esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Table 1 provides the results between the actual and expected scores. The mean actual score of the LwS group was significantly higher than those of the L and Lts groups (\(p\) < 0.001; Figure 4). No statistical difference was observed between the results of the L and Lts groups. In term of expected scores, the L group exhibited significantly lower estimates than the other groups that used the computer-aided simulation  
Between the actual and expected scores, the mean expected score was significantly lower than the mean actual score for the L group (\(p\) = 0.035). However, no statistical difference was found between the actual (\(p\) = 0.685) and expected (\(p\) = 0.114) scores for the Lts and LwS groups.  
The grading system illustrates the distribution of the actual scores for each group (Figure 5). In general, high percentages of excellent (grade A) and good (grade B) scores were observed with the application of computer-aided simulation in teaching compared with the sole use of the lecture method. The highest recorded score intervals for the LwS and L groups were grades A and C, respectively. For all teaching protocols, a failure score of grade F was not observed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score** & **Lecture** & **Lecture to Simulation** & **Lecture with Simulation** & \(p\)\({}^{*}\) \\ \hline Actual & 82.7 \(\pm\) 8.6 \({}^{\text{a}}\) & 85.2 \(\pm\) 7.4 \({}^{\text{a}}\) & 90.7 \(\pm\) 7.1 \({}^{\text{b}}\) & \textless{}0.001 \\ Expected & 75.7 \(\pm\) 16.9 \({}^{\text{a}}\) & 88.7 \(\pm\) 10.7 \({}^{\text{b}}\) & 89.7 \(\pm\) 12.5 \({}^{\text{b}}\) & \textless{}0.001 \\ \(p\)\({}^{\text{t}}\) & 0.035 & 0.114 & 0.685 & \\ \hline \hline \multicolumn{4}{l}{\({}^{*}\) Statistical differences within rows were calculated using one-way analysis of variance with [PERSON]’s post-hoc analysis. Same superscript lowercase letters show no significant differences among \(3\) groups within row. \({}^{\text{t}}\) Statistical differences within columns were calculated using the independent \(t\) test.} \\ \end{tabular}
\end{table}
Table 1: Mean and standard deviation of examination scores for groups following different teaching protocols.  
Figure 4: Box-plot diagram for examination scores according to the different protocols for occlusal education. \({}^{*}\) Statistical differences.
## 3 Methodology
### Compression of Convolutional Neural Networks Using Tensor Decomposition Methods
#### 3.2.1 ResNet Architecture  
In our work, we applied our compression method to convolutional layers within residual blocks of ResNet.  
A weight tensor of a convolutional layer can be written as \(W(S,T,K,K)\), where \(S\) and \(T\) give the number of the input and output channels, and \(K\times K\) is the kernel size of convolution (typically \(K=3\) or \(K=1\) for ResNet.)  
To achieve compression, we apply the CP, SVD, and Tucker decomposition techniques to weight tensors of convolutional layers. Details regarding these decomposition methods and their implementation will be discussed in the following sections.
## 4 Results and Discussions
### The Decision-Making Trial and Evaluation Laboratory (DEMATEL) Analysis
In terms of cause degree, \"Comfort\", \"Reliability\", \"Security\", \"Privacy\", \"Accuracy\", \"Sustainability\", \"Convenience\" and \"Interactivity\" belong to the affected indicators (d - r is negative). The indicators \"Energy Consumption\", \"Compatibility\", and \"Appearance\" belong to influential indicators because their correlation degrees were not much different from the average, and their cause degrees were greater than zero. The correlation degrees of \"Intelligence\", \"Foresight\", \"Integration\", \"Customization\", and \"Connectivity\" were greater than the average, and their cause degrees were greater than zero. Therefore, it is recommended that businesses which plan to develop wearable devices should pay more attention to \"Intelligence\", \"Foresight\", \"Integration\", \"Customization\", and \"Connectivity\", followed by \"Energy Consumption\", \"Compatibility\", and \"Appearance\". Figure 3: Product design indicators for wearable devices [cause-and-effect diagram].
# Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder
###### Abstract  
In order to investigate the effect and mechanism of coupled mechanical-chemical activation on the hydration activity of copper slag powder, copper slag powder with different grinding time and content was applied to prepare composite cement. The hydration heat and rate of the composite cement paste were tested for 120 h. The mechanical properties and microstructure of the samples were characterized by compressive strength activity index, XRD, and SEM. The findings revealed that the specific surface area of the copper slag powder increased by 27.84%, 20.14%, and 10.92%, respectively, when the grinding time increased from 30 min to 120 min. The particle size distribution of the copper slag powder after grinding for 90 min and 120 min was superior to that of cement. The compressive strength activity index of the paste specimen increased with the extension of the copper slag grinding time when the copper slag powder content remained constant. Chemical activator CaO further stimulated the hydration activity of copper slag powder, increased the hydration heat of copper slag powder-based composite cement paste, and promoted the compressive strength of composite cement-hardened paste at different ages. With the increase in copper slag powder content, the porosity of the hardened paste increased, resulting in a decrease in compressive strength at different ages.  
activation; copper slag; hydration activity; hydration heat; microstructure Article
# Semantic 3D Mapping from Deep Image Segmentation
## 3 Segmentation and Mapping in 3D
### Semantic Mapping
\begin{array}{cccc}\sigma_{\text{xx}}^{2}&\sigma_{xy}^{2}&\ldots& \sigma_{\text{zzz}}^{2}\\ \sigma_{yx}^{2}&\sigma_{yy}^{2}&\ldots&\sigma_{\text{yzz}}^{2}\\ \vdots&\vdots&\ddots&\vdots\\ \sigma_{ux}^{2}&\sigma_{uy}^{2}&\ldots&\sigma_{\text{zzw}}^{2}\\ \end{array} \right)\] (2)
2. Generate a random noise transform \(RT^{\prime}\) from the uncertainty using normal distributions of probability \[Noise_{Translation}=random(\mathcal{N}(0,\sigma_{xx}^{2}), \mathcal{N}(0,\sigma_{yy}^{2}),\mathcal{N}(0,\sigma_{zz}^{2}))\] \[Noise_{Rotation}=random(\mathcal{N}(0,\sigma_{rr}^{2}), \mathcal{N}(0,\sigma_{pp}^{2}),\mathcal{N}(0,\sigma_{ww}^{2}))\] \[RT^{\prime}=(Noise_{Translation},Noise_{Rotation})\] (3)
3. Calculate a new noisy transform \(RT^{\prime map\to sensor}\) \[RT^{\prime map\to sensor}=RT^{\prime}*RT^{\prime}*RT^{\text{dom- base\_ footprint}}*RT^{\text{base\_ footprint\to base\_link}}*RT^{\text{base\_link\to sensor}}\] (4)
4. We repeat N times the steps 2-3 for each \(\mathcal{O}_{t}\). We update \(\mathcal{M}_{t}\) from \(\mathcal{M}_{t-1}\) accumulating the probabilities of each cell as follows, being \(K\) a constant in range [0-1]. If \(K\) is near 1, the previous value in the cell has more importance than the new perception when fusing both values: \[\mathcal{M}_{t}=\sum_{i=1}^{N}(\frac{K}{N}*\mathcal{M}_{t-1}+\frac{1-K}{N}*RT _{i}^{\prime map\to sensor}*\mathcal{O}_{t})\] (5)  
The result is shown in Figure 11d. \(\mathcal{M}_{t}\) is a octomap in which each cell contains the class of an object coded as a color, and the occupancy probability.
## 3 GNSS Data and Results
### Lefkada Island  
On central Lefkada Island, data from two GNSS stations (SPAN in the north and PONT in the south) were processed for the period from 2009 to 2021 (Figure S2). Partial data from two other sites, EXAN and DRAN, were available after the strong (Mw6.4) 2015 earthquake (Table 2). For the two stations--SPAN and PONT--which operated prior, during and after the November 2015 event, a linear type of motion was observed for the pre-seismic period. There was no clear evidence of any kinematic changes or any alterations on the pattern of motion, in any of the three components, prior to the earthquake. Baseline changes between the two sites calculated for the pre-seismic period (2009 to 2015) indicated shortening of the distance, with a rate of V = (\(-\)2.50 \(\pm\) 0.03) mm/year. The latter is indicative of the compressional status that occurred along the island during the pre-earthquake period. The co-seismic displacements at the two sites (SPAN and PONT) were quite significant, mainly in the southern PONT station, which is located in the near vicinity of the epicenter (Appendix B). Static co-seismic displacements, which were smaller but not negligible, were also observed at stations located in Cephalonia (VLSM, KARA and SKAL), in Zakythos (ZAKY) and in western Greece (AGRI) (Appendix B). During the post-seismic period, the kinematic status of the area was characterized by a long relaxation process, expressed as a non-linear motion on the stations. This post-seismic kinematic behavior was observed at the two Lefkada stations, but mainly in PONT [90], as well as at sites of northern Cephalonia (FISK, ASSO). The slow relaxation was also evident at the two new sites that were established on the island, EXAN and DRAN (CRL GNSS net). The relaxation period on the PONT station was estimated to last up to August 2016, exhibiting a non-linear type of motion during that period. The estimation was performed with an average moving window of 5-60 days in length. The relaxation process was characterized by a significant southward motional component and a strong uplifted tension, following the significant co-seismic subsidence. For the subsequent period (from 2016 to 2021), the time
## 1 Introduction
This approach is particularly advantageous for addressing the multifaceted degradation issues inherent in FastSLAM, as it can simultaneously enhance the diversity and representativeness of the particle population, ensuring a more accurate and resilient SLAM performance. The motivation behind integrating MO-PSO with FastSLAM stems from the need to address the limitations of traditional single-objective optimization methods in the context of SLAM. By leveraging the multi-objective capabilities of MO-PSO, this research aims to propose a new method that not only mitigates particle degeneration, but also enhances the overall accuracy and robustness of the SLAM process. Our primary contributions include the development of a novel optimization strategy that harnesses the strengths of MO-PSO to optimize the distribution and diversity of particles within the FastSLAM framework. The proposed integration of FastSLAM with MO-PSO is underpinned by a carefully designed methodology that focuses on enhancing particle filtering through a multi-objective optimization approach. The design of our method involves the development of a bespoke fitness function that not only evaluates the particles' contribution to the current estimation, but also promotes diversity to counteract degeneration. To validate the effectiveness of our proposed method, we have meticulously designed a series of experiments. These experiments are conducted across various simulated environments, providing a controlled setting to assess performance. We have selected a set of state-ofthe-art SLAM algorithms for comparative analysis, ensuring a thorough evaluation of our method against existing solutions. The performance metrics for these experiments include accuracy of pose estimation, map quality, computational efficiency, and robustness in the presence of dynamic changes, providing a comprehensive assessment of our MO-PSO-enhanced FastSLAM approach. The structure of this paper is as follows: The first section sets the stage by introducing the background and motivation behind SLAM technology. The second section offers a concise review of the fundamental principles of SLAM and PSO algorithms, with an emphasis on the multi-objective optimization context. The third section details the design and implementation of the proposed FastSLAM-MO-PSO algorithm. The fourth section presents the experimental results and performance evaluation. Finally, the last section summarizes the findings and suggests directions for future research.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Steering Vector  
The Hermitian transpose of Equation (19) is the steering matrix or vector employed in all of the estimation techniques. For instance, in WSF and ML techniques, the matrix form of the three-signal scenario is the following:  
\[\mathbf{Y}(\mathbf{\Omega})=\left[\begin{array}{cccc}Y_{0}^{0}(\Omega_{1}),&Y_{1}^{-1} (\Omega_{1}),&Y_{1}^{0}(\Omega_{1}),&Y_{1}^{1}(\Omega_{1}),&\cdots&Y_{3}^{3}( \Omega_{1})\\ Y_{0}^{0}(\Omega_{2}),&Y_{1}^{-1}(\Omega_{2}),&Y_{1}^{0}(\Omega_{2}),&Y_{1}^{1}( \Omega_{2}),&\cdots&Y_{3}^{3}(\Omega_{2})\end{array}\right] \tag{35}\]  
The number of harmonic components is \(\left(N_{h}+1\right)^{2}=16\), because only two signals \(\Omega_{1}\) and \(\Omega_{2}\) are present, in contrast to PWD and MUSIC, whose steering is consistently 1-D with the form  
\[Y(\Omega)=\Big{[}Y_{0}^{0}(\Omega),\ Y_{1}^{-1}(\Omega),Y_{1}^{0}(\Omega),Y_{1 }^{1}(\Omega),\ldots,\ Y_{3}^{3}(\Omega)\Big{]}. \tag{36}\]  
The above matrices show the basic difference between the 2-D and 1-D search space used for WSF, ML techniques, and spectral-based techniques, respectively.  
For WSF and ML techniques, the localization function \(P(\Omega)\) is 2U-dimensional because \(\Omega\) has U azimuth and elevation values. The localization function of the minimum argument for WSF and ML techniques produces DoA estimates:  
\[\hat{\mathbf{\Omega}}=arg\underset{\Omega}{\min}\{P(\mathbf{\Omega})\}. \tag{37}\]  
The localization function \(P(\mathbf{\Omega})\) for spectral-based techniques is 2-D, because \(\mathbf{\Omega}\) has two dimensions: azimuth and elevation. Spectral-based techniques for DoA estimates are the U highest local minima in \(P(\Omega)\).
# Total Internal Reflection Lens for Optical Wireless Receivers
## 3 Measurements
### Optical Results  
Figure 5a shows the results of the \(I_{\mathrm{PD}}\) measurement. The photodiode without the lens shows quite similar \(I_{\mathrm{PD}}\) values for the observed \(\theta_{\mathrm{i}}\). In contrast, the TIR lens shows a strong dependence from \(\theta_{\mathrm{i}}\). It increases \(I_{\mathrm{PD}}\) within the center from 303 nA without the lens to 13.55 nA.  
Figure 5b shows the simulated and measured optical concentration ratio \(g_{\mathrm{o}}\) over the angle of incidence \(\theta_{\mathrm{i}}\). The simulation is based on Monte Carlo ray tracing within _Zemax Optic Studio 17_. For each simulation point, \(10^{6}\) rays are traced. The simulated lens features a center gain of up to 18.5 dB and enables data reception for \(\theta_{\mathrm{i}}\) < 7.5\({}^{\circ}\). For larger angles, the gain drops rapidly. The simulated acceptance angle (\(-\)3 dB) is \(\theta_{\mathrm{a}}^{\mathrm{sim}}\) = 5.5\({}^{\circ}\). From the experiment, we derive a concentration ratio of \(g_{\mathrm{o}}\) = 10 lg (44.7) = 16.5 dB at the center, which  
Figure 4: Measurement setup for the optical concentration ratio \(g_{\mathrm{o}}\).  
Figure 5: (**a**) Measured photocurrent \(I_{\mathrm{PD}}\) with and without the TIR lens. (**b**) Measured and simulated concentration ratio \(g_{\mathrm{o}}\).  
is 2 dB lower than the simulation. The measured curve has an acceptance angle of \(\theta_{\text{a}}^{\text{m}}=\pm 5^{\circ}\). Moreover, it drops slower outside the FOV compared to the simulated curve.  
Within our laboratory, the ambient light is 38 nA without the TIR lens and 0.8 nA with the TIR lens. This corresponds to an attenuation of ambient light by 16.8 dB.
# A Conceptual Model for Joint Graphic Representation of
## 5 MMCS Design Examples
### Synchronized Servo Axes Motion Applied to a Gantry Configuration  
This section studies the representation of temporal kinematic relations, being described by the source code and maintained by the motion controller.  
Specifically, an electronic gear is represented, equivalent to its mechanical version where the displacement of a slave servo axis is proportional to the one of a master servo axis.  
Figure 14: Combination of the simplification of mechanical drawings and trajectory representation. (**a**) Top view. (**b**) Side view.  
Figure 15: Program in PLCopen corresponding to two linear coordinated movements.  
As a mechanical case, a configuration of parallel servo axes was chosen for its application, carrying a rigid solidary structure that is perpendicular to their mobile carriages, also known as a gantry configuration [41]. This type of solution presents constructive advantages from the mechanical point of view, but it would be unfeasible without the technology of motion control. It is a typical example of the synergies of mechatronics.  
In Figure 16a, a 3D view of the aforementioned gantry configuration can be seen. Applying the mechanical simplification process described in Figure 6, we would obtain the upper view seen in Figure 16b. It can be appreciated that if the moving carriages do not maintain their alignment, the structure could be twisted and blocked. The servo axes are named as X1 and X2 according to the conventions of the ISO841 standard. It should be noted that information about a kinematic relation created by the motion controller is displayed on a simplified mechanical drawing.  
The motion command that defines and activates an electronic gear is called MC_GearIn. The name of the master servo axis, the name of the slave, and the relation of proportionality between their movements are indicated by its mains parameters. Once activated, it is enough for the master servo axis to move so that the slave servo axis proportionally replicates the movement. If the ratio is 1:1, the slave servo axis will behave as a duplicate of the master servo axis.  
Figure 17b shows the code corresponding to the activation of the electronic gear and a displacement of the master servo axis, which actually corresponds to the synchronized movement of the carriages of both servo axes. Its graphical representation is shown by Figure 17a. The arrow describing the movement of the slave servo axis is accompanied by a \"=\" sign, the name of the master servo axis, and the motion relation.
###### Abstract  
The removal of heavy metals from water has become a global environmental problem. Various materials have been applied as adsorbent to remove metals from water. In this field, nanomaterials have been gaining increasing interest due to their exceptional properties. In this work, we discuss the synthesis of a core-shell structure nanocomposite by the modification of magnetic chitosan (CS) (Fe\({}_{3}\)O\({}_{4}\)/CS) with polyethyleneimine (PEI) to produce Fe\({}_{3}\)O\({}_{4}\)/CS/PEI composite for the adsorption of arsenic ions (As(V) and As(III)) from aqueous solution. The synthesized materials were characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscope (TEM), and vibrating sample magnetometer (VSM). The results indicated the successful combination of three components of the nanocomposite. The adsorption conditions were optimized by studying the effect of different parameters included pH, contact time, initial concentration, and adsorbent dosage. The optimum adsorption pH was found to be 6.7 while the optimum adsorbent dosage was found to be 2.0 and 1.5 g/L for As(III) and As(V), respectively. The removal efficiency for the uptake of As(III) and As(V) ions over Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite at optimum conditions was found to be 99.5 and 99.7%, respectively. The experimental results were fitted using [PERSON]'s and [PERSON]'s isotherms. The data were more fitted to [PERSON] isotherm providing a suggestion of monolayer adsorption with maximum adsorption capacity equal to 77.61 and 86.50 mg/g for the removal of As(III) and As(V), respectively. Moreover, linear regression coefficient (R\({}^{2}\)) indicated that the adsorption of arsenic ions over the synthesized magnetic nanocomposite obeyed pseudo 2 nd order suggesting the chemisorption process. The reusability of the nanosorbent for arsenic uptake using sodium hydroxide as eluent was also assessed up to five cycles. Interestingly, Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite can be considered as a promising adsorbent for As ions' removal from water and should be tested for the removal of other pollutants.  
Keywords:magnetic nanomaterials; arsenic removal; adsorption; core-shell structures +
Footnote †: journal: Applied Sci. 2021, 11, 5630. [[https://doi.org/10.3390/app11125630](https://doi.org/10.3390/app11125630)]([https://doi.org/10.3390/app11125630](https://doi.org/10.3390/app11125630))
# Solutions to the Problem of Freight Transport Flows in Urban Logistics
## 1 Introduction
Each stakeholder group, in the context of this research, has individual objectives. These goals could be noted as follows:  
* Municipalities: to reduce the burden on a city's logistics system, improve the quality of life, and optimize heavy traffic flows;
* Logistics companies: use available resources more efficiently, reduce the cost of services, reduce hazardous substances, and CO\({}_{2}\) emissions;
* Service recipients and users: to receive the necessary loads promptly and plan their activities;
* City dwellers: to live in a safe environment where traffic flows are optimized and a decent standard of living is ensured.
# Semantic Traffic Sensor Data:
## 4 Data Annotation and Publishing  
The conversion of the data about traffic sensors and the measurements they take over a long period into Linked Data is within the scope of the TRAFAIR project. The approach implemented for this purpose is shown in Figure 4 and will be detailed in this section. Data related to the general information about the sensors and their measurements are stored into the TRAFAIR database. The tool Karma [62; 63] takes this data as input in CSV format and transforms it into Linked Data by using appropriate ontologies and by exploiting the Linked Geo Data ontology [64] for mapping the information of OpenStreetMap. The Linked Data produced by Karma is automatically loaded into a SPARQL endpoint, which is queried by the visualization tool called Lodview [65].  
In this section, the process that starts with the data storing into the operational database and progresses until the production of the Linked Open Data is described. Firstly, Section 4.1 focuses on the identification of the relevant concepts and properties. Secondly, Section 4.2 describes the process followed for data integration. Thirdly, Section 4.3 explains the implementation of a SPARQL endpoint by using Virtuoso [66; 67] and how Lodview [65] has been used to make the data available online. In the end, Section 4.4 summarizes the technological choices made and other potential alternatives.
## 3 Experimental Study
### Error Analysis  
The main sources of error in the experiments are the standard deviations of the rotational speed and the mean voltages from the thrust sensors. Given that the calibration is repeated frequently throughout the test series, the drift is not a concern and is not included in the uncertainty of the thrust. The error of rotational speed is related to the number of rotor magnets. Hall Effect sensor with 24 magnets was used in experiments, which causes an error of \(1/24\times 60=2.5r/\text{min}\) by the number of times for a magnet passing over the Hall Effect sensor in 1 s. Additionally, the thrust sensor is with a accuracy of 0.02% F.S. Therefore, the error of \(C_{T}\), \(C_{P}\), and \(FM\) can be calculated by the Kline-McClintock equation. The equations are as follows:  
\[\frac{\Delta C_{T}}{C_{T}}=\sqrt{\left(\frac{\Delta T}{T}\right)+4\left(\frac {\Delta\Omega}{\Omega}\right)^{2}} \tag{5}\]  
\[\frac{\Delta C_{P}}{C_{P}}=\sqrt{\left(\frac{\Delta Q}{Q}\right)+4\left(\frac {\Delta\Omega}{\Omega}\right)^{2}} \tag{6}\]  
\[\frac{\Delta FM}{FM}=\sqrt{\left(\frac{\Delta T}{T}\right)^{2}+\left(\frac{ \Delta Q}{Q}\right)+\left(\frac{\Delta\Omega}{\Omega}\right)^{2}} \tag{7}\]  
By the Kline-McClintock equation, the average error value of \(C_{T}\), \(C_{P}\), and \(FM\) is 1.2%, 1.1%, and 1.5%, respectively.