esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### Technical Report 069 (TR-069)  
TR-069 [83], also known as CPE WAN Management Protocol (CWMP), is a protocol designed for the remote management of customer premises equipment (CPE), including IoT devices. It provides a comprehensive framework for device provisioning, diagnostics, and maintenance in IoT networks. The protocol features auto-configuration capabilities, allowing IoT devices to be remotely configured and updated without manual intervention. Its diagnostic features enable administrators to monitor device health and troubleshoot issues efficiently. TR-069's secure communication framework ensures data integrity and confidentiality during remote management. It uses a hierarchical management model where a centralized Auto-Configuration Server (ACS) communicates with devices to perform operations such as firmware updates, configuration changes, and performance monitoring. The protocol is particularly suitable for large-scale IoT deployments due to its scalable architecture, enabling the efficient management of thousands of connected devices.  
TR-069 plays a crucial role in network management by enabling remote device configuration and provisioning, ensuring consistency and scalability across large IoT deployments. With its auto-configuration capabilities, administrators can remotely set up devices without requiring manual intervention, streamlining operations in dynamic network environments. One of its key advantages is support for over-the-air firmware updates, which help maintain device security and functionality by ensuring that all connected devices run the latest software versions. Additionally, TR-069 provides real-time device health monitoring and remote diagnostics, allowing network administrators to quickly identify and troubleshoot issues, minimizing downtime and enhancing overall network efficiency. Its scalable architecture makes it particularly well suited for managing large IoT deployments in geographically distributed networks, ensuring seamless and centralized control over numerous connected devices.  
In healthcare, TR-069 is widely used to manage the connectivity and firmware updates of medical devices, ensuring they remain functional and secure. Automated device updates play a crucial role in maintaining compliance with the latest security protocols, safeguarding sensitive patient data and protecting against cyber threats. Furthermore, TR-069 facilitates the real-time monitoring of device health and usage patterns in hospital environments, helping healthcare providers optimize device performance and proactively address potential failures. By ensuring the reliable and efficient management of medical IoT devices, TR-069 contributes to improved patient care and operational efficiency in healthcare networks.
## 8 Reflection
### Challenges and Limitations  
Despite its successes, several challenges were encountered:  
* Technical Challenges: Integrating heterogeneous data sources was complex due to semantic inconsistencies and data reliability issues.
* System Complexity: Designing an architecture flexible enough for various industrial scenarios required significant effort and time.
* Data Simulation: While historical data could be replayed, its analysis using machine learning (ML) techniques remains an open research question. A more advanced ML-based approach could detect patterns and anomalies, enabling predictive analysis and optimization, which necessitates further exploration.  
Future work should focus on refining data processing workflows and ensuring seamless ML integration, alongside the development of a user-friendly interface that allows non-experts to conduct complex analyses.
## 5 Lightweight Clear Channel Assessment (LW-CCA)
### Categories of RSSIs in LW-CCA  
The CC2420 in our application returns [\(-\)100,0] as the normal range of RSSI, where \(-\)100 is the minimum level of the noise floor. In performing each RSSI check, the values obtained for RSSI can be divided into three categories. Firstly, the values that are valid and determine the radio activity and ultimately, it results in the receipt of the data frame (positive WakeUP); Secondly, the values that are valid and determine the radio activity and ultimately, it results in the noise or interference (false WakeUp); and third the values that are invalid and do not specify any activity on the radio  
Figure 4: Performance of RSSI status checks based on radio hardware and ContikiMAC.  
Figure 5: Model 1: RSSI check-in Rx (28 symbols) and Tx(28 symbols) in Cooja timeline.  
Figure 6: Model 2: RSSI check-in Rx (8 symbols) and Tx (28 symbols) in Cooja Timeline.  
(idle listening). In the LW-CCA categories, false WakeUp and idle listening RSSIs are located in rssi_null. Figure 7 shows the RSSIs classified. Identification of idle listening and false WakeUp conducted via CC2420 driver's and ContikiMAC's side and the false WakeUp is reported to CC2420 driver through NETSTACK_RADIO [43].  
It is important to note that the classification of RSSIs depends on the return value of CCA check, so that if CCA value is 0 so ContikiMAC checks false/positive WakeUp, otherwise it is an idle listening. The diagnosis of positive WakeUp and false WakeUp is based on a timing mechanism in ContikiMAC. It is worth mentioning that, each node when it receiving a new RSSI value, performs the validation through CCA.
## 2 Materials and Methods  
### Column Experiments-Set Up  
To address the aim of the study, four 1D acrylic glass columns (160 cm long, diameter 15 cm) (Figure 1) were constructed. Glass beads (1 to 2 mm) were placed to a height of 8 cm at the column bottom to avoid washing out of finer sand grains above. Each of the columns was packed with different types of sandy soils to a total height of 140 cm, representing materials suitable for infiltration according to German DIN standard [8].  
The initial parameters describing the soil material were obtained from permeameter tests [22] and retention behavior experiments [23] by taking soil samples from each column (Table 1).  
For the estimation of spatial and temporal distribution of soil moisture, the experimental columns were equipped with Freeqency Domain Reflectometry (FDR) probes (Co. Stevenswater, Portland, OR, USA) to measure water content in two different depths (Obs1-0.25 m and Obs2-1.15 m below surface) every 10 min. A soil-specific calibration using TWWE was performed to minimize the error from this source. Suction cups (Co. Rhizosphere, Wageningen, NLD) were installed in five different depths (0.15/0.45/0.75/1.05/1.35 m below soil surface) for direct extraction of pore water for chemical analysis.  
The columns were placed in a climate chamber with a constant temperature of 10 \({}^{\circ}\)C in accordance with conditions (mean annual temperature 10-11 \({}^{\circ}\)C) in the lower layers of the unsaturated soil zone.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Soil 1** & **Soil 2** & **Soil 3** & **Soil 4** & **Glass Beads** \\ \hline Hydraulic Conductivity under full saturation (cm day\({}^{-1}\)) & 478 & 363 & 4234 & 2333 & 15,456 \\ Bulk density (g cm\({}^{-3}\)) & 1.68 & 1.70 & 1.67 & 1.65 & 1.52 \\ Content of sand (\%) & 99.6 & 92.5 & 100 & 100 & - \\ Content of silt (\%) & 0.4 & 7.1 & 0.0 & 0.0 & - \\ Content of clay (\%) & 0.0 & 0.4 & 0.0 & 0.0 & - \\ Van Genuchten shape parameter \(\alpha\) (cm\({}^{-1}\)) & 0.046 & 0.027 & 0.053 & 0.047 & 0.031 \\ Van Genuchten shape parameter \(n\) (-) & 4.08 & 4.26 & 3.93 & 2.96 & 5 \\ Residual water content \(\theta_{r}\) (cm\({}^{3}\) cm\({}^{-3}\)) & 0.084 & 0.094 & 0.072 & 0.069 & 0.036 \\ Saturated water content \(\theta_{s}\) (cm\({}^{3}\) cm\({}^{-3}\)) & 0.34 & 0.37 & 0.39 & 0.35 & 0.31 \\ Pore connectivity parameter in the conductivity function \(l\) (-) & \(-\)1.85 & \(-\)1.50 & \(-\)1.58 & \(-\)2 & unknown \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameters obtained from the permeameter test [22]; bulk density estimated by [24] and retention behavior laboratory test [23].  
Figure 1: Set-up of 1D-column experiment.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 4 Experiments
### Model Implementation  
#### 4.2.1 Preprocessing  
Considering the GPS data quality, we preprocessed the data before inputting the data into the proposed model, and this process mainly included three steps, as follows. (1) The trajectory data were analyzed, and discrete, large-error GPS points were removed [39]. (2) Multiple lanes were merged. Most road segments are related to multiple lanes. It is useful to know exactly how many lanes are on each road segment. However, these road attributes are not directly recorded in OpenStreetMap data. Therefore, a road segment with multiple lanes was merged into a single-lane road. (3) GPS trajectory points were matched to roads. Here, the interactive voting-based map matching algorithm (IVVM) [40] was selected for data matching. The algorithm, which considers the context information of the GPS sampling points, the mutual influence between the sampling points, and the attenuation of the influence with distance, has high precision for low-frequency GPS sampling points.
## 4 Discussion
The previous studies were also based on a limited number of temporal images, and thus improving the classification accuracy using limited temporal images, as carried out in this research, is an important contribution. The ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The advantages of pansharpened images and textural features have been reported by previous studies. [PERSON] et al. [110] described the effectiveness of pan-sharpening techniques to generate accurate vegetation maps in heterogenic and mixed ecosystems. Similarly, [PERSON] [111] reported high performance of pansharpened QuickBird images for mapping of olive trees. Another study by [PERSON] et al. [37] recommended the usage of texture features and pan-sharpening approaches for improved classification of tropical tree species from WorldView-3 satellite images. Therefore, the features extraction techniques implemented in this research are robust and trustworthy. In addition, the ultra-resolution suite expands these ideas by additional features retrieved from the color-transformation of the pan-sharpened images for distinguishing vegetation types at the community level such as Quercus DBF and Quercus Shrub. On the other hand, the study led by [PERSON] et al. [112] compared wet season, dry season, and multi-seasonal WorldView-2 images for the classification of agroforestry tree species and found that the multi-seasonal dataset produced the most accurate classifications. Another study by [PERSON] et al. [113] has also focused on multi-seasonal images for the classification of grassland ecosystem types. Therefore, there is a possibility of increasing the accuracy further for the discrimination of complex vegetation types by the addition of more seasonal images in this research.
## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems
#### 3.6.1 RSSI-IMU Hybrid Systems
As a comparison, the authors reid the study with Wi-Fi signals only, obtaining an accuracy of 5.73 m.
## 1 Introduction
Two global models were selected for analysis: Klobuchar GPS and NeQuick G as well the local EGNOS model, which all were compared to GPS/EGNOS positioning without any ionospheric model. The European EGNOS is a system used for safety-of-life (SoL) applications and can be used in land, water and, above all, air traffic [16; 17]. The main function of EGNOS is to deliver real-time data that can be applied by users to improve the accuracy of positioning and to get integrity information. In order to achieve these, EGNOS collects measurements from a network of 40 Ranging Integrity Monitoring Stations (RIMS) and uses the data to elaborate clock corrections for each GPS satellite in view, elaborate ephemeris corrections to improve the accuracy of spacecraft orbital positions, and elaborate a model for ionospheric errors over the EGNOS service area in order to compensate for ionospheric perturbations [18]. Since EGNOS requires a dense network of RIMS, the transmitted ionospheric model is not available for the entire geostationary coverage area, but rather only for a region within the coverage of monitoring stations. The motivation of this paper is the examination of the accuracy of GPS/EGNOS single-frequency positioning with the adoption of different ionospheric models to mitigate the impact of geomagnetic storm in the European coverage area. The main aim was to focus on ionospheric models that are officially accepted by the RTCA (Radio Technical Committee for Aeronautics). These models are EGNOS and Klobuchar, and currently, only these models can be used in practical aerial navigation. The research was extended with the NeQuick G model in order to test its quality and evaluate the possibilities of future application of this model in air navigation. The \"off\" solution was added just to show the importance of tested ionospheric models. In the article, calculations are based on a single-frequency GPS/EGNOS solution. The GPS L1 data with 1-s intervals were obtained from three permanent IGS stations situated near the EGNOS APV-1 availability border, in different geographical latitudes. The novelty of the solution is the use of EGNOS clock corrections for GPS satellites as well as EGNOS ephemeris corrections in every examined scenario while the part with an ionospheric model was modified. The Septentrio Post-Processing Software Development Kit (PP-SDK) [19] allows for manipulation with ionospheric model while using the EGNOS clock and ephemeris corrections. In order to conduct reliable analysis, the challenging ionospheric conditions were selected for the tests. The research was made for a one-week observation period, 6-12 September 2017, during which the last strongest geomagnetic storm took place. The ionosphere models examined in this article are briefly presented and described in Section 2 of the paper. Then, the datasets and processing strategies are described, followed by the comparison results of different ionospheric models with respect to the positioning quality. Finally, summary and conclusions are presented.
## 1 Introduction
used polypropylene and aramid fibers in hot mix asphalt and found that fiber improved the performance of asphalt pavement and reduced the occurrence of rutting, looseness, fatigue, and fracture [25]. [PERSON] et al. found that the simultaneous use of polyolefin-aramid fibers and Sasobit in asphalt mixtures improved the durability of the asphalt mixture and improved the mixing of the asphalt mixture, which would reduce implementation costs and extend the life of asphalt pavement [26]. [PERSON] et al. found that adding suitable polymer fibers to hot mix asphalt improved the dynamic properties of asphalt mixes [27]. [PERSON] et al. evaluated the low temperature cracking susceptibility through a bending test of rectangular beams and a semi-circular bending test based on fracture mechanics theory. The results showed that the use of aramid fibers improved the low-temperature performance of asphalt pavement [28]. [PERSON] et al. pointed out that aramid fibers generally exhibit higher tensile strength and modulus than aliphatic fibers [29]. [PERSON] et al. found that Aramid Pulp Fiber (APF) had very high tensile strength, modulus, and high cohesion, which effectively improved the fatigue life of asphalt mixture [30]. [PERSON] et al. showed that Poly-functional fibers significantly improved the softening point and viscosity of asphalt by a conventional asphalt performance test and rheological tests [31]. However, facing the emerging new materials, the selection of modifiers is still relatively blind, mostly relying on the macroscopic properties of modified asphalt binders. In addition, the research on the modification mechanism of modified asphalt and the comparison of the modification mechanisms of different modifiers are less clear. In this study, the properties and mechanisms of modified asphalt are explored from the perspective of composite materials. The modifier acts as a reinforcement and the base asphalt acts as a matrix. For the modifier selection, it is very meaningful to study the effects of different scale reinforcements on modified asphalt and to explore their modification mechanisms. In this study, the same dosage (2 wt% of asphalt) of modifiers were used to prepare modified asphalt binders. The penetration, softening point, and ductility of aramid fibers-, CaCO\({}_{3}\) whiskers-, and nano-CaCO\({}_{3}\) particles-modified asphalt was tested. The rheological properties of three modified asphalt binders were analyzed by dynamic shear rheometer (DSR). Scanning electron microscopy (SEM) was used to observe the microscopic morphology of three reinforcements. The performance of three different reinforcement-modified asphalt binders was compared and the mechanisms and causes of these differences were discussed.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 4 Data Acquisition  
The Data Acquisition stage obtains the load measurement at an adequate rate, aiming to identify distinctive load patterns in the next stages [44]. This phase is carried out to have a generalized perception of the energy supply and the demand by sensors from different energy manufacturers, being also responsible for a precise control by the actuators [76]. Therefore, in the Data Acquisition phase, two main entities collaborate. One is household appliances, and the other is metering devices. Once data are collected, the measurements are sent to the following stages to be processed, as shown in Figure 4.  
Household appliances have been classified in the literature depending on the target application. Figure 5 shows the main categories for appliances. For load monitoring systems, appliances are grouped in four categories, as proposed in [89]. These are as follows:  
* ON/OFF: Devices with only two operational states, e.g., toaster, EVs, kettle, etc.
* Multi-state: Devices which are represented by finite state machines (FSMs), e.g., washing machines, refrigerators, heat pumps, etc.  
Figure 4: Schematic diagram of the data acquisition stage in Home Energy Management System (HEMS).  
* Continuously variable: Appliances with variable power absorption characteristics, e.g., electric drills, laptops, etc.
* Permanent consumer devices: Appliances which remain active for a long period of time (weeks or days) consuming energy at a constant rate, e.g., TV receivers, telephones set, smoke detectors, etc.  
In terms of control, the authors of [1] defined it in two categories:  
* Uncontrollable: Refers to appliances which cannot be managed by HEMS, e.g., TVs, personal computers, and lighting.
* Controllable: Encompasses two subcategories: reducible appliances whose energy consumption can be reduced, e.g., air conditioner; and shiftable appliances which has two types of loads: interruptible (those whose functioning can be interrupted, such as ESS) and non-interruptible (such as the washing machine).  
The above categories have been widely discussed in literature [1, 14, 19, 30, 89, 90]. The authors of [90] modeled controllable appliances by a set of constraints. They claimed that controllable appliances should achieve their duty cycles within allowable time windows. In addition, they defined interruptible appliances as those whose operating cycle an be interrupted for convenience. The authors also explored thermostatically controlled appliances. On the other hand, metering devices have been less explored regarding standardization and categorization.
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 2 Thermal Analysis of Propeller Driver
### Boundary Condition Settings  
The driver model's boundary conditions include ambient characteristics, heating power parameters, and component material parameters. The material of driver boards is set to FR4. The materials of chips' substances are set to silicon. The materials of columns that link the 1 st, 2 nd, and 3 rd PCB layer are set to plastic. The material of the column joining the 4 th PCB layer and the base is set to copper. The materials of the heat sink and base are Al6063. The material and thermal conductivity for the main components in the model are listed in Table 2.
## 5 Experimental Results of Element Tests
### Wind Load Test Results  
The element performance is analyzed according to the resulting mid-span deflection of the element in correlation with the applied wind load on the outer facing (i.e., facing interior of chamber). Wind suction and pressure at the facings are represented by positive and negative values, respectively. In Figure 12, the global behavior of the two tested elements, namely single (S) and double (D) connector configurations, is shown as the wind load versus mid-span deflection (at locations 1-3 in Figure 8). The deflections, \(d1\)-\(d3\), were adjusted by deducting the average displacement at the position of the anchors, i.e., global displacements of the element with respect to the test rig. It should be noted that during the last wind load cycle, it was only possible to reach a pressure of approximately \(-1.9\) kPa due to small air leakage from the test chamber. It can be observed that both tested elements performed similarly under cyclic loading. Only minor differences in element deflections at the position of the three displacement transducers were noted, which confirms that the elements mainly bend about the x-axis, as per Figure 10. Both elements exhibit a somewhat larger mid-span deflection during wind suction compared to wind pressure, with a maximum deflection of approximately \(4\) mm at maximum wind suction (\(2\) kPa) which is less than \(L/500\).  
Given that the total wind load is distributed evenly between the four anchors, it can be assumed that there is a linear relation between the horizontal reaction force at each of the anchors and the applied wind load. Accordingly, this amounts to a maximum shear load at the anchors during testing of approximately \(4\) kN at maximum wind suction and wind pressure. This shear load is well below the  
Figure 11: Observed failure modes after shear load testing for cases (**a–d**).  
Figure 12: Wind load versus mid-span deflection (\(d1\)–\(d3\)) for single (S) connectors (**a**) and for double (D) connectors (**b**). The deflection values are adjusted with respect to displacements at the anchors.  
average measured capacities of the anchors reported in Section 5.1. Furthermore, after testing, some of the smaller pre-existing shrinkage cracks in the facings had propagated slightly, but only a few new minor cracks were detected. No cracks or damages were noted in the regions around the anchorages.
# Electrochemically Activated Screen-Printed Carbon Electrode for Determination of Ibuprofen
## 3 Results and Discussion
### Optimization of pH and Supporting Electrolyte Concentration  
The influence of supporting electrolyte type (0.25 mol L\({}^{-1}\) H\({}_{2}\)SO\({}_{4}\), CH\({}_{3}\)COONa + CH\({}_{3}\)COOH of pH 3.5, 4.0, 4.5, 5.0 and 6.0 \(\pm\) 0.1) on the IBP signal (20.0 uM) was tested (Figure 2A). The IBP oxidation peak was visible in all acetate buffer solutions. However, the highest signal was obtained in the acetate buffer solution of pH 4.5 \(\pm\) 0.1, so CH\({}_{3}\)COONa + CH\({}_{3}\)COOH of pH 4.5 \(\pm\) 0.1 was applied in subsequently studies. Then, the optimization of supporting electrolyte concentration was performed (0.025-0.50 M). As shown in Figure 2B, the oxidation peak current of IBP gradually increased with increasing buffer concentration from 0.025 to 0.25 M, and for concentration of 0.5 M, the peak current was decreased. Therefore, 0.25 M was used as the optimal acetate buffer concentration for each measurement of IBP.