esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 2 Materials and Methods
### Eligibility Criteria
#### 2.1.2 Intervention  
The considered trials concerned the use of systems enabling medical practitioners to remotely monitor disease-specific parameters of intervention group participants. Only trials in which patients in the intervention group received a telemonitoring device to collect disease-related parameter values or a smartphone app to send their data to the medical center were included. The remote patient monitoring devices must be able to transmit the collected data to the medical team so that the latter can make appropriate changes to the therapy.
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
## 1 Introduction
Many measurements were conducted in parking lots at frequencies below 6 GHz [27; 28; 29]. In [30], the passive Radio Frequency Identification (RFID) tag-to-tag communication paradigm within the context of a smart parking system was adopted to improve energy-efficiency and operational effectiveness. However, to the best our knowledge, the channel characterization of mmWave bands in outdoor parking lots environment has not been reported in the literature. In this paper, we went a step further in examining different scenarios involving 5G networks. Considering a parking lot case study, different single and multi-frequency path loss models were investigated. A new path loss model for parking lot environment that can work effectively in single and multi- frequency path loss model is proposed. For the single frequency path loss model, the close-in free space reference distance (CI) model, the floating-intercept (FI) model and parking lot path loss (PLM) proposed model were presented for single and dual parking lots environment at 28 GHz and 38 GHz frequencies. Then, they were extended to cover the multi-frequency models besides the alpha-beta-gamma (ABG) model. To show scalability of the proposed framework, some additional numerical results were provided in this work. The received power values at the transmitter (Tx)-receiver (Rx) separation distance beyond the measurement distance are predicted based on the proposed models. Section 2 describes the customized hardware channel measurements, the measurement locations and operating scenarios. Section 3 provides the path loss models for a single frequency. The proposed model is discussed in Section 4. Section 5 explains the path loss model for multi-frequency. The results and discussion are provided in Section 6. The conclusions are drawn in Section 7.
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion
### Effect of Time  
The effect of time on the degree of grafting of CPTMS onto halloysite nanotubes' surface has been discussed, and the data are reported in Table 1. The time was changed to be equal to 4, 35, and 48 h. The results of elemental analysis show that the degree of grafting decreased as the reaction time increased and the loss of the degree of grafting was not significant. The degree of grafting decreased according to the following order: 24.29, 22.5, and 21.16% for 4, 35, and 48 h, respectively.
###### Abstract  
In southern China, granite residual soil (GRS) is widely used as road base material. Thus, it is important to study the effects of hot and rainy climates and cyclic loads generated by trains on the dynamic properties of GRS. In this work, by means of dynamic triaxial tests, the effects of the number of drying and wetting (D-W) cycles, fines content and number of load cycles on the hysteresis curve, dynamic shear modulus \(G_{d}\) and damping ratio \(\lambda\) of GRS are systematically investigated. The experimental results illustrate the changes in the morphology of the hysteresis curve and dynamic parameters with the numbers of load and D-W cycles, as well as the fines content. Namely, the area \(S\), center offset \(d\), and residual strain \(\varepsilon_{\text{gr}}\) of the hysteresis curve decrease with the increase of load cycle number, increase with the growth of fines content, and first decrease with the increase of D-W cycle number, then slowly increase to stabilized values. However, the major axis gradient \(k\) exhibits exactly the opposite relationships. Meanwhile, the dynamic shear modulus \(G_{d}\) increases with the growing load cycles and decreases with the addition of fines content, and the damping ratio \(\lambda\) shows the opposite behavior. It is also shown that \(G_{d}\) and \(\lambda\) vary linearly with respect to log\(N\), where \(N\) is the number of D-W cycles. The dynamic properties of GRS are mostly affected by the number of load cycles, which is followed by the fines content and then the number of D-W cycles. The influence of the latter two factors on the dynamic properties of GRS may be primarily due to contact form changes between soil particles.  
Keywords:granite residual soil; drying-wetting cycle; fines content; hysteresis curve; dynamic property; deformation mechanism +
Footnote †: journal: Applied Sciences
## 2 Transcriptional Autoregulation Is a Characteristic Feature of Type II TA Cassettes  
As summarized above, type II complexes comprise a toxin and antitoxin that both are proteins [7]. Toxicity is neutralized by physical association of the two factors. When released from the complex, the toxin targets a specific intracellular component to trigger temporary stasis or cell death. In view of its  
Figure 1: Action of a generic type II toxin-antitoxin (TA) complex in which both factors are proteins. The antitoxin (A) and toxin (T) genes are expressed in an operon. A poorly structured domain within the antitoxin protein is remodelled by the toxin to produce a stable complex that autoregulates operon expression at the transcriptional level (**1**); In response to certain environmental conditions, e.g., nutrient limitation or antibiotic exposure, the antitoxin is proteolytically degraded (**2**); The toxin is thereby released to act on its intracellular target to induce cell dormancy or stasis (**3**). \(P\), promoter; \(O\), operator.  
latent potential to interfere with vital cellular processes, inadvertent release of the toxin is catastrophic for cell viability. To circumvent this possibility, transcriptional autoregulation is a characteristic feature of TA cassettes that ensures balanced toxin and antitoxin production.  
Transcriptional autoregulation of type II modules typically is mediated by the antitoxin that partially represses expression by binding to an operator site which overlaps the promoter motifs recognized by RNA polymerase. However, full repression is achieved only by binding of the TA complex to the site. Type II antioxins usually comprise a well-structured N-terminal domain that mediates DNA binding and a partly-structured C-terminal region. The toxin interacts with and restructures the latter thereby stabilizing the antitoxin for more effective operator binding (Figure 1). The following sections outline salient features of transcriptional autoregulation in selected type II TA complexes that illustrate the variations on a theme that have developed in TA gene expression control.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 3 Harmonic Suppression Methods
### Active Power Filter
Active Power Filters, designed to handle dynamic harmonics, offer better adaptability, stability, and control under varying conditions, making them a research focus. Hybrid APF systems combine passive and active components to enhance performance and cost-efficiency. More advanced methods, such as fractional lower-order statistics and Dynamic Voltage Restorers, address specific disturbances like impulsive noise and voltage sags, adding flexibility to harmonic suppression strategies. In addition to these methods, utilities and power system operators may also use load management techniques to reduce harmonic  
Figure 4: APF inverter circuit structure (**a**) voltage type (**b**) current type. distortion. This can include adjusting the power factor of large loads, such as industrial motors, to reduce harmonic currents, or installing harmonic measuring devices to monitor and identify sources of harmonic distortion. Figure 5: The circuit structure of various derived active power filter (**a**) parallel connection (**b**) series connection (**c**) small APF in series (**d**) parallel hybrid APF (**e**) series hybrid APF (**f**) with uniform power quality regulator (**g**) with series LC resonant circuit (**h**) with parallel LC resonant circuit. Figure 6: Block diagram of the harmonic suppression method based on self-sensing actuator.
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 1 Introduction
### Renewable Energy  
With the rapid development of global industrialization, it has been recognized that excessive consumption of fossil fuels will not only accelerate the reduction in fossil fuel reserves but also have an adverse impact on the environment. These influences will result in increasing health risks and threats of global climate change. In addition to fossil fuels and nuclear energy, renewable energy is currently the fastest-growing energy source. Renewable energy refers to reusable energy that can be recovered in nature, such as solar energy, wind power, hydropower, biomass energy, waves, tides, and geothermal energy. With characteristics of sustainability and low environmental pollution, the issue of renewable energy has attracted attention, and plenty of related studies have been performed recently. One of the most important challenges of renewable energy in the near future is the energy supply. The renewable supply is the integration of renewable energy sources into the existing or future energy supply structures [5]. The development of renewable-energy systems will be able to cope with essential issues of current energy problems, such as improving the reliability of energy supply and solving regional energy shortages. However, due to the huge volatility and the intermittent and random nature of renewable energy, this generation of various energy sources is intermittent and chaotic. Therefore, accurately dealing with the randomness of renewable energy data is still a work to be conquered. High-precision energy monitoring can improve the efficiency of the energy system. Energy forecasting technology plays a vital role in the development, the management and the policy making of energy systems. As ways of providing electrical energy from renewable energy sources increase, it is very important to develop proper technologies to store renewable energy [6]. Many studies have revealed that various machine-learning models have been employed in renewable-energy predictions. The data-driven models do provide practical ways of renewable-energy predictions. In addition, hybrid machine-learning models were designed to increase prediction accuracy of renewable energy. Various time intervals, such as minutes, hours, days, and weeks, were employed to predict renewable energy according to different purposes of predictions. Forecasting accuracy and efficiency were typically utilized to evaluate the performance of machine-learning models in renewable-energy predictions [7].
## 3 Gradations of SMA Mixtures  
The gradations of SMA5, SMA13, SMA20 and SMA30 are presented in Table 5.  
All the asphalt mixtures were designed with the standard Marshall method to determine the optimum asphalt content. The detailed design procedure can be seen in Figure 1. The Marshall Specimens of SMA5, SMA13 and SMA20 mixes were 63.5 mm high and 101.6 mm in diameter, and compacted using 75 blows on each side. The Marshall Specimens of SMA30 were 95.2 mm high and 152.4 mm in diameter, and compacted using 112 blows on each side.  
The mix design results for four SMA mixtures are summarized in Table 6, in which optimum binder content, Air voids (AV), Voids in mineral aggregate (VMA), Voids filled with asphalt (VFA), Stability, flow value and retained stability are included.  
\begin{table}
\begin{tabular}{c c c} \hline
**Properties** & **Value** & **Specification [12]** \\ \hline Penetration, 25 \({}^{\circ}\)C, 1/10 mm & 70 & 60–80 \\ Softening point, \({}^{\circ}\)C & 46.5 & 44–52 \\ Ductility, 15 \({}^{\circ}\)C, cm & 140+ & \textgreater{}100 \\ Solubility, \% & 99.9 & \textgreater{}99 \\ \hline Thin film oven (163 \({}^{\circ}\)C, 5 h) & Loss, \% & \(-0.04\) & \textless{}0.6 \\ Penetration ratio, \% & 62.9 & \textgreater{}55 \\ \hline Density, g/cm\({}^{3}\) & 1.028 & \textgreater{}1 \\ \hline \end{tabular}
\end{table}
Table 4: Properties of asphalt.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Sieve Size,** & \multicolumn{3}{c}{**SMA5**} & \multicolumn{3}{c}{**SMA13**} & \multicolumn{3}{c}{**SMA20**} & **SMA30** \\ \cline{2-9}
**mm** & **Test Result** & **Specification** & **Test Result** & **Specification** & **Test Result** & **Specification** & **Test Result** \\ \hline
37.5 & - & - & - & - & - & - & 100 \\
31.5 & - & - & - & - & - & - & 94.1 \\
26.5 & - & - & - & - & 100 & 100 & 77.2 \\
19 & - & - & 100 & 100 & 98.1 & 95–100 & 54.7 \\
13.2 & 100 & 100 & 96.2 & 95–100 & 82.6 & 75–90 & 50.3 \\
4.75 & 92.0 & 90–100 & 43.7 & 30–50 & 42.8 & 25–45 & 40.8 \\
2.36 & 42.3 & 35–50 & 34.6 & 20–35 & 34.5 & 20–35 & 34.3 \\
0.6 & 26.4 & - & 22.6 & - & 22.6 & - & 22.0 \\
0.3 & 17.5 & 15–25 & 16.1 & 13–20 & 16.1 & 15–20 & 16.1 \\
0.15 & 11.0 & - & 11.0 & - & 11.0 & - & 11.0 \\
0.075 & 8.3 & 8–13 & 8.3 & 8–13 & 8.3 & 8–13 & 8.3 \\ \hline \end{tabular}
\end{table}
Table 5: Aggregate gradations of SMA mixtures (percent passing, %).  
Table 6 shows that air void decreased, and VMA and VFA increased with the decrease of NMAS in aggregate gradation, but the stability failed to present inerratic changing trends for SMA mixtures with different NMAS. The optimum asphalt contents shown in Table 7 varied from 4.5% to 6.4%. By comparison, SMA30 had the lowest optimum binder content among all the mixtures. For the retained stability, it increased marginally with decreased NMAS.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 3 Scenarios for 5G Network and Its Requirements  
From the discussion of above Section II, the following four types of scenarios for 5G networks can be classified. Figure 5 illustrates the requirements and the technologies trends to achieve these requirements of 5G based smart health-care network.  
* Enhanced mobile broadband.
* Massive machine-type communications.
* High-reliability and low-latency communications.
* WRAN (Wireless Regional Area Networks).  
Every setup demands diverse requirements and different applications, discussed as follows.
# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 4 Radiomics in Osteoporosis Diagnosis
### Radiomics Based on CT Images in Osteoporosis Diagnosis
On the other hand, deep transfer learning algorithms, utilizing pre-trained networks for feature extraction, offered a solution to the challenge of limited labeled CT images by enabling accurate analysis on smaller datasets [60]. The integration of features derived from deep transfer learning with radiomics features has been shown to enhance diagnostic performance for osteoporosis [44; 61]. This integrated method not only leveraged the detailed analysis capabilities of radiomics, but also capitalized on the automation benefits of deep learning algorithms, resulting in an efficient and accurate diagnostic solution.
# Challenges for Teaching Wireless Communications Standards at the Graduate Level
## 4 Laboratory Experiences for Telecommunications Standards' Education
### Laboratory Experience for ZigBee Devices  
In the University laboratory, to understand wireless sensors networks and the Internet of Things environment practically, the students have available open source hardware and the OpenMote platform, with the OpenWSN OS, which integrates the OpenWSN protocol stack. The students can use the OpenMote platforms to learn, for example, how to compile the OpenMote firmware and how to use buttons or flashing LEDs. They need to use the Ubuntu operating system on the computer, program in C/C++ code for the ARM cortex platform, and in Python; therefore, they learn the operational aspects of a sensor network deployment. The devices are OpenMote CC2538, and in particular, CC2538 is an SoC (System on Chip) from Texas Instruments with a powerful wireless microcontroller system-on-chip; the radio operates at 2.4 GHz and is fully compatible with the IEEE 802.15.4-2006 standard, 6 LoWPAN, and ZigBee applications. For example, a prototype based on OpenMote-CC2538 has been implemented in the laboratory to highlight the security aspects for a wireless sensor network, for the Wi-Fi, or for the Ethernet connection, as shown in Figure 5.  
A smart gateway is implemented on the UDOO Quad platform with installed in the open source Intrusion Detection System (IDS) (e.g., Snort). When it detects abnormal behavior by some device connected to the network (Wi-Fi or Ethernet connection or the wireless IoT links), it provides an alert. This is transmitted to a server, and in this case, a database (it is possible to use different solutions in the future, such as a server in the cloud) will provide to the user a simplified and interactive visualization of the alert through a graphical user friendly interface. Then, the necessary actions against the intrusion are implemented automatically, e.g., disconnecting the terminal or sensor from the network. Three types of attacks are simulated: a ransomware that tries to communicate with a command to a control center, a port scan attack, and an unauthorized access/query to the sensors via the Constrained Application Protocol (CoAP) [23].  
By using this framework, the students can develop skills in wireless sensor networks, to connect devices with the gateway, learning to read the device hardware and firmware characteristics, and interrogate a sensor via the CoAP protocol. These hands-on experiences allow enlarging the technical knowledge and skills of students' approach to protocols and devices that they can use in their professional future.
## 2 State of the Art
### Condition Monitoring with Structure-Borne Sound  
Structure-borne sound is generated by a sudden, localized release of energy, such as in plastic deformation, impacts, friction, or crack formation, and produces mechanical, elastic waves that propagate through solid bodies [14]. Structure-borne sound is measured using the piezoelectric effect as a voltage signal [15]. Due to their smaller dimensions, structure-borne sound sensors are significantly more flexible and less expensive to use than force sensors [16]. Additionally, structure-borne sound sensing is particularly sensitive compared to force sensing and current measurements, which is particularly beneficial when monitoring fine machining processes [17].  
Generally, condition monitoring can be divided into monitoring tool wear and tool breakage on the one hand and detecting faulty machine states on the other [18]. Monitoring tool wear is of great importance due to its impact on surface quality and geometric deviations of the manufactured components. The emission of structure-borne sound signals when using worn tools is stronger than when using new tools because wear causes a larger contact surface between the tool and the workpiece. This results in increased friction, leading to increased energy input and increased heat generation [19].  
Numerous studies have successfully detected tool wear and breakage using structure-borne sound signals in various manufacturing processes. [PERSON] and [PERSON] [16] investigated tool wear in milling using spectral analysis, revealing significant differences between new and damaged tools. For gear honing, [PERSON] et al. [19] successfully classify tool wear into three classes using features of the frequency domain. [PERSON] et al. [20] use the power spectral density to characterize both tool wear and various wear mechanisms in turning, with an increase in the power spectral density value indicating an increase in wear.  
Using structure-borne sound, not only tool wear, but also faulty machine states can be detected, in many cases even identifying the specific damaged component. For example, the structure-borne sound is used to monitor bearings and transmissions, where strong sound emissions are an indication of bearing damage and lubrication loss [18; 21]. Additionally, the characterization of various forms of damage in transmissions, such as tooth breakage and pitting, is possible through acoustic emission [22]. Further, structure-borne sound enables the detection of unwanted conditions during turning, such as continuous chip formation, and it enables the investigation of the chip formation process itself [23; 24]. Continuous components in the structure-borne sound signal can be associated with plastic deformation and friction between the tool and chip, while chip winding, breaking, and collisions generate burst signals. Additionally, observations show a strong relationship between the RMS value of the acoustic signal and cutting energy.
## 2 Materials and Methods  
### Overview of the Existing CMVF System  
The CMVF system, which was developed in advance, is essentially a context-awareness system consisting of the CMVF server and client to provide context-aware services [10, 22]. The CMVF server performs the role of an ITS, and the CMVF client using a smartphone acts on behalf of a vehicle [10]. Thus, a smartphone itself, instead of attaching additional devices to existing vehicles, becomes an independent CMV client; it maintains a dynamic awareness radius that senses and reacts according to the vehicle's speed, which allows context-aware computing within its virtual boundary [10, 24]. Thus, vehicle-to-vehicle (V2V) communication occurs when vehicles with attached smartphones as CMV clients are connected to clients of other vehicles through mutual awareness [4, 24]. The mutual awareness mechanism already developed is implemented by exchanging integrated messages among CMV clients based on context-aware computing [24]. Here, integrated messages composed by the integrated messages framework, which was designed earlier, are added with context-aware information of CMVF clients after comparing and analyzing common fields of Basic Safety Message (BSM) and Cooperative Awareness Message (CAM)/Decentralized Environmental Notification Message (DENM) [25, 26, 27, 28].  
Therefore, this paper adds the following functions to improve the existing CMVF system. First, the exact cardinal direction of the smartphone is determined using the topological difference between latitude and longitude. Second, a representative heading direction for subgroups of connected cars is determined using the exact cardinal direction proposed in the first step. This is because connected cars move in the same direction, and if the movement direction is known, the sequence can be determined according to the differences in latitude and longitude of vehicles. Third, the topological sequence of each vehicle is determined within a subgroup of connected cars.  
Further details and algorithms for these three functions are presented in the following sections.