esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Industrial Application of Fast Carbonation of Recycled Concrete Aggregates
## 3 Application to Concrete
### Mechanical Properties of Hardened Concrete  
For a given 28-day compressive strength class measured on cylinders, carbonated RCA (CRCA) concrete differed only slightly from RCA concrete in the majority of compressive results (Figure 8). For low substitution rates (up to 20% sand and 50% gravel), the effects of introducing RCA or CRCA on other concrete properties were minor. For high substitution rates, the effect was visible for the largest w/c ratio. The evolution of the compressive strength with time led to the same strength class of concrete as that obtained with natural or recycled non-carbonated aggregates. The results were similar for other mechanical properties, such as Young's modulus or the pull-out strength [15].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Concrete Mix (kg/m3)** & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **B7** \\ \hline
0/4 NS & 844 & 571 & 607 & 592 & 411 & 444 & 420 \\
0/4 RS & & 143 & & & 274 & & \\
0/4 CRS1 & & & 152 & & & 296 & \\
0/4 CRS2 & & & & 148 & & & 280 \\
4/11 NG & 213 & 107 & 106 & 106 & & & \\
11/22 NG & 731 & 363 & 358 & 358 & & & \\
4/16 RG & & 471 & & & 870 & & \\
4/16 CRGI & & & 464 & & & 882 & \\
4/16 CRG2 & & & 464 & & & 873 \\ Cement CEMI/ A-LL 42,5R & 320 & 320 & 320 & 320 & 320 & 320 & 320 \\ Superplasticizer & 0.75\% & 1.20\% & 0.65\% & 0.65\% & 1.20\% & 0.90\% & 0.90\% \\ Setting retardant & & 0.4\% & 0.4\% & 0.4\% & 0.5\% & 0.5\% & 0.5\% \\ Water & 177 & 177 & 177 & 177 & 177 & 177 & 177 \\ Water to cement ratio & 0.55 & 0.55 & 0.55 & 0.55 & 0.55 & 0.55 & 0.55 \\ \hline \end{tabular}
\end{table}
Table 4: Composition of the 7 C25 concretes (mass proportions).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Concrete Mix (kg/m3)** & **B8** & **B9** & **B10** & **B11** & **B12** & **B13** & **B14** \\ \hline
0/4 NS & 748 & 710 & 708 & 708 & 712 & 712 & 712 \\
4/11 NG & 206 & 94 & 94 & 94 & & & \\
11/22 NG & 776 & 380 & 379 & 379 & & & \\
4/16 RG & & 473 & & & 877 & & \\
4/16 CRG1 & & & 478 & & & 877 & \\
4/16 CRG2 & & 450 & 450 & 450 & 450 & 450 & 450 \\ Cement CEMI/ A-LL 42,5R & 450 & 450 & 450 & 450 & 450 & 450 & 450 \\ Superplasticizer & 0.8\% & 1\% & 0.9\% & 1.1\% & 1.55\% & 1.5\% & 1.45\% \\ Water & 165 & 165 & 165 & 165 & 165 & 165 & 165 \\ Water to cement ratio & 0.37 & 0.37 & 0.37 & 0.37 & 0.37 & 0.37 & 0.37 \\ \hline \end{tabular}
\end{table}
Table 5: Composition of the 7 C45 concretes (mass proportions).  
Figure 8: Compressive strength measurements of the 14 concretes.  
Figure 6: Measured slumps for the C25 concretes.  
Figure 7: Measured slumps for the C45 concretes.
## 2 Inter-Floor Noise Dataset
### Selecting Type and Position of Noise Source  
The inter-floor noise dataset was designed based on a report provided by the Floor Noise Management Center [7]. From 2012 to March 2018, the center received 119,500 complaints from residents suffering from inter-floor noise, and 28.1% of victims were visited to identify inter-floor noise. The identified noise types and their portions are footsteps (71.0%), hammering (3.90%), furniture (3.3%), home appliances (vacuum cleaner, laundry machines, and television) (3.3%), doors (2.0%), and so on. Unidentified or unrecorded noise types account for 10.1%. 79.4% of the identified inter-floor noises originated from residents on the upper floor, while 16.3% of the identified inter-floor noises originated from residents on the lower floor [7].  
Based on the report, the top 4 noise types occupying 92.9% of the identified noise types were selected for classification. Because 95.7% of inter-floor noises were identified as noises from the upper floor and the lower floor, we focused on inter-floor noises generated on the two adjacent floors. In addition to these, inter-floor noises on the middle floor were also collected to check whether our model can distinguish noises generated on the same floor from noises generated on other floors.  
Figure 1 shows 5 inter-floor noise types included in the dataset. They are a medicine ball falling to the floor from a height of 1.2 m (MB), a hammer dropped from 1.2 m above the floor (HD), hammering (HH), dragging a chair (CD), and running a vacuum cleaner (VC). Generating reproducible footstep noises is very challenging. Furthermore, it could hurt the person who generates them. Thus, an impact ball, a bang machine (a tire), or a tapping machine was usually used to produce impulsive footstep noise dominated by low-frequency components [15]. In the dataset, a 2 kg, 0.2 m diameter medicine ball, which generates noises with a frequency characteristic similar to that generated when an impact ball is dropped, was used to mimic footsteps. Because it is difficult to transport and install a laundry machine and television, only a vacuum cleaner was used to generate noise from home appliances.
## 4 Joint Parameters Optimization
### Optimization of PBS Density  
As the MBS density can be set as a constant, the PBS density optimization problem can be converted to \(\rho_{p,m}\) optimization to maximize the network EE. After the optimized \(\rho_{p,m}^{*}\) is obtained, then the optimal PBS density can be calculated by \(\lambda_{p}^{*}=\lambda_{m}\rho_{p,m}^{*}\). Similarly, suppose that the PBS CRE bias \(B_{p}\) is a known quantity. Further suppose that \(\lambda_{u}\) is a constant value. Thus, the PBS density optimization problem can be formulated as follows:  
\[\begin{split}&\rho_{p,m}^{*}=\operatorname*{arg\,max}_{\rho_{p,m}}EE \left(\rho_{p,m}\right)\,\Big{|}_{\lambda_{u},B_{p}},\\ & s.t.\quad 0<\rho_{p,m}\leq 30,\\ &\quad B_{p}\text{ is an a arbitrary constant between 0 and 25}.\end{split} \tag{17}\]  
Assume that the variable step length of \(\rho_{p,m}\) is set to be 0.03. Thus, the optimal PBS density optimization for the network EE maximization can be also obtained by means of a linear search algorithm, which is shown in Algorithm 2.  
```
1. Initialization:

(1) Initialize the network scenario and the values \(\lambda_{u}\), \(\lambda_{m}\) and \(B_{p}\), where \(B_{p}\in(0,25]\).

(2) Set the initial value of \(\rho_{p,m}\)=0.03.

(3) Denote \(\sigma\) as the variable step length of \(\rho_{p,m}\). Denote \(EE^{*}\) as the optimal value of the network EE. Denote \(\rho_{p,m}^{*}\) as the optimized ratio between PBS density MBS density. Let \(\rho_{p,m}^{*}\)=\(\rho_{p,m}\), \(EE^{*}=EE\left(\rho_{p,m}\right)\left|{}_{\lambda_{u},B_{p}}\right.\) and \(\sigma\)= 0.03.

2. Calculate the optimal ratio of PBS density to MBS density while\(\rho_{p,m}\leq 30\)do \(\rho_{p,m}\)=\(\rho_{p,m}\)+\(\sigma\) \(EE^{\prime}=EE\left(\rho_{p,m}\right)\left|{}_{\lambda_{u},B_{p}}\right.\) according to Equation (14) if\(EE^{\prime}>EE^{*}\)then \(\rho_{p,m}^{*}=\rho_{p,m}\), \(EE^{*}=EE^{\prime}\) endif  endwhile  3. Obtain the optimal PBS density \(\lambda_{p}^{*}=\lambda_{m}\rho_{p,m}^{*}\)
```  
**Algorithm 2**PBS Density Optimization (PDO) Algorithm.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
###### Abstract  
We describe self-organizing network (SON) concepts and architectures and their potential to play a central role in 5G deployment and next-generation networks. Our focus is on the basic SON use case applied to radio access networks (RAN), which is self-optimization. We analyze SON applications' rationale and operation, the design and dimensioning of SON systems, possible deficiencies and conflicts that occur through the parallel operation of functions, and describe the strong reliance on machine learning (ML) and artificial intelligence (AI). Moreover, we present and comment on very recent proposals for SON deployment in 5G networks. Typical examples include the binding of SON systems with techniques such as Network Function Virtualization (NFV), Cloud RAN (C-RAN), Ultra-Reliable Low Latency Communications (URLLC), massive Machine-Type Communication (mMTC) for IoT, and automated backhauling, which lead the way towards the adoption of SON techniques in Beyond 5G (B5G) networks.  
self-organization; self-optimization; self-healing; SON applications and architecture; SON design and dimensioning; machine learning (ML) and artificial intelligence (AI) for SON; massive Machine-Type Communication (mMTC); IoT; URLLC; backhauling; SON for 3G/4G; 5G and B5G networks +
Footnote †: journal: _Future Internet_  
such as Network Function Virtualization (NFV), aiming to decouple the dependency between hardware and software in terms of network functions and migrate legacy network functions to a virtual or Cloud-based architecture in the core network domain. NFV combined with Software Defined Networking (SDN), with the aim to separate the data and control planes, shall be applied in the radio access network (RAN) part as well through the Cloud-RAN (C-RAN) concept, aiming to virtualize and aggregate the baseband units of the base stations in a central baseband pool [1; 2; 3; 4; 5; 6; 7; 8].  
Apart from NFV, SDN, C-RAN and techniques applied in the radio access domain, such as massive Multiple Input Multiple Output (MIMO), mmWave technologies, spectrum sharing, network slicing and cognitive radio, the key solution for automatically configuring, optimizing and tuning 5G and B5G network functions is using self-organizing network (SON) platforms, combined with the previously mentioned applications and empowered by intelligent machine learning (ML) algorithms, leading to full RAN automation, self-configuration, self-healing, and self-optimization capabilities [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23].  
The application of self-organization in mobile networks is derived from natural or biological sciences, mainly biology, and a big variety of papers exists in the literature referring to this specific issue; however, we shall not focus on describing the abstract characteristics of self-organization in nature, but describe and analyze the characteristics of SON technology, its benefits, its operation rationale for existing mobile networks, its evolution and the basic application fields in cellular communications networks [24].
## 3 Results and Discussion
### Scanning Electron Microscopy  
The bowl shape is the favored spot for pit nucleation, and it represents pitting growth initiation locations. The bowl shape takes a downward concave form because of gravity and its effect on electrolyte streaming, and it is the shortest path for mass transportation. In this study, an increase in time broadened the pit concave, and Fe(II) cations were formed generated because of Cl\({}^{-}\) ions flowing into the widening pit. These Fe(II) cations are the main reason for charge neutrality in the spot location. Scanning electron microscopy (SEM) image topography showed a pearlite phase in the deepest area all over the concave spot, indicating the aggressive effect of pearlite toward pit progression. Anion and cation vacancies at the chloride-metal interface form voids in the pit (Figure 13). If these vacancies penetrate the metal phase at a slower rate compared to their transference through the layer, it would  
Figure 11: Raman spectroscopy images in an alkaline solution after potentiodynamic polarization.  
Figure 12: Raman spectroscopy images in alkaline and Cl\({}^{-}\) solutions after potentiodynamic polarization.  
cumulate at the chloride-metal interface [52; 53]. Fitting is often responsible for the high concentration of ions, which may promote pit depth [52].  
Energy dispersive X-ray (EDS) analysis technique were conducted along with SEM examination to identify the elements in the oxide layers of the different samples. The results of the EDS spectra are shown in Figure 14. The atomic percentage content of iron, chlorine and oxygen representative data are presented graphically in Figure 14a conventional rebar and Figure 14b for DP rebar. The predominant elements detected on all samples were Fe and O. Trace amounts of Mn, S and Si were also detected. It is not possible to obtain quantitative compositional information from EDS analyses. However, qualitative or at best semi quantitative information can be derived from a careful comparison of the peak energy heights of the different elements. Thus, considering the peak heights of Fe and O in Figure 14b indicates that presence of Fe oxide, most likely in the form of Fe\({}_{2}\)O\({}_{3}\) or Fe\({}_{3}\)O\({}_{4}\) is higher in case of DP steel compared to non-conventional rebar.
## 2 UHPC-NC Interfacial Bonding Strength Test
### Slant Shear Test  
Slant shear tests are commonly used to determine the UHPC-NC interface bonding strength under the combined action of shear pressure [29]. Common specimen shapes include prisms and cylinders with a standard interface angle of 30\({}^{\circ}\) (Figure 8a). Based on the Moore-Coulomb theory, Figure 8b shows the relationships among the interface normal, shear, and maximum axial compressive stresses of an inclined shear specimen [29]. If the axial compressive stress at failure (\(\sigma_{0}\)) and the interfacial angle (\(\alpha\)) are known, the normal (\(\sigma_{n}\)) and shear (\(\tau_{n}\)) stresses at the interface can be calculated according to the diagram in Figure 8b.  
Figure 7: Shear test: (**a**) single-sided direct shear test; (**b**) L-shaped direct shear; (**c**) bisurface 1; (**d**) bisurface 2.  
[PERSON] [30] investigated the effect of different interface angles (55\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\)) on the specimen failure modes through the slant shear test. It was revealed that interface failure occurred in specimens with interface angles of 70\({}^{\circ}\), whereas substrate failure occurred in the remaining specimens. The specimen failure modes had different interface angles. As different specimen failure modes are obtained with different interface angles, selecting an appropriate interface angle can accurately reflect the actual interface bonding strength [44]. [PERSON] [45] discovered that interface angle and roughness considerably affect the slant shear test results. Moreover, an analytical method was developed for predicting the critical angle while considering the influence of the internal friction angle using numerous slant shear tests. The compressive stress required for the interface failure was minimal when the specimen was at a critical angle.  
[PERSON] [45] experimentally validated the aforementioned analysis method and determined the relationship between the ratio of the failure stress to the pure shear strength and the interface angle. The smooth, moderately rough, and extremely rough interfaces had critical angles of 27\({}^{\circ}\), 23\({}^{\circ}\), and 19\({}^{\circ}\), respectively. If the interface angle exceeds the critical angle, the interfacial failure stress ratio, defined as the ratio of the failure stress to the pure shear strength, rapidly increases. Thus, a small angle between the inclined planes should be used to promote the interfacial failure of specimens with rough surfaces. However, if the elastic modulus and [PERSON]'s ratio of the two materials do not match, further slant shear tests with the UHPC-NC samples are required.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Interfacing Protocols
#### 3.3.1 IEEE 802.11p  
For vehicular communication, IEEE 802.11p and 802.11p are widely used for wireless connectivity. IEEE 802.11p is considered as the cross-layer standard of PHY/MAC, incorporating OFDMA and frequency band specified for vehicular communication of 5.9 GHz. The IEEE 802.11p MAC is designed based on CSMA/CA, i.e., Carrier Sense Multiple Access with Collision Avoidance. A node initially senses the channel before transmission, and during the transmission process, the node and channel become inactive for a Short Inter-frame Space (SIFS). The transmission resumes only when the channel is idle. In the case of a busy channel, an arbitrary back-off mechanism is implemented with Contention Window (CW) of \(\left[0,max(CW)\right]\) and starts to transmit only at the lapse of the back-off timer. As part of the multi-channel access mechanism, the Control Channel (CCH) is dedicated to safety messages, and the Service Channel (SCH) is for non-safety communications. Control packets broadcast throughout the CCH with no acknowledgment required from the receiver. However, in SCH, the packets are retransmitted if no acknowledgment is received within the time frame.
## 3 Results
### The LUMA Interface
#### 3.5.1 The Mapping Window  
In the first step, users create mappings between their proprietary units and UCUM. In order to formulate these rules there are three requested inputs:  
* The proprietary unit term;
* A descriptor of the laboratory test;
* A valid LOINC code.  
After verifying whether the given LOINC code is valid, this incomplete tuple is added to the mapping table below. By selecting an incomplete mapping, users can opt to look up UCUM units associated with the LOINC code provided. On the right side of the LUMA interface, users may then find several possible matches for the proprietary unit. These recommendations are sourced from the LOINC data set in the form of a UCUM example unit (should it exist and be appropriate) and the ValueSet specified beforehand. As some UCUM units can appear rather cryptic, we also opted to add a human-readable written-out description of the unit that is generated automatically. When users find a match they deem appropriate, they may map the selected unit to their incomplete tuple. A completed table entry contains these additional values:  
* The UCUM unit selected for the mapping;
* Its written-out display name;
* The mapping status (Complete).  
Users may additionally manage these mappings by deleting entries. Once one or more mappings have been created, users can export them into a simple TSV file from which they can be recreated by loading them on the next start or during a running session. An exemplary view can be seen in Figure 6.
## 3 Results and Discussion
### Correction Factors Used for CO\({}_{2}\) Uptake
#### 4.1.2 Environmental Classes Involved  
Because carbonation is affected differently when it is either open or under a shelter, factors have also been assigned to the following classes: submerged or wet concrete, indoor concrete, outdoor concrete, sheltered concrete, and buried concrete. Table 6 provides the suggested values for k when using CEM I for different situations [19]. The k values are estimations derived from empirical data; therefore, they are considered when estimating k. This is true considering carbonation increases as the temperature increases, or the indoor walls in countries that receive snow are hotter than their corresponding outdoors; therefore, they have a higher value of k.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strength** & **\textless{}15 MPa** & **15–20 MPa** & **25–35 MPa** & **\textless{}35 MPa** \\ \hline Wet/submerged & 2 mm/\(\sqrt{\text{year}}\) & 1.0 mm/\(\sqrt{\text{year}}\) & 0.7 mm/\(\sqrt{\text{year}}\) & 0.5 mm/\(\sqrt{\text{year}}\) \\ \hline Buried & 3 mm/\(\sqrt{\text{year}}\) & 1.5 mm/\(\sqrt{\text{year}}\) & 1.0 mm/\(\sqrt{\text{year}}\) & 0.75 mm/\(\sqrt{\text{year}}\) \\ \hline Exposed & 5 mm/\(\sqrt{\text{year}}\) & 2.5 mm/\(\sqrt{\text{year}}\) & 1.5 mm/\(\sqrt{\text{year}}\) & 1 mm/\(\sqrt{\text{year}}\) \\ \hline Sheltered & 10 mm/\(\sqrt{\text{year}}\) & 6 mm/\(\sqrt{\text{year}}\) & 4 mm/\(\sqrt{\text{year}}\) & 2.5 mm/\(\sqrt{\text{year}}\) \\ \hline Indoors & 15 mm/\(\sqrt{\text{year}}\) & 9 mm/\(\sqrt{\text{year}}\) & 6 mm/\(\sqrt{\text{year}}\) & 3.5 mm/\(\sqrt{\text{year}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Suggested k values for concrete surfaces made from CEM I and exposed.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards
#### 2.1.7 ISO 15628 (ITS--Enhanced Crash Data for Automotive Vehicles)  
ISO 15628, as a standard for enhanced crash data exchange, not only defines the data structure and format but also represents a significant stride towards expediting post-crash assessment and emergency response. By providing a standardized framework for the exchange of detailed crash data between vehicles and emergency responders, this standard enhances the accuracy and speed of critical information dissemination. ISO 15628 contributes to more effective and timely emergency interventions, ultimately improving overall road safety and the outcomes of post-crash scenarios within the domain of ITS [49].  
Compliance with these established standards not only facilitates communication and interoperability within the IoV but also promotes an ecosystem that is crucial for the widespread adoption of connected and autonomous vehicles. As the IoV evolves, ongoing collaboration and the emergence of new standards will play a pivotal role in addressing emerging challenges, ensuring the resilience, security, and continual enhancement of ITS to support the seamless integration of vehicles into the broader smart city infrastructure.
## 1 Introduction  
In recent years, cancer studies have involved the consideration of cancer stem cell (CSC) theory. CSCs are a subpopulation of cells in tumors that have self-renewal, differentiation, and tumorigenity abilities [1]. These cells are related to therapy drug resistance, metastasis, and recurrent cancer [2]. The identification of CSCs is based on typical cellular surface markers, such as Cluster of Differentiation133 (CD133), CD44, CD24, and Aldehyde dehydrogenases (ALDH), of which CD133 appears in various types of cancer cells in solid tumors. This glycoprotein is among the most popular markers for isolation of CSCs [3]. CD133, also known as prominin-1, is a cross-membrane glycoprotein. Evidence has shown that CD133 might be related to metastasis, tumorigenesis, and drug resistance. Therefore, CD133 is used not only as a specific surface antigen to detect and isolate CSCs, but also in therapeutic strategies [4].  
Another typical feature of CSCs is immunosurveillance resistance [5]. Programmed death-ligand 1 (PD-L1) is also reported as a CSC surface marker which blocks PD-1 on the surface of T cells. Thus, PD-L1 limits the response of T cells, helping CSCs to escape the immune system for their growth and metastasis [6]. Since 2014, PD-L1 monoclonal antibody has been clinically approved for anticancer immunotherapy worldwide.  
CD133 and PD-L1 antibodies are reported to have the ability to detect and treat cancers, especially when combined with nanomaterials. Nanomaterials have improved the therapeutic index of clinical drugs by enhancing circulation time and increasing permeability and retention [7]. Nanomaterials help with probing, tracking, homing, and studying CSCs' behavior. In this area, rare-earth nanomaterials such as Terbium (Tb), a lanthanide, have attracted considerable attention. The advantages of lanthanide compounds include long luminescence lifetime, large stock displacement, and narrow spectral width, which are useful for fluorescent marking, probes, and sensors for use in tests and human body imaging [8]. Nanoscale lanthanides are highly stable, and it is easy to fabricate and functionalize their surfaces using biological substances such as antigens, monoclonal antibodies, enzymes, and aptamers. These molecules can be used to improve therapeutic efficacy or for locating nanoparticles in vivo. Therefore, in this study, Tb\({}^{3+}\) was used to produce nanomaterials to double conjugate with the monoclonal antibodies against CD133 and PD-L1 for the purpose of biolabeling and growth inhibition of cancer stem cells, which were NTERA-2 pluripotent human embryonic carcinoma cells.