esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Concept  
As described in Section 1, one of the main challenges is reducing the effort to control the manipulator in order to make it accessible even to severely impaired people. In the already mentioned work [5], the user is assisted in the manual control of the manipulator by several audio commands, which are associated to predefined movements of the manipulators. In this way, the capacity of controlling the manipulator to perform these movements is not required to the user. A similar idea is used in our previous works [8; 9; 10], where, limiting the manipulator functionalities to the pressure of a button or knocking on a door, the only task assigned to the user is communicating, through the HMI, where he wants to lead the manipulator end-effector. In these cases, the entire control of the manipulator is delegated to the system software, further reducing the user effort. To do that, the authors of [8; 9; 10] exploited the information contained in the images by a monocular camera to control the manipulator through a _Visual Servoing_ algorithm.  
This work exploits a similar approach, as shown in Figure 1, but it empowers the system through an _Object Recognizer_. The latter has the task of recognizing the objects to interact with inside the camera frames and to provide their coordinates expressed in pixels with respect to a reference point in the image, e.g., the camera center or a corner. Even in this case, camera frames are shown to the user on a dedicated HMI, in which the recognized objects are surrounded by a bounding box. After the user chooses one of the recognized objects through the HMI, the object camera plane coordinates are processed by the _Target Stabilizer_ block. It increases the robustness on the desired object position estimation against mechanical shaking, vibrations and frame losses. To do that, the _Target Stabilizer_ tracks the object area independently from the _Object Recognizer_, providing a better estimation of the 2-D coordinates. The latter are sent to the _Robot Logic_. Exploiting the coordinates of the selected object and the distance from it, obtained by the _depth sensor_, the _Robot Logic_ subsystem calculates the movements that each joint of the robot should carry out to approach the target. Such information is used by the _Robot Actuators_ to drive the _Robotic Arm_ motors. The use of _Object Recognizer_ introduces numerous advantages at the expense of increasing the computational complexity. First, as explained in Section 1, it increases the safety of the HMI toward the user. In fact, an incorrect pressure outside the bounding boxes is ignored by the system. In addition, as explained, the _Object Recognizer_ limits the user actions to the selection of the desired target. Such task can be easily performed through many standard HMIs, including manual raster, raster time, a keyboard, etc. Using these interfaces is much more tricky with systems such as those in [8; 9; 10], which require the user to select the area of the objects rather than the object itself. Thus, the introduction of a system for the target recognition increases the number of usable interfaces, for a higher system accessibility.
## 2 Background  
\(\gamma\)-Ray detectors generally belong to one of three broad categories: gas-filled, solid state, and scintillators. We focus here on NaI-based scintillator detectors, which are widely used because of the large variety of shapes, sizes, types, and low cost.  
\(\gamma\)-Ray spectroscopy using scintillation detectors is a relatively mature subject, dating to the demonstration in 1948 [4] that crystalline NaI with a trace of thallium iodide produces an exceptionally large scintillations light output. Although experiments continue using other light scintillation materials, NaI detectors continue to dominate in practical applications. In scintillation detectors, light of a characteristic spectrum is emitted following the absorption of radiation.  
Because this review focuses on RIID algorithms, we omit detailed discussion of the physical processes except to point out that \(\gamma\) s and their energies are indirectly detected in NaI scintillator detectors via voltage bursts arising from detected charged particles that arise from any of the three main \(\gamma\)-ray interactions: photoelectric effects, Compton scattering, and pair production, which are described next.  
In photoelectric absorption, which dominates at low \(\gamma\)-ray energies, an incident \(\gamma\)-ray is absorbed by an atom, ejecting a bound electron. This process can also release characteristic photons (\(x\)-rays) due to rearrangement of electrons from other energy levels of the atom. Compton scattering is the most frequent interaction for \(\gamma\)-rays in the 30 keV to 3,000 keV range, which is characteristic of most isotopes of interest in our context. In Compton scattering, the incident \(\gamma\)-ray is deflected rather than absorbed, transferring some of its energy to the recoiled electron. In pair production, which can only occur above 1,020 keV, the incident \(\gamma\)-ray disappears and is replaced by an electron-positron pair.  
One important feature that results from the voltage bursts is that high count rates can lead to voltage gain shifts. As described below, voltage must be calibrated to infer the energy of the incident \(\gamma\)-ray, and high count rates are known to shift the calibration relation (often referred to as the \"gain\") between energy and voltage [5, 6].  
The pulse shaping photomultiplier tube (PMT), which amplifies the signal from the incident \(\gamma\)s, is another important aspect of NaI spectroscopy. PMTs consist of a glass vacuum tube that contains a photocathode material that produces photoelectric effect electrons, plus several dynodes and an anode that together amplify the signal from an incident \(\gamma\) by approximately a factor of 10\({}^{6}\). Partly due to this amplification and largely due to the fact that a random number of initial electrons is created by each \"detected\" \(\gamma\)-ray leads to pulse broadening, or \"Gaussian energy broadening.\" Many peaks in NaI spectra are approximately Gaussian in shape [7], so peak fitting methods often assume a symmetric, Gaussian shape around the peak centroid.
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Modeling of the Academic Electromagnetic Device
#### 4.2.2 Distribution of Electric and Magnetic Fields  
At low frequencies, when the frequency tends towards zero, the distribution of the current density in the winding obtained with all models is similar, as observed in Figure 11(a), since this is equivalent to solve an electrokinetic problem. In this case, the distribution of the current density is only influenced by the geometry of the winding. When the frequency supplying the winding increases, coupled effects may be observed. To represent these effects in the winding, the distribution of the current density **J** is presented in Figure 11(b)-d. At \(f=10\) kHz, with the EQS model, it is clear that **J** will never be homogeneous along the winding; the majority of currents flow through the capacitor in terms of the displacement current. On the other hand, the current density with the MQS and Darwin models is restricted to a small layer at the boundary of the winding, and starts to flow in the outskirts of the conductor, as observed by the skin effect.  
Besides, due to the capacitive effects, the current density distribution obtained with the Darwin model is not the same as that of MQS. The irrotational current density is much larger in both cases, compared to the solenoidal one, such that the capacitive coupling cannot be distinguished from Figure 11(d). Moreover, the distribution of the electric field obtained from the Darwin model is presented in Figure 13 in a 2-D cutting plane for \(f=10\) kHz. A high field strength appears in the dielectric regions, particularly between the gaps of the winding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formulations** & **DoFs** & **Iterations** & **CPU Time** \\ \hline EQS & 2,466,002 & 18 k & \(\simeq\)2.5 h \\ MQS & 19,316,839 & 14 k & \(\simeq\)75 h \\ Darwin & 19,373,155 & 17 k & \(\simeq\)86 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the different models for a frequency \(f=10\) kHz.  
Figure 11: Considered mesh.  
In addition, when capacitive effects are neglected for low and middle frequencies, both MQS and Darwin problems give the same distribution of the magnetic flux density. Then, when the frequency tends towards zero, the distribution of the magnetic flux density is not uniform.  
Figure 12: Distribution of the current density \(\mathbf{J}\) (\(\mathrm{A}/\mathrm{m}^{2}\)) in the winding.  
Figure 13: Distribution of the electric field E (V/m) between the gaps of the winding at \(f=10\) kHz.  
obtained from MQS and Darwin models is equivalent to solve a magnetostatic problem, as shown in Figure 14.
## 3 Finite Element Model  
In the following, the proposed SDoF model is validated against the numerical simulations performed using the commercial software ABAQUS/Explicit version 6.14.  
The numerically examined beam with a size of \(l\times B\times H\) = 500 \(\times\) 20 \(\times\) 8 mm\({}^{3}\) is made of an elastic-perfectly plastic material with mass density \(\rho\) = 7850 kg/m\({}^{3}\), yield strength \(\sigma_{0}\) = 300 MPa, Young's modulus \(E\) = 206 GPa and Poisson ratio \(v\) = 0.3. The beam is modelled as deformable bodies using C3D8R, an eight-node linear brick element with reduced integration. In order to model a clamped boundary condition, constraints limiting all degrees of freedom are assigned to both ends of the beam.  
The striker is modelled as a rigid wedge to ensure no deformation and is relatively sharp with a small radius (\(r\)\(\sim\)1 mm) at the tip to model the concentrated force. Contact between striker and beam is accounted for by applying the \"Surface to Surface Contact.\"  
For the striker, the mass \(m_{0}\) = 30 kg and velocity \(V_{0}\) = 2.0 m/s are assigned to the reference point to ensure a relatively larger ratio of \(m_{0}/m_{b}^{c}\) and to weaken the influence of the necking phenomenon [19]. The motion of the striker is allowed only in the horizontal direction to transversely impact the beam at a point \(l_{1}\) from the left-hand support. To analyse the influence of the impact location on the overall dynamic behavior of the beam, numerical simulations are performed with four impact locations, i.e., \(l_{1}/l\) = 1/8, 1/4, 3/8 and 1/2.  
Convergence tests on different mesh sizes are performed using the impact case with \(l_{1}/l=1/2\). The mesh sizes of 1 mm, 1.5 mm, 2 mm and 4 mm are tested. The dependence of the characteristic response parameters, namely, the spring-back (i.e., \(W_{m}-W_{f}\)) and maximum contact force on the mesh size, are plotted in Figure 4. To balance the simulation accuracy and the computational cost, the mesh size is selected as 1.5 mm in the numerical study.
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
## 2 Materials and Methods
### Construction of an Expression Vector for the Heme Domain of CYP102A1 Mutant  
The polymerase chain reaction (PCR) was performed using a Thermal Cycler (BioRad, Richmond, CA, USA) and Phusion high-fidelity DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA). For the expression of the heme domain of M371, the gene-coding heme domain (1-472 residues) was amplified by the PCR with oligonucleotide primers BamHI/XbaI restriction sites: BM3-BamHI forward, 5\({}^{\prime}\)-AGCGGATCCATGACAATTAAAGAATGCC-3\({}^{\prime}\) and BM3-XbaI reverse, 5\({}^{\prime}\)-TCTAGACTATTAGCGTACTTTTTTAGCAGACTG-3\({}^{\prime}\). The PCR product was resolved on a 1% (\(v/v\)) agarose gel, purified, digested with BamHI and XbaI and ligated into a pCWori plasmid [18; 19]. The recombinant pCW vector containing M371-heme was expressed in _Escherichia coli_ DH5\(\alpha\)F1Q.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 4 Applications of Antimicrobial Nanoparticles
### Surface Films and Coating  
One of the most well-known applications of antimicrobial nanomaterials is the use of Ag NPs on the surfaces of objects, such as laptops and keyboards [131]. Recently, Corning(r) produced an antimicrobial glass for electronic mobile devices. Corning(r) Gorilla(r) Glass 3 is a scratch-resistant glass incorporating Ag to produce an antimicrobial effect. [PERSON] et al. reported that the glass was effective at reducing MRSA and _K. pneumoniae_ contamination [132].  
In 2016, diarrhoea disease was the ninth highest cause of death globally (WHO). The primary cause of diarrhoea is usually through the consumption of food or water that is contaminated with bacteria, including pathogenic strains of _E. coli_[133]. Since then, to control  
Figure 5: SEM images of PMMA fibre before (**left**) and after antimicrobial nanoparticles embedded into fibre mats (**right**). Reproduced with permission from [PERSON] et al., _Materials Science and Engineering C_; published by Elsevier, 2017. [11].  
contamination and to extend the life of fresh vegetables, fruit juice and meat, AMNPs are incorporated into prototype food packaging but still require studying to understand leaching and the effects of nanoparticle consumption. [PERSON] et al. used a compression moulding technique to produce plasticized polylactide composite films containing bimetallic Ag-Cu nanoparticles and cinnamon essential oils. The film was found to have antibacterial activity against common pathogens found on chicken meat [134]. Similarly, to produce low-density polyethylene (LDPE) films with Ag and ZnO, [PERSON] et al. mixed nanoparticle powders with LDPE resin pellets in a twin-screw extruder machine, and a single screw blowing machine was used to produce a 50 \(\upmu\)m thick nanocomposite film. Antimicrobial activity tests against _L. plantarum_ were done and showed that nano Ag films were more effective than nano zinc oxide films [135]. Lastly, [PERSON] et al. generated an Ag NP polymer-based coating for vegetables by adding AgNO\({}_{3}\) to polyvinylpyrrolidone, followed by glycerol. Asparagus spears were immersed in the coating, and it was found to prolong their life when evaluating the firmness, weight loss and colour [136].
## 2 Methodology  
This study employed a research methodology comprising three parts: (i) a comprehensive review of asphalt mixtures from the perspectives of energy consumption and gas emissions; (ii) an exploration of low-carbon development technologies; and (iii) the conclusions of this research.  
First, we defined the research focus as the energy consumption and gas emissions of asphalt mixtures, categorizing it into five areas: pavement types, temperature effects, production processes, energy consumption, and low-carbon development. This framework ensures the relevance and systematic nature of the literature review. We systematically reviewed the existing literature using the Web of Science and Scopus databases, conducting an initial analysis of high-quality studies published between 2010 and 2024 related to the energy consumption and gas emissions of asphalt pavements. The keywords employed in the search included \"energy consumption\" + \"asphalt\", \"gas emissions\" + \"asphalt\", \"energy consumption\" + \"pavement\", and \"green development\" + \"asphalt\". This process was supplemented by manual searches. The selection strategy identified 103 high-quality articles (i.e., peer-reviewed indexed research articles). These works enabled the authors to critically examine the latest technologies to address the research questions. Ultimately, five sections were established to analyze and categorize the energy consumption and greenhouse gas emission characteristics of asphalt mixtures. Through a detailed analysis of each research aspect, key insights were derived, as outlined below:  
* Pavement types: analyzing the impact of different pavement types on energy consumption and emissions, highlighting their significance.
* Temperature effects: investigating the influence of temperature on the performance and production efficiency of asphalt mixtures, noting the energy consumption and emissions issues associated with high-temperature production.  
* Production processes: providing a detailed description of the production process of asphalt mixtures, emphasizing the energy consumption and emission characteristics at each stage.
* Energy consumption: evaluating the overall energy consumption of asphalt mixtures, identifying high-energy consumption segments, and exploring methods to reduce energy usage.  
Additionally, we assessed green technologies and material recycling in the context of recycled materials and clean energy, focusing on their roles in reducing carbon emissions and achieving sustainable development. Finally, we distilled conclusions and recommendations that summarize the main findings across all aspects, emphasizing the importance of asphalt mixtures in environmental impact assessments and proposing future research directions that encourage the exploration of new technologies and materials (see also Figure 2).
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 6 Evaluation
### Performance Metrics  
In order to evaluate the performance and effectiveness of the proposed algorithm, some metrics of this paper are explained in this subsection.  
_Clustering efficiency_ is a comprehensive evaluation for clustering groups in a region, and it is mainly composed of _response time_ and _success rate_ in this paper.  
_Response time_ is used to measure the time taken for a clustering group from initiating clustering to meet the conditions of k-anonymity and l-diversity (i.e., \(th_{k}\) and \(th_{l}\) ) in a region. The smaller the response time, the more efficiency of a clustering group. It also means the faster a clustering group can send the aggregated package to LBSP.  
_Success rate_ is used to measure the ratio of the number of clustering groups that meet the conditions \(th_{k}\) and \(th_{l}\) to the number of all clustering groups in a region [3]. The higher the success rate, the more efficiency of cluster groups. It also means more users' requests can be sent to LBSP.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 4 Experiments and Results
### Link Prediction
#### 4.2.5 Ablation Study  
To explore the influence of different constraints and iterative learning, we perform an ablation study of Iterlogic-E on DB100K with nine configurations in Table 5. Compared to the completed model Iterlogic-E, the first and second variants remove the non-negativity and \(l_{2}\)-norm constraints (\(l_{2}\)). The third setting is the combination setting. The fourth removes iterative learning (IL), which only uses a rule for reasoning reason once. The fifth, sixth, and seventh variants remove the additional loss item from the sixth variant. The eighth setting is another variant of the Iterlogic-E model based on the ninth setting: after the ComplEx fitting, based on the scores of the conclusions, the top \(n\) (where \(n\) is the rounded product of the number of conclusions and their confidence value) conclusions of each rule are selected and added to the KG to continue training. The ninth setting (AC) adds all conclusions inferred from the rules as positive examples, and the tenth setting (WC) uses the rule confidence as soft labels of conclusions.  
As seen in Table 5, we can conclude the following: (1) When removing NNE constraints, the performance of Iterlogic-E decreases slightly. Without \(l_{2}\)-norm constraints on entities, the performance of Iterlogic-E degrades by 2.3% in HITS@1 and by 5.1% in MRR. One explanation may be that \(l_{2}\)-norm constraints are sufficient to constrain embedding norms on DB100K. However, the performance will suffer dramatically if no \(l_{2}\)-norm constraints exist. (2) Removing iterative learning decreases performance slightly. One reason may be that the number of rules on DB100K is relatively small, so the number of conclusions added through iterative learning is relatively small. (3) Removing the additional loss item of the conclusions decreases performance slightly. The result illustrates that Iterlogic-E can filter out incorrect conclusions and dense the KG. Surprisingly, even if we directly use ComplEx to filter and learn the conclusions that can achieve such high performance, this method is  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**FB15K-Sparse**} \\ \cline{2-5}  & **MRR** & **HITS@1** & **HITS@3** & **HITS@10** \\ \hline TransE & 0.398 & 0.258 & 0.486 & 0.645 \\ DistMult & 0.600 & 0.618 & 0.651 & 0.759 \\ ComplEx & 0.616 & 0.540 & 0.657 & 0.761 \\ IterE & 0.628 & 0.551 & 0.673 & 0.771 \\ SoLE & 0.668 & 0.604 & 0.699 & 0.794 \\ \hline Iterlogic-E(ComplEx) & **0.674** & **0.611** & **0.701** & **0.800** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of link prediction on the test sets of FB15K-sparse. This dataset’s settings are derived from IterE. The training set is identical to FB15K, but the validation and test sets mainly consist of triples involving sparse entities. The boldface scores indicate the best results.
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 4 Results and Discussion
### Effect of pH on Particle Formation Behavior  
The following paragraphs have been adapted to provide clarity and improve the overall flow of the information presented. We conducted an experiment where we adjusted the C\({}_{6}\)H\({}_{15}\)NO\({}_{3}\) levels to examine the influence of the pH value on particle formation behavior. The results, as depicted in Figures 8 and 9, yielded some noteworthy findings. Figure 8 reveals that at a neutral pH level of 7, there is a maximum grain count of 102.2 meshes per cubic micron, and the nucleation time is the shortest. However, as the pH level increases or decreases, the time required for nucleation formation extends, and the number of nucleations decreases. Figure 9 offers more insights. At a pH level of 7, the D\({}_{50}\) is 0.19 nm, and the particle size distribution (PSD) curve is normal with 0.19 nm acting as the vertical center in the 0-1.2 nm range. At this point, the particle size is small with a concentrated distribution, and the D\({}_{50}\) mass fraction is also high, at 14.48%. In an acidic environment, the lower the pH level, the higher the H\({}^{+}\) concentration. This inhibits the hydrolysis of TiCl\({}_{4}\), leading to fewer TiO\({}_{2}\) grains precipitating from the solution. An increased H\({}^{+}\) concentration also accelerates the polycondensation reaction of H\({}^{+}\) and the dehydration condensation product of the titanium hydroxyl complex, causing the grain size of the TiO\({}_{2}\) to increase rapidly. Hence, the particle size in acidic conditions is larger than in neutral conditions. As the amount of C\({}_{6}\)H\({}_{15}\)NO\({}_{3}\) dropped increases, the system's pH level also increases. This leads to an increased hydrolysis degree of the TiO\({}_{2}\) precursor. When the pH level is neutral, the rate of polycondensation is moderate, resulting in a smaller TiO\({}_{2}\) particle size and a narrower distribution. However, at high pH levels, the severe hydrolysis of the TiO\({}_{2}\) precursor due to the high OH\({}^{-}\) concentration in the water leads to more TiO\({}_{2}\) grains precipitating. The OH\({}^{-}\) also inhibits the polycondensation reaction, slowing down the growth rate of TiO  
Figure 6: Variation curve of TiO\({}_{2}\) crystal nucleus number with time under different water amounts.  
Figure 7: Distribution curves of TiO\({}_{2}\) particles under different water amounts.  
grains. These rapidly formed TiO\({}_{2}\) grains tend to agglomerate on the original particles, causing an increase in the TiO\({}_{2}\) particle size and a widening of the PSD curve distribution. As the pH value increases, the mass fraction also decreases.