esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Effects of External Sulfate Attack on Low-Carbon Cementitious Materials at Early Age
###### Abstract  
Placed in a sulfate-rich environment, concrete reacts with sulfate ions, influencing the long-term durability of reinforced concrete (RC) structures. This external sulfate attack (ESA) degrades the cement paste through complex and coupled physicochemical mechanisms that can lead to severe mechanical damage. In common practice, RC structures are generally exposed to sulfate at an early age. This early exposition can affect ESA mechanisms that are generally studied on pre-cured specimens. Moreover, current efforts for sustainable concrete construction focus on replacing clinker with supplementary cementitious materials, requiring a 90-day curing period, which contradicts real-life scenarios. Considering all these factors, the objective of this study is to explore ESA effects at an early age on cement-blended paste samples using various low-carbon formulations. The characterization techniques used demonstrated that the reference mix (100% CEM I) exhibits the weakest resistance to sulfate, leading to complete deterioration after 90 weeks of exposure. This is evident through the highest mass gain, expansion, cracking, formation of ettringe and gypsum, and sulfate consumption from the attacking solution. Conversely, the ternary mix, consisting of CEM I, slag, and metakaoin, demonstrates the highest resistance throughout the entire 120 weeks of exposure. All the blended pastes performed well in the sulfate environment despite being exposed at an early age. It can be recommended to substitute clinker with a limited quantity of metakaoin, along with blast furnace slag, as it is the most effective substitute for clinker, outperforming other combinations.  
c 2024 14 2831 1 1
## 4 Simulation Verification
### Simulation of Double-Exposure Holographic Interferometry  
Figure 6 presents the workflow for double-exposure holography:  
Procedure:  
(1) Model construction: Identical to Section 4.1;  
Figure 5: (**a**) Diffraction pattern of object wave before deformation. (**b**) Hologram before deformation. (**c**) Diffraction intensity after deformation. (**d**) Reconstructed image under reference beam illumination. (**e**) Reconstructed image under deformed object beam illumination. (**f**) Interference fringes from simultaneous illumination with reference and deformed object beams.  
Figure 6a: Diffraction pattern of object wave before deformation. Fresnel diffraction field of a flat rectangular object at distance \(\mathrm{z_{o}}\), showing a central bright rectangle + Fresnel diffraction rings (edge oscillations).  
Figure 6b: Hologram before deformation. Interference between reference wave R and object wave O forms the hologram; the fringe tilt angle is controlled by alpha and beta, showing tilted parallel straight fringes.  
Figure 6c: Diffraction pattern of deformed object wave. Peak phase modulation causes wavefront distortion, corresponding to object deformation, showing central region distortion + diffraction ring distortion.  
Figure 6d: Hologram after deformation. Deformation alters the object wavefront, causing local fringe distortion, showing local fringe bending/density variation.  
Figure 6e: Double-exposure reconstructed image. Interference superposition of two exposed holograms; fringes in the image reflect the phase difference caused by deformation, showing a rectangular image overlaid with bright/dark interference fringes. Numerical simulation of double-exposure holographic interferometry was implemented based on the fast Fourier transform (T-FFT/S-FFT) algorithm. The hologram recording and reconstruction process was simulated by constructing an initial rectangular object plane and a deformed object plane with superimposed peak phase modulation. Simulation results show that object plane phase modulation is successfully transformed into interference fringe distribution in the reconstructed image, verifying the mapping relationship between phase change and fringe density/orientation.
# The Application of Numerical Ductile Fracture Simulation in the LBB Evaluation of Nuclear Pipes
## 3 Numerical Ductile Fracture Simulation
### Calibration of Parameters  
The basic material properties for TP304L stainless steel are obtained by tensile tests according to ASTM E 8 [31] with the specimen as shown in Figure 5a. From tensile tests, the yield strength and tensile strength are obtained as 269 MPa and 529 MPa, respectively. The material constants in Equation (2) are determined based on the tensile test results of smooth and notched specimens (Figure 5). A quarter of the FE models for these specimens are constructed to simulate the tensile behaviors, as shown in Figure 6. The simulation results agree well with the test results for four kinds of specimens (Figure 7a). The stress triaxiality and strain in the middle of the specimen are extracted from the FE simulation, as shown in Figure 7b. The area-averaged stress triaxiality is used to fit the relation between critical fracture strain and stress triaxiality for considering the loading history effect, as shown in Figure 7c, and the values of \(A\) and \(B\) are determined as 4.5 and 0.22, respectively.  
A half-model of a 0.5T-CT specimen is constructed to study the effect of the element-size-dependent critical damage parameter (\(\omega_{c}\)), as illustrated in Figure 8a. To accurately capture crack-tip propagation behavior, a fine mesh of C3 DBR hexahedral elements was implemented along the crack propagation path. From the simulation, the  
Figure 5: Tensile specimens: (**a**) smooth bar; (**b**) notched bar (R = 1.5, 3, 6 mm).  
Figure 6: Tensile specimens: (**a**) smooth bar; (**b**) notched bar (R = 6 mm).  
Figure 7: Test and simulation results for smooth and notched bars: (**a**) engineering stress–strain curves; (**b**) stress triaxiality–equivalent plastic strain curves; (**c**) fracture strain–stress triaxiality curve.  
load-displacement curve and load-crack extension relationship curve of the model can be obtained. Then, based on the J-integral calculation formula provided in ASTM E1820 [4], these simulation data were converted into J-R resistance curves characterizing the material's fracture toughness.  
Figure 8b demonstrates the significant influence of different \(\omega_{\rm c}\) parameter values on the predicted J-R curves of the CT specimen when a characteristic element size of 0.4 mm is adopted. As shown in Figure 8c, the values of \(\omega_{\rm c}\) are determined as 0.22, 0.41, and 0.69 for the element size L\({}_{\rm e}\) = 0.8, 0.4, and 0.2 mm, respectively. These calibrated parameter values were subsequently applied in numerical simulations of ductile crack propagation behavior in full-scale pipelines, providing a critical basis for balancing computational accuracy and efficiency in large-scale pipe models.
## 3 Results
Data visualization includes individual data points and condition-specific means with standard error bars. analysis of the FREQ variable for RF (Figure 2e) revealed a significant main effect for TIME POINT (\(F\) = 38.69; \(p<0.001\); \(\eta_{p}\)\({}^{2}\) = 0.226). The interaction between TIME POINT \(\times\) Side was not significant (\(F\) = 0.042; \(p\) = 0.838), and the between-side analysis showed no significant effect for the Side factor (\(F\) = 0.226; \(p\) = 0.637). Post hoc comparisons confirmed a significant improvement in the variable from pre- to post-intervention, with a \(\Delta\) of 1.15 (\(t\) = 6.22; \(p\) < 0.001). The analysis of the FREQ variable for MOS (Figure 2d) revealed a significant main effect for TIME POINT (\(F\) = 7.30; \(p\) = 0.010; \(\eta_{p}\)\({}^{2}\) = 0.037). The interaction between TIME POINT \(\times\) Side was not significant (\(F\) = 2.82; \(p\) = 0.101), and the between-side analysis showed no significant effect for the Side factor (\(F\) = 1.10; \(p\) = 0.301). Post hoc comparisons confirmed a statistically significant pre-post difference, with a \(\Delta\) of 0.75 (\(t\) = 2.70; \(p\) = 0.01). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multirow{2}{*}{**Side**} & \multicolumn{2}{c}{**PRE**} & \multicolumn{2}{c}{**POST**} & \multicolumn{2}{c}{**ANOVA Effects**} \\ \cline{3-8}  & & \(\mathbf{M\pm SD}\) & **Shapiro–Wilk** & **M \(\pm\) SD** & **Shapiro–Wilk** & **M \(\pm\) SD** & **Shapiro–Wilk** & **Time** & **Side** & **Time \(\times\) Side** \\ \hline \multirow{3}{*}{AT} & \multirow{3}{*}{R} & 0.98 \(\pm\) 0.16 & \multirow{3}{*}{0.94 (0.21)} & 0.99 \(\pm\) 0.18 & \multirow{3}{*}{0.91 (0.05)} & \multirow{3}{*}{0.91 (0.05)} & \multirow{3}{*}{\(p>0.05\)} \\  & & (0.91, 1.05) & & & & & \\ \cline{1-1} \cline{6-8}  & & \(\mathbf{L}\) & 1.07 \(\pm\) 0.21 & \multirow{3}{*}{0.95 (0.28)} & 1.13 \(\pm\) 0.21 & \multirow{3}{*}{0.95 (0.31)} & \multirow{3}{*}{0.95 (0.31)} & \\ \cline{1-1} \cline{6-8}  & & (0.97, 1.17) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics and ANOVA results for DEC variable.
## 4 Experiments and Discussion  
The experiments and results are outlined and discussed in this section. In order to simplify the RCS filtering process and speed it up for industrial deployment, non-textual data, such as images, video and audio, are omitted in the proposed models and methods. This section uses real industrial data to find the filtering results based on textual data.  
Figure 3: Proposed CNN for RCS filtering.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Key Management
#### 5.2.3 Indirect Key Establishment Phase  
In case there is no common key-space between two neighboring nodes, it is needed to establish a path key through one or more intermediate nodes. Our solution for this problem is as follows.  
After the _direct key establishment_ phase, every node \(A\) knows a set of secure neighboring nodes, denoted as \(S_{A}\). Node \(A\) wants to establish a pair-wise shared key with its neighbor \(B\), but \(B\) and \(A\) do not share any key-space. In this situation, \(A\) generates a session key, called \(K_{S}\), and find a node say \(C\) in \(S_{A}\) that have the same group ID with node \(B\) or neighboring group ID of group containing node \(B\). Node \(A\) then sends a message containing \(K_{S}\) encrypted by key \(K_{AC}\) to node \(C\). In turn, node \(C\) sends to \(B\) a session key through a secure channel protected by the key \(K_{CB}\). The key \(K_{S}\) then is used as pair-wise shared key between two nodes \(A\) and \(B\).  
After above three phases, every node stores a table containing neighbor IDs and pair-wise shared keys equivalently. The existence of key materials allows sensor networks to be able to add new nodes for replacement later.
## 8 Limitations and Future Recommendations  
Although this study used thorough research procedures, there are some possible limitations that might be pointed out. First, the findings and implications from the two models' assessments were inferred from the African developing countries, specifically the East African nations. Thus, the generalizability of the findings should be treated with caution due to cross-cultural differences between developing countries. In regards to that, a longitudinal study that would include samples from a larger number of developing nations may find more exciting insights and enhance the generalizability of the study. Second, at odds with other studies that express ICT fluency and digital divide based on the students' ICT skills, this study did examine ICT fluency and digital divide based on the individuals' internal and external proxy variables that influence the students' online resources access. An investigation based on the socioeconomic status of the participants might serve to explore the in-depths of the digital divide while dividing the constructs into more small groups, for instance, one can study ICT fluency and digital divide based on cultural capital and level of wealth considering the urban and rural origin students.  
Finally, since this study's data collection was performed by a cross-sectional method and the models' assessments are conducted at a single given moment, it must be noted that personal opinions may change over time. Hence, future research should consider assessing the models by conducting panel studies in order to continue deepening the understanding of the causal and relationships between the variables that are crucial for ICT fluency and evolution of digital inequality in developing countries.
## 3 Proposed IGVDD-1 DCNN Fault Diagnosis Model
### IGVMD-1 DCNN Model  
A one-dimensional convolutional neural network generally contains input, convolutional, pooling, fully connected, and output layers.  
The 1 DCNN network used in this paper has 13 layers, including input, convolutional, pooling, dropout, fully connected, softmax, and output layers, among which the dropout layer is mainly used to randomly remove some hidden layer units to further avoid the phenomenon of overfitting of the model. The dropout layer probability in this experimental network is 0.5. A batch normalization (BN) layer is added between each convolutional and activation layer, making the input data distribution of each layer relatively stable and accelerating the learning speed of the model. The parameter settings of the 1D convolutional neural network are shown in Table 1.  
The network structure of fault identification detection in this paper is shown in Figure 4 and is mainly divided into four stages: fault signal preprocessing, signal input, fault feature intelligent extraction, and fault feature identification stages. In the signal processing stage, the improved grey wolf optimized variational modal function algorithm proposed in this paper extracts the characteristics of the fault signal from the noise background. The input signal in the signal input stage is a one-dimensional vibration signal of length 1024 [35,36]. The input signal is convolved for intelligent feature extraction. Then, PReLU learns negative parameter features and solves the zero-gradient problem, downscales by maximal pooling, further downscales by the dropout layer, and executes a fault diagnosis by softmax for fault classification. The output layer has ten neurons to classify the ten health states of the bearing. Figure 5 shows a flowchart of the rolling bearing fault detection system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network Layer Type**} & **Convolution Kernel and** & **Activation** & **Output Data Length and** \\  & **Number of Channels** & **Functions** & **Number of Channels** \\ \hline Input & - & - & 1024, 1 \\ \hline Conv\_1 & \(55,27\) & PReLU & 1024, 27 \\ Pooling\_1 & \(16,27\) & - & 64, 27 \\ Conv\_2 & \(55,27\) & PReLU & 64, 27 \\ Dropout\_2 & - & - & - \\ Conv\_3 & \(55,27\) & PReLU & 64, 27 \\ Pooling\_3 & \(16,27\) & & 4, 27 \\ Conv\_4 & \(55,27\) & PReLU & 4, 27 \\ Dropout\_4 & - & - & - \\ Conv\_5 & \(55,27\) & PReLU & 4, 27 \\ Flatten & - & - & 108 \\ Softmax & - & - & 10, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 1 DCNN parameters.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### Comparison of Simulated and Measured Results for S11 Parameter and Antenna's Total Efficiency  
This subsection compares the simulated and measured S11 results. Figure 10 presents the simulated S11 result (in which the antenna is fully attached on the skin layer surface) as well as S11 parameters measured on the abdomen and skin with cloth. It is noted that a similar tendency can be found in simulated and measured results. There are some fluctuations along the evaluated frequency range. At certain frequencies, simulated and measured S11 parameters match better than in others. However, the differences are moderate and antenna matching is good in both simulated and measurement cases.  
Next the antenna total efficiencies are studied by simulations and measurements. In the simulations, total efficiencies are measured in the cases when the thicker cable is used (antenna skin distance is 3.2 mm) as well as in the case when the antenna is fully attached  
Figure 10: Comparison of the S11 parameters obtained by layer model simulations and measurements on the hand (with and without the cloth).  
Figure 9: Impact of the bending of the antenna, measured on the abdomen (blue curves) and on the hand (red curves).  
on the skin (skin partially cut below the thinnest cable). The measured efficiency is obtained when the antenna is on the hand phantom. The simulated and measured efficiencies at different frequencies are summarized in Table 3. The efficiency results obtained from simulations, in which the antenna is fully attached on the skin layer, correspond well with the hand phantom measurements at lower frequencies. At higher frequencies the difference is several decibels. This is assumed to be due to the hand phantom properties. The hand phantom is designed for a lower UWB band, and thus the results obtained at higher UWB range are less reliable. In the simulations, as the antenna-skin distance increases, the efficiency improves slightly.
## 2 The Method of Multi-DOF Image Automatic Acquisition  
In practical industrial applications, due to the complexity of the image acquisition environment, the angle of the acquired image is variable, and the illumination is different. In order to simulate the image acquisition situation comprehensively in the industrial application, we designed a multi-DOF automatic image acquisition system. This system can collect data under different shooting angles and different illumination conditions. Figure 1 shows the general idea of the system design. The system is composed of a motion control module and an illumination control module. The lighting control module takes random lighting in different directions to construct different lighting conditions. The motion control module controls the random motion of multi-degree of freedom motion axis to realize different angles in the spatial range. Considering the relative position between the industrial camera and object and the acquisition time-consuming problem, it is necessary to calculate the camera's spatial pose and optimize the whole random acquisition path when controlling the random motion of the multi-DOF motion axis.
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 3 Cluster-Based Control Message Management for FANET
### Control Message Dissemination Operations
The UAV sends more then one message only if it does not receive the acknowledgment after a certain amount of time. On the other hand, as SDN-FANET do not employ an acknowledgment packet, the UAV keeps sending information in every \(t\) time interval. 2. The UAVs follows the PPRZM mobility model. When a UAV is performing the Stay-at, Eight, or Oval movement pattern, it stays hovering in the same area, next to one or two close coordinates. In this case, CAPONE assumes that the UAV stays stationary in the same region. On the other hand, when the UAV is performing Way-point or Scan movement, CAPONE assumes that the UAV is moving between far distinct locations. In this case, the UAV sends its next location to the controller right before it moves, so that the controller can keep this information for future planning. However, in SDN-FANET, the UAVs send beacons without a previous decision-making process. 3. CAPONE employs a clustering approach to improve the control message management. The CN takes into consideration the location of each UAV and then selects the best candidates RNs to become CH. Each CH behaves temporarily as a controller forwarder (\(CN_{fw}\)), connecting the CN to the other UAVs. The CHs changes according to position configuration of all UAVs.
## 3 Results and Discussion
### Sensory Evaluation of Aroma  
Regarding the fermentation protocol used for each trial, there was a noticeable variability in the olfactory perceptions of the cane wines (Table S1). The intensity of the odors varied significantly across the different treatments: SC15 had the highest value (6.5) and SC2 the lowest (2.3). Odor complexity was also highest in SC15 (7.2) and lowest in SC2 (1.5). The fermentation protocol in SC15 seemed to promote the generation of a wider range of active aroma compounds, such as esters and higher alcohols, contributing to a more complex aroma profile [67].  
Among the specific attributes, the apple odor was more pronounced in SC14 (5.6), while the floral odor was stronger in SC15 (4.5). These attributes, including apple, apple-like, and cooked apple, and floral odors, are commonly associated with different rums undergoing sensory analysis [75]. The vanilla odor was mainly detected in SC11 (3.5). This aroma, as noted by [PERSON] and [PERSON] [76], becomes more prominent as the rum ageing process in barrels progresses. The melon odor reached its peak in SC12 (5.1). Melon is recognized in the characterization of fruity aromas in many rums, though it is more pronounced in cachaca, a rum-like spirit [77]. The alcohol odor was most intense in SC4, with citrus and spices being the most prominent odors in SC4 (3.4) and SC15 (7.6), respectively. These attributes are typical of rum, and, together with herbaceous notes, define its main aromatic component [78]. The vinegar odor was most pronounced in SC1 (7.0), while the sulfurous and herbaceous odors were generally low in all trials. In trials SC1-SC5, the presence of AAB, which remained until the end of alcoholic fermentation, oxidizing ethanol into acetic acid, contributed to the increased perception of vinegar odor [79]. The overall acceptance levels were highest in SC15 (7.5) and lowest in SC9 (2.3). These results indicate a significant variability in the olfactory perceptions of the different cane wine samples.  
The agglomerative hierarchical clustering analysis (AHC) of the sensory data of the cane wines grouped the 16 cane wines into four distinct clusters (Figure 7), each characterized by specific olfactory attributes. Cluster 1 included trials SC1 to SC4, characterized by a high perception of vinegar odor and low aromatic intensity and complexity. Cluster 2 included eight treatments (SC5-SC10, SC13, and SC14), characterized by a dominance of apple and sulfurous odors. Cluster 3 consisted of the SC11 and SC15 trials, characterized by high complexity and floral, alcohol, citrus, spicy, and vanilla odors, and high overall acceptance. Finally, cluster 4 consisted of the SC12 and SC16 trials, characterized mainly by the smell of melon. The total variance was 27.54% within the cluster and 72.46% between clusters, suggesting an optimal separation between the groups.  
Figure 7: Agglomerative hierarchical clustering obtained by the olfactory analysis of cane wines after the end of alcoholic fermentation.