esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
The values of the variables are organized from lowest to highest according to their respective ranges, and the corresponding RLST and RHST values are plotted accordingly. Figure 16: Behavior of the RHST and RLST in relation to the variable of slightly impermeable soil. Figure 14: Behavior of the RHST and RLST in relation to the variable of highly impermeable soil. Figure 15: Behavior of the RHST and RLST in relation to the variable of moderately impermeable soil across the cities. Figure 12 illustrates the relationship between the representative low surface temperature (RLST) and the representative high surface temperature (RHST) with the variable of land area, measured in square kilometers (km\({}^{2}\)). We observe that the trends for the RHST (R\({}^{2}\) = 0.28) and RLST (R\({}^{2}\) = 0.28) are similar. Specifically, on the corresponding day in July 2020, the low temperature occupied a larger area, while the high temperature tended to be more contracted. In the case of Figure 13, which considers the number of inhabitants, the RLST (R\({}^{2}\) = 0.31) shows an improved correlation compared to the RHST (R\({}^{2}\) = 0.21). In both cases (land area and population), the graphs exhibit similar behavior. Regarding highly permeable soils (Figure 14), moderately permeable soils (Figure 15), and poorly permeable soils (Figure 16), the best correlation was observed for RLST in  
Figure 17: Behavior of the RHST and RLST in relation to the variable of the high NDVI across the cities. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{2}{c}{**Representative Low Surface Temperature (RLST)**} \\  & **Representative High Surface Temperature (RHST)** \\ \hline  & **RHST (R\({}^{2}\))** & **RLST (R\({}^{2}\))** \\ \hline Area or extension in km\({}^{2}\) & 0.28 & 0.28 \\ Number of inhabitants & 0.21 & 0.31 \\ Highly impermeable pavements & 0.16 & 0.51 \\ Moderately impermeable pavements & 0.07 & 0.05 \\ Slightly impermeability pavements & 0.14 & 0.19 \\ High NDVI & 0.34 & 0.32 \\ Low NDVI & 0.22 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: R\({}^{2}\) values and correlation coefficients. Figure 18: Behavior of the RHST and RLST in relation to the variable of the low NDVI across the cities. highly permeable soils, with R\({}^{2}\) = 0.51. Moderately permeable soils showed the weakest correlation, while poorly permeable soils exhibited similar correlation values for the RHST (R\({}^{2}\) = 0.14) and RLST (R\({}^{2}\) = 0.19). The high NDVI (Figure 17) values indicated that the RLST (R\({}^{2}\) = 0.32) and RHST (R\({}^{2}\) = 0.34) exhibit an inverse trend; however, both show similar correlation levels. Considering all cases, the low NDVI (Figure 18) was the least strongly correlated with the variable of mobility, where the RLST obtained a value of R\({}^{2}\) = 0.01. Figure 19 shows the average of all temperatures recorded on the two days in July 2019 and 2020 as an indicator of overall trends.
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
## 3 Selection of the Best TEG + DC/DC Converter Combination  
As explained, TEGs generate very low voltage under small temperature gradients, so a suitable DC/DC converter is required to supply the sensors and communications module in WSs. This section analyzes two TEGs specially designed for low temperature gradients and two DC/DC converters designed for very low input voltage. The performance of the four possible combinations between the selected TEGs and the DC/DC converters summarized in Table 1 under very low temperature gradients is also studied. The results obtained will allow selection of the most appropriate combination to supply the low power WSs electronics.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Analysis of Residents' Travel Time Characteristics  
To investigate the travel characteristics of residents, we began by focusing on the temporal aspect. Utilizing the OD points derived from trajectory data, our initial analysis involved tracking variations in the number of taxi trips taken by residents across different dates and time intervals. Alterations in travel duration, taxi operation duration, and passenger load capacity were also examined. Additionally, we compared the proportion of passenger travel time and similar factors across various dates and time segments. This approach aims to provide a more comprehensive understanding of residents' travel patterns. Specifically, different types of days were considered, including both weekdays and weekends, and time periods were broken down into hourly intervals.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 3 Smart Mobility in Smart Cities
### Opportunities  
Smart mobility solutions can bring unprecedented benefits for the smart city ecosystem. Smart traffic management to intelligent land use planning and management can be made possible with the help of smart mobility solutions. City planners can exploit the intelligent mobility approaches to come up with effective and efficient design plans which can be realized in sustainable and environmentally friendly ways. There are several opportunities associated with smart mobility approaches for the city planners, authorities, users and other inhabitants of the smart city [5; 6; 7; 8; 9; 10; 11]:  
Strategic Route Planning and Development: The Big Data generated from the ubiquitously connected IoT devices can be harnessed to extract better insights about the traffic and city routes. This information can further be used for strategic route planning and development.  
Business Marketing Campaigns--Intelligent Ads Placement: Businesses can use digital sign boards and hoardings to effectively place their marketing advertisements at critical locations (where they can be made visible to maximum vehicles/traffic).  
Digital Ads and Revenue Generation: The rudimentary poster and banner-based advertisements can be replaced with digital counterparts. This has two-fold benefits. One,  
Figure 6: The primary connected vehicle models for smart mobility.  
it reduces paper wastage, and secondly, since it can be dynamically updated with simple coding/programming, it consumes less time for setup and updates.  
Alternate Route Management and Incentivizing Citizens for Cooperating in Decision Making and Following Instructions: law makers can take real-time data-based dynamic decisions for traffic management. This information can be shared with commuters to avoid possible traffic congestions by suggesting alternative routes. Governments can also incentivize the law-abiding citizens who help in following and enforcing these laws. This step can promote citizen centric participative governance.  
Opportunities for Builders, Businesses and Manufacturers: Builders, businesses and manufacturers can come up with innovative solutions for providing sustainable and environment friendly alternatives for the classical approaches. The area/techniques focused startups can exploit this opportunity for growing their businesses.  
Cheaper and Multiple Options for Transportation: With smart mobility solutions in place, citizens have the option for a better quality of service (QoS) in terms of comparative costs, improved and multiple options for transportation and hassle-free commuting experience.  
Improved Serviceability: Data analytics approaches can be applied to provide citizen centric services to the inhabitants of the smart city. Since the decisions and policies are data driven, they can surely improve the QoS.
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 1 Introduction  
Computer vision can be described as the way machines interpret images and is a field of AI that trains computers to comprehend the visual world [1]. During the last 20 years, computer vision has evolved rapidly, with deep learning and especially Deep Convolutional Neural Networks (D-CNNs) standing out among other methodologies. The accuracy rates for object classification and identification have increased to the point of being comparable to that of humans, enabling quick automated image detection and reactions to optical inputs.  
CNNs are considered unquestionably the most significant artificial neural network architecture for any computer vision and image analysis project at the moment. Making an appearance in the 1950s with simple and complex cell biological experiments [2; 3] and officially introduced in the 1980s [4] as a neural network model for a mechanism of visual pattern recognition, they have progressed greatly over the last years until today's complex pre-trained computer vision models. One of the main applications of deep learning and CNN's is that of image classification where the system tries to identify a scene or an object inside it. CNNs can also be taken a step further, by using one or more bounding boxes to recognize and locate multiple objects inside an image.  
Many traditional machine learning models such as Support Vector Machine (SVM) [5] or K-Nearest Neighbor (KNN) [6] were used for image classification before CNNs, where each individual pixel was considered a feature. With CNNs, the convolution layer was introduced, breaking down the image into multiple features, which are used for predicting the output values. However, since convolution itself is a demanding computation, pooling was introduced to make the overall process less resource intensive along the network. This method reduces the overall amount of computations required, essentially downsampling the input every time it is applied while trying to maintain the most important information.  
In this review, we attempt to summarize many of the pooling variants along with the advantages and disadvantages of each individual method, while also comparing their performance in a classification scenario with three different datasets.  
Initially, the pooling methods are presented one by one, providing an overview of each approach. In the end, we summarize the models and datasets that each method usesin a table, as a preamble to the testing methodology, which is explained right after. Finally, we present and analyze our benchmark results, focusing on the performance and ability to retain the details of the original input.
# Recovery/Reuse of Heterogeneous Supported Spent Catalysts
## 3 Methods for Recycling Heterogenous Catalysts
### Magnetic Separation
#### 3.2.1 Magnetic Nanoparticles (MNPs)
These materials can be easily modified and functionalized without altering their magnetic properties. Appropriate surface changes are required to improve the dispersion, the catalytic activity, and the physicochemical and mechanical properties of MNPs, and make them biocompatible [58]. For example, cobalt (0) nanoparticles supported on ceria were used as catalyst in the hydrolysis of ammonia borane for hydrogen production. The presence of cerium(III) defects improved the catalytic activity of metal nanoparticles through a more favorable substrate-metal interaction [59]. After completion of reaction, the catalyst was isolated by a permanent magnet and then redispersed for a subsequent run of hydrolysis retaining its initial catalytic activity even after the fifth use [60]. Among the magnetic nanoparticles, iron oxides are widespread used in the field of MNPs catalysis owing to their optical, electrical, magnetic, and catalytic properties [61]. Up to now, 16 pure phases of iron oxides, in the form of oxides, hydroxides, and oxyhydroxides have been found [62]. Particularly, magnetite (Fe\({}_{3}\)O\({}_{4}\)), an important type of ferrite, is highly regarded and applied due to their low cost, availability, nontoxicity, and easy functionalization with other metallic species or organocatalysts [38]. Fe\({}_{3}\)O\({}_{4}\) is characterized by strong superparamagnetic and electron properties due to the presence of iron cations in both capacities (Fe\({}^{2+}\), Fe\({}^{3+}\)) [62]. Their superparamagnetic behavior makes magnetite NPs possible to use such as the magnetic core to an eventual core-shell structure. Adding Fe\({}_{3}\)O\({}_{4}\) into catalysts endows the catalysts with excellent magnetic properties and improves even further the catalytic performances [38].
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
###### Abstract  
The 2D point location problem has applications in several areas, such as geographic information systems, navigation systems, motion planning, mapping, military strategy, location and tracking moves. We aim to present a new approach that expands upon current techniques and methods to locate the 2D position of a signal source sent by an emitter device. This new approach is based only on the geometric relationship between an emitter device and a system composed of \(m\geq 2\) signal receiving devices. Current approaches applied to locate an emitter can be deterministic, statistical or machine-learning methods. We propose to perform this triangulation by geometric models that exploit elements of pole-polar geometry. For this purpose, we are presenting five geometric models to solve the point location problem: (1) based on centroid of points of pole-polar geometry, PPC; (2) based on convex hull region among pole-points, CHC; (3) based on centroid of points obtained by polar-lines intersections, TLI; (4) based on centroid of points obtained by tangent lines intersections, TLI; (5) based on centroid of points obtained by tangent lines intersections with minimal angles, MAI. The first one has computational cost \(O(n)\) and whereas has the computational cost \(O(nlogn)\) where \(n\) is the number of points of interest.  
Keywords:signal processing; 2D point location; computational geometry; pole-polar geometry +
Footnote †: journal: Sensors
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 5 Average PAoI Minimization
By setting \(\frac{\partial\lambda}{\partial L_{1}}=0\) and \(\frac{\partial\lambda}{\partial L_{2}}=0\), we obtain the following fixed-point problem for the two variables \(L_{1}\) and \(L_{2}\):  
\[\big{(}L_{1}^{*}(K),L_{2}^{*}(K)\big{)}=\operatorname*{argmin}_{(L_{1},L_{2})\in \mathbb{N}^{2}}\tilde{A}(K,L_{1},L_{2}). \tag{11}\]4 \[\big{(}L_{1}^{*}(K),L_{2}^{*}(K)\big{)}=\operatorname*{argmin}_{(L_{1},L_{2})\in \mathbb{N}^{2}}\tilde{A}(K,L_{1},L_{2}). \tag{11}\]5  
For a given set of system parameters \((N,K,S,\rho_{1},\rho_{2})\), we can numerically solve for \(L_{1}\) and \(L_{2}\) by applying a fixed-point iteration method [31] to Equations (19) and (20). The blocklengths in (11) are then calculated as \(L_{1}^{*}(K)=\lfloor L_{1}\rfloor\) and \(L_{2}^{*}(K)=\lfloor L_{2}\rfloor\), where \(\lfloor\cdot\rfloor\) denotes rounding to the nearest integer. We note that even for large-scale systems (i.e., systems with a large number of sensors \(N\)), the fixed-point problem remains scalable, as \(N\) is treated as a constant in Equations (19) and (20), and a conventional fixed-point algorithm can be applied. However, the subsequent step in the optimization procedure, formulated in (13), involves comparisons over all \(k\in K\subset\{1,2,\ldots,N\}\), resulting in a computational complexity of \(O(N)\) with respect to the system size \(N\). Since the fixed-point iteration can be executed offline using standard numerical methods, the proposed scheme is considered computationally feasible for real-time or resource-constrained systems.
## 4 Experimental Verification
### Validation of Measured Datasets
#### 4.2.1 Outer Ring Fault  
The number of sampling points of the outer ring fault data is 8192, and the outer ring fault signals with loads of 500 N and 1500 N are collected, respectively. The time-frequency spectrum and envelope spectrum are shown in Figure 7,b. From the figures, it can be observed that the fault information is not clearly visible due to the strong noise interference.  
Figure 8 shows the envelope demodulation spectrum after VMD optimization by SSA. When the load is 500 N, the number of modes \(K\) after the SSA optimization of VMD is 10, and the penalty factor \(\alpha\) is 333; when the load is 1500 N, the mode number \(K\) is 9, and the penalty factor \(\alpha\) is 1441. The limitations of the SSA-VMD-MCKD method can be attributed to the following: (1) SSA falls into the local optimal solution. (2) The MCKD algorithm, while effective in enhancing periodic impulses, is highly sensitive to noise, which limits its performance in noisy environments. So, under 500 N load and 1500 N load, the fault frequency and its harmonics are not clearly identifiable in the envelope spectrum. The fault information is drowned out by noise, indicating that the SSA-VMD-MCKD method struggles to extract fault features under strong noise conditions.  
The parameter optimization VMD-NLMS algorithm proposed in this paper is used for fault diagnosis. The fault signals of the outer ring of the motor bearing with loads of 500 N  
Figure 8: SSA-VMD-MCKD (outer ring).  
Figure 7: Outer ring fault signal spectrum and envelope spectrum.  
and 1500 N are decomposed by VMD, respectively. The modulus selection indicator under different loads is shown in Table 4. Respectively, the VMD modal numbers are chosen to be \(K=5\), \(K=6\). The optimized optimal penalty factor is shown in Figure 9. Table 5 shows the correlation coefficient (outer ring). When the load is 500 N, IMF3 and IMF4 are selected for reconstruction. When the load is 1500 N, IMF3 are IMF4 are selected for reconstruction.  
improves the SNR value, thereby enhancing the identifiability of fault characteristic signals. Quantitative results show that the SNRs are 11.12 dB under 500 N and 13.33 dB under 1500 N.  
In Figure 11, the fault characteristics of the outer ring of the motor bearing with a load of 500 N and 1500 N can be clearly observed. A comparison with the SSA-VMD-MCKD method reveals that the proposed parameter optimization VMD-NLMS method clearly identifies the fault frequency and its harmonics, up to 8\(\times\) under 500 N and 4\(\times\) under 1500 N, demonstrating superior noise reduction and fault feature extraction capabilities. The quantitative results show that the NLMS algorithm improves the SNR by 26 dB under 500 N and 23.62 dB under 1500 N, while reducing the RMSE to 0.012 and 0.015, respectively, highlighting the advantages of the proposed method.
# Integrating BIM and GIS for an Existing Infrastructure
## 5 Case Study: Madrid, Calle 30
### BIM Model from GIS Data  
The input data to generate a BIM model of Calle 30 were provided by the infrastructure management company in GDB and SHP formats. First, this information was processed. All the information was unified to CRS ETRS89, obtaining all the coordinates. This work was carried out using the open application QGIS. On the other hand, a library of BIM objects was developed for each element of the infrastructure, such as road markings, road equipment, safety barriers, or facilities. Based on the XYZ coordinates, several programs were developed in Dynamo to automate the modelling of the roadway and the positioning of all the elements that make up the road [67]. This BIM model was developed in Revit 2023 software with a LOD of 200, as can be seen in Figure 3.  
Figure 3: Infrastructure BIM model.  
Figure 2: Main formats used to develop the BIM-GIS model for the development of an FM app.  
All the available data from the original documentation, the information generated from it, the information generated by the BIM methodology, and the complementary information to the O&M were structured in an external relational database (SQL). For this purpose, the open-source database manager PostgreSQL was used, as it allowed the management of spatial data. All the elements that make up the model were uniquely identified using the existing real coding used by the infrastructure manager. This coding was defined as a new ID parameter in the BIM model. Figure 4 shows the ID parameter with the DB linked for an element and other information that has been input from the GIS data, such as UTM coordinates.
# BMP-2 Delivery through Liposomes in Bone Regeneration
###### Abstract  
Bone regeneration is a central focus of maxillofacial research, especially when dealing with dental implants or critical sized wound sites. While bone has great regeneration potential, exogenous delivery of growth factors can greatly enhance the speed, duration, and quality of osseointegration, making a difference in a patient's quality of life. Bone morphogenic protein 2 (BMP-2) is a highly potent growth factor that acts as a recruiting molecule for mesenchymal stromal cells, induces a rapid differentiation of them into osteoblasts, while also maintaining their viability. Currently, the literature data shows that the liposomal direct delivery or transfection of plasmids containing BMP-2 at the bone wound site often results in the overexpression of osteogenic markers and result in enhanced mineralization with formation of new bone matrix. We reviewed the literature on the scientific data regarding BMP-2 delivery with the help of liposomes. This may provide the ground for a future new bone regeneration strategy with real chances of reaching clinical practice.  
often has a poor quality, and implants fail to integrate. A good osteoinductive material has a role in support, as well as the capability to recruit mesenchymal stromal cells (MSCs), deliver the growth factors which are necessary in the differentiation process, and rendering a faster, enhanced bone formation [8; 9].  
In bone remodeling, the main growth factors are the members of the TGF\(\beta\) superfamily, mainly bone morphogenetic proteins ( BMPs) [10; 11; 12; 13]. The delivery of these factors is a problem not yet solved. In the literature, numerous drug delivery systems are described but concerns are that none of these are ideal. Adding growth factors to metals increases the price of the implants significantly. Delivery by calcium-phosphate ceramics increases the necessity of growth factors and there is a risk for factor degradation [14]. Liposomes are bioactive vesicles which can encapsulate many types of molecules and specific genetic sequences that transfected into the cells can increase the secretion of specific proteins. They are excellent carriers, highly biocompatible, but overlooked in many areas, such as bone regeneration. Including growth factors in liposomes, reduces the amount used, and subsequently the costs and the risks of side effects [12].  
In this review article, the aim is to compile the evidence regarding the use of liposomes as growth factor delivery systems in bone regeneration, most specifically, in the osseointegration process of dental implantation.