esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### Trust Establishment and Link Duration Estimation
#### 3.1.2 Direct Trust  
The direct trust evaluation in our work is computed using the number of legal (\(L\)) and malicious (\(M\)) interactions between two UAVs \(i\) and \(j\) and it is computed following Equation (3):  
\[DT(i,j)=\frac{\#L(i,j)}{\#M(i,j)+\#L(i,j)}\cdot\left[1-\frac{1}{L(i,j)+1}\right] \tag{3}\]  
Figure 3: UAVs trusting each other and moving with similar mobility patterns.  
where \(L(i,j)\) and \(M(i,j)\) represent the number of legal and malicious actions, respectively, between \(i\) and \(j\) from the perspective of the UAV \(i\). \(\frac{\#L(i,j)}{\#M(i,j)+\#L(i,j)}\) represents the percentage of legal actions compared to the total number of actions, and \(1-\frac{1}{L(i,j)+1}\) is a factor approaching 1 when the number of legal actions increases. Hence, many legal actions are required to increase the direct trust to respect the general rule that says trust must be hard to win and easy to lose.  
An action is considered legal or malicious based on a similarity measurement between the effective action taken by a UAV \(j\) and the same action simulated from the point of view of the UAV \(i\). Based on the semantic kernels method [29], we use the function \(\phi(x)\) to represent an action \(T\) having \(n\) features.  
\[\phi:d\mapsto(t_{1},t_{2},...,t_{n})\]  
\(t_{1}\) is one of the features (\(1\leq t\geq n\)) and \(n\) is the total number of features of the action \(t\). Similarity of two action \(T1\) and \(T2\), \(\hat{k}(T1,T2)\) is given by Equation (4).  
\[\hat{k}(T1,T2)=\big{\langle}\frac{\phi(T1)}{\|\phi(T1)\|},\frac{\phi(T2)}{\| \phi(T2)\|}\big{\rangle}=\frac{k(T1,T2)}{\sqrt{k(T1,T1)k(T2,T2)}} \tag{4}\]  
To compute \(k(T1,T2)\) we use the following formula:  
\[k(T1,T2)=\big{\langle}\phi(T1),\phi(T2)\big{\rangle} =\phi_{u}(T1).\phi_{v}(T2)\] \[=\left(\sum_{T1:u=\phi(T1)}1\right)\left(\sum_{T2:v=\phi(T2)}1\right)\] \[=\sum_{(T1,T2):u=v}1\]  
where \(u\) is a feature of \(T1\) and \(v\) is the same feature for \(T2\). Finally, if the similarity is higher than 0.5 the action made by \(j\) will be considered legal, otherwise malicious.
# Evaluation of Chronic Kidney Disease Risk Factors after Radical Nephrectomy
###### Abstract  
Intraoperative hypotension (IOH) and loss of blood during radical nephrectomy (RN) cause postoperative clinically significant renal dysfunction, which after 12 months can cause a reduction in serum creatinine clearance of \(<\)60 mL/min. We conducted a prospective study of 93 adult patients in which we investigated the risk factors for developing chronic kidney disease (CKD) after RN. Forty-six (49.5%) patients had CKD, and of them, 43 patients had acute kidney injury (AKI) 48 h after surgery. Sixty-six (73.1%) of the postoperative AKI patients had CKD upstage. With each 1 mL estimated blood loss during RN (OR 1.01, \(p<\) 0.001), IOH was evaluated as the main risk factor of postoperative CKD development (OR 1.09, \(p<\) 0.01). [PERSON]'s \(t\)-test revealed that only clinically significant AKI had a main effect (g = -1.08, \(p<\) 0.0001) on renal function 1 year after RN. A higher preoperative estimated glomerular filtration rate (eGFR), OR 0.89, \(p=\) 0.02, and contralateral kidney CT volume (OR 0.97, \(p=\) 0.04) had a clinically significantly decreased risk of postoperative CKD. Risk factors of AKI with CKD upstage were a small contralateral kidney CT volume (OR 46.70), NLR \(>\) 3.5 (OR 1.42), higher primary eGFR (OR 1.13) and longer IOH (OR 1.05), and for all of these, \(p<\) 0.03. A half of all patients after RN are at increased risk of CKD. Longer IOH and increased blood loss during RN are significant risk factors for CKD. Clinically significant postoperative AKI is related with a developed risk for postoperative eGFR decline and the presence of CKD 12 months after RN, and can be predicted by NLR \(>\) 3.5. A higher preoperative eGFR and contralateral kidney CT volume reduces the risk of postoperative CKD.  
radical nephrectomy; intraoperative hypotension; blood loss; chronic kidney disease; clinically significant acute kidney injury
## 4 Performance Analysis  
With the basic knowledge of the SM and MP-WFRFT aided RFDA-DM scheme, we next analyze the secrecy performance of the system through the symbol error rate (SER) and bit error rate (BER), which are important metrics to measure the performance of wireless communication systems. Moreover, we analyze the anti-interception performance and provide comparisons with different DM schemes.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 3 Results and Discussion
### Effect of Incentse Ash on Mechanical Properties  
Figure 2 presents the compressive strength of specimens made with different water/binder ratios and replacement ratios and cured at 25 \({}^{\circ}\)C. Under two water/binder ratios, specimens with different replacement ratios exhibited a similar trend in their strength. Specifically, the strength by replacement ratio was 5% > control group > 10% > 20% > 30%. Generally, when the replacement ratio was less than 5%, the compressive strength of the specimens was comparable to that of the control group, regardless of their curing time, although some specimens did exhibit a greater compressive strength than did the control  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Incentse Ash** & **Initial Setting Time** & **Final Setting Time** & **Amount of Water Required** \\ (\%) & **(min)** & **(min)** & **for Normal Consistency (\%)** \\ \hline
0 & 136 & 224 & 28.7 \\
10 & 135 & 223 & 30.5 \\
20 & 132 & 220 & 31.9 \\
30 & 128 & 218 & 33.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amount of water required for normal consistency and setting times of incense ash paste.  
Figure 1: SEM analysis of incense ash (\(\times\)5000).  
group. This differs from the finding of [PERSON] [37]; replacing 5% of cement with agarose ash resulted in a greater compressive strength than the control group. At the 5% replacement ratio, incense ash serves as the filling material. However, at a ratio greater than 5%, it obviously loses the filling function, which explains why no significant improvement in strength was observed in replacement ratios greater than 10%. Furthermore, although strength did increase with curing time, it was still far inferior to that of the control group. This is because at a higher replacement ratio, the areas in cement mortar that are formerly filled by water form large voids that cannot be filled entirely by hydration products. The voids thus become defects that compromise compressive strength. Although the compressive strength at a W/C = 0.3 was considerably greater than that achieved at a ratio of 0.4, both had the same result--that 5% was the optimal replacement ratio. Therefore, 5% was established as the threshold for the replacement ratio. Figure 3 presents the compressive strength of specimens cured at 75 \({}^{\circ}\)C. The specimens cured at 75 \({}^{\circ}\)C with a water/binder ratio of 0.4 achieved a compressive strength comparable to those cured at 25 \({}^{\circ}\)C with a water/binder ratio of 0.3. Such a result amply signifies that, at a high curing temperature, the greater activation energy facilitates the formation of Ca(OH)\({}_{2}\); increases cement's contact with incense ash, SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\); and expedites the hydration reaction [29; 30]. Thus, the strength of the specimens was enhanced.  
Figure 3: Compressive strength developments of incense ash paste (75 \({}^{\circ}\)C).  
Figure 2: Compressive strength developments of incense ash paste (25 \({}^{\circ}\)C).
## 3 Performance Analysis
### Landing Performance  
To reveal the advantage of the SRVL, this work compares the performances of the SRVL with those of the conventional landing method. The touchdown state boundaries of the two landing methods are calculated for comparison by establishing the attainable equilibrium sets of the controllable flight envelope based on the flight dynamics model established in Section 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Physical Quantities** & **Value** & **Value** & **Value** & **Value** & **Value** \\ \hline Lift fan thrust (\%) & 30\% & 40\% & 50\% & 60\% & 70\% \\ Taxi time (s) & 4.7 & 4.49 & 4.28 & 4.04 & 3.78 \\ Taxi distance (m) & 50.14 & 39.80 & 29.11 & 17.79 & 4.40 \\ Liftoff speed (m/s) & 20.48 & 17.94 & 14.96 & 11.212 & 5.228 \\ Tilt angle (deg) & 26.10 & 32.22 & 38.77 & 45.99 & 54.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculation results of the takeoff performance.  
Figure 4: The variation curves of the throttle of the lift fan and the deflection angle of the 3 BSD.  
Figure 3: Time history of lift fan during rolling to take off from the ground.  
Principally, the state constraints exist in the attitude angles, flight path angle, and the flight speed, which are given by Equation (1):  
\[\left\{\begin{array}{l}\dot{\alpha}=0,\dot{q}=0,q=0\\ 0<\theta<\theta_{\max}\\ \gamma=\gamma_{0}\\ V_{t}=V\\ \delta_{\min}<\delta_{N}<\delta_{\max}\end{array}\right. \tag{1}\]  
where \(\dot{\alpha}\) and \(\dot{q}\) are the derivatives of the angle of attack and pitch angular speed, \(\theta\) and \(\theta_{\max}\) are the pitch angle and its maximum value, \(\gamma\) is the flight path angle, whose default value is \(\gamma_{0}=-4^{\circ}\), \(V_{t}\) is the airspeed, \(\delta_{N}\) is the longitudinal deflection angle of the thrust vector nozzle, and \(\delta_{\min}\) and \(\delta_{\max}\) are its boundaries, which are \(\delta_{\min}=0^{\circ}\) and \(\delta_{\max}=90^{\circ}\) in SRVL and \(\delta_{\min}=0^{\circ}\) and \(\delta_{\max}=0^{\circ}\) in conventional landing. The calculation procedures are given in the flow chart (Figure 5).  
The lower boundary of the mass is 13 kg, which is the dead load of the aircraft. The calculation lower boundary of the velocity is 0 m/s, and the upper boundary is 25 m/s, which was determined by the runway range and deceleration performance.  
The calculation results are given in Figure 6, which shows that when the mass is 13 kg the touchdown speed reduces from 18.5 m/s to 6 m/s if the SRVL is adopted and that if the minimum touchdown speed is fixed the landing load enhances from 13 kg to 20.5 kg. This means that the SRVL may expand the feasible region of touchdown speed and landing load to twice as much as before, which is definitely in favor of landing strategy formulation and flight mission planning.  
Figure 5: Landing performance calculation logic.
## 3 RUL Prediction Method Based on Hierarchical Clustering and Transformer-GRU
### Transformer-GRU Combination Model  
To fully explore the relationship between the bearing degradation process and the RUL, a Transformer-GRU hybrid model is constructed, as illustrated in Figure 6. After inputting the data into the model, the multi-head attention mechanism in the Transformer encoder is first employed to capture various dependencies across different time steps in parallel subspaces [27]. This allows the original input feature sequences to be transformed into high-level feature representations rich in contextual information. Subsequently, the GRU, with its strong ability to capture temporal dependencies and trend patterns in sequential data [37], is used to extract the degradation trends from these high-level features. Finally, a fully connected layer maps the extracted features to the bearing's RUL.  
Figure 5: Hierarchical clustering to redundancy flow chart.  
The mean absolute error (MAE) and root mean square error (RMSE) [6] are employed as evaluation metrics to assess the prediction performance of the proposed model. Their definitions are given in Equations (18) and (19), respectively:  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}\lvert y_{i}-\hat{y}_{i}\rvert \tag{18}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{m}\left(y_{i}-\hat{y}_{i}\right)^{2}}{m}} \tag{19}\]  
where \(m\) denotes the number of samples, \(y_{i}\) represents the predicted value, and \(\hat{y}_{i}\) denotes the true value.
# Nanosized Ni/SBA-15 Catalysts for CO\({}_{2}\) Reforming of CH\({}_{4}\)
## 4 Conclusions  
Several catalysts were prepared using S15 as a support, including Ni, Co, and Ni-Co. The support was also modified by doping S15 with Ce or Zn for Co-Ni bimetallic catalysts. A metallic catalyst loading of 5 wt % was used for all experiments. The catalysts were activated at 500 \({}^{\circ}\)C using a mixture of H\({}_{2}\) and He. The prepared catalysts were tested for CO\({}_{2}\) reforming of methane under atmospheric pressure at 800 \({}^{\circ}\)C. Different characterization methods were used to investigate the textural and chemical properties of all categories of catalysts.  
Over 8 h of reaction time, Ni/S15 demonstrated the best activity and stability, where the observed conversion was 90% for CH\({}_{4}\) and CO\({}_{2}\). Co-Ni/S15 and Co-Ni/Ce-S15 exhibited reasonable activity and stability. Regarding the H\({}_{2}\)/CO ratio in the product stream, Ni/S15 exhibited the highest selectivity among all catalysts. Promoting/doping Ni/S15 had a remarkable effect on the catalyst activity. Co/S15 and Co-Ni/Zn-S15 exhibited low activity and selectivity. The addition of Zn/Ce oxides had a remarkably negative influence on the reduction properties and catalytic performance of the catalysts. All catalysts showed a high surface area, which is attributed to the high surface area of S15 support. The NH\({}_{3}\)-TPD profiles indicated that doping S15 with Ce/Zn oxides increased the catalyst acidic sites. Ni/S15 exhibits excellent potential for the commercial CO\({}_{2}\) reforming of methane.  
[PERSON], A.A., A.S.A.-F., S.O.K. and A.H.F. synthesized the catalysts, performed all the experiments and characterization tests as well as wrote the manuscript. N.S.K., A.S.A.-A., A.M.E.-T., A.E.A. contributed in analysis of data and proofread the manuscript.  
The Deanship of Scientific Research at King Saud University funded the work under the research group project No (RGP-119). The authors also thank RSSU at King Saud University for their technical support.  
The authors declare no conflicts of interest.  
Figure 8: Weight change during the temperature-programmed oxidation (TPO) test as recorded by TGA for Co/S15 and Co–Ni/S15.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 2 Materials and Methods
### Soil Depth Estimation Using Seismic Refraction Survey  
Seismic refraction survey is an indirect exploration technique, appreciated for its cost and time effectiveness. Since the early 1960s, the seismic refraction method has been extensively used to characterize geological structures for landslide investigation and has proven a useful geophysical tool. This technique involves generating elastic waves at the surface and analyzing those reflected or refracted at the boundary between two media with different velocities to investigate subsurface structures. The results of a seismic refraction survey provide a cross-sectional view of subsurface structures depicted by seismic wave velocities.  
The seismic refraction survey equipment used in this study included the Geode Exploration Seismograph (Geometrics, San Jose, CA, USA) and 35 Hz geophone (Geospace Technologies Corporation, Houston, TX, USA). In-line arrays for both source and receiver have been implemented to acquire seismic reflection data. In our study, geophones were spaced uniformly at intervals of 5 m along the slope direction. The seismic waves were generated artificially by hitting the ground with a 5 kg hammer at some source points. The data obtained from the seismic refraction survey were processed using GeisImager/2D software 2.4 (OYO Corporation, Tokyo, Japan) to generate seismic tomography sections. Seismic tomography provides insights into subsurface structures and allows for the identification of seismic wave velocity distributions within the subsurface strata of slopes.  
When estimating soil depth using seismic refraction surveys, it is crucial to define the seismic wave velocity that distinguishes between the bedrock and soil layers. In general, rocks have relatively large seismic wave velocities compared with those of soils, but their range could vary depending on the soil type and ground conditions. The P-wave velocities for various materials are presented in Reynolds [34], with topsoil having a range of 300-900 m/s. [PERSON] et al. [18] reported that the range of P-wave velocities for landfill and alluvial soil was 0-700 m/s. In South Korea, a P-wave velocity of 700 m/s is recognized by the Ministry of Land, Infrastructure, and Transport (MOLIT [35]) as the standard fordistinguishing soil layers from other geomaterials. In our study, the seismic refraction survey was conducted along the slope direction, and a P-wave velocity criterion of 700 m/s was applied to estimate the depth of the soil layers (\(d_{n}\): surface normal direction soil depth; \(d_{S}\): gravity direction soil depth), as shown in Figure 3.
# Environmentally Friendly Anticorrosive Polymeric Coatings
## 4 Developments in Environmentally Friendly Polymeric Coatings
### Polyester  
Base polyesters have been modified in several ways to obtain resin structures. Particularly important are hyperbranched/dendritic polymers as polyester due to their extraordinary properties as promising resins for both development of low-VOC coatings [59] and protection against severe corrosive environments [60]. Modification of a hyperbranched hydroxy-functional polyester to a thermoset resin structure is readily achieved via catalyzed esterification of the hydroxyl end-groups with acid chlorides or suitable anhydrides [61]. Different resins can be obtained depending on the type of the end-groups and structure and source of fatty acids or triglyceride oil, as can be seen in Figure 3. Some bio-based monomers have been proposed for substituting not renewable diacid parts. [PERSON] et al. [62] developed a Sacha inchi oil-based alkyd resin. Results showed that the addition of penterythritol and Sacha inchi oil promoted an increase of anticorrosion behavior and outstanding adhesion properties in harsh conditions.  
Figure 3: Principle of synthesis routes for different resin architectures based on hyperbranched polymers [61].  
The application and curing process can emit toxic VOC from solvent-based coatings. This has resulted in a gradual shift from solvent borne coatings to waterborne coating minimizing VOC, other solutions are UV-curable coatings and high solid coatings. Alkyd resins based on glycolyzed PET waste and different aliphatic hyperbranched polyesters with fatty acid were developed by [PERSON] et al. [63] in order to produce low-VOC coatings and to recycle PET materials. [PERSON] et al. [64] reported the encapsulation of pigments with UV-curable hyperbranched polyester acrylate to improve printing performance and durability.
## 3 Results
### Microstructure and Defects
#### 3.2.2 Porosity and Aluminum Content  
Figure 6 displays the gas porosity of the bulk samples (see Figure 1e) in dependence on the beam power on the left and the aluminum content on the right. The powder properties are indicated by the dotted lines. Misconnections are not found in the samples. The gas porosity declines from roughly 0.5% for small beam powers to 0.1% around 900 W. For higher beam powers, it increases to 0.3-0.4%. The aluminum content shows a similar trend. After an initial drop up to 900 W, it rises again for higher beam powers. The slight deviations between batches 1 and 2 can be attributed to the different aluminum content in the powder. Both microstructural features indicate that the melt pool becomes hotter, larger, and longer-lasting up to a peak at approximately 900 W followed by a reduction of these melt pool characteristics for higher beam powers. These results fit well with the previous findings of the nearly lamellar starting area. The connection between the melt pool dimensions and the final microstructure features is discussed in the following section.  
Figure 5: The nearly lamellar area at the bottom of the melt pool during the beginning of the melting over the beam power for both powder batches in x and y deflection. The NL area decreases with rising beam power until around 15 mA and increases for higher beam power from this point on.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 2 Data and Methods
### Methodology
#### 2.2.2 Correction of Remote Sensing Precipitation by ANN Model  
A three-layer ANN model was set up for remote sensing precipitation correction. The model has five input variables (i.e., gauged precipitation (Ps), elevation (DEM), longitude (X) and latitude (Y) of the gauge stations, and TRMM precipitation). Specifically, the Ps is the target value of the model, and the four others are the variables of the model input layer. The output variable is only the corrected precipitation in the model output layer. The hidden layer nodes are set to 20 by the preferred selection. The structure of the ANN model in this paper is shown in Figure 2.  
The model training function is 'trainlm' which updates weight and bias values according to Levenberg-Marquardt optimization. At the same time, we chose the 'tansig' as the model neural transfer function to calculate a layer's output from its net input. The divide function is accessed automatically whenever the network is trained, which is used to divide the data into training, validation and testing subsets. In this study, the net divide function is set to 'dividerand', and the ratios for training, validation and testing are 0.7, 0.0 and 0.3 according to the needs of the training function of 'trainlm'. The prepared sequence data is randomly divided into the training subset (70%) and testing subset (30%) using the division parameters by the divide function. The fusion models for 3B42 RT and 3B43 correction completed 103 and 127 times of training, and the convergence error (mean square error) was 0.0036 and 0.0041, respectively.  
The process of data fusion mainly includes four steps: (1) the Pt is extracted by combining ArcGIS technology according to the spatial locations of the gauge stations. Moreover, the data of Ps, DEM, X, Y and Pt are stored in a one-to-one correspondence; (2) training and testing the model after setting each function of the artificial neural network. When the parameters such as the R\({}^{2}\) reach certain conditions, the construction of the model is completed; (3) correction remote sensing precipitation based on the fusion model which is established in the previous step; (4) the fused precipitation data are inversed to the research area on a spatial-temporal scale according to the spatial information of the grids.