esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of a Demand-Side Management Solution for South Korea's Demand Response Program
## 4 Discussion  
Unlike the traditional energy management models that focus on the supply side, the DSMS considers the energy demand and control on the interactions between customers and supplier to manage electricity usage reduction and money saving. Through the implementation of the DSMS technology, the end user can automatically check necessary information, such as the CBL, the RRMSE value, and the amount of DR reduction, without the need for complex formulas and contents. Furthermore, the CBL and current usage can be checked in real time by monitoring power usage every 5 min. The DSMS operated in the actual South Korea DR market for a year and based on these results the developed solution was verified. In addition, the result illustrates that the integrated demand-side management solution contributes by participating in the DR market and provides a benefit and satisfaction to the consumer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amount** & **Large** & **Medium** & **Small** \\ \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**N Company**} & \multicolumn{2}{c}{**Provincial**} & \multirow{2}{*}{**G Store**} \\  & & & & \\ \hline
**Type** & **Factory** & **Building** & **Retail** \\ \hline \multicolumn{4}{c}{Contracted capacity (kW)} & 10,000 & 1000 & 120 \\ \hline \multirow{4}{*}{CBL (kW)} & 14:00–15:00 & 18,059 & 1944 & 356 \\ \cline{2-5}  & 15:00–16:00 & 17,303 & 1904 & 349 \\ \cline{2-5}  & 16:00–17:00 & 17,545 & 1882 & 302 \\ \hline \multirow{4}{*}{Real load (kW)} & 14:00–15:00 & 8378 & 669 & 158 \\ \cline{2-5}  & 15:00–16:00 & 7980 & 634 & 121 \\ \cline{2-5}  & 16:00–17:00 & 7958 & 470 & 113 \\ \hline \multirow{4}{*}{DR reduction result (kW)} & 14:00–15:00 & 9681 & 1275 & 198 \\ \cline{2-5}  & 15:00–16:00 & 9323 & 1270 & 227 \\ \cline{2-5}  & 16:00–17:00 & 9587 & 1412 & 189 \\ \hline \multirow{4}{*}{DR delivery rate (\%)} & 14:00–15:00 & 97\% & 128\% & 165\% \\ \cline{2-5}  & 15:00–16:00 & 93\% & 127\% & 189\% \\ \cline{1-1} \cline{2-5}  & 16:00–17:00 & 96\% & 141\% & 158\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result of demand response from customer.  
Figure 9: N company demand response result (data of 20 July 2017, 14:00–17:00).  
The researcher and stakeholders of the DR market should consider the criteria value of the RRMSE. As previously mentioned in Section 2, the U.S. PJM power suggests a RRMSE value less than 20%, but the South Korea DR market sets the value within 30%. This consideration helps an effective DR operation and derives a successful outcome. This unique demand-side manage experience in South Korea could provide the rest of the world with a model to efficiently maintain a national power grid and potentially suggest the development of novel energy managing plans for local situations and policy.
## 2 Main Blocks for Wirelessly-Powered Cage System
### Closed-Loop Power Control (CLPC)  
Compared to the wireless power transmission system between the fixed Tx and Rx coils, as shown in Figure 1c, the Rx coil attached to the animal body continuously moves inside the wirelessly-powered cage resulting in the coupling variation between Tx and Rx coils. In addition, the power consumption in the mobile device is typically not constant for recording or stimulation operation. Therefore, CLPC, which can dynamically compensate for coupling distance and load variations due to animal movements and implant functions, is required to provide enough power for the mobile device [16]. When the mobile device receives more than enough power, the CLPC reduces the Tx power automatically to minimize the power dissipation on the Tx and the EM exposure on the animal subject, resulting in the improvement of wireless link efficiency and ensuring safety.  
The CLPC is typically composed of the data communication channel from the Rx to the Tx, the control unit, DC-DC converter, and the PA as shown in the exemplar design of Figure 9a. The rectifier voltage in the Rx, \(V_{\text{rec}}\), is monitored and sent to the Tx through the data communication channel which can be either near-field or far-field data communication. The control unit, such as a microcontroller, in the Tx collects the rectifier information through data demodulator block and determines whether the Rx receives enough power or not. If the Rx is not receiving sufficient power, the microcontroller controls the digital potentiometer to reduce the feedback voltage of DC-DC converter. Then, the DC-DC converter increases the PA supply voltage, \(V_{\text{DD-Tx}}\), to increase the amount of transmitted power. Otherwise, the microcontroller adjusts the digital potentiometer to decrease \(V_{\text{DD\_Tx}}\) if the Rx is receiving surplus power. Figure 9b shows the exemplar operation of CLPC in [16]. The CLPC starts to increase the transmitted power by increasing \(V_{\text{DD\_Tx}}\) when the rat moved to low PTE areas or stood up resulting in the weak coupling from the Tx to Rx coils as shown in the inset t = 14,817 s. When the Rx coil was close to the Tx coil located the bottom of the homecage or high PTE areas as shown in the inset t > 14,817 s, the Rx receives more power than necessary resulting in the increase of rectifier voltage, \(V_{\text{rec}}\). Then, the CLPC immediately decreases the \(V_{\text{DD\_Tx}}\) to reduce the transmitted power for the regulation of received Rx power. In the result, the Rx can always receive the constant power from the Tx regardless of any environmental variations during the experiment.
# Virtual Sensing Directional Hub MAC (VSDH-MAC) Protocol with Power Control
## 2 Medium Access Control Protocol  
This section outlines the operation of the virtual sensing directional hub MAC protocol (VSDH-MAC) and the carrier sensing version (DIFS-VSDH-MAC) named after the DCF Interframe Space (DIFS), the period during which carrier sensing occurs, in the IEEE 802.11 CSMA/CA/DCF.  
The proposed VSDH-MAC protocol is similar to the IEEE 802.11 DCF (distributed coordination function), which uses CSMA/CA/DCF protocol [21], and the IEEE 802.15.4 protocol which is a CSMA/CA protocol. However, continuous physical channel sensing is not performed. Instead, virtual channel sensing is enabled using request-to-send/clear-to-send (RTS/CTS) packets in a similar way to the CSMA/CA/DCF protocol. The packet exchange procedure of the VSDH-MAC protocol follows the IEEE 802.11 CSMA/CA/DCF method with the RTS/CTS and DATA/ACK (data/acknowledgement) packet structure.  
In this paper, we consider a network with a single hub node which has a number of directional antennas which can operate simultaneously, each in half-duplex (HDX) mode. The power consumption of the hub node is not considered to be constrained. The sensor nodes are assumed to have a single omni-directional antenna to reduce hardware complexity. It is assumed that the transmit power of each sensor node can be adjusted to minimize the transmit power consumption and inter-node interference. Furthermore, we assume that all communications are initiated by the sensor nodes so that they can remain quiescent and minimize energy consumption when they have no data to transmit.
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## Materials and Methods
### Dual-Polarization Ship Detection Algorithm
As described in [7], the clustering algorithms employed in  
Figure 1: Example of vessel detection results obtained from the dual-polarization-based algorithm by applying both a global and local adaptive threshold to the \(C_{VV-VH}^{un-norm}\) extracted from a Sentinel-1 IW image. vessel detection chains include thresholding approaches followed by a cluster growing procedure, morphological-based operations or target extractions that fit a Gaussian profile. In this study, we propose to apply a segmentation technique based on the k-nearest neighbours that allows clustering the detected pixels into objects. The minimum number of pixels per object was set to five and objects smaller than this size are considered as sea clutter. The maximum number of pixels per object was equal to the size in pixels of the world's longest ship in service (i.e., 450 m). Since the signature of a vessel depends on its structure and its orientation with respect to the SAR sensor, it can be represented as a fractured object which cannot be completely clustered by the segmentation. Therefore, a dilation filter is applied subsequently to the segmentation in order to regroup clusters corresponding to the same object. The entire procedure of the proposed dual-polarization detector is summarized in the pseudo-code described in Algorithm 1. ```
1 functionVesselDetect \((C^{un-norm}_{VV-VH},f)\); Input : Dual-polarization coherence \(C^{un-norm}_{VV-VH}\) Output :\(SAR_{vessels}(lon,lat)\)
2\(V_{detected}=\text{zeros}(dimsC^{un-norm}_{VV-VH})\)
3 for\(swht\gets 1\) to \(N_{swht}\)do
4 estimate \(\mu^{sea-clutter}_{swht}\) & \(\sigma^{sea-clutter}_{swht}\);
5\(T(\mu^{sea-clutter}_{swht},\sigma^{sea-clutter}_{swht})=\mu^{sea-clutter}_{swht }+f\times\sigma^{sea-clutter}_{swht}\);
6 if\(\text{clut-norm}(i_{swht},j_{swht})\geq T\)then
7\(V_{detected}(i_{swht},j_{swht})=1\)
8
9 end for
10
11 end for
12\(SAR_{vessels}(lon,lat)=\text{Cluster}(V_{detected},SAR_{metadata})\);
13 return \(SAR_{vessels}(lon,lat)\);
```  
**Algorithm 1**\(C^{un-norm}_{VV-VH}\)-based detector.
## 3 Reliability Models of Electric Distribution System Components
### Distributed Generation Sources
#### 3.2.2 Renewable Energy Sources  
Among the most popular renewable energy sources in electric distribution systems, there are small hydro power plants (SHPP), small wind-turbine power plants (WTPP) and photovoltaic power plants (PVPP).  
The parameters of different energy carriers (i.e., a river flow, wind speed and solar radiation) can be modeled as the homogenous Markov chain with the states representing different intervals of available energy and the transition rates between \(\lambda_{ij}\) (transition rate from state \(i\) to state \(j\)). The general reliability model of a renewable generation system is shown in Figure 2.  
In order to represent the reliability of particular types of renewable energy sources, the authors of this paper have found an exemplary number of states, fraction of DG rated apparent power corresponding to the state and values of the transition rates between the states [29; 62; 65]. All of these are presented in Tables 4-6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Fraction of DG Rated Apparent Power**} \\ \cline{2-6}  & **0\%** & **60\%** & **100\%** & **0\%** & **0\%** \\ \hline FROM\(\backslash\)TO & U.1 & U.2 & U.3 & U.4 & D \\ U.1 & 0 & 200 1/a & 0 & 0 & 4 1/a \\ U.2 & 20 1/a & 0 & 400 1/a & 0 & 4 1/a \\ U.3 & 0 & 300 1/a & 0 & 10 1/a & 4 1/a \\ U.4 & 0 & 0 & 500 1/a & 0 & 24 1/a \\ D & 90 1/a & 90 1/a & 90 1/a & 24 1/a & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reliability parameters of WTPP [62].  
Figure 2: N + 1-state reliability model of a renewable generation system. \(U.i\) is an up unit state (normal operation) with \(i\)-th of \(n\) level of energy carrier [1,..., \(i,j,\)..., \(n\)] and \(D\) is a down unit state (failure).  
It is necessary to mention the values of fraction the DG rated apparent power given in Table 4. The state U.1 concerns a situation when a wind speed is less than the cut-in wind speed of a wind turbine and the power generation equals 0. No power generation is also in the state U.4. In this case, the wind speed is greater than the cut-out wind speed when a wind turbine is switched off.
## 5 Results and Discussion
### Fault Diagnosis Phase
#### 5.2.2 Diagnostic Data Set Generation  
According to the prediction data set obtained in the fault prediction stage and the normal operation index of each operating parameter, the time interval of fault occurrence is determined. When the operating parameters fluctuate unsteadily, this is the time point at which the fault occurs. According to the fault occurrence interval, the fault diagnosis data set is selected for fault diagnosis analysis, as shown in Figure 21, and the fault interval is determined.
## Nomenclature
### Parameters
  
**C. Variables**  
\(OCa_{s}(t)\) & Cost of active power at time \(t\) in scenario \(s\) \\ \(OCr_{s}(t)\) & Cost of reactive power at time \(t\) in scenario \(s\) \\ \(d_{p}^{u}\) & Traveled distance of \(p\)-th PEV in iteration \(q\) \\ \(DEC_{s}(t)\) & PEVs fleet total battery degradation cost in the scenario \(s\) \\ \(D_{LS}^{TOT}\) & Total degradation for \(p\)-th PEV in scenario \(s\) \\ \(D_{LS}^{PAV}(T_{c,s}^{cyc})\) & Calendar degradation for \(p\)-th PEV at \(c\)-th charging cycle in scenario \(s\) \\ \(D_{Ps}^{CXC}(T_{c,s}^{cyc})\) & Cycling degradation for \(p\)-th PEV at \(c\)-th charging cycle in scenario \(s\) \\ \(DOD_{Ps}(t,S_{c,s}^{cyc})\) & Depth of discharge for \(p\)-th PEV at \(c\)-th charging cycle in scenario \(s\) \\ \(f(x|\mu,\sigma^{2})\) & Normal distribution function \\ \(f(x|\mu,\kappa)\) & Weibull distribution function \\ \(La_{s}(t)\) & Active load at time \(t\) in scenario \(s\) \\ \(Lr_{s}(t)\) & Reactive load at time \(t\) in scenario \(s\) \\ \(Pr(s)\) & Probability of the scenario \(s\) \\ \(P^{u}(t)\) & Active power of equipment u at time \(t\) \\ \(P_{p}^{dr}(t)\) & Charging rate of \(p\)-th PEV ad time \(t\) \\ \(P_{p}^{dr}(t)\) & Discharging rate of \(p\)-th PEV at time \(t\) \\ \(PEVa_{s}(t)\) & PEV's active power at time \(t\) in scenario \(s\) \\ \(PEVr_{s}(t)\) & PEV's reactive power at time \(t\) in scenario \(s\) \\ \(Plossa_{s}(t)\) & Active power loss at time \(t\) in scenario \(s\) \\ \(Plossr_{s}(t)\) & Reactive power loss at time \(t\) in scenario \(s\) \\ \(p_{n_{u},t}^{val}\) & Power generated by the wind turbine \\ \(P_{Wmax}\) & Nominal generated power by the wind turbine \\ \(Q^{u}(t)\) & Receive power of equipment u at time \(t\) in scenario \(s\) \\ \(Q_{p}^{USE}\) & Useful capacity of \(p\)-th PEV \\ \(Q_{p}^{p}\) & Nominal capacity of \(p\)-th PEV \\ \(Ra_{s}(t)\) & Purchesed active power at time \(t\) in scenario \(s\) \\ \(Rr_{s}(t)\) & Purchesed reactive power at time \(t\) in scenario \(s\) \\ \(SOC_{unit,p}^{ct}\) & SOC at initial time \(t\) for \(p\)-th PEV \\ \(SOC_{ps}(t_{4 pp})\) & SOC at departure time \(t\) for \(p\)-th PEV \\ \(SOC_{ps}(t)\) & SOC at time \(t\) for \(p\)-th PEV \\ \(SOC_{ps}(T_{c,s}^{cyc})\) & SOC of \(p\)-th PEV at \(c\)-th charging cycle in scenario s \\ \(SOC\) & Nominal SOC of \(p\)-th PEV \\ \(T_{c,s}^{cyc}\) & \(c\)-th charging cycle \\ \(t_{s}\) & Charge time \\ \(Wa_{s}(t)\) & Wind active power at time \(t\) in scenario \(s\) \\ \(Wr_{s}(t)\) & Wind reactive power at time \(t\) in scenario \(s\) \\ \(V_{s}(i,j,t)\) & Line voltage between bus \(i\) and \(j\) at time \(t\) in scenario \(s\) \\ \(V_{l}(t)\) & Voltage value for bus p at time \(t\) \\ \(v_{t}\) & Wind speed \\ \(v_{ci}\) & Wind turbine cut-in speed \\ \(v_{\alpha}\) & Wind turbine cut-out speed \\ \end{tabular}
# Research on Bidirectional Reservation Method for Anti-Permeation Geomembrane Slack
## 2 Geomembrane Slack Methods
### Two-Way Reservation Method  
#### 2.2.1 Two-Way Reservation Concept  
The existing wave-, groove-, and Z-shaped concentrated reservation lay-ups are unidirectional. That is, they can only address the tensile stress on the geomembrane from one direction. However, numerous studies have shown that in practice, geomembranes are mostly stretched in two directions due to various factors, resulting in bidirectional stresses in geomembranes [27; 28; 29; 30; 31]. The existing method of reserving slack in one direction for geomembranes cannot address the protection of geomembranes when stretched in two directions.  
To address this problem, we propose the concept of a bidirectional geomembrane slack reservation method. Geomembranes are laid with slack reserved in two directions to stretch freely when subjected to tensile stresses in different directions. Based on the concept of bidirectional reservation and the existing unidirectional reservation methods, we propose three bidirectional reservation methods so that the geomembrane can address the effects of tensile stresses from any direction during geomembrane stretching.
## 2 Design Procedure
### Passbands Optimization and Tuning of the Filter  
One of the major merits of the proposed dual-band BPFs is their configurable passbands so that they can be tuned throughout the C-band without the need for any active or additional modules. The lower band of the proposed filter can be tuned by varying the capacitive section of the flag-shaped resonators (A\({}_{14}\)), where decreasing A\({}_{14}\) would decrease the capacitive effects, shifting the resonance frequency to higher frequencies, resulting in 600 MHz dynamic range (4.4 GHz to 5 GHz) as shown in Figure 8. The second passband can be adjusted by either varying the capacitive (A\({}_{21}\)) or inductive (A\({}_{18}\)) sections of the stepped-impedance resonators. Indeed, increasing A\({}_{21}\) or A\({}_{18}\) would have the same effect on the upper band, increasing the associated capacitor and inductor, respectively, shifting the upper band to lower frequencies. As shown in Figures 9 and 10, the upper band has a large dynamic range of 1000 MHz (from 7 GHz to 8 GHz).  
Figure 8: Adjustability of the lower band of the proposed BPF by varying A\({}_{14}\): (a) \(\text{S}_{21}\), (b) \(\text{S}_{11}\).  
Figure 7: Current density distributions of the proposed dual-band BPF.  
Figure 10: Adjustability of the upper band of the proposed BPF by varying A\({}_{18}\): (**a**) \(\mid\)S\({}_{21}\)L, (**b**) \(\mid\)S\({}_{11}\).  
Figure 9: Adjustability of the upper band of the proposed BPF by varying A\({}_{21}\): (**a**) \(\mid\)S\({}_{21}\)L, (**b**) \(\mid\)S\({}_{11}\).  
The tunable effects of the filter passbands are depicted in Figure 11. As shown in this figure, the fractional bandwidths (FBWs) of both passbands increase slightly as a result of pushing down the first and second center frequencies, while the actual bandwidths remain almost unchanged. It needs to be mentioned that re-prototyping is required for the practical implementation of this tunability.  
Quality factor (Q. factor) is defined as the ratio of operating frequency and bandwidth as shown in (4). Figure 12 shows the Q. factor of the proposed filter as a function of the three physical parameters of A\({}_{18}\), A\({}_{21}\) and A\({}_{14}\). As depicted in this figure, the Q. factor decreases by increasing A\({}_{18}\), A\({}_{21}\) and A\({}_{14}\).  
\[Q.factor=\frac{f_{0}}{B.W} \tag{4}\]  
Figure 11: (a) Variations of bandwidth as a function of A\({}_{18}\), A\({}_{21}\) and A\({}_{14}\), (b) Variations of fractional bandwidth (FBW) as a function of A\({}_{18}\), A\({}_{21}\) and A\({}_{14}\).
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 2 System Model
### Communication Model
To reconstruct the aggregated gradient, the AP performs gradient-based linear estimation post-processing on the received signal \(r_{m,c}^{(n)}\), given as  
\[y_{m,c}^{(n)} =\frac{1}{K}\Bigg{(}\frac{r_{m,c}^{(n)}}{\sqrt{\theta_{m}^{(n)}} }+\sum_{k\in\mathcal{K}_{m}}\bar{a}_{k}^{(n)}\Bigg{)}\] \[=\frac{1}{K}\left(\sum_{k\in\mathcal{K}_{m}}a_{k,c}^{(n)}+\frac{r _{m,c}^{(n)}}{\sqrt{\theta_{m}^{(n)}}}-\sum_{k\in\mathcal{K}_{m}}\left(a_{k,c }^{(n)}-a_{k}^{(n)}\right)\right)\] \[=a_{m,c}^{(n)}+\varepsilon_{m,c}^{(n)}, \tag{9}\]  
where \(a_{m,c}^{(n)}=\frac{1}{K}\sum_{k\in\mathcal{K}_{m}}a_{k,c}^{(n)}\) is the desired average gradient; \(\varepsilon_{m,c}^{(n)}\) represents the aggregation error due to noise, power mismatch, and inter-cell interference, given as  
\[\varepsilon_{m,c}^{(n)}=\frac{1}{K}\Bigg{(}\sum_{k\in\mathcal{K}_{m}}\Bigg{(} \frac{\sqrt{p_{k}^{(n)}}|h_{m,k}^{(n)}|}{\sqrt{\theta_{m}^{(n)}}}-v_{k}^{(n)} \Bigg{)}s_{k,c}^{(n)}+\sum_{j\in\mathcal{M}\setminus\{m\}}\sum_{i\in\mathcal{ K}_{j}}\frac{\sqrt{p_{i}^{(n)}}h_{m,i}^{(n)}(h_{j,i}^{(n)})^{H}}{\sqrt{\theta_{m}^{(n) }}|h_{j,i}^{(n)}|}s_{i,c}^{(n)}+\frac{Z_{m,c}^{(n)}}{\sqrt{\theta_{m}^{(n)}}} \Bigg{)}. \tag{10}\]  
By collecting all \(C\) dimension model parameters, the average global gradient received by AP \(m\in\mathcal{M}\) is given by  
\[\bar{a}_{m}^{(n)}=\Re\{\mathbf{y}_{m}^{(n)}\}=\Big{(}\mathbf{a}_{m}^{(n)}+\Re\{\mathbf{e}_ {m}^{(n)}\}\Big{)}, \tag{11}\]  
where \(\mathbf{a}_{m}^{(n)}=[a_{m,1}^{(n)},\ldots,a_{m,c}^{(n)}]^{T},\mathbf{y}_{m}^{(n)}=[y _{m,1}^{(n)},\ldots,y_{m,c}^{(n)}]^{T},\mathbf{e}_{m}^{(n)}=[\varepsilon_{m,1}^{( n)},\ldots,\varepsilon_{m,c}^{(n)}]^{T}\). Finally, the global model of cell \(m\in\mathcal{M}\) is updated as  
\[\mathbf{a}_{k}^{(n)} =\frac{1}{C}\sum_{c=1}^{C}a_{k,c}^{(n)}, \tag{5}\]0  
where \(\eta_{m}^{(n)}\) is the learning rate. This process continues until convergence or until the maximum number of communication rounds is reached.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Model Non-Batch By Category  
#### Service Time Modelling  
To recap on the importance of modelling by \"Non-Batch By Category\", we refer the reader back to Section 3.2.1. We now try and fit a parametric distribution to our data using different techniques from the classification model. First, we analyse the measures of dispersion using the \"Non-Batch By Category\" model. We note from Table 15 that for the service times, one-third of the messages are removed when we remove messages of zero duration. We also note a slight difference in the service times in the 95 th percentile between non-batch messages and when zeros are removed from the dataset. Our dataset is highly skewed, with a reporting skewness greater than 20. With the zeros removed from the dataset, the lowest service time is 0.001 s.  
Our closest model to fit a parametric distribution is in Figure 19. We apply the model to the \"Normal Period\" using the \"Tail\" of the data and removing messages that are zero seconds in duration.  
Table 16 shows the top two best models for parametric fitting. We note a large AD score and ascertain that even the best fitting model is not particularly good, and so we have not identified a suitable parametric model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test** & **No Transform** & **Log Transform +1** & **Square Root** & **Cube Root** \\ \hline log-normal & 3942 & 3966 & 3942 & 3942 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Non-batch by category classification model.  
Figure 19: Non-batch by category classification model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Data** & **Log** & **Sqrt (data)** & **Exp (data)** & **Sqrt (data+1)** & **Log (log** & **Sqrt (data+1)** & **Sqrt (data)** \\ \hline \hline \multicolumn{10}{c}{AD Score} \\ \hline Burr & **3.9** & 6.6 & 4 & Inf & 6.6 & 8.5 & Inf \\ \hline \hline \end{tabular} In the interest of space, only AD scores are shown in the table. The majority of \(p\)-values round to 0.00.  
\end{table}
Table 14: IAT \(>\) 1 s, Split Count = 1: AD Test.  
When modelling the head of our data, we again found evidence of discrete values leading to quantisation noise. We refer to Section 4.4 for a discussion around the effects of quantisation.  
Using the classification model, we attempt to fit our data to a parametric distribution. We now focus our efforts on non-parametric modelling.