esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### CAT-M1 for LTE/5G Public Network Support of DG Management Communication  
In LTE [14], MTC end-user equipment (UE) contains the same USIM card as H2H traffic, so that LTE does not handle MTC traffic differently from H2H traffic in the radio network. Massive MTC applications (e.g., smart metering or sensor applications) typically send small data volumes, are latency-tolerant, but require low cost, long battery life and superior coverage in hard-to-reach areas such as basements or deep indoor environments. Support for massive MTC applications was introduced in 3 GPP Release 13 [15] in the form of two LTE-based technologies: NB-IoT and LTE CAT-M1, which are suited for different types of use cases.  
Narrowband-Internet of Things (NB-IoT) [15] is a low-power wide-area (LPWA) technology which provides greatly improved coverage compared to LTE CAT-M1, supports a massive number of low throughput devices, enables ultra-low device cost, and has low device power consumption. However, NB-IoT's latency can be up to 1-10 s, which is not suitable for the autarkic microgrid black start use case.  
LTE CAT-M1 is a LPWA air interface that connects MTC devices with medium data rate requirements (375 kb/s upload and download speeds in half-duplex mode) and latencies of 50-100 ms, which satisfy the requirements of the autarkic microgrid black start use case. It enables long battery lifecycles and greater in-building range. It supports the EAB Override and ACDC mechanisms discussed in Section 3.4 below. As well as being in LTE, it will continue to be supported in the 5G specifications [16].
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 4 Discussion  
NHATS data provide significant insight into ageing trends and the lifestyle of elderly citizens. We used activities of daily living as parameters. We identified important parameters from these to create machine learning models that can assist in clinical methods of diagnosing dementia. More importantly, this model can help with non-intrusive detection of dementia based on available trends. This can ensure that people on the cusp of dementia receive treatment to prolong their quality of life.  
There are several insights we gain through this study.  
* The ability to draw a clock or not (cg9 dclkdraw) is a strong indicator of dementia.
* We find that despite the intuitive sense of the relationship between diabetes and dementia, we did not find any significant impact between diabetes and dementia.
* The size of social circle, going out with friends and socializing have bearing on the onset of dementia. (sn9 dnums, pa9 outfrenjy).
* This data also tells us that there is a significant awareness among dementia patients about their cognitive decline. They are aware of the sense of losing control by stating that other people now control their day-to-day life. (wb9 agrwstmt1--Perception of control of one's own life).  
\begin{table}
\begin{tabular}{l l} hcfdepresan1 & Little interest or pleasure \\ pa@outfrenjy & Enjoys an outing with friends \\ snf@dnumsn & Total people that the subject trusts \\ tefcellphone & Use of cell phone \\ wb@fagrwstmt1 & Perception of control of one’s own life \\ \end{tabular}
\end{table}
Table 15: Parameters identified during temporal analysis. ‘#’ refers to the NHATS round.  
Figure 5: Accuracy Metrics.  
* One of the major discoveries was that ability to use technology is strongly correlated with a delay in the onset of dementia (te9 cellphone).
* Ability to recall the first and last name of the current head of state (cg9 presidna2, cg9 presidna4).  
These insights helped us to design a testing protocol that consists of the above parameters. These can serve as input to our model to predict risk of onset of dementia.  
Table 16 presents the compilation of selected questions for our proposed testing model, designed to categorize individuals as at either high risk or low risk for dementia. The responses to these questions can be taken as an input through a mobile application; a mobile app can then connect with the proposed machine learning model via an API (application programming interface) and provide binary risk assessment of whether the person is at risk of dementia or not. Most of the shortlisted questions belong to the NHATS cognitive battery subset. The NHATS has stated that it takes around 7 min on an average to administer the cognition related questions [14].
## 5 Experiments and Results  
In this section, the experimental setup is described, starting from the characteristics of the data used in this paper. Then, the performed experiments and the achieved results are reported and discussed. We analysed the effect of all the parameters on the system and how they affect the rebalancing operations in terms of number of critical stations fixed.  
This section describes the results of the following analyses:  
* The dataset used to test the developed framework is described and analysed, together with the preprocessing steps made to clean the data.
* The effect of different input parameters are evaluated to properly define the preprocessing pipeline for the data cleaning process. In particular, we analysed the effects of the thresholds \(f\) and \(varianceThreshold\).
* The effects of the framework's parameters on the rule extraction process are analysed.
* The effects of all the framework's parameters and the number of available vehicles \(N_{l}\) on the rebalancing process are analysed.  
For these analyses, all the data partitioning approaches introduced in Section 4.1.1 are considered and compared to determine the most prominent approach for the analysed dataset.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 2 Related Work
### Cloud Continuum  
With the term cloud continuum, we intend the edge computing extension of cloud services towards the network boundaries [25] reaching out to end devices (i.e., the IoT devices). Edge computing revealed itself as a valid paradigm to bring cloud computation and data storage closer to the data source of applications. On the one hand, edge computing provides better performance for delay-sensitive applications, whereas, on the other hand, applications with a high computational cost have access to more processing resourcesdistributed along the continuum. This continuum reaches out to the IoT devices as sensors are the source of massive amounts of data that are challenging for infrastructure, data analysis, and storage. The Cloud-Edge-IoT computing continuum has been explored in various areas of research. For instance, it has been investigated to provide services and applications for autonomous vehicles [26], smart cities [27], UAV cooperative schemes [28] and Industry 4.0 [29], where most of the data require real-time processing using machine learning and artificial intelligence.  
One of the main challenges of the cloud continuum is the orchestration between the components of the system, which includes computational resources, learning models, algorithms, networking capabilities and edge/IoT device mobility. Context awareness is required in the continuum to decide when and where to store data and perform computation [30]. For IoT environments, context information is generated by processing raw sensor data to make decisions in order to reduce energy consumption, reduce overall latency and message overload [31], achieve a more efficient data transmission rate in large-scale scenarios using caching [32], and store and process placement across the continuum [33; 34]. The mobility-aware multi-objective IoT application placement (mMAPO) method [25] proposes an optimization model for the application placement across the continuum considering the edge device mobility to optimize the completion time, energy consumption and economic cost. Task offloading from ground IoT devices to UAV (unmanned aerial vehicle) cooperative systems to deploy edge servers is explored in [35], where authors also present an energy allocation strategy to maximize long-term performance.  
The use of the cloud continuum for SAR operations has the potential to extend the capabilities and resources available and by this enhance the situational awareness of first responders. The orchestration process of the different systems, including robots and rescue teams, is a challenging aspect that might obtain support from models from industry, health and military systems [36].
# Limitations on the Use of Recycled Asphalt Pavement in Structural Concrete
## 4 Mechanical Behaviour of Concrete Containing RAP
### Durability
The optimal RAP percentage of replacement in terms of freeze-thaw durability for high strength concrete is instead 35% by mass [5]. The coefficient of thermal expansion of concrete has a significant influence on the opening and closing of construction joints and it is therefore of interest in transportation infrastructures. Asphalt materials have a high thermal expansion, which could lead to a high coefficient of thermal expansion of concrete containing RAP. [PERSON] et al. [23] determined coefficients of thermal expansion for normal strength concrete with RAP coarse aggregate, and reported an increase of up to 10% when 40% of virgin coarse aggregate was replaced with RAP. According to [PERSON] [5], the coefficient of thermal expansion of high strength concrete is minimally affected by the replacement of up to 50% of natural coarse aggregate with RAP. With 35-40% RAP, the coefficient of thermal expansion is higher than the high strength concrete, with 100% of natural aggregate. In [5], other parameters, such as the initial rate of absorption and capillary absorption coefficient, were studied and the results demonstrate that these parameters decrease with the increase in RAP aggregate content: both the initial rate of absorption and capillary absorption decreased by 50% for a mix containing 75% of RAP in relation with a mix with only virgin aggregates. A decrease in sorptivity parameters is due to the clogging of pores with drying before the absorption process. An investigation into durability is essential to define the possible behaviour of concrete if coupled with steel reinforcement or used under critical temperature conditions, in order to define the most suitable applications: further studies are therefore needed to cover the gaps found in the literature.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Analysis of Experimental Results  
The selected fault features are input into the Naive Bayes classifier for bearing fault classification, thus completing the entire process of bearing fault diagnosis.
## 3 Smart Energy Management in the Future IoT
### AI-Enabled Battery Optimization in IoT Devices  
Depending on the application area, sometimes, primary (non-rechargeable) batteries are the perfect choice for low drain IoT devices [23]. When this happens, there is a need to optimize their batteries with techniques that can prolong their usage. Some of these techniques include the use of high capacity batteries with a long shelf life and the use of battery management strategies that prevent unnecessary battery discharge. Other approaches include the use of low-rate wireless personal area networks (LR-WPANs) transmission techniques such as ZigBee, BLE, 6 LoWPAN, WirelessHART, etc. which makes data communication energy efficient. Some of the factors that influence the rate of energy depreciation in a battery-powered sensor node include the type of load, battery model, operating  
Figure 5: Some input data and their potential usage with AI algorithms.  
Figure 6: Some input data and their potential usage with AI algorithms.  
temperature, transmission rate and type [24]. In general, the total power consumption of an IoT node can be described in Equation (4) as  
\[\mathbf{P_{tot}}=\mathbf{P_{rec}}+\mathbf{P_{trans}}+\mathbf{P_{sleep}}+\mathbf{P_ {idle}} \tag{4}\]  
where \(\mathbf{P_{tot}}\) is the total power consumed, \(\mathbf{P_{rec}}\) is the power consumed during data reception, \(\mathbf{P_{trans}}\) is power consumed during data transmission, \(\mathbf{P_{sleep}}\) is the power consumed during sleep and \(\mathbf{P_{idle}}\) is the power consumed when there is no data to be sent or received. For an ideal case, \(\mathbf{P_{idle}}\) tend toward zero i.e., the device is only active when there is data to be collected or sent. AI can be used to minimize the power consumed by the individual components of Equation (3). For example, \(\mathbf{P_{rec}}\) can be minimized by leveraging AI in data prediction [25], reduction, and compression schemes [26]. In the case of \(\mathbf{P_{trans}}\), an effort is still ongoing in the research community on how to integrate cognitive capabilities in LPWAN with the aim of further improving performance and energy efficiency. For example, authors in ref. [27] propose a cognitive LPWAN architecture that safeguards stable and energy-efficient communications in a heterogeneous IoT. The proposed architecture uses an AI-enabled LPWA hybrid method to provide the smart control of wireless-communication technology and intelligent services for heterogeneous IoT devices. Furthermore, the use of cognitive radios to support the operations of LPWAN is a new research area with the potential for promoting energy-efficient communications [28].
# A Virtual Prototype for Fast Design and Visualization of Gerotor Pumps
## 3 Methodology
### Geometric Model  
The internal profile of the gerotor is generated according to the parameterization proposed by Ref. [16]:  
\[x_{i}(\alpha_{pc}) = R_{2}\cos\biggl{(}\frac{1}{Z-1}\alpha_{pc}\biggr{)}\pm e\cos \biggl{(}\frac{Z}{Z-1}\alpha_{pc}\biggr{)}-\frac{S}{m}\biggl{[}R_{2}\cos \biggl{(}\frac{1}{Z-1}\alpha_{pc}\biggr{)}\pm r_{2}\cos\biggl{(}\frac{Z}{Z-1} \alpha_{pc}\biggr{)}\biggr{]}\] (1) \[y_{i}(\alpha_{pc}) = -R_{2}\sin\biggl{(}\frac{1}{Z-1}\alpha_{pc}\biggr{)}\mp e\sin \biggl{(}\frac{Z}{Z-1}\alpha_{pc}\biggr{)}+\frac{S}{m}\biggl{[}R_{2}\sin\biggl{(} \frac{1}{Z-1}\alpha_{pc}\biggr{)}\pm r_{2}\sin\biggl{(}\frac{Z}{Z-1}\alpha_{pc} \biggr{)}\biggr{]}\] (2) \[m = \sqrt{r_{2}^{2}+R_{2}^{2}\pm 2r_{2}R_{2}\cos\alpha_{pc}}\] (3) where the parameter \(\alpha_{pc}\in[0,2\pi]\) corresponds to the turning angle of the rotor. Figure 4 shows the resulting shape of the internal profile as the trace of contact point \(P^{\prime}\), whose position is determined by the radii \(R_{2}\) and \(r_{2}\), the eccentricity \(e\), and the number of chambers \(Z\). In Figure 4, the external profile is determined by a set of outer circumferences that are truncated by a larger cutting circumference.  
This construction method of the external profile, even though simple and widely used, limits the performance of the pump, as the resulting shape does not mesh perfectly with the internal profile shape [17]. We have implemented an additional method to build the external profile as the conjugated curve of the internal shape.  
Suppose the curve \(C\) that corresponds to the internal profile (Equations (1) and (2)) is put through a series of affine transformations that are defined by the rolling without slipping of the circumference defined by \(r_{1}\) with respect to the circumference defined by \(r_{2}\) in Figure 4. Subsequently, the external profile shape will be defined as the envelope curve of the locus of \(C\) as it moves through the rotation domain.  
Figure 5 shows the locus of curve \(C\), as generated by the movement of the circumferences. The envelope curve of the locus can be used as the external profile shape, with the advantage that by using this external shape both curves mesh perfectly, hence improving the performance by avoiding fluid recirculation. Once the inner and outer profile shapes are defined, the geometric quantities of each chamber are calculated by sampling both the internal and external curve to form a closed polygon, finding the area \(A_{i}\) and perimeter \(P_{i}\) of the polygon corresponding to chamber \(i\).  
Figure 4: Parameterization of internal profile shape as in Equation (2).
# Seismic Performance of Corroded Reinforced Concrete Columns Strengthened with Basalt Fiber Sheets
## 4 Conclusions  
By carrying out low-cycle repeated tests of four reinforced concrete columns, the failure process and formation of concrete columns under working conditions of no corrosion, corrosion and corrosion reinforcement were described. The effect of changes in parameters, such as the corrosion rate and the reinforcement layers of basalt fiber on the seismic performance of reinforced concrete columns, were compared and analyzed. The conclusions are as follows:  
1. The basalt fiber sheet bulged under horizontal repeated loads, indicating the crush of concrete. However, the concrete was still well wrapped in the basalt fiber sheet, which plays a good role in restraining the concrete.
2. The hysteresis curve of the intact specimen is much fuller than the corroded specimens and the hysteresis loop area is obviously larger, indicating that the energy-dissipation capacity and ductility of specimens is reduced after corrosion. The hysteretic curve of the specimen strengthened with basalt fiber sheet is much fuller than that of the corroded specimen with a larger hysteretic loop area, indicating that the bearing capacity, energy-dissipation capacity and ductility is greatly improved when reinforced with basalt fiber sheet.
3. The bearing capacity and ductility of the corroded specimens under repeated horizontal loads were significantly weakened compared with the intact specimens, while the bearing capacity and ductility of the corroded and reinforced specimen all exceeded those of the intact specimen.
4. The restraint effect of basalt fiber sheet on the specimen increases with the reinforcement layers. The more reinforced layers of basalt fiber sheet, the more obvious the ductility of the reinforced specimens is improved.  
Conceptualization, methodology, formal analysis and investigation, writing--original draft preparation, J.Y.; writing--review and editing, resources, supervision, validation, D.S. All authors have read and agreed to the published version of the manuscript.  
The financial support of the National Key R&D Program of China (Grant No. 2018 YFC0705800) and the National Natural Science Foundation of China (Grant No. 51879092) are gratefully acknowledged. This work is also sponsored by Enterprise Practice Training of Young Teacher Project of Vocational College of Jiangsu Province of China.  
Not applicable.  
The authors declare no conflict of interest.
## 4 An IoT-Based Real-Time Location Monitoring System by Using BLE
### State-of-Art  
Underground communication inside mines is a major factor for the safety and security concerns of the mineworkers. The advent of IoT technology and its usefulness can be beneficial for the mining industry. It is believed that a robust communication infrastructure using IoT technology inside the mines may enhance the safety of the workers and is also capable of providing real-time information resulting in quick action to avoid lethal situations. Several researchers have proposed various frameworks and ideas for efficient communication inside the mines based on IoT technology, which includes low-power and short-range communication.  
The authors of [212; 213] proposed a wireless sensor network (WSN)-based monitoring system for underground mines. In this proposed technique, various sensors are placed at different locations to collect activities and positions of the employees, and the collected data are transferred to the end user or the central server via BS. [PERSON] et al. [214], proposed an IoT-based smart mine monitoring system that uses radio frequency (RF) technology for communication purposes inside the mines. With this proposed technique the real-time location and real-time sensing of the dynamically varying environment can be achieved by using RF technology and WSN network, respectively. The major drawback of this proposed model is that large-signal transmission loss occurs through the walls of underground mines. An IoT-based mine safety system using WSN was proposed in [215; 216]. In these proposed techniques, the authors used a Zigbee module for information collection from the cloud and measured the surrounding parameters of underground mines with the help of various sensors. A mine safety system using WSN was proposed in [217], where the authors constructed a prototype by using Zigbee and WSN to monitor safety issues and to measure the ambient properties, e.g., temperature, humidity, airflow, etc., inside the underground mines. A Zigbee compliant RFID-based safety system for underground coal mines was proposed in [218], where a unified wireless mesh-network infrastructure was used to monitor and locate the workers and measure the different environmental parameters inside the coal mines. Similarly, an IoT-based system for underground coal mines that uses a microcontroller, a node MCU and various sensors to measure the environmental conditions and safety measures of workers was proposed in [219]. A LoRaWAN-based coal safety and health monitoring system was proposed in [220]. In this proposed methodology, LoRaWAN uses low-power RF with a wide communication range and IoT technology for monitoring the workers' health and observing the status of the circumstances in the coal mines.  
There are several existing technologies used for communication purposes in underground mines. The most common approaches are RFID, Zigbee, Bluetooth, GPS, etc. Table 6 presents a comparative analysis of some existing technologies in terms of their pros and cons [214; 215; 216; 217; 218; 219; 220; 221].