esa-sceva/satcom-chunk-collection huggingface dataset:
# Cost-Effective Temperature Sensor for Monitoring the Setting Time of Concrete
###### Abstract  
Concrete and Portland cement-based products are the most widely used materials in the construction industry. According to the Global Cement and Concrete Association (GCCA), 14 billion cubic meters of concrete are consumed worldwide every year. Knowledge of their properties is essential to ensure the quality of concrete products and structures. Knowing the evolution of certain parameters related to their durability makes it possible to prevent situations that affect compliance with quality requirements. Thanks to advances in IoT (Internet of Things) technologies, it is possible to know the evolution of these parameters in real time. The following work pursues the development and application of a prototype to monitor the setting time of concrete. This equipment provides real-time measurements, taking advantage of the Internet of Things (IoT) technology, allowing effective monitoring of the thermal behavior of concrete during its setting process. By measuring the temperature of the process and evaluating the resistance acquired during the setting time, we can correlate these two parameters, thus ensuring their correct evolution and allowing quick action to avoid future problems. For the development of this work, temperature measurements were made during the setting of 12 concrete specimens corresponding to four different mixtures (two types of cement with and without additives), assessed at three setting ages (28, 90, and 180 days). Through detailed experimental tests, the sensor was accurately and reliably validated, showing its ability to detect temperature changes, indicating the initial and final setting time. In addition, it was observed that the integration of the DS18B20 sensor does not compromise the structural properties of the concrete. The prototype's cost-effectiveness, efficiency, and easy installation make it a valuable tool for construction professionals, offering an innovative solution to ensure the quality and durability of the concrete. This breakthrough could represent a significant step towards the digitalization and improvement of construction processes, with direct implications for the efficiency and sustainability of modern infrastructures.  
digitalization; IoT; sensor; concrete; setting time measure +
Footnote †: journal: _Sci. 2024, 14_, 4344. [[https://doi.org/10.3390/app1414344](https://doi.org/10.3390/app1414344)]([https://doi.org/10.3390/app1414344](https://doi.org/10.3390/app1414344))
# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
###### Abstract  
This study examines variations in personal exposure to PM\({}_{2.5}\) and NO\({}_{2}\) while commuting by bicycle, vehicle, and walking during heating and non-heating seasons in Gliwice, an industrial city in Upper Silesia, Poland. Understanding these variations is crucial for assessing health risks and developing effective mitigation strategies. Personal exposure was measured using low-cost sensors, while stationary measurements provided comparative background concentrations. The results indicate statistically significant seasonal differences in pollutant concentrations. NO\({}_{2}\) levels were higher during the heating season (mean: 30.84 ug/m\({}^{3}\), median: 25.60 ug/m\({}^{3}\)) than in the non-heating season (mean: 22.61 ug/m\({}^{3}\), median: 20.37 ug/m\({}^{3}\); \(p=0.025\)). In contrast, PM\({}_{2.5}\) concentrations were higher in the non-heating season (mean: 12.1 ug/m\({}^{3}\)) compared to the heating season (mean: 9.5 ug/m\({}^{3}\); \(p=0.032\)). Inhaled doses instead of concentrations evaluated the exposure of participants. The inhaled doses of NO\({}_{2}\) and PM\({}_{2.5}\) per km were significantly higher for walking (mean: 141.3 and 30.7 ug/km for the male participant; 77.9 and 31.6 ug/km for the female participant) than for bicycle and walking (\(p<0.05\)). These findings underscore the impact of transport mode and seasonality on air pollution exposure, highlighting the necessity for targeted mitigation strategies to reduce commuters' exposure to traffic-related pollutants.  
Article
## 5 Results and Discussion
### Effectiveness on the Explainability Problem (Case Study)  
We conducted a real-world case study of user #7 and candidate item #861 in the Movielens-1M dataset, to present the capability of ALIEN to explain the recommendation results from two perspectives: (1) feature interactions, and (2) a user's historical behavior. The MovieLens-1M dataset was utilized as the dataset of this case study.
## 3 Materials and Methods
### Soil Water Retention and Bulk Density Determination  
The volumetric soil water content, \(\theta_{h}\), close to saturation, i.e., in the range of soil pressure head (\(h\)) values \(-100\leq h\leq-5\) cm, was obtained by a hanging water column apparatus [33]. The soil samples were placed on the surface of the porous plate of a glass funnel and saturated from below by applying four successive equilibrium steps of about 24 h each at \(h\) values of \(-20\), \(-10\), and \(-5\) cm, followed by submersion (i.e., \(h=0\)) [34]. Starting from saturation, soil samples were drained by imposing a drainage sequence of eleven h values: \(-5\), \(-7.5\), \(-10\), \(-15\), \(-20\), \(-25\), \(-30\), \(-40\), \(-50\), \(-70\), and \(-100\) cm. For each equilibrium \(h\) value, the volume of drained water was recorded, and these volumes were added backwards to the equilibrium volumetric water content, \(\theta_{h}\) (cm\({}^{3}\)/cm\({}^{3}\)), determined at \(h=-100\) cm by weighting the sample after oven-drying at 105 \({}^{\circ}\)C for 24 h [34]. For each soil/amendment mixture, the water retention data at pressure heads in the range \(-15\),\(300\leq h\leq-330\) cm were determined in pressure plate extractors using samples of 5-cm-diameter by 1-cm-height [35]. The duration of the overall laboratory experiment, i.e., the sample wetting/saturation phase and the determination of water retention during the drainage transient, lasted 2 to 3 weeks.  
Therefore, the soil water retention curve was parameterized for each soil/amendment mixture considering 15 pairs of volumetric soil water content vs. soil water pressure head values. The unimodal van Genuchten (vG) water retention function [36] was fitted to the data by the RETC software (version 6.02) [37]. To accurately estimate the water retention curve, model fitting was performed by optimizing all the parameters, namely the residual and saturated volumetric soil water content (i.e., \(\theta_{r}\), \(\theta_{s}\)) and the scale and shape parameters  
Figure 1: Flow chart of the steps for soil physical quality (SPQ) parameters determination: 1. preparation of sieved soils (Ginosa, GIN, and Castellaneta, CAS), and amendments, i.e., compost (COM), vermicompost (VER), and biochar (BIO); 2. soil/amendment mixture in cylindrical samplers; 3. soil water retention determination; 4. parameterization of soil water retention function; 5. determination of the SPQ parameters.  
(\(\alpha\) and \(n\)) of the vG model [4]. The modeled water retention curves were used for the calculation of the SPQ parameters (Figure 1).  
The soil bulk density (\(BD\)) was determined using the 204 cm\({}^{3}\) oven-dried soil samples by standard laboratory procedures [4; 16].
## 4 FDM-DEM Coupling Model
### Numerical Model  
According to the case mentioned above, the FDM-DEM coupling model is shown in Figure 5. The model has a length of 60 m and a height of 40 m. The discrete domain is 20 m in length and 18 m in height. The radius of shield tunnel is 3.0 m. The distance between the isolation pile and the right tunnel is 3.7 m. The length of the isolation pile is 22 m, and the width is 0.8 m. Ground loss induced by shield tunnelling is simulated by controlling walls shrinkage. The ground loss rate is estimated to be 2.5% based on feedback from field construction parameters. Isotropic elastic constitutive model is adopted in FLAC\({}^{\text{2D}}\) zone. In order to accurately simulate the characteristics of rock and soil, linear parallel bond constitutive model is adopted in PFC\({}^{\text{2D}}\) ball. Macroscopic and microscopic physical and mechanical parameters of soil are shown in Table 1.  
Figure 4: Coupling Mechanism Based on FLAC\({}^{\text{2D}}\) and PFC\({}^{\text{2D}}\).
## 4 Analysis Example and Methods
### Bidirectional FSI Simulation and Experimental Methods for Bionic Pipelines
#### 5.1.2 The Results of SEM Experiment on the Composite Materials  
Under different SWCNT contents, the fracture morphology for each specimen is shown in Figure 13.  
In Figure 13a-c, when no SWCNTs are added to the matrix material, the fracture morphology is relatively smooth and has distinct folds, which is an obvious feature of a brittle fracture in the elastic material. As the content of the SWCNTs increases, the fracture morphology of the material gradually becomes rough and wrinkled, so the energy required in the stretching process increases, indicating that the tensile strength and elastic modulus of the material increase accordingly [32; 33]. The surface roughness of the material in Figure 13d is similar to that in Figure 13c, but there are obvious pits on the surface. The pits are caused by the agglomeration of the SWCNTs, which indicates that the filling capacity of the SWCNTs has reached the supersaturated state. In Figure 13e,f, the pit phenomenon is more obvious, and the agglomeration is intensified, which indicates that the probability of CNTs agglomeration intensifies with the increase in the content.  
Combined with the existing tensile experimental and SEM test data (the additional amount of the SWCNT is 0-4 vol%), when the content of the SWCNTs is 0.5 vol%, the mechanical properties of the RTV-SWCNTs composites are the best. Therefore, the RTV-SWCNTs composite material with the SWCNTs content of 0.5 vol% is used as the elastic layer material in the BHP.  
Figure 13: The fracture morphology for the specimens with different SWCNTs content under SEM. The content of the SWCNTs is: (**a**) 0 vol%; (**b**) 0.25 vol%; (**c**) 0.5 vol%; (**d**) 1 vol%; (**e**) 2 vol%; (**f**) 4 vol%.
## 2 Nonlinear Finite-Element Modeling
### Material Model
#### 2.5.4 Damage Evolution  
The definition of damage parameter in the CDP model is demonstrated in Figure 5, where the plastic strain in tension and compression were calculated as:  
\[\dot{\varepsilon}_{c}^{pl}=\dot{\varepsilon}_{c}^{in}-\frac{d_{c}}{(1-d_{c})} \frac{\sigma_{c}}{E_{c}} \tag{21}\]  
\[\dot{\varepsilon}_{t}^{pl}=\dot{\varepsilon}_{c}^{ck}-\frac{d_{t}}{(1-d_{t})} \frac{\sigma_{t}}{E_{c}} \tag{22}\]  
where \(d_{c}\) and \(d_{t}\) are the compressive and tensile damage parameter, respectively; \(\dot{\varepsilon}_{c}^{in}\) is the (inelastic) crushing strain, and \(\dot{\varepsilon}_{t}^{ck}\) is the cracking strain (Equation (23)).  
\[\begin{array}{l}\dot{\varepsilon}_{c}^{in}=\varepsilon_{c}-\frac{\sigma_{c }}{E_{c}}\\ \dot{\varepsilon}_{t}^{ck}=\varepsilon_{t}-\frac{\dot{\varepsilon}_{t}}{E_{c }}\end{array} \tag{23}\]  
The definition of the damage parameter does not affect macro responses such as the moment-deflection and crack patterns; therefore, with the purpose of saving calculation costs, a simple, linear damage evolution was assumed in the present study, which can be written as:  
\[d_{i}=1-\frac{\sigma_{i}}{f_{i}};i=t,c \tag{24}\]  
where \(t\) and \(c\) represent tension and compression, respectively; \(\sigma\) is the general stress in each time increment, and \(f\) is the peak stress.  
Furthermore, to ensure the stability of models, damage parameters in both the compression and tension were limited to below 0.8, resulting in a predetermined residual stress of approximately 20% peak stress.  
Figure 4: Post failure stress–fracture energy curve.
## 5 Application of the Approach  
The use case for demonstrating the approach is located in the foundry industry. As the melting processes are considered especially energy-intensive, the foundry industry can significantly influence the power grid [56]. In the use case, four furnaces are used to melt raw iron, iron ore, and scrap iron. The ladles are transported to the casting fields via forklifts. Molds and cores are produced on site. The whole process of melting, molding, core preparation, casting, and post-processing is considered for a time period of 65 time units (TU), which is equivalent to 16.25 h. A detailed description of the use case can be found in [PERSON] et al. [6].  
The approach requires the risk treatment path profiles obtained in [PERSON] et al. [6] as well as process-specific risk and measure data, which are gathered with the help of expert interviews and failure mode and effects analysis (FMEA). Six risks with their potential extents of damage and probability of occurrence were identified and are listed with the related nine different measures in Table 3. At this point, it is important to note that risks that are directly related to quality management processes were not considered, as they are not in the sphere of influence of the PPC.  
Figure 5: Schematic initial production schedule and the effects of disturbances on the load profile.  
The measures are further subdivided into process-altering and supplementary process measures, e.g., M2: casting with increased post-processing effort alters the post-processing, and M3: preparation of an additional mold is a supplementary process to be planned in addition to the scheduled mold preparation processes. For every process-altering measure, the measure-induced deviation in duration and load profile, and for every supplementary process measure, the absolute duration and load profile is filed.  
Additional input parameters for the scheduling are the planned load profile, the QDC, and the initial energy-optimal production schedule, including production-specific requirements.  
In the following, the application of the approach is described. The section is structured based on the three main steps of the approach, with the calculation of the final criteria weights, the classification of the relevant measures, and, finally, the integration of the measures.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks
### Drawbacks of COMP  
* To sustain the low delay and high capacity of the backhaul network, the need to conserve QoS with the support of COMP is a real challenge.
* Maintaining consolidated processing is another key challenge [60].  
Owing to the mentioned discussion, the non-3 GPP multihoming and COMP mechanisms provide improved support for adaptability criteria, such as AL5 (having the option of granularity on a per-channel basis for MM mechanisms) and AL4 (permitting the probability of connecting to multiple access points). Similarly, the criteria for 5G and future MM mechanisms for dependability are fulfilled with the help of parameters DL4 (permitting unbroken and seamless connection at the time of handover) and DL5 (permitting the option of redundant physical connections).  
Eventually, we will conclude in Section 4 by summarizing the findings in Table 3, wherein we have indicated all of the explored mechanisms in the light of dependability, adaptability, extensibility, and power-saving criteria. Furthermore, we have also included the references in Table 3 for supporting the analysis and arguments. The review, discussion, and Table 3 show that none of the legacy mechanisms could satisfy and cope with the requirements of 5G and future network in light of the considered parameters. However, 3 GPP-based LTE MM mechanisms facilitate better on the basis and provision of 5G and future MM mechanisms, provided that they jointly fulfill the maximum parameters amongst other considered techniques. Furthermore, with the help of qualitative analysis, we have formed conclusions regarding the worth and challenges of legacy mechanisms for 5G and future MM networks in the light of parameters mentioned in Table 2 of Section 3. Specifically, we concluded that legacy MM techniques are insufficient in terms of the stated worth and challenges. Hence, we investigate novel state-of-the-art MM solutions for 5G and future networks in the subsequent section.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Considered Approaches**} & \multicolumn{6}{c}{**Dependability**} & \multicolumn{6}{c}{**Adaptability**} & \multicolumn{6}{c}{**Extensibility**} & \multicolumn{6}{c}{**Power Saving**} \\ \cline{2-19}  & **DI1** & **DI2** & **DI3** & **DI4** & **DI5** & **RF** & **AL1** & **AI2** & **AI3** & **AI4** & **AL5** & **RF** & **EL1** & **EL2** & **
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Underwater  
The applications of Underwater Wireless Communication (UWN) range from ocean pollution monitoring to environment, climate, natural disturbances, and marine ecosystem monitoring for survey and overview operations, surveillance, offshore exploration, navigation, and disaster prevention. The primary objective of UWN is the detection and monitoring of the underwater environment, as well as the navigation of Autonomous Underwater Vehicles (AUVs) and information provision to offshore centers via intermediate data collection points or floating sinks above sea level. Some additional limitationsspecific to the underwater application scheme include propagation delay or time-varying multiparty propagation [138], multivariate attenuation [139], the effect of noise [140], limited bandwidth [141], high transmission power [142], big error rates [143], limited energy harvesting options [144], and no position information [145]. A summary of the related underwater works is illustrated in Table 4.
## 1 Hair Growth Cycle  
To understand the problems of hair loss, it is important to understand the hair cycle. From the beginning of the growth of a hair until it falls out, every hair goes through three phases. These are the anagen (growth phase), catagen (intermediate phase), and telogen phase (resting phase) [3]. These three phases will now be described in more detail.  
The anagen phase starts with the growth of the hair. The duration of this growth phase is genetically predetermined and lasts between three and six years (in exceptional cases, even longer) [4]. With a growth of 1 cm per month, the hair can grow up to 75 cm long. This means that the maximum achievable length of the hair depends primarily on the duration of the growth phase. However, the older a person gets, the shorter the anagen phase becomes [5]. In addition to age and genetic predisposition, sex also plays a role--male hair does not tend to grow as long, but it usually grows faster than female hair [6, 7].  
The individual hairs are all in different phases; otherwise, we would develop a bald head from time to time, similar to the trees in autumn, shedding their leaves. However, if a hair is pulled out, the next period of hair growth is initiated.  
The anagen phase is followed the so-called catagen phase. This transition from anagen to catagen phase is regulated by the growth-maintaining insulin-like growth receptor 1 (IGF-1) [5] and the catagen-promoting transforming growth factor-beta2 (TGF-82) [8]. This phase lasts for about two to four weeks. In this phase, the keratinocytes of the hair root degenerate, and hair growth comes to a halt. The hair root loses volume, moves slowly to the surface of the skin, and the connection to the hair papilla is severed, causing the hair to be shed.  
In the telogen phase, which lasts 3-4 months, the hair falls out naturally due to external influences such as washing the hair, brushing, or the pressure of a new root forming under the scalp. This is followed by a rest phase until a new cycle begins.  
Normally, over 80% of our hair is in the anagen phase. However, under certain circumstances, the proportion of hair in the telogen phase can rise to 30%, and our main hair visibly loses density quickly.
## 1 Introduction
Thus, this study aimed to create the closest model to the produced electricity poles, optimize the use of materials, and reduce the need for expensive experimental studies. Theoretical and experimental modal analysis methodologies are employed to determine the mode shapes, damping ratios, and natural frequency values, which are recognized as the dynamic characteristics of engineering structures. The Theoretical Modal Analysis Method is currently executed using computational tools and structural analysis software. In the analytical phase of this method, it is essential to develop a mathematical model that closely approximates the structural reality to facilitate analysis ([PERSON], 2017; [PERSON] et al., 2023). The finite element (FE) method, a numerical approach that considers material properties, support conditions, and geometric characteristics to ascertain the dynamic properties of structures, will be utilized to evaluate the mechanical properties of the system in question. The literature contains numerous studies dedicated to the modal analysis of reinforced concrete structures. [PERSON] and [PERSON] conducted a study on the free vibration and modal analysis of a typical tower crane frame utilizing SAP2000 V13 and ANSYS software. They developed a numerical model using three-dimensional beam elements for the vertical and horizontal frames, as well as rod elements for the connecting rods ([PERSON] and [PERSON], 2017)([PERSON] and [PERSON], 2014). In a related study, [PERSON] et al. (2021) [18] explored the dynamic characteristics of masonry minarets through the FE model, focusing on a specific mosque minaret. Their analysis incorporated the interaction between the structure, foundation, and soil within the FE modeling framework. The FE solutions were achieved with the ANSYS 19 r1 FE package program. [PERSON] and [PERSON] investigated a historical minaret that sustained damage from two significant earthquakes, with magnitudes Mw = 7.7 and Mw = 7.6, that struck the eastern region of Turkiye on 6 February 2023. They developed an FE model to evaluate the seismic performance of the mosque, examining its seismic responses under the earthquake's influence [19] ([PERSON] and [PERSON], 2023). Currently, there is limited information available regarding the modal properties of the most commonly used reinforced concrete electric transmission poles. To address this gap in the literature, our study presents a detailed analysis of the modal characteristics of reinforced concrete electric transmission poles, considering various lengths, diameters, and material properties, along with an estimation model for the corresponding modal frequencies.