*2.2. Study Participants*

The sample consisted of students on campus who voluntarily wished to participate. Data were collected during the COVID-19 pandemic between March and May 2020 from public libraries on the campuses of the four universities. It was ensured that the participants' privacy was protected and that they would not be disturbed during participation. The students agreed to use a browser to search for information pertaining to the preset health questions and have their screens recorded during the process. The final sample comprised 31 students from one university in central Taiwan and 70 students from three universities in southern Taiwan—resulting in a total sample size of 101. A total of 101 valid responses were thus obtained for image content analysis.

### *2.3. Study Tools*

To investigate the online health information-seeking behavior of the undergraduate students, this study referred to the "14 Health Topics of the Health Promotion Administration, Ministry of Health and Welfare (Taiwan)", the "Top 10 Health Education and Teaching Issues in the United States", and the six categories of health information in Liao et al.'s [7] study, as the basis for formulating the example health issues. Liao et al.'s categories included disease treatment, diet and nutrition, exercise and fitness, health and aging prevention, medical consultation and treatment, and preventive health care, being supplemented with health issues of public concern. The health issues of concern to

undergraduate students in this study comprised four topics: "balanced diets", "obesity prevention", "health and fitness promotion", and "sleep management", each of which was extended to two questions, for a total of eight questions. The students were asked to select one of the eight questions and provide written answers to it, in order to reveal the status of their online health information-seeking behaviors and actual behaviors. Taking "balanced diet" as an example, the design concept of the health-related questions used in the research is shown in Table 1.

**Table 1.** Example of the design concept of health-related questions of concern to undergraduate students.

1 Data source: [25].


3.1.3. Fault Angle

The previous results show that the largest thermal differences between steady state and transient temperatures are found in the hanging wall part of the basin. For all the tested fault angles the results are broadly similar, but the temperature effect will change with the fault angle. As the fault angle is changed, so is the affected area of the hanging wall. With a steeper fault angle a smaller part of the basin experiences the largest thermal instabilities (Figure 9a). The opposite is the case for lower fault angles, a larger part of the basin will then experience higher thermal instabilities before the basin regain steady state (Figure 9b). Five different fault angles have been tested (Figure 9a,b). All scenarios show temperature differences up to 45 ◦C between transient and steady state models. Around 10 Myr after fault slip all scenarios regain steady state temperatures.

**Figure 9.** Temperature difference between steady state and transient model 10 kyr after fault slip for the steepest (**a**) and the least steep (**b**) studied listric fault.

### 3.1.4. Thermal Conductivity and Specific Heat Capacity

The thermal conductivity (W·m<sup>−</sup>1·K−1) of a lithology relates to the rock's ability to transfer heat. Rocks with low thermal conductivity result in warm basins, while high thermal conductivity rocks give colder basins. A lithology's specific heat capacity (J·kg−1·K−1) is the physical property crucial for the time frame needed to transfer heat through the stratum and thus for the time needed for basins to regain steady state after sediment deposition or erosion. So far the section has been modeled as all shale with properties as listed in Table 2. Commonly, basins consist of altering layers of different lithology types, often shale and sandstone. Therefore, the following four scenarios have been compared: all shale, all sandstone, all shale with one sandstone layer, and all sandstone with one shale layer.

Thermal conductivities and specific heat capacities of shale and sandstone show a large variation (e.g., values in Table 2). The default values are given in Table 2 and to illustrate the span of values within the same lithology segment, we refer to Cerm ˇ ác and Rybach [38]. Their minimum and maximum specific heat capacities and the average and maximum thermal conductivities of sandstone and shale are used in this study (Table 2). In order to determine the thermal conductivity values based on the rock's porosity, the mixing law arithmetic mean model [66] has been used:

$$k = \Phi \cdot k\_f + (1 - \Phi)k\_{s\_f} \tag{1}$$

where the thermal conductivity (*k*) is obtained on the basis of the rock porosity (Φ*)* by combining the thermal conductivity of rock matrix (*ks*) with that of the pore fluid (*kf*).

Generally, sandstones have higher thermal conductivities than shales, resulting in colder basins where they are abundant. The results from the homogeneous basins, either all shale or sandstone, coincide with this, showing high temperatures in the basin when the thermal conductivity is low, and lower temperatures when the thermal conductivities are high. A comparison between two basins with the same fixed specific heat capacity, but with one basin modeled with average shale conductivity, to another basin modeled with default shale conductivity, shows up to 140 ◦C difference between the two models shortly after fault slip (Figure 10a). With fixed specific heat and changing conductivity the results show that the basin needs longer time to regain steady state in basins with low thermal conductivity values due to the higher temperatures obtained in these basins (Figure 10b). This applies for both scenarios with all shale and all sandstone of varying thermal conductivity. Steady state is regained from 3–22 Myr after fault slip and the quickest scenario to regain steady state is the sandstone basin with maximum thermal conductivity.

**Figure 10.** (**a**) Thermal difference between transient temperatures of basin (10 kyr after fault slip) modeled with average conductivity to basin modeled with default conductivity. (**b**) Point plot results from location of point given in Figure 8b, showing the steady state and transient temperatures for the three tested conductivities for shale and sandstone. Conductivity values given in Table 2.

In the case where the thermal conductivity is fixed and the specific heat capacities are changed in the different modeling scenarios (Table 2, lower part), the steady state temperatures for all the scenarios are, as expected, the same (Figure 11). However, with increasing specific heat, the time needed for the basin to obtain steady state increases (Figure 11). This is especially visible in the point plot for sandstone basin in Figure 11, because of large difference in the published heat capacity values of sandstone, but the effect is also visible for the shale basin. For all tested scenarios steady state is obtained between 8–18 Myr after fault slip.

**Figure 11.** Resulting temperatures for the three tested specific heat capacities for shale and sandstone. Location of studied point indicated in Figure 8b. Heat capacity values given in Table 2.

To accommodate the fact that basins commonly consists of different lithologies, the polygon with syn- and post-rift deposits have been changed in the following simulation (Figure 12a). The thermal conductivity is fixed with values as for the default shale or sandstone (Table 2). However, the specific heat capacity is changed using default, minimum, and maximum values for the sandstone and shale (Table 2). With the presence of a sandstone layer in an otherwise shale basin, the results show that the temperatures in the basin increases. For the opposite case, a layer of shale in an otherwise sandstone basin, the temperatures in the basin decreases (Figure 12b).

**Figure 12.** (**a**) Shale and sandstone basins with sandstone layer and shale layer respectively. The red point indicates location for point plot in (**b**). (**b**) Results show the temperatures for the three tested heat capacities for shale basin with one sandstone layer and sandstone basin with one shale layer. For comparison, temperatures for homogeneous basin are also plotted. For heat capacity values see Table 2.

The variation of specific heat capacity in a homogeneous basin resulted in quite a time gap (8–18 Myr) for the sandstone basin to regain steady state (Figure 11). However, the shale basin required less time to arrive at steady state for the tested specific heat capacities (8–10 Myr). For homogeneous basins, shale or sandstone, steady state was regained around 10 Myr after fault slip (Figure 12b). The presence of another lithology influences the basin temperatures and thus the time needed for the basins to regain steady state. For a shale basin with a sandstone layer, steady state was obtained around 13 Myr after fault slip for all the specific heat capacities. Sandstone basin with a shale layer regains steady state around 6 Myr after fault slip regardless of the specific heat capacities (Figure 12b). These results are related to the porosities at the depth at which the di fferent lithology layers are modeled and the contrasting thermal conductivity for shale and sandstone.


*7.1. Evaluation Environment*

The evaluation environment contains a network of control points (6 pillars, 16 ground points, 15 building points) that covers an area of about 1.5 km2 with variable GNSS conditions (Figure 13). The control point coordinates were determined in a network adjustment using measurements from total stations, GNSS baselines, and levelings. The network is linked to the official German coordinate systems, i.e., ETRS89/UTM32 (European Terrestrial Reference System 1989, Universal Transverse Mercator, Zone 32) for position, and DHHN16 (Deutsches Haupthöhennetz 2016) for physical heights. In addition, the ellipsoidal heights w.r.t. GRS80 (Geodetic Reference System 1980) were determined. The connection between the ellipsoidal heights and the physical heights was realized by using the German Combined Quasi Geoid Model (GCG16) [83]. The network was surveyed in June 2017 and July 2018 for checking its stability [84]. The final control point coordinates are based on a combined network adjustment of both epochs and have estimated standard deviations of <1 mm.

**Figure 13.** Network of control points with six pillars, 15 building points, and 16 ground points. The control points can be signalized with BOTA8 (Bonn Target 8) laser scanning targets (Source: Google Earth, modified).

In addition to the control points, TLS reference point clouds of all buildings in the area of the control points were measured (Figure 14). The TLS scans were captured from multiple stations in two faces and georeferenced using the network of control points like it was done for the calibration field in Section 6.1. The mean absolute error after georeferencing was 1.2 mm. The TLS reference point clouds can be used for an area-based evaluation of mobile laser scanning systems. In addition, a point-based evaluation is possible based on the control points. For this purpose, the pillars and building points can be signalized with special BOTA8 laser scanning targets [82] (cf. Figure 15), which are applicable to both TLS registration and evaluation of mobile laser scanning systems.

**Figure 14.** TLS (Terrestrial Laser Scanning) reference point clouds georeferenced using the control points and BOTA8 (Bonn Target 8) laser scanning targets.


**1. Introduction**

Silicone rubber has been widely used as an electrical insulating material of polymer insulators of power transmission and distribution lines, because of advantages like light weight, easy handling, and water repellency. Electrical and mechanical degradation of silicone rubber is caused by discharges on its surface, exposure to ultraviolet ray, surface contamination, and heavy rain.

With respect to degradation of silicone rubber caused by discharge, arc discharge gives serious and irreversible damage to silicone rubber when compared with partial discharge. To improve resistance to tracking and erosion of silicone rubber caused by arc discharge, alumina trihydrate (ATH) is usually added to silicone rubber [1]. Water of crystallization contained in ATH evaporates by absorbing energy of arc discharge, resulting in good performance. Arc discharge usually occurs on silicone rubber surface under sever condition such as high electric field, heavy contamination and wetting [2]. However, occurrence probability of such conditions may be low.

On the contrary, partial discharge occurs more easily under less sever condition. Partial discharge is a localized breakdown observed in a small portion of an insulation system subjected to high electric field. It does not cause short-circuit of the insulation system. In the case of polymer insulators, partial discharge may be observed at the triple junction point-like interface of silicone rubber/electrode/air and silicone rubber/water droplet/air [3,4]. Partial discharge generated in air attacks the surface of silicone rubber. It is difficult for partial discharge to breakdown silicone rubber by one attack because its energy is not sufficient enough. However, silicone rubber subjected to partial discharge over a long period of time will be eroded gradually from the surface to the bulk and finally breakdown. Knowledge of partial discharge degradation of silicone rubber is considered essential to electrical insulation design of composite insulator. However, researches focused on partial discharge degradation of silicone rubber have not been performed much.

Analysis of contact angle and SEM images of surface after 48 and 96 h partial discharge treatment show that the resistance to partial discharge is improved by adding nano-sized silica to silicone rubber [5]. Effect of partial discharge on rheological and chemical properties of silicone rubber has been discussed in terms of oxidization and localized temperature rise, moisture, and depolymerization [6]. Another paper reports that deposited charge on silicone rubber surface by partial discharge may have an impact on its flashover voltage, but the material degradation caused by partial discharge is limited [7]. To understand partial discharge degradation phenomena of silicone rubber, further fundamental long-term experiments under various conditions in accordance with appropriate procedures seem necessary.

The authors have carried out experiments in rather actual conditions, where silicone rubber was subjected to partial discharge in salt or clean fog [8,9]. To simplify the experimental condition by avoiding the effect of water droplet on silicone rubber, we started experiments in the atmosphere without fog [10]. The present paper reports a series of results obtained in accordance with proposed experimental procedures, which are suitable to study degradation of hydrophobic silicone rubber caused only by partial discharge. The repeatability of results is good and the acceleration rate can be changed in a certain range by adjusting applied voltage and spacing of the gap where partial discharge occurs. The effect of alumina trihydrate (ATH)—a typical additive for mitigating tracking and erosion of silicone rubber—on partial discharge degradation and breakdown is studied by using five kinds of silicone rubber samples including different amount of additives. Results of chemical analysis show that ATH is decomposed by partial discharge as reported in the case of arc discharge. However, it is suggested addition of ATH lowers permittivity and resistivity of silicone rubber, which results in active partial discharge in air gap and shorter lifetime to breakdown.

## **2. Experimental Procedures**

#### *2.1. Sample and Electrode*

Five kinds of silicone rubber samples of the same size (45 × 45 × 2 mm) were used as samples. The matrix of each sample was identical. Respective samples had different amount and surface treatment of additives: ATH and silica. The physical properties of samples [11] are summarized in Table 1.

**Table 1.** Physical properties of samples.

1 Part by weight. 2 Treated stands for rounded surface of particle.

A bundle of four steel round bars of 6 mm diameter was used as high voltage electrode; each bar had a hemispherical tip of 3 mm radius of curvature. The ground electrode was a stainless steel disc of 30 mm in diameter. A silicone rubber sample was placed on the ground electrode. An air gap of 1 or 2 mm spacing was formed between the tip of the round bars (high voltage electrode) and a sample surface. The electrode system is shown in Figure 1. Five electrode systems in total, each sample was placed in one electrode system, were set in an acrylic chamber of 1 m3.

The level of applied ac voltage was determined so that no arc discharge occurred throughout the experiment and surface degradation progressed as fast as possible only by partial discharge. In the present study, 6.6 and 8.5 kVrms (60 Hz) were selected for air gap spacing of 1 and 2 mm, respectively. Voltage was applied simultaneously to five electrode systems for 8 h and then interrupted for 16 h for recovery of hydrophobicity of sample surface. The 24-h cycle was repeated 50 or 100 times, during these cycles degradation performance of five samples is categorized into 3 groups. Applied voltage and current of each sample were monitored and recorded. A schematic diagram of the experimental setup is shown in Figure 2.

In the present study, samples were not tested under the same partial discharge conditions (for example, amount of charge, and number of pulses). The magnitude of applied voltage was fixed instead, considering the actual usage condition of polymer insulators at the site. It result in di fferent partial discharge activities among samples because localized electric field in the air gap depends on permittivity and conductivity of the sample.

**Figure 1.** Electrode system.

**Figure 2.** Schematic diagram of experimental setup.

#### *2.2. Analyses of Surface Erosion*

Contact angle was measured with a Drop Master DM500 (Kyowa Interface Science Co., LTD.) just before voltage application and just after voltage interruption of every cycle. A water droplet of 1 μl (conductivity: ~50 μS/cm) was dropped on a horizontally placed sample. Measurement was performed at five spots on the eroded area of a sample by partial discharge and the average contact angle was calculated.

Surface erosion of a sample was evaluated after 8-h voltage application with a surface roughness meter (TOKYO SEIMITSU CO., LTD., Surfcom 1400D) every three cycles. Measurement was carried out by 3 mm-long linear motion of a sensor on a sample surface.

Confocal microscope (Lasertec Corporation, OPTELICS HYBRID C3) enabled close observation of a sample surface of the area 1.5 mm × 1.5 mm. Images of samples were obtained every ten cycles. The arithmetic average roughness Ra of the surface in the observed area was also available. Ra gives the average of the absolute values of the roughness profile ordinates [12].

Infrared absorption spectrum was obtained every three cycles with a Fourier transformation infrared spectrophotometer (JASCO Corporation, FT/IR 6300).