esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper presents the investigation on the possibility of damage assessment and monitoring at adhesively bonded interfaces between steel and concrete. The ultrasonic tests were carried out on steel-concrete specimens subjected to push-out loading. The idea of the procedure was to monitor the changes in the amplitude and phase shift of signals measured during subsequent phases of degradation of the adhesive joint. To quantify discrepancies between the reference signal and other registered signals, the damage indicator based on the \(C\)-error of the Sprague and Gears metric was proposed. The damage assessment procedure was compared with results of the monitoring of the interface slip.  
The tested specimens revealed three types of failure mechanisms, namely, the adhesive, cohesive and mixed adhesive-cohesive failure. The adhesive failure occurred in a sudden manner and detection of damage before the complete separation of steel and concrete was not possible based on ultrasound monitoring. In the other cases of failure modes, i.e., the cohesive and mixed adhesive-cohesive failure, the proposed damage indicator enabled detection of potential damage. The gradual increase of the \(C\)-error was observed before the initiation of cracks in the bulk of the adhesive layer or in the bulk of the concrete cube.  
The monitoring of the interface slip revealed sudden increases of its value in the case of specimens with the cohesive failure preventing earlier detection of damage. For specimen with the adhesive or mixed adhesive-cohesive failure, the gradual increase in the slip value at the final stage of the test may indicate approached failure. However, in some specimens, no increase of the slip was observed. A possible application of the interface slip monitoring in the case of the adhesive-type failure should be tested in a larger number of cases.  
The results showed the possibilities and limitations of the proposed approach in diagnostics of adhesive connections between steel and concrete depending on the failure modes. The conducted research should be considered as an initial step for further analyses related to the condition assessment of adhesively bonded shear connectors.  
The author would like to acknowledge the National Science Centre, Poland for the support for this research within project No. 2015/19/B/ST8/00779. The help of [PERSON], [PERSON] and [PERSON] during preparation of steel-concrete specimens is gratefully acknowledged.  
The author declares no conflict of interest.
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
###### Abstract  
Metamaterials research has developed perfect absorbers from microwave to optical frequencies, mainly featuring planar metamaterials, also referred to as metasurfaces. In this study, we investigated vertically oriented metamaterials, which make use of the entire three-dimensional space, as a new avenue to widen the spectral absorption band in the infrared regime between 20 and 40 THz. Vertically oriented metamaterials, such as those simulated in this work, can be experimentally realized through membrane projection lithography, which allows a single unit cell to be decorated with multiple resonators by exploiting the vertical dimension. In particular, we analyzed the cases of a unit cell containing a single vertical split-ring resonator (VSRR), a single planar split-ring resonator (PSRR), and both a VSRR and PSRR to explore intra-cell coupling between resonators. We show that the additional degrees of freedom enabled by placing multiple resonators in a unit cell lead to novel ways of achieving omnidirectional super absorption. Our results provide an innovative approach for controlling and designing engineered nanostructures.  
metamaterial; split-ring resonator; micro-structure; absorption; vertical metamaterial +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Further Analysis  
#### 4.4.1 Specific Analysis of Ablation Study on ToolBench  
In this paper, we examine the experimental results from 500 test cases across I2-Ins, I2-Cat and I3-Ins in ToolBench. We summarize and categorize the types of problems in ToolBench that directly lead to task failure and provide further specific analysis. Figure 6 shows the number of various problem types present in ToolBench and their distribution across experimental conditions. The CoReaAgents framework without the Tool Agent makes more errors in tool selection and usage. The experimental results show that the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**HotpotQA**} \\
**Methods** & **EM** & **F1** \\ \hline CoT & 34.80 & 42.32 \\ ReAct & 35.47 & 42.18 \\ Self-Consistency & 39.40 & - \\ AMOR & 39.60 & 49.30 \\ UALA & 41.30 & - \\ Self-ask & 43.98 & 54.67 \\ HGOT & 47.37 & 59.48 \\ CoReaAgents & **48.83** & **61.77** \\ \hline w/o TA & 46.64 & 58.28 \\ w/o RA & 44.87 & 56.34 \\ w/o TA\&RA & 41.93 & 54.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of different baselines and the ablation study on HotpotQA in terms of EM (%) and F1 (%). The table highlights the highest scores in bold, with underscores indicating the highest scores achieved by prior methods. - indicates that the method is not publicly available.  
CoReaAgents framework using the Tool Agent for tool augmentation and subtask execution can more effectively select the appropriate tool.  
The CoReaAgents framework without the Reflect Agent makes more mistakes in both tool application and task planning. This indicates that the Reflect Agent enables the timely correction of mistakes in selecting or using tools, significantly reducing the occurrence of API hallucinations. In addition, the reflection on the overall planning by the Reflect Agent can guide the task direction to the correct reasoning path in time, which effectively reduces task planning errors.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 2 Materials and Methods
### IRI-2016 Model  
The International Reference Ionosphere (IRI) is an international project that is jointly established by the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI) [27].  
The IRI model is an empirical ionospheric model that is based on data from ionospheric vertical observing stations and satellite observations. Its goal is to offer systematic and all-encompassing ionospheric parameters. The IRI model may anticipate electron density data in a steady manner and serve as a reference for the four-dimensional ionospheric electron density model.  
The IRI-2016 version is used in this study, which operates in a Linux Python environment. To utilize it, it was installed, and the iri2016 module package was invoked in a Jupyter Notebook. To acquire electron density data, the corresponding longitude, latitude, altitude, and time were entered. The performance of EDG-DNN was assessed by comparing it with the IRI-2016 prediction results.  
Figure 1: Example of the global distribution of the COSMIC trajectory visualisation during 12 January 2010: (**a**) 3-D distribution of the electronic density trace; (**b**) horizontal distribution of the electronic density trace.  
Figure 2: Distribution of electron density over longitude, spanning 15\({}^{\circ}\) latitude.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 3 Location and Velocity Prediction
### Kalman-Filter-Based Trajectory Prediction
\tag{12}\]  
On this basis, we can predict the positions and velocities for all nodes via the previous prediction and current measurement results.
###### Abstract  
This paper presents a novel approach, using hybrid feature selection (HFS), machine learning (ML), and particle swarm optimization (PSO) to predict and optimize construction labor productivity (CLP). HFS selects factors that are most predictive of CLP to reduce the complexity of CLP data. Selected factors are used as inputs for four ML models for CLP prediction. The study results showed that random forest (RF) obtains better performance in mapping the relationship between CLP and selected factors affecting CLP, compared with the other three models. Finally, the integration of RF and PSO is developed to identify the maximum CLP value and the optimum value of each selected factor. This paper introduces a new hybrid model named HFS-RF-PSO that addresses the main limitation of existing CLP prediction studies, which is the lack of capacity to optimize CLP and its most predictive factors with respect to a construction company's preferences, such as a targeted CLP. The major contribution of this paper is the development of the hybrid HFS-RF-PSO model as a novel approach for optimizing factors that influence CLP and identifying the maximum CLP value.  
construction labor productivity; predictive modeling; hybrid feature selection; optimization; machine learning +
Footnote †: journal: _Algorithms_  
0021
## 3 General Properties of Hyaluronic Acid-Catechol
### Catechol-Involving Reactions  
The catechol groups in HA backbones can undergo chemical and physical reactions during the manufacture of formulations (i.e., hydrogels, thin films, and nanoparticles) and help them adhere to various substrates (i.e., tissues, metal/metal oxide, polymer surfaces). For example, hydrogels can be prepared by the covalent and/or non-covalent crosslinking of polymers via catechol-involving reactions (Figure 3) [37; 39; 43; 46; 47; 48; 50; 51; 52; 53; 54; 55; 56; 57; 58]. Catechol groups in basic pH conditions transform into catechol quinone, which is highly reactive towards amines, thiols, and other catechol groups because of their pKa values (pKa1: 9.25; pKa2: 13.0) [90]. Oxidants (e.g., sodium periodate, NaIO4) are used to achieve the rapid gelling of HA-catechol hydrogels [37; 46; 51; 52; 53; 54]. HA-catechol achieves a crosslinked gel state with instantaneous solidification upon the addition of NaIO4. The optimized stoichiometric ratio of NaIO4/catechol for the preparation of HA-catechol hydrogels is 1.5:1, which results in rapid gelation within 10 s [37]. Additionally, oxidoreductase has previously been used to prepare HA-catechol hydrogels [39; 57]. When H2O2 (0.01% \(v/v\)) and HRP (0.05 mg/mL and 0.1 mg/mL) were added to an HA-catechol solution, the gelation time significantly decreased to 12.5 s for 0.05 mg/mL of HRP and to 5.2 s for 0.1 mg/mL of HRP [57]. In addition to covalent crosslinking, HA-catechol hydrogels are prepared by non-covalent reactions of polymers. In general, the catechol groups can bind to various molecules and substrates by hydrogen bonds, \(\pi\)-\(\pi\) stacking, cation-\(\pi\) interactions, and metal coordination. In particular, catechol groups strongly bind to Fe\({}^{3+}\) ions, as previously reported [91; 92; 93; 94; 95; 96]. The rapid gelling of HA-catechol hydrogels is achieved by the addition of Fe\({}^{3+}\) ions to a solution of HA-catechol [47; 59]. The elastic modulus value of Fe\({}^{3+}\) ion-mediated HA-catechol hydrogels at a concentration of 10 mM FeCl\({}_{3}\) was -4.6 kPa, which is far higher than that of the HA-catechol solution (-1.7 Pa) [47]. In addition, the gelation time could be controlled by changing the Fe\({}^{3+}\)-catechol stoichiometric ratio. For instance, the gelation time decreased by increasing the Fe\({}^{3+}\)-catechol ratio from 1:1 (6.5 mM Fe\({}^{3+}\) and catechol) to 1:3 [59]. Therefore, catechol-involving reactions of HA-catechol can be utilized to prepare injectable hydrogels, drug delivery depots, and tissue engineering scaffolds.
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 5 Simulation Results and Analysis
### Impact of Energy Consumption  
Energy consumption is measured by the product of energy consumed by a single MN and the total number of mobile nodes in MANET. The lower the energy consumption, the more efficient the method.  
Table 2 shows energy consumption for the proposed FFROR Technique and the existing methods. The number of mobile nodes is 50 to 500 for experimental purposes. The table value clearly shows that the proposed FFROR technique has achieved less energy consumption than the existing works. The FFRO algorithm chooses the minimum energy consumption MN for transmitting the data packets. From the results, the energy consumption of the proposed FFROR technique is reduced by 22%, 43%, 13%, and 36% compared to the existing EAER [1], MECOR Protocol [4], EESMR technique [25], and OEERA scheme [26], respectively.
## 4 Simulation
### Variable Load Operation  
To further simulate the variation in the feeding process of the belt conveyor, this study introduces a change at 20 s by doubling the material mass on the conveyor belt from 0 m to 240 m of a traditional belt conveyor. The material mass was increased by a factor of two. In response to the load disturbance, the speed and tension waveforms at various observation points on the conveyor belt during the recovery to a stable operating state are illustrated in Figure 15.  
Figure 16 displays the simulation results of the improved conveyor. At the observation point of 300 m, which is closest to the location of the sudden load increase, the speed decreased to a minimum of 5.64 m/s, and the peak value of tension amplitude was 6.68 kN. At the observation point of 600 m, which is separated by four permanent magnet direct drive motors from the 300 m point, the tension fluctuations were attenuated due to the adjustment of the direct drive motors. Around 3 s, the speed at the 600 m observation point decreased by approximately 0.1 m/s, and the range of tension fluctuations increased only to 6.2 kN. The observation point of 900 m, which is separated by another two permanent magnet direct drive motors from the 600 m point, experienced a further reduction in tension fluctuations. It was challenging to detect any significant variations in tension and speed at the 900 m point due to the increased load. At the observation point of 1200 m, the impact of the localized load increase was almost negligible due to the cumulative attenuation effect of the permanent magnet direct drive motors.  
Comparing Figure 15 with Figure 16, it is evident that under localized load disturbances, the dynamic adjustment time for the new conveyor is approximately 2 s, with tension fluctuations confined to the conveyor belt at 900 m. The speed reduction at 300 m is about 26.9% of the speed reduction at 300 m of the traditional conveyor. In contrast, the traditional conveyor has a dynamic adjustment time of about 20 s, with tension fluctuations affecting the entire conveyor system. Therefore, the new conveyor demonstrates better resistance to interference when faced with localized load changes.  
Figure 15: Belt response curves on conventional belt conveyor systems under variable load operation. (**a**) Velocity curves. (**b**) Tension curves.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis
### Comparison of Recognition Performance under Different Parameters
#### 4.2.3 Comparison of Recognition Accuracy with Different Feature Dimensions  
In the training and testing process, we used the distances between the sample features and the prototypes to measure the attribution, and the dimensionality change of the features also had an impact on the recognition performance. The experiments were conducted when the other parameters were optimal. Figure 8 shows the recognition performance of the network when the feature dimensions were 2, 3, 4, and 5 and the SNR was \(-4\) dB and 0 dB, respectively. It can be seen that using a lower feature dimension could achieve a higher recognition accuracy, and a higher feature dimension improved only the computational complexity of the network rather than the recognition performance.  
As shown in Figure 8, the feature dimension had a certain impact on the recognition performance at a low SNR, and the impact became smaller at a high SNR. At the same time, according to the results of several experiments, the recognition accuracy of three-dimensional features was slightly improved compared with that of two-dimensional features. When the dimensions continued to increase, the recognition accuracy did not change significantly, but this increased the network complexity. Therefore, three-dimensional features were finally chosen for the model.
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 6 Numerical Studies
### Validation via Monte Carlo Simulations  
The average PAoI expression in (10) forms the basis for the JBG optimization. Accordingly, we first validated this expression prior to performing the optimization. To this end, we developed a MATLAB-based simulator that emulates the two-hop status update system illustrated in Figure 1 under AWGN channels. Using this simulator, we conducted Monte Carlo simulations with different random seeds for each run. To validate our analysis under various channel conditions, we considered different SNR settings, specifically \(\rho\in\{1,3,5\}\) dB. Furthermore, we performed simulations over two different time scales--long-term and short-term. For the long-term case, we performed 100,000 runs and computed the empirical mean. For the short-term case, we performed 30 runs and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of sensors \(N\) & 30 \\ System bandwidth \(B\) & 1 \\ Information bits per status packet \(S\) & 100 bits \\ Average SNR \(\rho\) & 1 dB, 3 dB, 5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
computed the 95% confidence interval. We then compared the simulation results with the analytical values obtained from (10). Figure 3 presents this comparison. As shown in the figure, the analytical results closely match the simulation results at both long-term and short-term time scales across different SNR settings, thereby confirming the correctness of the derived expression.
# From Forest Berry Leaf Waste to Micellar Extracts with Cosmetic Applications
## 1 Introduction
The first berries can appear as early as June and may persist until the end of September [23]. According to the literature, the fruit of this plant is a rich source of anthocyanins, flavonoids, tannins, organic acids, minerals, vitamins, sugars, and pectin [23]. Due to its anthocyanin content, _Vaccinium myrtillus_ fruit can be used for the nutritional prevention of neurodegenerative diseases, cardiovascular disorders, cancer, and age-related conditions [12; 13; 23]. The anthocyanins and flavonoids found in _Vaccinium myrtillus_ help delay the aging process and protect against damage to blood vessel walls. Blueberry extracts are also known for strengthening capillary walls, enhancing the resilience of the blood vessel endothelium, reducing bleeding, and exhibiting anti-inflammatory and antithrombotic properties [12; 13; 23]. Many of these beneficial properties can be utilized in cosmetic formulations [14; 24]. For example, research conducted by [PERSON] et al. [14] confirmed that a berry extract used in creams positively influenced the pigmentation and brightness of the skin while enhancing its hydration, elasticity, and smoothness. Plant extracts are well-known ingredients in cosmetics. In principle, any part of the plant can undergo the extraction process. While plant extracts for cosmetic purposes are most commonly derived from fruit, leaves can also serve as valuable raw materials for various industries, including cosmetics, pharmaceuticals, and food. However, their application potential is often underutilized, as they are frequently regarded as waste raw materials. This perspective is flawed, as leaves, just like fruit, can be a valuable source of substances such as minerals, vitamins, and polyphenols. An ecological method for obtaining active substances is micellar extraction, a technique whose benefits are well documented in the literature [25; 26; 27; 28]. Previous research [29] has also demonstrated the potential of micellar extracts (e.g., sourced from dock root, ash bark, and bistort rhizome) as valuable ingredients for cosmetics. This study aimed to explore the potential of using leaves from selected forest berry plants (wild strawberry, blackberry, and blueberry), typically considered waste, for the production of micellar extracts. Additionally, it sought to evaluate the application of these extracts in bath wash formulations and empirically investigate their influence on specific properties of the developed prototype bath cosmetics.
## 4 Result
### Convergence Analysis  
The ability of TTUR and SN modules to enhance training stability requires further validation. To assess the effectiveness of these improvements, we compared the generator and discriminator losses of the Original DCGAN, TTUR, and SN-TTUR. The resulting training losses are presented in Figure 9. The figure demonstrates that with an increasing number of training epochs, the generator loss in the Original DCGAN continues to rise, while the discriminator loss approaches zero, which indicates that the generator's performance is weaker than that of the discriminator. In contrast, with TTUR, the generator loss exhibits a smoother decrease, and is significantly lower than that of the Original DCGAN. The SN-TTUR generator loss values are further reduced, and its fluctuation stabilizes, indicating that combining these two modules enhances the training stability of the DCGAN model. With the simultaneous application yielding the most favorable results.
# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 4 Results and Discussion
### Optimization of Operating Parameters
#### 4.3.2 Regression Analysis  
The DPS software was used to process the test data. The regression coefficients in the regression equation were tested by the \(F\)-test at a significance level of 0.05, taking the engine speed variation rate \(y_{1}\), the crushing rate \(y_{2}\), the impurity rate \(y_{3}\), the corn kernel loss rate \(y_{4}\), and the cylinder speed variation rate \(y_{5}\) as the objective functions. The regression equation was as follows:  
Figure 6: Contour maps of operating quality. (**a**) Threshing cylinder speed variation rate; (**b**) engine speed variation rate; (**c**) corn kernel crushing rate; (**d**) corn kernel impurity rate; (**e**) corn kernel loss rate.  
\[y_{1}=1.53+6.74x_{1}+8.97x_{2}+5.31x_{3}+5.94{x_{1}}^{2}+7.90{x_{2}}^{2}+11.90{x_{3 }}^{2}+36.56x_{1}x_{2}-11.69x_{1}x_{3}-12.44x_{2}x_{3}, \tag{8}\]  
\[y_{2}=4.18+0.40x_{1}-0.19x_{2}+0.38x_{3}+0.28{x_{1}}^{2}-0.11{x_{2}}^{2}+0.05{x_{ 3}}^{2}-0.38x_{1}x_{2}-0.06x_{1}x_{3}+0.38x_{2}x_{3}, \tag{9}\]  
\[y_{3}=1.87-0.27x_{1}+0.12x_{2}+0.16x_{3}+0.10{x_{1}}^{2}-0.28{x_{2}}^{2}-0.19x _{3}}^{2}-0.19x_{1}x_{2}+0.03x_{1}x_{3}+0.30x_{2}x_{3}, \tag{10}\]  
\[y_{4}=10.11-2.16x_{1}-0.85x_{2}+2.04x_{3}+2.83{x_{1}}^{2}-2.16{x_{2}}^{2}-2.67 {x_{3}}^{2}+2.35x_{1}x_{2}-1.29x_{1}x_{3}-0.24x_{2}x_{3}, \tag{11}\]  
\[y_{5}=1.91+0.29x_{1}-0.34x_{2}+3.42x_{3}+1.56{x_{1}}^{2}+2.23{x_{2}}^{2}+2.83{ x_{3}}^{2}+6.93x_{1}x_{2}-3.71x_{1}x_{3}-3.37x_{2}x_{3}. \tag{12}\]  
A mathematical regression model was obtained from the analysis of variance (ANOVA); the ANOVA results are shown in Table 5. As can be seen in Table 5, the significance test of \(y_{2}\), \(y_{3}\), and \(y_{4}\) was \(F_{2}>3.02\), \(1>p>0.05\), which was less significant but had a tendency to be significant. The main reason for this was that the factors affecting the crushing rate, impurity rate, and loss rate were not limited to the three mentioned in the text. For example, the parameters of wind speed and scavenging section would determine the size of the impurity rate to some extent. The \(y_{1}\) parameter had a good fit, with a significance test of \(F_{2}=3.519\), \(p=0.0359<0.05\), indicating that the regression equation was significant. The \(y_{5}\) parameter also had a good fit, with a significance test of \(F_{2}=3.473\), \(p=0.0373<0.05\), indicating that the regression equation was significant.