esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
###### Abstract
Previous data-driven assessments have either focused on real-world driving, which precludes testing under hazardous conditions [7], or on specific, isolated manoeuvres in simulators [31]. Our research expands upon these approaches by (1) using a high-fidelity simulator to evaluate a complex, integrated ADAS suite across multiple critical scenarios and (2) implementing a fuzzy logic model as a new analytical tool. This model's primary innovation is its capacity to synthesise various, nonlinear performance metrics into a single, interpretable score. By explicitly managing the inherent uncertainty of driver performance data, our framework offers a more robust and nuanced evaluation of driver competence than has been previously demonstrated. This study was designed to test the following primary hypothesis: more comprehensive and interactive training methods will result in significantly better driver performance in operating an ADAS than brief, text-based instruction. Specifically, we posited that (H1) participants undergoing practical, simulator-based training would demonstrate the highest level of competence, followed by those using the e-learning platform. It was further hypothesised that (H2) both practical training and e-learning would be significantly more effective than the brief manual instruction, which represents the current de facto learning method for many drivers. Performance was operationalised through objective metrics including reaction times and system operation attempts, as measured in a controlled simulator environment.
# GNSS-Based Monitoring Methods for Mining Headframes
## 2 Overview of the GNSS Monitoring Scheme Design and Monitoring Platform for the Headframe  
### GNSS Station Location Design and Installation  
The Liuzhuang Coal Mine is a large-scale mine located in Fuyang City, Anhui Province, covering an area of 82 km\({}^{2}\), with coal reserves reaching 1.56 billion tons, of which 679 million tons are minable. The annual output reaches 11.4 million tons. The mine's headframe is over 40 m high, equipped with two sheave wheel platforms, with a sheave wheel radius of about 1.5 m.  
To monitor the stability of the headframe structure, a network of nine GNSS stations has been established. A single GNSS reference station is positioned approximately 1 km away on a stable rooftop in an open area. The remaining eight GNSS monitoring stations are strategically arranged as follows: two are located on the upper sheave wheel platform, with one on each side of the sheave wheel axis, and six are situated on the lower sheave wheel platform, with one at each of the four endpoints of the platform and one on each side of the sheave wheel axis. These monitoring stations facilitate real-time observation of deformation and settlement in the headframe supports and sheave wheels. The selection of sites for the GNSS monitoring stations is based on a comprehensive evaluation of three key factors. Firstly, the arrangement is designed to fulfill the safety monitoring requirements of the mine by enabling dynamic observation of the headframe structures. Secondly, it complies rigorously with the signal reception conditions stipulated in the GNSS Surveying Standards and the subsidence avoidance principles outlined in the Coal Pillar Retention Standards. Thirdly, the selection process incorporates extensive on-site verification and is integrated with mining and excavation plans, surface movement patterns, and other relevant considerations. Figure 1 shows a comprehensive overview of the Liuzhuang Mine frame and the design of monitoring stations.  
GNSS site equipment mainly consists of GNSS monitoring dedicated receivers, solar panels, main control cabinets (which house the main control transmission modules), and mounting brackets. One reference station is powered by solar energy, while the other reference station is powered by municipal electricity. Both utilize 4G wireless network communication technology to remotely acquire monitoring data through a fixed IP and port settings. The installation site of the GNSS site equipment and the structural components of the GNSS monitoring dedicated receivers are shown in Figure 2.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 2 Coherent Optical Receiver
### Turbulence Model
Therefore, several statistical models  
Figure 2: \(C_{n}^{2}\) profile according to altitude for moderate turbulence and several wind speed values. The same behavior is obtained for weak and strong turbulence, changing only the \(C_{n}^{2}(0)\) value. for simulating the PDF of received power fluctuations have been proposed for different turbulent regimes. This study will use the Log-Normal and Gamma-Gamma distributions among the various models to determine the PDF of the irradiance. The Log-Normal distribution model is valid only for weak and moderate turbulence regimes and is given by [2]:  
\[p(I)=\frac{1}{I\sqrt{2\pi\sigma_{I}^{2}}}\exp\left[-\frac{\left(\ln\left(\frac{ I}{I_{0}}\right)+\frac{1}{2}\sigma_{I}^{2}\right)^{2}}{2\sigma_{I}^{2}}\right], \qquad I>0 \tag{6}\]  
where \(I_{0}\) is the irradiance in the absence of turbulence. For strong turbulent regimes, the Log-Normal PDF deviates from the experimental data. Thus, the Gamma-Gamma distribution is used for moderate to strong turbulence regimes. Therefore, the PDF of the optical signal scattered by the atmospheric turbulence can be represented as:  
\[p(I)=\frac{2(\alpha\beta)^{(\alpha+\beta)/2}}{\Gamma(\alpha)\Gamma(\beta)}I^{ (\alpha+\beta)/2-1}K_{\alpha-\beta}(2\sqrt{\alpha\beta I}),\qquad(I>0) \tag{7}\]  
where \(K_{b}\) is the modified Bessel function of the second kind and order \(b\). The \(\alpha\) and \(\beta\) are the effective turbulence parameters related to the large-scale and small-scale scintillation of the optical wave. The expressions defining each parameter and their relation according to the FSO satellite link can be found in [2] (Chapter 12). Thus, based on the previous definitions regarding scintillation, the modulated optical signal is calculated according to the realization of random fluctuations with PDF defined by the Gamma-Gamma function or Log-Normal distribution of the VPIphotonics turbulence model.
## 2 Adaptive Inverse Hyperbolic Tangent Algorithm  
This AIHT form fits data obtained from measuring the electrical response of photo-receptors to flashes of light in various space [3]. It has also provided a good fit to other electro-physiological and psychophysical human visual function measurements [4; 5; 6]. The inverse hyperbolic tangent of a value \(x\) is the value \(y\) for which the hyperbolic tangent of \(y\) is \(x\). The inverse hyperbolic tangent function is only defined in the open range (\(-1\), \(+1\)). This corresponds to the output range of the hyperbolic tangent function.  
The calculation for \(\tanh^{-1}(x)\) can be derived either algebraically from the definition of \(\tanh^{-1}(x)\), or by converting the derivative to a series and then integrating. The contrast of an image can be enhanced using inverse hyperbolic tangent function by Equation (1):  
\[y=\tanh^{-1}(x)=\frac{1}{2}\log\biggl{(}\frac{1+x}{1-x}\biggr{)} \tag{1}\]  
Adding the _bias(x)_ and _gain(x)_ parameters to control the shape of the inverse hyperbolic tangent curve leads to Equation (2):\[Enhance\Big{(}x_{ij}\Big{)}=\left(\log\left(\frac{1+x_{ij}^{bias}(x)}{1-x_{ij}^{ bias}(x)}\right)-1\right)\times gain\big{(}x\big{)} \tag{2}\]  
where _bias(x)_ represents the Bias Power Function, and _gain(x)_ represents the Gain Function described in the following paragraphs.  
In the AIHT algorithm, the dark and bright regions are under- and over-saturated, respectively. There is insufficient enhancement in the dark and bright regions. In order to address this problem, and to avoid expending the large amount of time required in traditional contrast enhancement algorithms--which search optimal gray transform parameters in the whole gray transform parameters space--a new criterion is proposed with sub-band processing. This method introduces a new contrast enhancement algorithm designed specifically for segmentation applications using an image with different sub-bands. The underlying assumption of the proposed algorithm is that a sub-band can be best segmented if it is locally enhanced at a scale that corresponds to the image feature. The contrast type of the original image is determined by the new criterion. Gray transform parameter space is respectively determined by different contrast types, which dramatically shrinks the gray transform parameter space. Nonlinear transform parameters are based on a multi-scale _bias_ and _gain_ parameter algorithm so as to obtain optimal gray transform parameters [7, 8, 9, 10, 11]. This method is adaptive where the enhancement is applied locally, based on local image properties.
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Developed Tool** & **Purpose** & **Phase, Type of the Structure(s), and** & **Limitations** \\  & & & **Type of BIM(s)** & \\ \hline \multirow{3}{*}{[PERSON] et al. [2017]} & \multirow{3}{*}{IFC-based QC system} & Finding common patterns in IFC-based datasets to detect discrepancies & \begin{tabular}{c} *Design \\ Buildings, \\ Infrastructures, and Metro \\ \"Architecture \\ \end{tabular} & \begin{tabular}{c} *The possibility of a loss of information \\ during IFC export. \\ \"Not automated. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [2017]} & \multirow{3}{*}{IFC data model QC algorithm} & Analyzing the IFC data model to identify discrepancies automatically & \begin{tabular}{c} *Conservation \\ Buildings \\ “As-built \\ \end{tabular} & \begin{tabular}{c} *The possibility of a loss of information \\ during IFC export. \\ \"Investigating only the quality of \\ architectural models. \\ \"Containing errors during IFC export. \\ \"Not automated. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [2018]} & \multirow{3}{*}{IFC-based QC system} & Conducting the quality control of BIM models & \begin{tabular}{c} *Design \\ Buildings \\ “Architecture \\ \end{tabular} & \begin{tabular}{c} *Transforming only the quality of \\ architectural models. \\ \"Not automated. \\ \"Not automated. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [2018]} & \multirow{3}{*}{IBM QC method} & Evaluating the quality of BIM models based on customized & \begin{tabular}{c} *Design \\ Buildings \\ “Architecture \\ \end{tabular} & \begin{tabular}{c} *Considering only the architectural BIM QC. \\ “Not automated. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [2019]} & \multirow{3}{*}{Integrated IFC-based QC solutions} & Utilizing neural network algorithms to evaluate the construction quality & \begin{tabular}{c} *Conservation \\ Buildings \\ “As-built \\ \"Not considering the design quality. \\ \end{tabular} & \begin{tabular}{c} *The possibility of a loss of information \\ during IFC export. \\ “Not automated. \\ \"Not considering the design quality. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] and Basaga [2019]} & \multirow{3}{*}{IFC-based automated checking compliance system} & Automating the checking & \begin{tabular}{c} *Design \\ Buildings \\ “Architecture \\ \end{tabular} & \begin{tabular}{c} The possibility of a loss of information during \\ IFC export. \\ \"Architecture \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [2018]} & \multirow{3}{*}{IFC-based QC system} & \multirow{3}{*}{IFC-based QC system} & Proposing a method to check the quality of BIM models & \
\begin{tabular}{c} *Design \\ Buildings \\ “Architecture \\ \end{tabular} &
\begin{tabular}{c} The possibility of losing information during \\ IFC export. \\ Not automated. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous studies on BIM-based QC solutions.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 4 Modeling the Performance of Bluetooth Low Energy for Opportunistic Sensor Data Collection
### Advertisement-Based Approach
#### 4.1.1 Sensor Node Current Consumption and Lifetime
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**State Number**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Duration**} & \multicolumn{2}{c}{**Current Consumption**} \\ \cline{3-6}  & & **Variable** & **Value (ms)** & **Variable** & **Value (mA)** \\ \hline
1 & wake-up & \(T_{\mathit{var}}\) & 0.728 & \(I_{\mathit{var}}\) & 9.595 \\
2 & radio preparation & \(T_{\mathit{pre}}\) & 0.247 & \(I_{\mathit{pre}}\) & 17.506 \\
3 & transmission & \(T_{\mathit{tx}}\) & 0.398 & \(I_{\mathit{tx}}\) & 41.046 \\
4 & channel change & \(T_{\mathit{ch}}\) & 0.134 & \(I_{\mathit{ch}}\) & 21.467 \\
5 & radio off & \(T_{\mathit{off}}\) & 0.190 & \(I_{\mathit{off}}\) & 10.543 \\
6 & postprocessing & \(T_{\mathit{post}}\) & 0.818 & \(I_{\mathit{post}}\) & 10.523 \\
7 & sleep & \(T_{\mathit{slap}}\) & - & \(I_{\mathit{slap}}\) & \(1.193\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of states and relevant variables for the advertisement-based approach. In the measurements, the slow clock function offered by the BLE121 LR platform was disabled. Figure 4: Current consumption profile of an advertising event for the BLE121 LR platform operating as a non-connectable advertiser. Three-advertisement (leftmost) and single-advertisement (rightmost) advertising events are shown. where, assuming that \(N\) denotes the number of advertising packets sent within an advertising interval (with \(N\in\ \{1,\ 2,\ 3\}\)), \(T_{act}\) can be computed as:  
\[T_{act}=T_{wu}+T_{pre}+N\cdot T_{tx}+(N-1)\cdot T_{ch}+T_{off}+T_{post} \tag{2}\]  
and \(I_{act}\) is the average current consumption during \(T_{act}\):  
\[I_{act}=\frac{T_{wu}\cdot I_{wu}+T_{pre}\cdot I_{pre}+N\cdot T_{tx}\cdot I_{ tx}+(N-1)\cdot T_{ch}\cdot I_{ch}+T_{off}\cdot I_{off}+T_{post}\cdot I_{post}}{T_{act}} \tag{3}\]  
Note that \(T_{sleep}\) can be obtained as:  
\[T_{sleep}=advInterval+E\{advDelay\}\ -T_{act} \tag{4}\]  
Finally, assuming that the sensor node operates on a battery, the sensor node lifetime, denoted \(T_{lifetime}\), can be derived taking into account the battery capacity, \(C_{battery}\) (expressed in mA\(\cdot\)h), as follows:  
\[T_{lifetime}=\ \frac{C_{battery}}{I_{avg\_adv}} \tag{5}\]  
As a final remark, note that possible communication bit errors do not affect the sensor node average current consumption or the sensor node lifetime in the advertisement-based approach. In fact, transmitting advertisements consumes an amount of energy that is independent of whether those advertisements will suffer communication errors, and it does not require feedback from the receiver.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### X-ray Diffraction (XRD)  
XRD is an analytical measurement technique that identifies the crystalline nature of the foulant(s) either organic or inorganic, polymeric or metallic deposited on the membrane surface. It reveals important information about the crystal structure, size, shape, etc. Different type of polycrystalline samples can be measured. The foulants deposited on the membrane surface can be extracted and ground to a powder form, and then arranged in a very thin layer on a sample holder. On the other hand, membrane samples can also be measured directly. Both types of samples are usually analyzed with a diffractometer equipped with monochromatic Cu-K\(\alpha\) radiation [95]. The irradiated crystals disperse the X-rays only in some determined directions with intensities that depend on how the atoms are ordered at the microscopic level. With this information, direction and intensity of each ray, it is able to obtain the molecular structure of crystals. More details on XRD technique and analysis can be found in [125].  
It is necessary to point out that XRD has widely been applied to study membrane fouling [15], even in MD. As example, [PERSON] [46] used XRD to analyze the components of scaling on PP membrane. As can be seen in Figure 8, the spectra showed the presence of calcite crystals when the antiscalant was not used, and the calcite peak disappeared after using the antiscalant. [PERSON], et al. [126] characterized the components making up the crystals formed in the crystallizer of an integrated MD and membrane crystallization (MDC) system for shale gas produced water (SGPW) treatment.
## 3 Results
### Modeling GPP from the Validation Data  
When applied to the 2014 validation dataset, NDMI had a stronger relationship with GPP than MOD17A2, such that RMSE decreased from 7.46 g C m\({}^{-2}\) (training) to 6.96 g C m\({}^{-2}\). This 6.96 g C m\({}^{-2}\) validation RMSE represented 20% of the observed range in GPP 8 day sums (e.g., 5.29 g m\({}^{-2}\)-39.75 g m\({}^{-2}\)) (Figure 6).  
During 2014, the GPP modeled from the NDMI relationship had a well-resolved phenological pattern and was more similar to flux GPP than the MOD17A2 product (Figure 7). The MOD17A2 GPP product also lacked an obvious summer maximum. Our new GPP model exhibited a more reasonable phenology than MOD17A2 in the observed year, especially during the growing season (from May to October). MOD17A2 also overestimated GPP in the non-growing season. This demonstrates the ability of the proposed model to improve the data quality of MODIS GPP products for wetlands.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **R\({}^{2}\)** \\ \hline NDVI & 0.04 \\ SAVI & 0.38 \\ NDMI & 0.46 \\ VARI & 0.05 \\ EVI & 0.38 \\ WDRVI & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The goodness of fit for the relationship between the Vegetation Indices and GPP for the Training Data.  
Figure 5: The composited cloud and tide-free candidate vegetation indexes for modeling GPP over the years 2014 and 2015. Gaps represent windows where no suitable data were available to composite.
# Effect of Waste Engine Oil and Waste Cooking Oil on Performance Improvement of Aged Asphalt
## 5 Conclusions  
In this study, some conventional and specific experiments were conducted to evaluate the regeneration properties of the aged asphalts. Based on the experiments results and analysis from different aged asphalts, the following conclusions can be drawn.  
(1) WEO and WCO could improve the basic physical properties of aged asphalts to the normal level. To satisfy better performance of regenerated asphalt, the WEO or WCO dosage should be in a reasonable range, which was different for different aged asphalts.  
(2) In comparison, WCO had better function than WEO as a regenerative agent. Less WCO content could achieve better regeneration properties. According to the different aging time, aged asphalt could recover its basic physical performance with 1-5% WEO content and 1-4% WCO content (by mass of asphalt).  
(3) The addition of WEO or WCO could increase saturate, resin, and asphaltene content and decrease aromatics dosage, intensity of carbonyl, and sulfoxide of aged asphalt, but it could not change its colloidal structure. Moreover, there was a suitable oil content related to the aged asphalt condition and actual construction requirements. An excessive amount of oil content would affect other properties of aged asphalt.  
(4) The rheological properties of aged asphalts could also be regained to normal level of VA with different contents of WEO or WCO. Overall, WEO or WCO were adaptable regenerative agents to improve aged asphalt performances. It provided a new application method for WEO or WCO in asphalt pavement recycling field, which realized the wastes cyclic utilization and environmental protection.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 3 Testing Methodology
### Accelerated Aging of DRAM Modules  
In order to observe the wear-out effects on the DRAM chips, a test similar to the burn-in test is performed on the DRAM modules. The module is exposed at an elevated temperate and DC voltage for a prolonged period of time to simulate a stress condition. This stress condition accelerates the aging process in the cell transistors. When DC stress is applied to the DRAM node, the leakage current increases due to trapped emission electrons, and the DRAM cell capacitors suffer from asymmetric damage, degrading the retention behavior [28]. Evaluating the retention error at this accelerated aging condition can be used to identify the impacts of wear-out mechanisms (e.g., BTI, HCI, electromigration) and detect the aged device as well as the infant mortality failures and unexpected failures [2].  
The following steps are involved in the accelerated aging process:  
1. Initially, a data pattern is written to the entire row using standard timing parameters. In order to ensure maximum stress, all ones (0 xFF) are written to the true cells, and all zeros (0x00) are written to the anti-cells.
2. Auto-refresh is enabled. The timing parameter \(t_{REFI}\) (the gap between refresh command) is set to 0, a minimum value (one clock cycle), so that DRAM cells are refreshed more frequently and create maximum stress on the storage capacitor.
3. The DRAM module is placed under the thermostream device at a high temperature of 70 \({}^{\circ}\)C and a high-voltage condition (\(V_{dd}=1.55\) V instead of standard 1.5 V. At this stressed condition, wear-out effects are accelerated in the DRAM cells. The module is left in this condition for 8 h for wear-out mechanisms to affect the DRAM cells.  
After the aging process in stress conditions, the module is cooled down to room temperature, and the retention test is performed to evaluate the bit flips. The retention errors are compared with the fresh chips and the additional errors are attributed to various aging mechanisms.