esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Strength Development at Hardened Stage on High-Strength Concrete Using NDT
## 2 Experimental Procedure
### Experimental Design and Mix Proportions  
Table 3 shows the experimental design in this study. The water/cement (W/C) ratios of concrete were set to 0.48, 0.41, and 0.33 to cover ordinary to high strengths. As for the curing of the concrete specimens, the cylinder molds were subjected to curing in room temperature of \(20\pm 2\)\({}^{\circ}\)C, humidity of \(60\pm 5\%\) for one day and subsequently water-cured. The mock-up specimens were subjected to air-curing in the outdoors.  
As for the properties on fresh concrete, the slump (mm) and air content (%) were measured immediately after mixing and after 60 min. Measurement after 60 min was performed to ensure workability in the field. In addition, a thermocouple(\(\mathcal{O}1.6\times 3000\) mm sheath + 0.65 mm\({}^{2}\times 1000\) mm thermo shield wire, JOOSHIN, Korea) was embedded in the middle of each mock-up specimen to measure the temperature history (\({}^{\circ}\)C). Based on the measurement value, the maturity of concrete (D-D) was calculated. As for the properties of hardened concrete, the compressive strength (MPa), rebound value (R), and ultrasonic pulse velocity (km/s) were evaluated to analyze their relation. The compressive strength was evaluated using the \(\omega 100\) mm \(\times\) 200 mm onsite-fabricated specimens and the core specimens extracted from the mock-up members.  
Table 4 shows the mix proportions of concrete. To set the compressive strength of concrete in the 24-60 MPa range when the unit water content in the concrete mixes was 165 kg/m\({}^{3}\), the cement content per unit volume was set to 340 kg/m\({}^{3}\), 400 kg/m\({}^{3}\), and 500 kg/m\({}^{3}\). The slump of concrete was set to ensure workability in \(180\pm 25\) mm and the air content was set to \(4.5\pm 1.5\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{8}{c}{**Chemical Composition (\%)**} & \multicolumn{8}{c}{**Loss on Ignition (LOI) 2**} \\ \cline{2-9}  & **CaO** & **SiO2** & **Al2O3** & **MgO** & **Fe2O3** & **SO3** & **K2O** & **Others** & \\ \hline OPC 1  & 60.34 & 19.82 & 4.85 & 3.83 & 3.30 & 2.90 & 1.08 & 0.86 & 3.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of used cement.  
Figure 1: Gradation sieve analysis curves of aggregates used: (**a**) fine aggregates; (**b**) coarse aggregates.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 1 Introduction
More specifically, the contributions of this paper can be listed as follows:  
* We develop an analytical model for the energy consumed (per round) by sensor nodes supporting the LPL mechanism implemented in TinyOS. With no loss of generality, we consider the MicaZ [8] hardware platform integrating the CC2420 radio in the evaluation, as it has been one of the most widespread commercial solutions. In addition, this selection allows for validating the resulting model via simulation with Avrora [9]. Avrora is a very accurate and highly scalable sensor node simulator that offers a complete framework for evaluating applications written in TinyOS 1.x/2.x for the Mica family nodes, which include an AVR microcontroller. * We develop an analytical model for the duty cycle of nodes in terms of relevant parameters describing their traffic load and energy harvesting capability. This model results from the formulation of the condition for energy neutral operation and the assumption that solar irradiance follows a periodical pattern. * We show that, in general, duty cycle adjustment is not enough to support non-disrupted operation, but an initial level of energy is also necessary. So, in addition, the proposed model includes a closed-form expression for the energy that is initially required for self-sustained operation. To the best of our knowledge, the work presented in this paper represents the first attempt to tackle the characterization of the node duty cycle for energy neutral operation in an analytical way. The rest of the paper is organized as follows. In Section 2, we revise the state of the art on MAC protocols for battery-powered and energy-harvesting WSN. Then, in Section 3, we describe the LPL mechanism implemented in TinyOS for duty cycling the communication activity of sensor nodes. In Section 4, we construct an analytical model for the energy consumed by any node in a TinyOS WSN supporting an active-monitoring (time-driven) application. In Section 5, we validate the proposed model via simulation. Section 6 describes an accurate model for energy harvesting based on solar cells. Then, by using both the energy consumption and energy harvesting models, in Section 7 we develop closed-form expressions for the duty cycle and initial energy storage that guarantee energy neutral operation. Finally, in Section 8, we draw the main conclusions and suggestions for further research.
###### Abstract  
In this article, the authors conducted electromagnetic radiation research on telecommunication base stations using Unmanned Aerial Vehicles (UAVs). Until now, UAVs have only been capable of performing visual inspections, without investigating electrical parameters. The authors suggest a method that involves using a spectrum analyzer and an automated flight with a predefined trajectory around the base stations to conduct electromagnetic radiation research, which is used by telecommunication regulatory organizations. Two different types of UAVs were used in this work: a drone and a fixed-wing aircraft, each with distinct characteristics. The authors successfully designed and tested both types of UAVs under real conditions and performed measurements. A specialized algorithm with software was developed for processing measurement results, which accurately presents the data in a graphical format. Experiments were performed, and the results, at distances of 200 m or further from the telecommunication base tower by changing the altitude by 5 m, were collected.  
drones; electromagnetic radiation; antenna measurements; radiation monitoring 2025 IEEEexample:BSTcontrol
## 9 Drag Coefficient Prediction
### Compared to Using Shape as Input  
[PERSON] sets the internal aerodynamic shape to 1 and the external to 0 to express the aerodynamic shape, then uses it as the model input for training. Since the sampling area in this paper is large to ensure the normal training of the model, the internal aerodynamic shape is set to 0 and the external is set to 1 to express the aerodynamic shape as the model input. In this paper, three distance fields (\(I_{Dist}\), \(I_{X}\), and \(I_{Y}\)) are innovatively proposed to express the aerodynamic shape and flow field information. This paper uses the UNet model determined in Chapter 6 and exactly the same parameters for training and compares the accuracy of the drag coefficient to illustrate that the three distance fields (\(I_{Dist}\), \(I_{X}\), and \(I_{Y}\)) proposed in this paper can effectively improve the prediction accuracy of the model. Taking an ellipse with an aspect ratio of 4 as an example, the 0-1 distance field is shown in Figure 12. The average relative error of the drag coefficient of each basic shape predicted by the model is shown in Table 5.  
It can be seen from Table 6 that the average relative error of the R = 0.3 hexagon reaches 45.33%, and the average relative errors of the R = 0.4 hexagon and rectangle also exceed 20%, making the total average relative error reach 22.67%. In contrast, the average relative error of the three distance fields (\(I_{Dist}\), \(I_{X}\), and \(I_{Y}\)) acting as the model input is 9.42%, which is 13.25% higher than the average relative error of the drag coefficient when the 0-1 distance field is used as the model input. It can be seen that the wall distance field and spatial coordinate field can make the model learn and predict more efficiently.  
The average relative error when using the 0-1 distance field as the model input is much higher than the average relative error of the wall distance field and the spatial coordinate field proposed in this paper. The main reason for this is that the 0-1 distance field is too simple to express the aerodynamic shape and cannot provide effective distance information and flow field information, while the drag coefficient is closely related to the distance information and flow field information. It is, therefore, difficult for deep learning models to establish a relationship between the aerodynamic shapes and the drag coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Shape**} & **R = 0.1** & **R = 0.2** & **R = 0.3** & **R = 0.4** & \multirow{2}{*}{**Circle**} & \multirow{2}{*}{**Rectangle**} & \multirow{2}{*}{**Diamond**} \\  & **Hexagon** & **Hexagon** & **Hexagon** & & & & & \\ \hline Average relative error (\%) & 3.450 & 7.740 & 12.14 & 5.010 & 9.110 & 13.08 & 15.42 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average relative error of predicted drag coefficient after changing output.  
Figure 12: Partial 0–1 distance field.
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 9 Conclusions  
This paper has presented solutions for detecting web attacks using SDN. Two modules were designed and implemented to detect web attacks, specifically utilizing signatures and regular expressions. These modules were evaluated and several important questions were addressed: can SDN be used as an effective and efficient WAF? Can traditional web attack solutions be implemented without modifying the SDN? How should we choose between an SDN-based WAF and traditional WAFs? These questions were answered and the most notable contribution was the demonstration of the concept using SQL injection attacks. Initially, an SDN-based WAF was designed, its stages were discussed, and it was implemented on the SDN. Several modifications were made to the POX controller to enable deep packet inspection. Then, three modules were implemented to detect SQL injection attacks: signature-based, regular-expression-based, and traditional WAFs using ModSecurity. The experimental results show that all of these solutions were able to detect infected web application injection attempts.  
This work aimed to determine if a controller can achieve or surpass the efficacy of traditional WAFs in detecting SQL injection attacks using a simple SDN switch configuration. Furthermore, by showcasing custom SDN-based security solutions, we highlight the possibility of defending against these attacks without relying on costly corporate software and hardware.  
In addition, other important aspects were measured to assist network managers in selecting the most appropriate solution. Specifically, the packet latency and CPU overhead were evaluated. The proposed solutions were better in terms of TCP latency, with average packet transmission times of 35.5 ms for regular expressions, 43.6 ms for signatures, and 50.6 ms for the traditional solution. This is due to the packet routing and processing processes, where, in the ModSecurity implementation, the packet is transferred to the controller for routing, then to the firewall for inspection, and finally to the web application for request processing. On the other hand, ModSecurity showed a lower CPU overhead, with an average CPU usage of 7.02%, compared to 7.046% for regular expressions and 14.78% for signatures.
# A Hybrid Group-Based Food Recommender Framework for Handling Overlapping Memberships
###### Abstract
The purpose of the food RS presented in [10] was to generate custom daily meal plans for the users based on their dietary requirements and historical food preferences. Although there have been many studies targeted at developing computational tools for food consumption trends, most of them do not manage user preferences and dietary data directly. In [2], the authors presented a food RS that relies on daily personalized meal plans according to users' previous food preferences and nutritional necessities. The authors proposed an algorithm named analytic hierarchy process sort (AHPS), which is a multi-criteria decision making (MCDM) algorithm used to filter all foods that are not appropriate for a user. Recent works based on optimization approaches focused on maximizing user preferences while fulfilling nutritional requirements for menu generation. In this context, an ant colony optimization technique that implements recommendations based on patients' dietary needs considering pathology data was proposed in [1]. None of the aforementioned works explicitly addressed the challenges that emerge when an individual functions as a member of a collective group, instead restricting their scopes to the analysis of isolated individuals. To tackle the challenges inherent in current group-based food recommendation systems (RSs), we proposed an integrated two-stage group recommendation (ITGR) framework. This framework leverages deep neural collaborative filtering (DNCF) networks to efficiently learn from both explicit user preferences, such as ratings and recipe attributes, and implicit preferences, including interactions among group members. A critical challenge in group recommendations is the overlapping of user interests across multiple groups. To address this, the proposed model integrated fuzzy C-means (FCM) clustering with collaborative filtering to enhance intragroup similarity and promote diverse memberships across distinct clusters. Our contributions are summarized as follows:  
* We introduced ITGR, a group-based recipe recommendation system employing deep neural networks to effectively incorporate both explicit user ratings and implicit recipe features, thereby facilitating personalized recommendations. * To address the challenge of intragroup similarity in group-based recipe recommendations, we employed FCM clustering and collaborative filtering, enabling users to belong to multiple groups. * We proposed a method for generating the top-\(k\) recommendations for a group, utilizing an advanced aggregation approach. This method enhances generalization by allowing users to be part of multiple groups. * We addressed the issue of data sparsity by leveraging latent factors of both users and recipes, thus improving prediction accuracy. * We demonstrated the effectiveness of our model through simulations, showing that it outperforms existing methods in generating the top-k recommendations.
## 3 RMNCE-Based Satellite Selection
### Deficiency of GDOP Based Methods  
After the compensation of satellite clock offset, ionospheric delay and tropospheric delay, the pseudo-range observation model [36] is reduced to:  
\[\rho_{GNSS}=\sqrt{\left(x_{s}\ -\ x\right)^{2}+\left(y_{s}-\ y\right)^{2}+\left(z _{s}-z\right)^{2}}\ +\ \delta t_{u}\ +\ \Delta\rho \tag{15}\]  
where \([x_{s},y_{s},z_{s}]^{T}\) is the position of the satellite, \([x,y,z]^{T}\) is the position of receiver, \(\delta t_{u}\) is the receiver clock offset delay, and \(\Delta\rho\) denotes the measurement noise. Meanwhile, replacing \([x,y,z]^{T}\) with the INS result \([x_{INS},y_{INS},z_{INS}]^{T}\), a prediction of the GNSS observation, which can be taken as a redundant measurement, can be derived and denoted as \(\rho_{INS}\). Because the INS and the GNSS are two independent systems, the measurement noise of \(\rho_{GNSS}\) and \(\rho_{INS}\) are uncorrelated. By using the RMNCE approach the variance of \(\Delta\rho\) can be estimated as \(\partial_{\rho}^{2}\). Similarly, the measurement noise variance \(\partial_{\rho}^{2}\) of the pseudo-range rate can be estimated simultaneously.  
According to the measurement of pseudo-range Equation (15), the GNSS positioning accuracy can be analyzed as below:  
\[\Delta\mathbf{x}=\left(\mathbf{H}^{T}\mathbf{H}\right)^{-1}\mathbf{H}^{T} \Delta\rho,\quad\mathbf{H}=\left[\begin{array}{cccc}\frac{\partial p_{1}}{ \partial x}&\frac{\partial p_{1}}{\partial y}&\frac{\partial p_{1}}{\partial z }&1\\ \frac{\partial p_{2}}{\partial x}&\frac{\partial p_{2}}{\partial y}&\frac{ \partial p_{2}}{\partial z}&1\\ \vdots&\vdots&\vdots&1\\ \frac{\partial p_{n}}{\partial x}&\frac{\partial p_{n}}{\partial y}&\frac{ \partial p_{n}}{\partial z}&1\end{array}\right] \tag{16}\]  
where \(\Delta\mathbf{x}\) is the error vector of the receiver and \(\mathbf{H}\) is the Jacobian matrix with respect to the expanded vector \([x,y,z,\delta t_{u}]^{T}\). GDOP \(=\sqrt{\text{trace}\left\{\left(\mathbf{H}^{T}\mathbf{H}\right)^{-1}\right\}}\) is the traditional index to select the optimal satellite combination. For embedded systems, because the directions of the satellites in the sky change slowly, \(\mathbf{H}\) can be periodically updated to decrease the computational burden.  
In order to consider other factors besides geometry, weighted GDOP defined as below based on prior knowledge has been implemented and proved more effective than standard GDOP:  
\[\text{GDOP}_{W}=\sqrt{\text{trace}\left\{\left(\mathbf{H}^{T}\mathbf{W} \mathbf{H}\right)^{-1}\right\}} \tag{17}\]  
Although weighted GDOP and SNR/CNR have been utilized to select optimal satellites in previous research, the uncertainty of weight and the inconsistency between the SNR/CNR and the measurement accuracy will degrade the effectiveness. The exclusion method using elevation and SNR/CNR has the risk of selecting the satellites with bad quality or missing the satellites with good quality. Hence, if the accuracy of pseudo-range can be evaluated precisely in real time, the selection reasonability and the subsequent positioning accuracy can be improved.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 3 Direct Wave Signal Parameter Estimation Method Based on Template Matching  
Different from conventional PBR based on a civil illuminator that directly use the direct wave as the reference signal for cross correlation with scattered wave, we first estimate the parameters of the direct wave signal to achieve the time and frequency synchronization of PBR system. Therefore, the parameter estimation of the direct wave signal is a very important step in the whole signal processing flow and should be accurately completed in real time. Therefore, combined with the parameter template library of PAR signal, we propose a direct wave parameter estimation method based on template matching.
## 3 Materials and Methods
### Extraction Procedure and Sample Preparation for UPLC-PDA-MS Analysis  
Dried lyophilized food material was grounded separately in a mortar with liquid nitrogen. A powdered aliquot (ca. 30 mg) was then homogenized with 2.5 mL 70% MeOH containing 5 mg/mL umbelliferone (internal standard) with sonication for 30 min. Extracts were vortexed, centrifuged at 3000\(\times\)\(g\) for 30 min to remove debris, and stored. Chromatographic separation was performed on an Acquity UPLC system (Waters Corp., Milford, MA, USA) equipped with an HSS T3 column (100 \(\times\) 1.0 mm, particle size 1.8 mm; Waters Corp.) applying the following binary gradient at a flow rate of 150 mL/min: 0 to 1 min, isocratic 95% A (water:formic acid, 99.9:0.1, \(v/v\)), 5% B (acetonitrile:formic acid, 99.9:0.1, \(v/v\)); 1 to 16 min, linear from 5 to 95% B; 16 to 18 min, isocratic 95% B; 18 to 20 min, isocratic 5% B.
## 3 Data Processing Methods
### SAR Images Formulation Based on ULA Communication Signals with the BPA  
#### 3.2.1 Availability Analysis of the BPA  
From the preceding discussion, it can be concluded that the PDP derived from the echo signals collected by the CBS through frequency-domain matched filtering can be treated as the result of pulse compression for imaging processing. In this context, the BPA has been selected for azimuth focusing, thereby avoiding the construction of overly complex azimuth matched filters. It is well known that the BPA algorithm is a time-consuming time-domain imaging algorithm. However, the relatively small observation area of a CBS mitigates the need for excessive time resources. Verification of the data processing in this experiment has shown that obtaining a SAR image of the target area through the BPA requires only 0.4 s, which meets our data processing requirements.
## 3 Results and Discussion
### Relevance for the Production of Volatile Compounds during Alcoholic Fermentation
#### 3.3.1. Biotin Influence on Wine Volatile Compounds
Although the exact metabolic impact of biotin in the production of these compounds is not clear, such a behavior can be overall associated with the role held by the vitamin as a cofactor to several carboxylases, including pyruvate and acetyl-CoA carboxylases as well as in the Ehrlich pathway decarboxylation step of \(\alpha\)-keto acids towards their associated aldehydes [49; 50; 55]. Amongst these impacted contents, both isovaleric acid and isoamyl alcohol were found in concentrations above their odor detection thresholds of 0.7 and 7 mg/L, respectively [52], further supporting the idea that biotin might play a role in the sensory properties of final wines. Interestingly, the ratio between the total ethyl esters (calculated as sum of the ethyl esters quantified here) and total acetate esters (calculated as sum of the acetate esters quantified here) appears to be significantly (\(p<0.05\)) influenced by the initial biotin in the medium, increasing alongside the vitamin. Surprisingly, though, the total esters (calculated as sum of the esters quantified here) do not present any significant (\(p>0.05\)) dependency regarding biotin, suggesting that the vitamin indeed modulates the ester profile of the wines, orienting it preferentially towards ethyl esters rather than acetate esters, and offering the first evidence of such an effect of the vitamin on the ester formation in wine. Central Carbon Metabolism-Derived Compounds  
Notable increases in the diethyl succinate concentrations were found when increasing the initial biotin contents in the synthetic must medium (Table 6, Figure 2C), rising from 0.35 \(\pm\) 0.05 to 0.45 \(\pm\) 0.03 \(\upmu\)g/L when increasing biotin from 0 to 3 \(\upmu\)g/L. On the other hand, significant decreases in the concentration of benzaldehyde formed during fermentation appeared when raising the initial biotin doses (Table 6, Figure 2), dropping from 52.50 \(\pm\) 16.62 \(\upmu\)g/L to 29.62 \(\pm\) 4.91 \(\upmu\)g/L when reaching 3 \(\upmu\)g/L of the initial vitamin in the synthetic must. All in all, such results further suggest the influence that biotin might have on the yeast central carbon metabolism during alcoholic fermentation.