esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 4 Results and Discussion
### Shrinkage of RPC  
Figure 5 shows the variation in the shrinkage strain of the RPC samples with age. At the early ages, the shrinkage strain increases at a fast rate, and the shrinkage reaches a steady state at approximately 200 days. However, the shrinkage of RPC is much smaller than that of normal strength concrete, which is usually on the order of several hundred micro strain or more. With the improved uniformity and minimized pore size of the RPC, the escape of moisture is inhibited, capillary stress is reduced, and as a result, the shrinkage is also reduced. The shrinkage decreases with increasing steel fibre content. The final shrinkage of the RPC samples with 1% and 2% steel fibre content is 10.6% and 15.0% less, respectively, than that of the RPC samples without steel fibre. This is similar to the effect of steel fibre on shrinkage in normal strength concrete [41; 42]. The positive effect of steel fibre for reducing the shrinkage of RPC can be explained as follows:  
1. During the process of mixing the steel fibre into the RPC matrix, some micron-scale water films are formed on the steel fibre surface. As a result, calcium hydroxide crystals form directly on the surface of the fibre and grow with no constraints, which forms a loose reticular structure at the interface of the steel fibre and RPC matrix, thereby reducing shrinkage [43].
2. The distribution of steel fibres presents a three-dimensional random state. The steel fibres cross and overlap to form a skeleton, which can hinder the development of free shrinkage owing to the high elastic modulus of the steel fibre [44; 45].  
Figure 4: Compressive strength and elastic modulus of the RPC samples: (**a**) Compressive strength; (**b**) Elastic modulus.  
Figure 5: Shrinkage strain of the RPC samples with age.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 1 Introduction
### Topic Introduction and Research Objectives
We, therefore, propose a new algorithm that gives a better answer to the design and the modification of a backbone network while maintaining the functions guaranteed by the EAFFB algorithm developed in [6] and, at the same time, achieving significantly improved energy efficiency. This study analyzes the power consumption of a WDM network, especially the IP transmission over a WDM backbone network. The proposed scheme reduces the power consumption while at the same time ensuring network connectivity in the case of natural disasters. It does not explore aspects such as data transmission delays post-earthquake disasters. The remainder of this article is organized as follows. In Sections 1.2 and 1.3, background knowledge of IP over WDM networks and related work are presented, respectively. The proposed algorithm along with the energy contribution, seismic measurements, system configuration setup, and data are presented in Section 2. Section 3 includes the evaluation of the results along with the performance analysis. Finally, Section 4 concludes the article.
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 2 System Architecture and Design
### Printed Circuit Board Design  
For the reliable mass production of smart tag devices, a Printed Circuit Board (PCB) has been designed with the purpose to:  
* Reduce the time cost by automating the production process;
* Reduce the total cost of the device. A cost reduction of about EUR 15 has been achieved;
* Increase the reliability and sustainability of the board.  
The PCB includes all the necessary components, including the SIM2070 module, and was implemented using the Autocad Eagle design tool. The schematic of the PCB is presented in Figure 4.  
In detail, the developed PCB of the smart tags is composed of the below main components (group blocks in the schematic):  
1. The ESP32 microcontroller which is the main processing unit of the device, along with decoupling capacitors and a 32.768 kHz ABS05 crystal;
2. The USB to UART module, which consists of a FT231X chip, a micro-USB port, a UMH3N dual digital transistor and decoupling capacitors and resistors. The USB to UART module enables the MCU to be programmed via USB and can provide power the device when the battery is not available;
3. The voltage regulator, which includes an MP2161 chip, a diode and decoupling capacitors. The voltage regulator provides steady voltage supply of 3.2-3.3 V to the ESP32 from the battery or the USB;
4. The SIM7020 module that provides cellular LPWAN connection to the device and communicates with the ESP32 via the UART interface;
5. The SHTC3 module, which consists of the SHTC3 sensor and pull up resistors. The sensor measures temperature, relative humidity and dew point;
6. The SY-DS-1L sensor with a resistor that is used to define the Dry/Wet state of the walls;
7. The battery monitor circuit that includes P-MOS and N-MOS transistors and a voltage divider. It is used to monitor the battery power level;
8. The TP4056 solar charger module that charges the battery using solar energy by the solar panel;
9. The battery and the power switch that turn on/off the device;
10. The reset and boot buttons that restart the ESP32.  
The printing of the PCB was implemented by a PCB manufacturer. The PCB includes surface mount technology (SMT) components and through hole components. The SMT assembly was implemented by a re-flow oven and the through hole components were soldered to the PCB by hand. The finalized PCB board before and after the placement and soldering of SMT and through hole components is presented in Figure 4.
## 5 Results and Discussions
### Physical Properties of THFS Modified Asphalt Binder  
Physical properties of asphalt binders with different THFS contents were measured, and are listed in Table 8.  
The test results indicated that there was a remarkable improvement in 135 \({}^{\circ}\)C RV, softening point and decrement in 25 \({}^{\circ}\)C penetration of THFS modified asphalt binders compared to SK-90. It could be demonstrated that the high-temperature properties of asphalt binder improved due to the addition of THFS and increased as THFS content increased. However, the test results in Table 8 also mentioned that there was a remarkable reduction in 10 \({}^{\circ}\)C ductility of THFS modified asphalt binder compared to SK-90, which indicated that the low-temperature properties of asphalt binder decreased after adding THFS. Furthermore, when THFS content was higher than 6%, the 10 \({}^{\circ}\)C ductility of asphalt binder failed to meet the technical requirement specified by China (\(\geq\)10 cm). A possible explanation for this tendency was THFS with higher asphaltene content reduced the flexibility of asphalt binder at low temperatures but improved the stiffness and hardness of asphalt binder at high temperatures. The movement of asphalt binder molecular chains might be limited by the layers of THFS at high temperatures during formation of an exfoliated structure, which led to an increase in softening point and RV, but the penetration and ductility decreased.  
Therefore, it could be noticed from Table 8 that asphalt binders with different THFS contents had a slight decrease in residual penetration ratio and residual ductility values after being subjected to RTFO aging. This decrease was statistically insignificant at the 5% significance level (95% confidence interval), which addressed that aging had a little effect on the properties of THFS modified asphalt binder.  
Compared with SK-90, as THFS content increased, the high-temperature PG value of the asphalt binder increased from 58 \({}^{\circ}\)C to 64 \({}^{\circ}\)C, but the low-temperature PG value decreased from \(-\)22 \({}^{\circ}\)C to \(-\)16 \({}^{\circ}\)C. This indicated that there was an interaction of THFS with asphalt binder that would affect not just high-temperature properties of asphalt binder, but also low-temperature properties. The optimum THFS content should be recommended while considering a balance between the high-and-low temperature properties of asphalt binder. In this study, the optimum THFS content was recommended to be 6%.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV
### IoV Architecture
#### 3.2.5 Sensing Layer  
This layer is responsible for the sensing of objects to collect the data from multi-data operators. Data can be collected in form of details about vehicles and RSUs, and these systems are interlinked with cloud server to give information about the vehicle's location. In this process, edge/cloud devices are used. Sensing layer is also used for cleaning and normalizing the data [37]. Physical data space takes care of driving pattern and leverages, for example, Advance Driver Assistance System (ADAS), behavior pattern, and emotions.  
As a new technology, 5G is characterized by high speed, low delay, wide coverage and support for Device-to-Device (D2D) communications, IoV creates a huge opportunity for further enhancements and performance improvements in VANET [38; 39]. When compared with traditional sensor network, there are higher requirements on perception accuracy, stability in data transmission, real-time analysis, intelligent decisions and network reliability for IoV, demanding for more complex architectures. For that reason, the proposed architecture meets the requirements of IoV, comprising application layer, control layer, cognition layer, communication layer and sensing layer as shown in Figure 3.
# Usability Test for an Over-Ground Walking Assistance Robotic Device Based on the Mecanum Wheel
## 2 Methods
### Procedure  
A usability test was conducted by comparing two robotic walking devices: a commercialized over-ground walking assistance robot (COWAR, Andago) and the newly developed OWAR-MW. The procedure of the usability test is illustrated in Figure 1.  
All participants were instructed to walk freely for a total distance of 100 m using both devices. The sequence of device usage was randomly assigned by drawing lots, and a 30 min break was given between the use of the first and second devices. The test was performed in a rectangular corridor measuring 15 \(\times\) 35 m\({}^{2}\). During each trial, the participants wore a harness that provided 10% body weight support. While walking, each robotic system detects the user's movement intention in real-time and assists in walking by tracking and following the user's trajectory. No time limit was imposed to complete the 100 m walk. A trained assistant accompanied each participant for safety, offering physical  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Physical Therapists (n = 9)** & **Stroke (n = 9)** \\ \hline Sex (male/female) & 4/5 & 2/7 \\ \hline Age (years) & 29.44 \(\pm\) 6.61 & 77.33 \(\pm\) 8.50 \\ \hline Height (cm) & 164.89 \(\pm\) 12.84 & 158.44 \(\pm\) 9.88 \\ \hline Weight (kg) & 65.44 \(\pm\) 12.78 & 58.17 \(\pm\) 7.42 \\ \hline BMI (kg/m\({}^{2}\)) & 23.99 \(\pm\) 3.36 & 23.27 \(\pm\) 3.14 \\ \hline Clinical experiences (years) & 5.33 \(\pm\) 5.67 & - \\ \hline Onset duration (month) & - & 6.22 \(\pm\) 2.68 \\ \hline Affected side (Right/Left) & - & 3/6 \\ \hline MAS, lower extremity (0/1) & - & 8/1 \\ \hline MMT, knee extensor (P/F/G) & - & 2/5/2 \\ \hline FAC (0/1/2) & - & 1/1/7 \\ \hline BBS (score) & - & 24.22 \(\pm\) 9.90 \\ \hline MBI (score) & - & 58.56 \(\pm\) 14.57 \\ \hline K-MMSE (score) & - & 25.11 \(\pm\) 2.03 \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) SD or frequency. BMI: Body Mass Index, MAS: Modified Ashworth Scale, MMT: Manual muscle test, P: Poor, F: Fair, G: Good, FAC: Functional Ambulation Categories, BBS: Berg Balance Scale, MBI: Modified Bathel Index, K-MMSE: Korean Mini-Mental State Examination.  
\end{table}
Table 1: General characteristics of the participants (n = 18).  
Figure 1: Flowchart of usability test.  
support only in emergencies. Additionally, the participants with stroke used an additional walking aid (a cane) while using the two types of ground-based walking robots if they wished. The walking task was conducted independently using a robotic assistance system. After completing the walking task with each device, the participants completed a usability satisfaction questionnaire covering three domains: safety, operability and functionality, and convenience. Following the survey, one-on-one in-depth interviews were conducted to explore participants' subjective experiences, perceived limitations, and suggestions for device improvement. The same procedure was applied to both robotic systems.
## 1 Controlling Thermal Radiation in Photonic Quasicrystals Containing Epsilon-Negative Metamaterials
It has a high melting point, which means it can endure high temperatures. It has chemical stability, which means it does not react easily with other substances. It has mechanical strength, which means it can resist deformation and fracture. ENG metamaterials are artificial structures that could also exhibit negative effective permittivity. Arrays of metal rods that are covered with dielectric materials can be used to make ENG metamaterials. Dielectric materials are materials that can be polarized by electric fields. These arrays can produce plasmonic resonances and strong electric polarization. Plasmonic resonances are the collective vibrations of the electrons in the metal rods that are excited by the electric field of the light [44]. The effective parameters of such metamaterials depend on the geometry and material properties of the rods, as well as the frequency and polarization of the incident waves. High transparency in the infrared (IR) region, and strong magneto-optical effects are the photonic properties of InSb. It can specifically be used for THz and IR spectral filtering, nonreciprocal surface plasmon polaritons, and quantum nanostructures [45; 46]. Plus, it also has high conductivity and thermal tunability, which can be exploited for sensing, switching, and modulating applications. The refraction index of all the elements could be calculated by \(n_{i}=\pm\sqrt{\varepsilon_{i}\mu_{i}}\)[47] according to the refraction index definition. Regarding signs \(\pm\), if both \(\varepsilon_{i}\) and \(\mu_{i}\) are negative quantity, then \(n_{i}\) takes sign \(-\); otherwise, sign \(+\) assigned to \(n_{i}\). Therefore, this relation takes sign \(-\) just in the case of double-negative (DNG) metamaterial, and all the material utilized in the proposed structure takes sign \(+\). It is also worth indicating that as all the materials are classified as diamagnetic or paramagnetic material, which means that they have no permanent magnetic moments and there is only a weak (negligible) induced magnetic response to the external magnetic field, their magnetic permeabilities were considered 1. Of course, it would be noticed that ENG and MNG metamaterials could also show negative refractive index over some frequency range.
## 4 Results and Discussion
### Robustness of Methodology
Furthermore, the developed heat after 7 days, presented in Figure 13B, suggests that the reactivities of the synthetic slags are similar to the industrial slag. Based on the presented chemical composition and specific surface area measurements, there are certain differences between the slags. However, the comparison further suggests that the synthesis generates a relevant sample. Figure 12: Particle size distributions and d\({}_{50}\) values for the three samples constituting the triplicate. Figure 13: R\({}^{3}\) calorimeter results showing (**A**) cumulative heat and (**B**) evolved heat after 7 days with error bars of one standard deviation. Figure 13B presents the final values for the developed heat after 7 days, representing the overall reactivity of the slags. The standard deviation based on the population was calculated to be 8.4 J/ g SCM, which corresponds to 3.6% of the average value of the triplicate. Thus, the repeatability of the whole process is considered remarkable since [PERSON] and [PERSON] reported that the standard error of the R\({}^{3}\) isothermal calorimetry test was 2-3% [22]. Comparing Figures 13B and 9B, the repeatability is, however, lower for the synthetic samples in comparison to remelting and granulating an industrial slag. Possibly, this methodology has potential for improvement using iterative grinding aimed at a specific surface area since the differences in reactivity presented in Figure 13B partially follows the variations in specific surface area. In conclusion, the synthesis of iron silicate slags on a laboratory scale using iron crucibles in an inert atmosphere provides relevant and repeatable results. This offers a methodology to synthesize slags of different compositions, enabling the evaluation of the relationship between chemical composition and inherent reactivity.
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 2 Bone-Conducted Speech Blind Enhancement Technology
### Equilibrium Method  
[PERSON] et al. [18] first proposed the idea of the equalization method and built the system by modeling and constructing an inverse filter. The content of BC and AC speech collected in different ways is the same, but their transmission routes are changed. The equalization method considers that the transmission channel can be modeled, and the BC and AC speech can be mapped through the modeling function. As shown in the transformation function Formula (1), the transformation from BC speech to AC speech is completed by combining the inverse transformation of the transmitted signal.  
\[A(n)=B(n)*T_{\text{B}}^{-1}(n)*T_{\text{A}}(n)=B(n)*Y(n) \tag{1}\]  
where \(B(n)\) represents the BC speech signal, \(A(n)\) represents the AC speech signal, \(T_{B}(n)\) is the BC speech transmission channel function, \(T_{A}(n)\) is the function of the AC speech transmission channel, and \(Y(n)\) is the transformation function from BC speech to the AC speech transmission channel.  
The speech conversion function is not limited to the time-domain signal but also can extract different speech features to build different transformation models, which are usually called equalization filters, for modeling the speech signal feature transformation. For example, the linear prediction cepstrum coefficient (LPCC) features and power spectral density features of BC speech and noisy speech are extracted for equalization filter modeling [19; 20]. In the equalization method, prior AC and BC speech signal characteristics are first used to train the relevant parameters of the equalization filter because, in the enhancement phase, there is only a BC speech signal available. Finally, the BC speech signal is enhanced through the trained equalization filter. For example, Ref. [21] selects the corresponding bone- and air-conducted speech short-time amplitude spectrum to obtain an equalization filter, that is  
\[\hat{C}(f)=\frac{\text{Short}}{\text{Short}}\frac{(AS_{\text{ac}}(f))}{(AS_{ \text{bc}}(f))} \tag{2}\]  
where short refers to the short-time spectrum, and the filter coefficients are smoothed. Let the amplitude spectrum of the AC speech signal and BC speech signal be individually expressed as \(AS_{\text{ac}}(f)\) and \(AS_{\text{bc}}(f)\).  
However, it is difficult to reconstruct the high-frequency components of speech signals using the above BC speech blind enhancement method because when the high-frequency energy of BC speech is almost zero, even if the response of \(\hat{C}(f)\) at high-frequency is large, it is difficult to play the role of energy enhancement; the equalization filter \(\hat{C}(f)\) designed in the literature is fixed. In fact, \(\hat{C}(f)\) will change with the change in BC speech. Because the construction of the filter is related to the content of the speaker, the speaker's vocal tract, and the characteristics of solid tissue, such an equalization filter cannot be designed any better, nor can it be mathematically modeled to obtain such a complex nonlinear relationship.
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 3 Integration of the Enabling Technologies
### Core Network Platforms  
In this sub-section, the main solutions to deploy the core network are discussed briefly. The comparison between the core solutions/platforms is based on seven factors such as the operational mode, support for core functions/components, supported interfaces, network slice, type of license, main contributors, and type of community support. The comparison shown in Table 2 shows that OAI-CN and free5 GC projects focus only on the development of the 5G Standalone (SA) operational mode, however, the Open5 GS, Open5 GCore, SD-Core, Magma Core, and Nokia 5G core implement both 5G SA and 5G Non-Standalone (NSA) modes. In addition to this, some of these platforms also provide support for 4G, LTE, and NB-IoT LTE modes. In terms of network functions, most of the platforms can deploy AMF, SMF, AUSF, UDM, UDR, NRF, etc functions. However, OAI-CN, free5 GS, Open5 GCore, and Nokia 5G Core platforms can also deploy the newly introduced Non-3 GPP Interworking Function (N3 WIF) which is responsible to interwork between untrusted non-3 GPP networks and the 5G Core. It acts as a gateway for the 5G core network providing the support for N2 and N3 interface towards the 5G core. Additionally, N3 IWF also provides a secure connection for the user equipment accessing the 5G core over a non-3 GPP access network with support for internet protocol security (IPsec) between the UE and the N3 IWF. Magma Core can only deploy the AMF function since the remaining functions are still under development. Considering the importance of intelligence in 5G core networks, it is important to highlight that only the Nokia 5G Core platform can deploy the NWDAF function which supports intelligent and autonomous network operations and service management. On the other hand, most of the platforms listed in Table 2 bring great convenience to the implementation of network slicing such as OAI-CN, free5 GC, Open5 GCore, etc. Finally, in terms of the license, the OAI-CN, free5 GC, and COMAC are distributed under the Apache V2.0 license, Open5 GS is under the GNU AGPL3 license, Magma Core is under Berkeley Source Distribution (BSD) license, and the SD-Core is released with an ONF member-only license.
# Assessment of Student Music Performances Using Deep Neural Networks
## 3 Material and Methods
### Dataset  
The dataset that we use is provided by the Florida Bandmasters' Association (FBA). This dataset contains the audio recordings from the Florida all-state auditions for three student categories: (i) _Middle School_ (7 th and 8 th grade), (ii) _Concert Band_ (9 th and 10 th grade), and (iii) _Symbolic Band_ (11 th and 12 th grade) for three consecutive years (2013-2015). The auditions cover various wind and percussive instruments. Each student is required to perform several exercises, which are rated by expert judges along several assessment criteria. The scores performed vary between instruments, years and also between different student categories. All ratings are normalized to a range between 0 to 1. However, the number of judges and the grading rubrics are not available.  
For this study, performances of three pitched wind instruments, i.e., _Alto Saxophone_, _Bb Clarinet_, and _Flute_ from the Middle School and Symphonic Band categories are considered. The choice of instruments is considering that these instruments have the highest number of performance recordings over the three years. The two student categories are expected to have a greater difference in overall proficiency levels. The year-wise distribution of the number of students, the average duration of the performances and the percentage split amongst the instruments is shown in Table 1. The four assessment criteria used to grade the performances are _Musicality (M)_, _Note Accuracy (N)_, _Rhythmic Accuracy (R)_, and _Tone Quality (T)_.