esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Activated carbons are materials characterised by a porous internal structure that gives them great surface area, volume, and distribution of pore size; additionally, they have functional groups that are prepared at the edges of grapheme layers and have been used in industrial processes that involve the adsorption of contaminants at the liquid-solid interface [1]. One of the most studied pollutants in adsorption from solution is phenol, because it is normally present as a residue in water sources, since it is part of the effluent from chemical plants, oil refineries, the processing of paper pulp, gasification, the production of phenolic resins, and paintings, among others [2].  
The surface of activated carbon is heterogeneous, composed of both hydrophobic and hydrophilic regions, with a variety of functional groups that are mainly oxygenated. The process of adsorption has been described using different mechanisms, such as van der Waals interactions, hydrogen bonds, electrostatic interactions, ion exchange, and hydrophobic links. Many organic molecules like phenol possess hydrophilic and hydrophobic regions. The polar groups in molecules tend to interact with polar groups on the activated carbon surface using hydrogen bonds, permanent dipoles, and permanent dipole-induced dipole or ion-ion interactions, which are enthalpy-driven processes, while the non-polar portions of the molecule are adsorbed onto the basal surface of activated carbon through entropy-driven hydrophobic links [3].  
When activated carbon is placed in contact with a liquid there are different phenomena such as wetting, penetration of the molecules into the pores, and the formation of a layer that involves specific interactions; these can be studied by immersion calorimetry, because the heat exchanged during this processes provides important information about adsorbent-adsorbate interactions [4; 5; 6; 7; 8].  
This study seeks to compare specific and non-specific interactions between superficially modified activated carbons and phenol in adsorption during immersion calorimetry at equilibrium. An activated carbon was modified by impregnation with solutions of nitric acid, phosphoric acid, nitric acid with subsequent reflux in solution of ammonium hydroxide, and carbonisation in a nitrogen atmosphere to 1073, 1173, and 1273 K. The immersion enthalpy was determined in aqueous solutions of phenol in a range from 0.1 to 10 mmol L\({}^{-1}\). The phenol adsorption during the calorimetric process of immersion was compared with the adsorption obtained when it reached equilibrium, taking into account that there is an effect on the interaction due to contact time; in addition, it verifies the validity of the energy interaction, which is determined by calorimetry with the adsorption that is measured at equilibrium.
## 3 The Main Content and Key Technologies of SC Supported by DTs
### Content Architecture and Key Technologies of DTs
Algorithms based on big data and machine deep learning have increasingly strong autonomous learning and decision-making functions. The working principle of a three-dimensional laser scanning system is using the transmitter to transmit laser pulse signals to the target object. The pulse signals are reflected on the surface of the object, and the DTs are received by the laser receiver on the instrument. Finally, the distance between the target object surface and the scanner is calculated according to the time from transmitting to receiving the pulse signals and the propagation speed of the pulse signals. According to the distance between the scanner and the object surface measured by the scanner and the angle information in the horizontal and vertical directions recorded in the scanner, the three-dimensional data of the object surface can be accurately recorded. Finally, these data can be calculated and processed to carry out high-precision point cloud data. Finally, the processed point cloud data can be used to reconstruct the reverse three-dimensional model. DTs have a wide range of application scenarios in various industries. DTs are innovative applications integrating a series of technologies, such as perception, transmission, computation, modeling, and simulation, and their architecture includes a physical layer, data layer, model layer, and functional layer [74, 75, 76]. The physical layer refers to the objects in the physical world, which are divided into tangible objects, such as the human body, objects, and physical space and intangible objects such as business processes. The data layer is the basis of DTs application that consists of data acquisition, transmission, processing, and storage. The model layer is the core of DTs application that adopts modeling and other technologies to represent the digital mirror image of real objects. The function layer is the direct embodiment of the value of DTs. It encapsulates the simulation results and visual services together and provides them to business system applications to satisfy various application scenarios.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 1 Introduction
Similarly, although smartphone collaborative positioning (CP) using inter-device distance has been investigated in several studies [24; 25; 26], it is uncommon to express the distance as an inequality constraint while taking into account vehicle length information. Therefore, it is crucial to explore methods that can utilize range information as an inequality constraint to further improve smartphone positioning accuracy. In this study, we aim to improve the accuracy of smartphone positioning using range information via an inequality constraint estimation approach. [PERSON] et al. previously demonstrated that the error variance of a solution obtained through inequality-constrained state estimation is equal to or less than that of a solution obtained through unconstrained estimation [27]. Various methods have been proposed to solve the inequality constraint problem, such as the active sets method, the Bayesian method, and the projection method [27; 28; 29]. However, it has been noted that projecting state estimates onto the constraint hyperplane does not necessarily place the mean of the state near the mean of the feasible region [30]. As such, we choose the probability density truncation (PDF) method as our estimator, which updates the state with the mean and covariance of the PDF region [31]. Additionally, an iterative procedure is used to ensure that the probability within the constraint range exceeds a given threshold. To validate our method, we introduce three applications: vertical velocity constraint, direction constraint, and inter-smartphone distance constraint. The structures of these constraints are introduced, and their effectiveness is demonstrated in the experimental section. The rest of the paper is organized as follows. Firstly, the positioning model is reviewed, and then we introduce the design of the inequality constraint matrices in different applications. Secondly, the density function truncation method is introduced in detail, including the estimator and the iterative procedure. Thirdly, the performance of the method is analyzed using a real-world collection of datasets from three smartphones. Finally, we conclude the paper.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes  
According to the domain of insertion, the watermarking techniques proposed in the literature can be grouped into two classes: those that operate in the spatial domain and those in the transformed domain [7]. A watermark can be embedded in the spatial domain by involving a matrix of pixels in a digital image [62], and it can also be embedded in the frequency domain by modifying the frequency spectrum of an image generated through some type of frequency transformation [63]. However, there are techniques that combine several domains to achieve better watermarking performance [64]. The robustness and capacity of the approach are directly impacted by the domain selection. This choice depends on the targeted application, since each domain has its own characteristics. Further, in this section, we review various works in the most widespread domains of digital image watermarking.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 2 Related Work
### Wi-Fi Offloading in Small Cells  
Substantial research has been performed on Wi-Fi offloading due to the benefits associated with offloading mobile network data to Wi-Fi that mainly include BS power saving, easing of network congestion and quality-of-service improvement for the users, especially in dense areas. Work in this area started more than a decade ago; however, due to its overwhelming advantages and network upgrades, research in this area still continues and Wi-Fi offloading is also being considered for 5G and B5G. [PERSON] et al. [26] proposed a bid-based heterogeneous resource allocation framework that enables MNOs to efficiently use both cellular and operator-owned Wi-Fi resources simultaneously. An auction-based mechanism is used to achieve dynamic Wi-Fi offloading with awareness of user valuations. The operator-domain offloading helps avoid selfish user behavior and ensures near-optimal profit and social utility. [PERSON] et al. [27] proposed a scheme for joint Wi-Fi and cellular offloading to optimally reduce the energy consumption and latency of mobile terminals in task processing.  
[PERSON] proposed a Wi-Fi offloading algorithm based on traffic congestion awareness in [28]. The algorithm assumes user traffic information based on usage pattern (e.g., video streaming or emails etc.), i.e., user priority level. Users with high priority level are offloaded to Wi-Fi on priority. However, it does not describe the overhead signaling involved in updating the priority level information which likely varies over time. [PERSON] et al. [29] proposed an incentive mechanism to encourage Wi-Fi AP owners to participate in data offloading. Their scheme (Delay-constraint and Reverse Auction-based Incentive Mechanism) solves an optimization problem to maximize the revenue of the MNO while jointly considering the delay constraint for various user applications. A Vickrey-Clarke-Groves scheme-based payment rule is also proposed to guarantee the individual rationality and truthfulness properties of users while considering different traffic load scenarios. [PERSON] et al. [30] proposed an optimal pricing scheme to maximize the user satisfaction through Wi-Fi offloading and minimize the monetary losses to MNO incurred due to users offloading to Wi-Fi.  
Previous works have individually considered cooperative SC network or Wi-Fi offloading for rate maximization. However, no work has considered the conditions in which either of the techniques is more useful compared to the other. In this work, we provide a comparison of the two schemes and discuss the scenarios where one is beneficial over the other.
## 3 Performance Prediction of a Cement-Chemically Inert Quartz Filler Binary Mixture
### Prediction of the Compressive Strength  
After determining the reaction level, we can use it to estimate the strength development of the cement and concrete. The compressive strength can be represented as a linear function of the cement's reaction level, as described in Equation (8) [15]:  
\[f_{c}(t)=A_{1}\frac{C\alpha}{W0}-A_{2} \tag{8}\]  
Here,\(f_{c}\) represents the compressive strength, and A\({}_{1}\) and A\({}_{2}\) are strength coefficients. Figure 4a illustrates the relationship between strength and \(\frac{C\alpha}{W0}\). For different mix ratios and ages, the overall compressive strength is a linear function of \(\frac{C\alpha}{W0}\), with a coefficient of determination for the linear regression of 0.77. However, for the specimen with a water-to-(cement + quartz) ratio of 0.2, the one-day strength deviates from the regression curve. When we excluded the one-day strength data for the specimen with a water-to-(cement + quartz) ratio of 0.2 and performed the linear regression again, the coefficient of determination increased to 0.91, which is significantly higher than that shown in Figure 4a.  
Figure 3: Evaluation results of properties of cement–quartz blends. (w/b = 0.2, M4, M5, and M6, water availability coefficient 1.8).  
Figure 5 illustrates the impact of the chemically inert quartz filler content and the water-to-(cement + quartz) ratio on the 28-day strength. In Figure 5a, where the water-to-(cement + quartz) ratio is 0.5, the compressive strength decreases as the substitution amount of chemically inert quartz filler increases from 0 to 20%. In Figure 5b, where the water-to-(cement + quartz) ratio is 0.2, the change in compressive strength is minimal as the substitution amount of chemically inert quartz filler increases from 0 to 20%. This indicates that at a low water-to-(cement + quartz) ratio, replacing cement with chemically inert quartz filler is beneficial. It reduces cement consumption and carbon dioxide emissions without significantly affecting the strength.
## 2 Methodology
### Cavitation Model  
The mass transfer mechanism between the vapor and liquid phases is described using the Zwart-Gerber-Belamri cavitation model. The difference between the local and vapor pressures in the flow field drives the mass transfer rate between the vapor and liquid phases (i.e., the rate of cavitation in the flow field). The cavitating flow field is determined by the mass transfer rate between the vapor and liquid phase [24]. The cavitation rate is governed by the transport equation:  
\[\frac{\partial}{\partial t}(\alpha_{l}\rho_{v})+\
abla\cdot\left(\alpha_{l} \rho_{v}\overset{\rightarrow}{V_{v}}\right)=R_{\rm e}-R_{\rm c}, \tag{10}\]  
where \(\alpha_{l}\) is the volume fraction of liquid phase, \(\rho_{v}\) is the density of the vapor phase, \(\overset{\rightarrow}{V_{v}}\) is the velocity of the vapor phase, and \(R_{\rm e}\) and \(R_{\rm c}\) are the mass transfer source terms related to the evaporation and condensation, respectively, of the vapor.  
The evaporation mechanism responsible for cavitation in the hydrodynamic torque converter flow field is governed by \(R_{\rm e}\). When the local pressure in the flow field is less than the vapor pressure, the liquid phase partially converts to the vapor phase, which determines the formation of cavitation in the flow field. The source terms in Equation (10) for the evaporation are expressed by  
\[R_{\rm e}=F_{\rm vap}\frac{3\alpha_{\rm nuc}(1-\alpha_{v})\rho_{v}}{R_{\rm B}} \sqrt{\frac{2}{3}\frac{p_{v}-p}{\rho_{l}}}. \tag{11}\]  
The cavitation bubbles inside the hydrodynamic torque converter compress when they move toward the high-pressure region, and the cavitation condensation mechanism is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\beta^{*}\) & \(\alpha_{1}\) & \(\beta_{1}\) & \(\sigma_{\rm k1}\) & \(\sigma_{\omega 1}\) & \(\alpha_{2}\) & \(\beta_{2}\) & \(\sigma_{\rm k2}\) & \(\sigma_{\omega 2}\) \\ \hline
0.09 & 5/9 & 3/40 & 0.85 & 0.5 & 0.44 & 0.0828 & 1 & 0.856 \\ \hline \end{tabular}
\end{table}
Table 1: Constant coefficients in the shear stress transport (SST) \(k\)-\(\omega\) model.  
determined by \(R_{\rm c}\). When the local pressure in the flow field exceeds the vapor pressure, the vapor phase starts to condense. The source terms in Equation (10) for the condensation are expressed as  
\[R_{\rm c}=F_{\rm cond}\frac{3\alpha_{v}\rho_{v}}{R_{\rm B}}\sqrt{\frac{2}{3}\frac {p-p_{v}}{\rho_{l}}}, \tag{12}\]  
where \(p\) is the mixture pressure, \(p_{v}\) is the vapor pressure, and \(\rho_{l}\) is the density of liquid phase. Different empirical factors are assumed in ANSYS Fluent help profiles as follows: \(R_{\rm B}=10^{-6}\) m, \(\alpha_{\rm nuc}=0.0005\), \(F_{\rm vap}=50\), and \(F_{\rm cond}=0.01\).  
The turbulence level of the flow is clearly related to the cavitation, and fluctuations in the turbulence velocity are greatly influenced by cavitation shedding. Therefore, the cavitation and turbulence models need to be combined to resolve the multiphase problem of the hydrodynamic torque converter, which is usually solved as a flow of pure oil with large prediction errors.
## 4 Finite Element Modeling
### Simulated Conditions
#### 4.3.2 Reinforcement Methods  
To address the effects of loose soil, erosion voids, and caisson tilting on the stability of caisson structures, corresponding reinforcement measures need to be identified. Permeable polymers can penetrate and diffuse into the pores between soil particles, solidifying to reinforce the soil and achieve waterproofing, thereby enhancing soil density and stability. This material effectively mitigates the impact of soil looseness on engineering structures. In the FE model, soil consolidation and reinforcement with permeable polymers were simulated by modifying the field variables in the model, adjusting the modulus of the surrounding soil to simulate soil consolidation, and incorporating the permeable polymer reinforcement.  
Foam polymer materials are two-component polyurethane materials that, after absorbing water, expand to form a gel-like substance with high strength and density, bonding with the surrounding soil to fill voids around the caisson [19; 20]. In the FE model, voids were filled by \"killing\" the soil and \"activating\" the foam polymer material using the Model Change function.  
For structural tilting, anchor reinforcement technology was typically employed, transmitting loads through anchor rods to deep soil layers to disperse the load and reduce concentrated loading on the surrounding soil. Therefore, anchor reinforcement technology was chosen in this study to address caisson tilting. The inclination angle of the anchors was set to 15\({}^{\circ}\). In the FE model, the pre-embedded anchor rods were gradually \"activated\" during the excavation process, anchored to the caisson wall through node coupling, and the influence of variables such as anchor rod size and length on the reinforcement effect was clarified through scenario simulations.  
The arrangements for the permeable polymer reinforcement of loose soil, foam polymer filling of voids, and anchor rod reinforcement of tilting are illustrated in Figure 5.
# Vibration and Reliability Analysis of Non-Uniform Composite Beam under Random Load
## 3 Numerical Studies, Results and Discussion  
A non-uniform rectangular cantilever composite beam with positive exponential cross-section variation function is chosen for numerical case studies. The length of the beam is fixed as \(L\) = 0.2 m and the thickness of the left end of the non-uniform composite beam is \(H_{0}\) = 0.003 m. The dimensions at \(x\) position along the length of the beam are \(b(x)=b_{0}e^{hx}\), \(h_{1}(x)=h_{0}e^{hx}\) and \(h_{2}(x)=(H_{0}-h_{0})e^{mx}\). \(h\) is the taper ratio of the width variation of the composite beam. \(n\) and \(m\) are taper ratios for the variations of thickness of the host beam and the composite layers, respectively. The material properties of the composite beam are \(\rho_{1}\) = 2.7 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\), \(\rho_{2}\) = 7.8 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\), \(E_{1}\) = 7 \(\times\) 10\({}^{10}\) Pa, \(E_{2}\) = 68.9 \(\times\) 10\({}^{9}\) Pa, for the host beam and composite layers, respectively. The reference value of maximum strain is \(\varepsilon_{i}\) = 0.0035.
# Validation of an RF Image System for Real-Time Tracking Neurosurgical Tools
## 2 Materials and Methods
### Signal Processing for Imaging  
After having described the hardware components of the system, we will focus on the algorithms used to obtain the images for the surgical navigation system from the measured information. A Python script that manages all the communications with the VNA and the Arduino microcontroller was created for the measurement process. On the one hand, the communications with the VNA are made according to the TCP-IP protocol through a laboratory local area network (LAN). To that end, a set of instructions were defined using the VISA (Virtual Instrument Software Architecture) protocol. On the other hand, the communication with the microcontroller uses a serial communication channel via USB.  
To eliminate, as much as possible, the interferences and reflections caused by the switches, cables, and other electronic elements of the system, as well as the offset of the equipment, an individual SOL (short-open-load) calibration for each antenna is carried out with the VNA before any measurement. The calibration is made once all the antennas and equipment are placed in their corresponding places and properly connected. This calibration is saved and later loaded for each antenna during the measurement process, taking into account the active antenna at each moment. In addition, to reduce the effect of the different elements composing the hardware system, such as the rest of the antennas or the switches (as well as further electronic equipment), a reference measurement for each of the antennas is carried out. This measurement will be used during the signal processing to eliminate reflections that are not strictly due to the bodies to be tracked with the aim to increase the sensitivity and accuracy of the system.  
In this work we study the use of two different algorithms for building the medical image: Delay and Sum (DAS) and Delay Multiply and Sum (DMAS). The following subsections will provide an overview of each of them.
## 4 Conclusions  
In the last years, the use and abuse of veterinary drugs has represented a very important issue for animal and human health. Consequently, the need of sensitive and rapid analytical techniques for the analysis of veterinary drug residues in food products has become a challenge. This review highlighted the potential role of CE in this topic, as summarized in Table 1. In fact, it is evident that the recent progress in sample pre-concentration methodologies coupled to CE allowed the sensitivity limits of this technique to be overcome and to improve its versatility in the analysis of different types of molecules, in complex food matrices, too. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CE-Technique** & **Food Matrix** & **Sensitivity** & **Ref.** \\ \hline \multicolumn{4}{c}{**Nitroimidazoles**} \\ \hline LLE-SPE-CEC-UV & bovine milk & LOQ: 19–96 (\(\mu\)g/L) & [38] \\ DLIME-CEC-UV & water & LOQ: 5.7–9.3 (\(\mu\)g/L) & [41] \\ DLIME-CZE-MS/MS & poultry and porcine meat & LOQ: 4–16 (\(\mu\)kg) & [44] \\ DLIME-CESI-sweep-MEKC-UV & water & LOQ: 20-58.14 (\(\mu\)g/mL) & [43] \\ SPE-CSEI-sweep-MEKC-UV & egg & LOQ: 6.99–16.8 (\(\mu\)g/g) & [45] \\ \hline \multicolumn{4}{c}{**Fluorquinolones**} \\ \hline SPE-CZE-UV & bovine milk & LOD: 7.5–11.6 (\(\mu\)g/L) & [28] \\ MMMIPs-CZE-UV & bovine milk & LOD: 12.9–18.8 (\(\mu\)g/L) & [29] \\ PPT/SPE-CZE-UV & bovine milk & LOQ: 0.06–0.1 (mg/kg) & [50] \\ MISPe-CZE-LIF & bovine milk, pig kidney & LOQ: 0.55–35 (\(\mu\)g/kg) & [52] \\ MISPE-CZE-MS/MS & bovine milk & LOQ: 3.2–4.7 (\(\mu\)g/kg) & [53] \\ PLE-SPE-CZE-MS/MS & meat & LOQ: 130–470 (\(\mu\)g/kg) & [54] \\ FASS-sweep-CZE-UV & milk, meat & LOD: 570, 7.39 (mg/mL) & [55] \\ FESI-CZE-UV and CZE-MS & bovine milk & LOQ: 2.3–8.3 (\(\mu\)g/kg) & [56] \\ DLILME-NACE-UV & water & LOQ: 5.43–461 (\(\mu\)g/L) & [57] \\ SD-LLIME-NACE-UV & water & LOD: 10.1, 55.3 (\(\mu\)g/L) & [58] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CE-modes in the analysis of the mainly used veterinary drugs in different food matrices. Method sensitivities in terms of LOD or LOQ values are reported. Figure 3: CE-capacitively coupled contactless conductivity detection (CE-C\({}^{4}\)D) electropherograms of four \(\beta\)2-agonists in pig feed. (**a**) Samples (10 mg/L in background electrolyte (BGE)) without pre-concentration step; (**b**) samples (0.1 mg/L in methanol) with field-enhanced sample injection (FESI) preconcentration step; (**c**) samples (5 mg/L in methanol) with FESI preconcentration step [106]. **Author Contributions:** Conceptualization, R.C., A.P. ; Writing-Original Draft Preparation, R.C., A.P. ; Writing-Review & Editing, R.C., A.P. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest.