fiber lengths (1-5 cm) and three fiber contents (0.2-0.6%). Principal component analysis (PCA) and Response Surface Methodology (RSM) were applied for optimization. (3) Results: The results show that coir fiber increases soil cohesion, shear strength, porosity, and permeability while reducing bulk density. The best reinforcement occurred at a 3-4 cm fiber length and 0.4-0.6% content, enhancing both the shear strength and crack resistance. Correlation analysis indicated a positive relationship between porosity and shear strength and a negative correlation between crack ratio and shear strength, confirming fiber reinforcement benefits. RSM analysis identified 3.051 cm + 4.07% as optimal for sandy soil and 3.376 cm + 0.456% for silty soil. (4) Conclusions: The optimal coir fiber combination significantly improves soil stability, providing theoretical support for optimizing spray substrates.  
Keywords:soil spray seeding; spray substrate; coir fiber; fiber reinforcement; shear strength +
Footnote †: journal: Applied sciences
# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 3 Methodology
### The Inputs for the Negative Sample Selector  
The negative sample selector takes the randomly sampled unobserved edges as inputs. To promote the negative sample selector to select high-quality negative samples from the randomly sampled unobserved edges, a state vector is designed for each randomly sampled unobserved edge. Two kinds of information are utilized to compose the state vector of a randomly sampled unobserved edge.  
To avoid choosing missing edges as negative samples, the negative sample selector needs to evaluate the existence of the randomly sampled unobserved edges. Therefore, we use similarity-based matrices, including the Adamic-Adar (AA) [21], Jaccard Coefficient (JC) [22], and Preferential Attachment (PA) [23], and the Spectral Clustering (SC) [24] method to obtain four evaluated score functions. The evaluated score functions are denoted as \(AA(\cdot,\cdot)\), \(JC(\cdot,\cdot)\), \(PA(\cdot,\cdot)\), and \(SC(\cdot,\cdot)\) for the AA, JC, PA matrices, and SC method, respectively.  
In another scenario, some of the randomly sampled unobserved edges can be easily distinguished from the observed edges in \(E^{pos}_{train}\) by the GNN model. These easily distinguishable negative samples contribute insufficiently to the GNN model. Therefore, we use the edge probability \(PR(\cdot,\cdot)\) of each unobserved edge in the current sampling period to reflect the ability of the GNN model to distinguish it from observed edges in \(E^{pos}_{train}\). The easily distinguishable negative samples are with lower edge probabilities, while the hard distinguishable ones are with higher edge probabilities.  
Figure 3: The architecture of the negative sample selector. The conceptions of the AA, JC, PA, SC, PR, and Action are introduced in Sections 3.1 and 3.2.  
Figure 2: The training procedure of GNN models with the proposed PbTRM in the link prediction task. GNN models are used as the encoder to extract the convolved features of nodes in the input network. The decoder calculates the edge probabilities \(PR(\cdot)\) of positive and negative samples.  
Combining the aforementioned two kinds of information, the negative sample selector builds a state vector for each randomly sampled unobserved edge. For a randomly sampled unobserved edge \((v,v^{\prime})\), its state vector \(s(v,v^{\prime})\) can be expressed as follows:  
\[\begin{array}{c}s(v,v^{\prime})=AA(v,v^{\prime})\oplus JC(v,v^{\prime})\oplus PA (v,v^{\prime})\\ \oplus SC(v,v^{\prime})\oplus PR(v,v^{\prime})\end{array} \tag{1}\]  
where \(\oplus\) is the concatenation operator.
## 4 Simulation Experiments and Analysis Based on Error Sensitivity
The calculation results of \(S(x)\) are listed in Table 7. The error sensitivities \(S(x)\) of different error sources changing along with the BD disturbance frequencies are shown in Figure 16.  
\[S(x)=100\sqrt{\frac{\sum_{i=1}^{n_{l}}\left[\triangle\theta_{i}(x)-\overline{ \triangle\theta(x)}\right]^{2}}{n_{l}-1}},\ \overline{\Delta\theta(x)}=\frac{\sum_{i=1}^{n_{l}} \triangle\theta_{i}(x)}{n_{l}}, \tag{24}\]  
From the above tables and Figure 16, the most sensitive error sources are identified as x-axial component of pitch axis perpendicularity deviation \(\delta_{apx}\), y-axial component of the azimuth gyroscope assembly deviation \(\varepsilon_{ay}\), and z-axial component of the pitch gyroscope assembly deviation \(\varepsilon_{p2}\). Error sensitivities of the identified three errors are ranked as the order of \(S\big{(}\delta_{apx}\big{)}>S\big{(}\varepsilon_{ay}\big{)}>S\big{(} \varepsilon_{p2}\big{)}\). Additionally, with the rising of disturbance frequency, the stability accuracies \(\triangle\theta\) can be improved, and the error sensitivities \(S(x)\) decrease. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Error Sensitivity}} & \multicolumn{3}{c}{**BD Frequency [Hz] (For Each Column, the First Data Is the Value of the**} \\ \multicolumn{1}{c}{**St(x) Data in the Center Area with**} & \multicolumn{3}{c}{**BD Frequency, and the Following Are the \(S(x)\) of Each Error Type on the**} \\ \cline{2-6}  & **Three Decimal Places)** & **0.5** & **1** & **2** & **3** & **4** \\ \hline \multirow{6}{*}{Error Types (Each line contains the \(S(x)\) of the error type under different BD frequency on the top)} & **0.5** & **1** & **2** & **3** & **4** \\  & **\(\delta_{\text{bary}}\)** & 8.704 & 2.007 & 0.673 & 0.261 & 0.249 \\  & \(\delta_{apx}\) & 324.476 & 156.998 & 74.231 & 47.222 & 34.866 \\  & \(\delta_{apx}\) & 4.305 & 0.968 & 0.303 & 0.277 & 0.277 \\  & \(\triangle\alpha_{apx}\) & 5.213 & 1.287 & 0.502 & 0.261 & 0.339 \\  & \(\triangle\beta_{\text{am}}\) & 5.007 & 3.078 & 0.704 & 0.217 & 0.187 \\  & \(\triangle\alpha_{\text{pm}}\) & 7.989 & 1.612 & 0.612 & 0.507 & 0.297 \\  & \(\triangle\beta_{\text{pm}}\) & 2.646 & 1.223 & 0.838 & 0.421 & 0.152 \\  & \(\varepsilon_{ax}\) & 5.817 & 0.817 & 0.853 & 0.477 & 0.158 \\  & \(\varepsilon_{ay}\) & 325.078 & 150.672 & 70.202 & 44.542 & 31.868 \\  & \(\varepsilon_{px}\) & 6.014 & 1.408 & 0.912 & 0.265 & 0.308 \\  & \(\varepsilon_{px}\) & 121.427 & 86.072 & 53.342 & 38.140 & 30.272 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Error sensitivity of different error sources. Figure 16: Error sensitivity of different error sources.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods
### Dataset Preparation  
In any deep learning-based object detection, the initial step is data preparation and data cleaning. This is a very important step in any artificial intelligence technique because datasets contain duplicates and missing values which cannot be used to train deep learning models, as they may lead to larger errors in object detection [13]. The major steps in general include gathering the dataset, removing missing data and outliers, analysing the data, and finally converting it into a suitable format for the deep learning model for further processing [14]. In this work, actual weld plates were laser cut into eight different asymmetrical seam shapes to address the industrial welding process. The reason for choosing these shapes is that defected parts in industries are not symmetrical, so an attempt was made to prepare the dataset to identify the different shapes using a 2D camera with random positions at both the robot workspace and the welding table to create robust datasets. Furthermore, in order to increase the robustness of dataset and improve model training, data augmentation techniques were applied [15]. This was needed because datasets are prepared in limited conditions, but environmental conditions may vary in real-time scenarios, so different flips, rotations, saturation and grayscale were applied to the datasets. Additionally, weld fume images with arc light were also included to improve the weld seam detection. The major reason to choose a 2D camera is that they are easily available and the overall setup cost can be reduced. The main goal of this work was to train to the robot to detect and trace the weld plates anywhere in the workspace. About 2286 images were obtained which were manually labelled with the Roboflow image annotation tool. In data pre-processing, original data of 640 x 480 were resized to 416 x 416 pixels, making them suitable to train all the YOLO algorithms. The weld plate and augmentation specifications are tabulated in Table 1. The sample dataset of weld plates with different rotations is shown in Figure 2.  
Figure 1: Process flow diagram of weld seam detection and tracing using TAL BRABO robotic manipulator.
# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 3 Validation of Mobility Models
### Average Neighbor Percentage  
This metric describes the cumulative percentage of the total number of neighboring nodes. That is the percentage of total MNs' neighbors during the whole validation time. Low values of this metric reflect the degree of model instability in attaining neighbors, which further leads to worse network performances [12]. To strongly verify the results shown in Figure 3, we validate the average neighbor percentage during the simulation. Figure 4 shows the results of this feature. We can highlight the following.  
Figure 4: The average node neighbor percentage. (**a**) The RWPMM; (**b**) the MGMM; (**c**) the RPGMM; (**d**) the NCMM; (**e**) SLAW; (**f**) SMOOTH.  
* According to Figure 4a, the RWPMM presents the worst outcomes, where it fluctuates in a range of 0-16% with many detected time instants without any neighbors. At these moments, if a mobile node sends a packet, it finds no receiver to which these data can be forwarded. This behavior further increases the number of lost packets, and decreases the PDR of the RWPMM.
* From Figure 4b, the MGMM shows small fluctuations in the range of 4-9%, and more than two neighbors from the total of 49 nodes are always present. This allows this model to perform better than the RWPMM, but it stays enough to offer best performances.
* From Figure 4c,e, SLAW and the RPGMM show better results than the RWPMM and the MGMM. These models fluctuate in the ranges 0-18% and 0-25%, respectively, with fewer time instants are without any neighbors.
* However, from Figure 4d,f, SMOOTH and the NCMM have ranges of 18-70% and 8-98%, respectively. They both provide the best results, having more than four neighbors during the entire simulation. This feature allows them to outperform other models in mobile networks as _they can deliver all the packets_[36]. These models are _more stable_ than other models.  
This metric gives a general view of the density wave phenomenon via the average percentage within the validation period. These results strongly endorse the outcomes depicted in Figure 3, as summarized in Table 4.
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion
### Effect of the Temperature on the Deoxygenation of Benzaldehyde  
We also tried to use this catalyst for the deoxygenation of benzaldehyde into the saturated compounds, which was also very useful in the synthesis of value-added chemicals and liquid fuels. As the hydrogenolysis of hydroxyl group wasmuch more difficult than the reduction of the carbonyl groups into alcohols, we performed the reduction of benzaldehyde at 2.5 bar and elevated temperatures, and the results wereshown in Figure 3. We couldfind that the reaction ratewas greatly affected by the reaction temperature. As the reaction temperature increased, the yield of toluene increased, while the yield of benzyl alcohol decreased. When the reaction temperature below 70 \({}^{\circ}\)C, the yield of toluene wasvery low in 12 h, while the temperature exceeded70 \({}^{\circ}\)C, the yield of toluenecould reach to 95% in a shorter time (Table S3). These results showed that higher reaction temperature benefited the hydrogenolysis of hydroxyl group, indicating the activation energyoftheydrogenolysis  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**10 bar H\({}_{2}\)**} & \multicolumn{2}{c}{**R-CH\({}_{2}\)OH**} \\  & & \multicolumn{2}{c}{**R-CH\({}_{2}\)O\#HAP-Pd**} & \multicolumn{2}{c}{**R-CH\({}_{2}\)OH**} \\ \hline
**Entry** & **Substrate** & **Product** & **Time (h)** & **Conv. (\%)** & **Yield (\%)** \\ \hline
1 & **FC** & **CHO** & **FC** & **CH**OH & 1.5 & 100 & 99.1 \\
2 & **F** & **CHO** & **F** & **CH**OH & 1.5 & 100 & 99.3 \\
3 & **CI** & **CHO** & **CI** & **CI**OH & 2 & 100  
of hydroxyl group is higher than the hydrogenation of the aldehyde group. At the reaction temperature of 130 \({}^{\circ}\)C, toluene could be produced with an excellent yield of 97.9% in a short time of 1 h over the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst, whichwascomparable with the reported homogeneous catalyst of Ru\(-\)pincer complexes that promoted the deoxygenation of carbonyl groups at 130 \({}^{\circ}\)C and 2 bar H\({}_{2}\)[21].
# Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks
## 2 System Model for Crowdsourcing Incentive Mechanism
### Problem Formulation  
Considering the MUs' selfishness and the limited resources, the MUs may intend to shirk or act less carefully in crowdsourcing tasks. For example, because crowdsourcing tasks consume the MUs' resources (i.e., battery, memory, and time), the MUs may like to obtain their benefits from the SP to maximize their own utilities with little crowdsourcing effort. Thus, the MUs may not take the full responsibilities for their tasks. Because of the asymmetry of network information, the MUs' crowdsourcing actions are unobservable to the SP, which leads to the moral hazard problem. This moral hazard problem influences the crowdsourcing's performance. Therefore, the SP needs to design a contract-based incentive mechanism to motivate the MUs to participate in crowdsourcing tasks efficiently and credibly.  
As shown in Figure 2, when the SP designs the optimal contract, the SP broadcasts a set of contract items to the potential MUs. Then, after receiving the contract, the MUs willing to accept certain contract items inform the SP of their choices. Next, after receiving the MUs' confirmations, the SP informs the employed MUs' crowdsourcing tasks, and the MUs help to participate in crowdsourcing sensing or computing. Finally, after receiving the data from the MUs, the SP checks for the required information. If the MUs succeed in the crowdsourcing tasks, the SP rewards the MUs according to their contracts. However, if the information fails to meet the requirement, the employed MUs obtain no reward. Because this requires limited interaction with potential MUs, this contract-based incentive mechanism is simple to implement, and can effectively reduce communication and computation overhead.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network
#### 3.4.2 Correlation Filter Initialization  
Given an initial target box T in the second frame of the tracking video sequence, the corresponding ideal gaussian response \(G\) is constructed based on the initial target box, and the peak point of the response corresponds to the position of the tracked target. The output feature maps of the last layer of the deep-pruned feature network are \(F_{CL,1},F_{CL,2},\ldots,F_{CL,KN_{CL}}\), and the size of each feature map is M \(\times\) N. Each feature map corresponds to one correlation filter channel. According to the literature [14; 30; 32; 33], the correlation filter channel \(W^{l}\) can be initialized by Equation (15) as:  
Figure 7: The deep-pruned feature network is applied to the second frame of the tracking video sequence for feature extraction. (#\(i\) indicates the \(ith\) picture in the video sequence).  
\[W^{l}=\frac{\hat{F_{CL,l}^{\ast}}(T)\odot\hat{G}^{\ast}}{\sum_{k=1}^{KN_{CL}}F_{CL,k} ^{\ast}(T)\odot\left(F_{CL,k}^{\ast}(T)\right)^{\ast}+\lambda}\ (l=1,2,\ldots KN_{CL}) \tag{15}\]  
where \(\odot\) refers to the Hadamard product, \(\ast\) represents the complex conjugate, \(\wedge\) represents the Fourier transform, \(W^{l}\) represents the correlation filter channel corresponding to the \(lth\) feature map channel, \(\lambda\) is the regularization coefficient, and \(\lambda\geq 0\).