esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Data-Driven Integral Sliding-Mode Control and Its Automation Application
### Traditional MFAC Design  
Consider the following performance-metric function:  
\[\lambda(O(i))=\partial|O(i)-O(i-1)|^{2}+|I_{d}(i+1)-I(i+1)|^{2} \tag{7}\]  
where \(I_{d}\) is the desired output and \(\partial>0\) is a weighting factor.  
The control law is obtained by taking (7) to the first order partial differentiation of \(O(i)\) and making it zero as follows:  
\[\begin{array}{c}O(i)=O(i-1)+\frac{\rho_{L_{I}+h_{I}+1}(i)(I_{d}(i+1)-I(i))}{ \partial+\left|h_{L_{I}+1}(i)\right|^{2}}\\ -\frac{h_{y^{+1}}(i)\left(\sum\limits_{k=1}^{L_{I}}\rho_{k}h_{k}(i)\Delta I(i- k+1)+\sum\limits_{k=L_{I}+1}^{L_{I}+1}\rho_{k}h_{k}(i)\Delta O(i-L_{I}-k+1) \right)}{\partial+\left|h_{L_{I}+1}(i)\right|^{2}}\end{array} \tag{8}\]  
where \(\rho\in(0,1]\) is the step factor.  
The following estimation function is utilized to accurately estimate the time-varying parameter vector \(\mathbf{H}(i)\):  
\[J(\mathbf{H}(i))=\left|\Delta I(i)-\mathbf{H}^{\text{T}}(i)\Delta\mathbf{L}(i-1 )\right|^{2}+\mu\big{\|}\mathbf{H}(i)-\mathbf{\hat{H}}(i-1)\big{\|}^{2} \tag{9}\]  
where \(\mu>0\) is the weight factor.  
The parameter estimation algorithm is given by the minimization of the parameter estimation criterion function (9):  
\[\mathbf{\hat{H}}(i)=\mathbf{\hat{H}}(i-1)+\frac{\eta\Delta\mathbf{L}(i-1) \big{(}\Delta I(i)-\mathbf{\hat{H}}^{\text{T}}(i-1)\Delta\mathbf{L}(i-1)\big{)} }{\mu+\left\|\Delta\mathbf{L}(i-1)\right\|^{2}} \tag{10}\]  
where \(\mathbf{\hat{H}}(i)\) is the estimated value of \(\mathbf{H}(i)\) and \(\eta\in(0,2]\) is the step factor.  
Design parameter reset algorithms to further enhance the system's anti-interference ability:  
\[\mathbf{\hat{H}}(i)=\mathbf{\hat{H}}(1)\;\;\text{if}\;\;\big{\|}\mathbf{\hat{ H}}(i)\big{\|}\leq\varepsilon\;\;\text{or}\;\;\big{\|}\Delta\mathbf{L}(i-1) \big{\|}^{2}\leq\varepsilon\]  
or \(\;\text{sign}\Big{(}\hat{h}_{L_{l}+1}(i)\Big{)}\
eq\text{sign}\Big{(}\hat{h}_{ L_{l}+1}(1)\Big{)}\)  
The control scheme is obtained as follows:  
\[\left\{\begin{array}{l}\Delta\text{O}(i)=\frac{\rho_{L_{l}+\hat{h}_{L_{l}+1} (i)\left(I_{l}(i+1)-I(i)\right)}}{\delta+\left|\hat{h}_{L_{l}+1}(i)\right|^{2} }-\\ \frac{h_{l_{y}+1}(i)\left(\sum\limits_{k=1}^{L_{l}}\rho_{k}\hat{h}_{k}(i) \Delta I(i-k+1)+\sum\limits_{k=l+2}^{L_{l}+L_{l}}\rho_{k}\hat{h}_{k}(i)\Delta \text{O}(i-L_{l}-k+1)\right)}{\delta+\left|\hat{h}_{L_{l}+1}(i)\right|^{2}}\\ \mathbf{\hat{H}}(i)=\mathbf{\hat{H}}(i-1)+\frac{\eta\Delta\mathbf{L}(i-1) \left(\Delta I(i)-\mathbf{\hat{H}}^{\text{T}}(i-1)\Delta\mathbf{L}(i-1) \right)}{\mu+\left\|\Delta\mathbf{L}(i-1)\right\|^{2}}\\ \mathbf{\hat{H}}(i)=\mathbf{\hat{H}}(1)\;\;\text{if}\;\;\big{\|}\mathbf{\hat{ H}}(t)\big{\|}\leq\varepsilon\;\;\text{or}\;\;\|\Delta\mathbf{L}(i-1)\big{\|}^{2} \leq\varepsilon\;\;\text{or}\;\;\text{sign}\Big{(}\hat{h}_{L_{l}+1}(i)\Big{)} \
eq\text{sign}\Big{(}\hat{h}_{L_{l}+1}(1)\Big{)}\\ \end{array}\right. \tag{12}\]  
where the \(\rho\in(0,1]\) is the step factor.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 4 Future Perspectives
### Towards Magnetoelectric Switching Dynamics  
Now that non-destructive access to a buried ferroelectric domain configuration is gained using SHG, tracking its dynamic response to external perturbations is enabled. It is possible, for example, to apply an electric field to magnetoelectric devices and track the resulting shifts in the magnetic domain pattern. To the best of our knowledge, only a few experiments were carried out with the scope of monitoring the multiferroic, respectively magnetoelectric switching dynamics [13; 142; 143]. In the case of BFO thin films, the kinetics of the electric field induced reversal of the canting of the antiferromagnetically aligned spins has been investigated using time dependent dual-frequency PFM  
Figure 17: Characterization of the polar state during the growth. (**a**) Schematic of the in situ X-ray diffraction experiment, (**b**) Example of diffraction results for a BTO/STO superlattice. (**a,b**) Reprinted from [21]. (**c**) Schematic of the in situ SHG experimental setup. (**d**) In situ SHG measurement showing the emergence of polarization during the growth of a BTO thin film. (**e**) In situ SHG during the deposition of a BFO/SRO multilayer with a parallel polarization configuration. (**c–e**) Reprinted with from [61].  
studies and first principle calculations [13]. A two-step switching process of the coupled polarization and the antiferromagnetic order results in the observed electric field induced magnetization reversal.  
In another study, SHG measurements performed on bulk MnWO\({}_{4}\) revealed a surprisingly slow reversal of the magnetic order parameter by an electric field on the time scale of milliseconds [143]. Hence, although understanding the _switching dynamics_ rather than solely the _presence_ of a magnetoelectric coupling is essential for any type of device application, research in this direction remains an underdeveloped topic. The non-invasive nature of SHG may now pave the way to such investigations [88; 113] in multifunctional thin film architectures.  
Parallel to this, alternatives to multiferroic and magnetoelectric switching mechanisms are gaining increasing interest in the push for low-energy-consuming electric-field control of magnetization. Spin-orbit-torque-based magnetic domain switching may serve as an example here [144]. Recent experiments have demonstrated a current induced magnetic switching in the picosecond regime [145].
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 3 Problem Statement and Mathematical Model
### Mathematical Model
\begin{split} Y_{h,d,c,j,s}=\{0,1\}\forall h\in H,\ d\in D,\ c\in C\cup M,\ j\in C\cup M,\ s\in S\\ X_{h,d,cj}=\{0,1\}\forall h\in H,\ d\in D,\ c\in C\cup M,\ j\in C\cup M \\ Z_{h}=\{0,1\}\forall h\in H\\ R_{s}=\{0,1\}\forall s\in S\end{split}  \tag{15}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \multicolumn{3}{c}{**Indices and Sets**} \\ \hline C & The set of customers \\ S & The set of all items (shipments, parcels) \\ D & The set of all drones \\ H & A set of possible locations for mobile points (mobile hubs—MHs) \\ c, j & Delivery point (customer) index (c, j \(\in\) C) \\ s & Shipment index (s\(\in\)S) \\ d & Drone index (d\(\in\)D) \\ h & Mobile point index (h\(\in\)K) \\ \hline \multicolumn{3}{c}{**Parameters**} \\ \hline VS\({}_{s}\) & Parcel volume (volumetric weight) s (s\(\in\)S) \\ VC\({}_{d}\) & Drone’s d payload (d\(\in\)D) \\ RA\({}_{s,c}\) & If parcel k should be delivered to customer o, then RA\({}_{s,c}\) = 1, otherwise RA\({}_{s,c}\) = 0 (s\(\in\)S, c\(\in\)C) \\ FA\({}_{cj}\) & Cost of travel between customers c and j (c, j \(\in\) C\(\cup\)M)—average values include 2D and 3D \\ TA\({}_{cj}\) & Transfer time between customers c and j (c, j \(\in\) C\(\cup\)M)—average values include 2D and 3D \\ TX & Time within which the delivery should be made \\ MS & Maximum number of mobile points (mobile hubs—MHs) \\ ST & A large constant \\ UA\({}_{s}\) & If parcel \(s\) was delivered from the mobile point (MH) to the customer, then UA\({}_{s}\) = 1; otherwise, UA\({}_{s}\) = 0 (s\(\in\)S). The parameter value determined the delivery direction. \\ \hline \multicolumn{3}{c}{**Decision variables**} \\ \hline X\({}_{h,d,cj}\) & If a drone d traveled from customer c to customer j and took off from MH, h then X\({}_{h,d,c,j}\) = 1, otherwise X\({}_{h,d,c,j}\) = 0, \\ X\({}_{h,d,cj,s}\) & (h\(\in\)H, d\(\in\)D, cj\(\in\)C\(\cup\)M). \\ Y\({}_{h,d,c,j,s}\) & If a drone d traveled from customer c to customer j and took off from MH, h carrying parcel s, then Y\({}_{h,d,c,j,s}\) = 1, \\ Y\({}_{h,d,c,j,s}\) & otherwise Y\({}_{h,d,c,j,s}\) = 0, (h\(\in\)H, d\(\in\)D, c, j\(\in\)C\(\cup\)M). \\ R\({}_{s}\) & If parcel s could not be delivered, then R\({}_{s}\) = 1 otherwise R\({}_{s}\) = 0 (s\(\in\)S). (The introduction of this decision variable meant that the model will always be solvable.) \\ Z\({}_{h}\) & If there were take-offs from mobile point h, then Z\({}_{h}\) = 1; otherwise, Z\({}_{h}\) = 0. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the constraints.
## Nomenclature  
\begin{tabular}{l l} \(A\) & area (m\({}^{2}\)) \\ \(A_{c}\) & the outer surface area of absorber tube (m\({}^{2}\)) \\ \(C_{b}\) & synthetical conductance (W/(m \({}^{\circ}\)C)) \\ \(C_{B}\) & bond conductance (W/(m \({}^{\circ}\)C)) \\ \(\epsilon_{p}\) & specific heat (kJ/(kg \({}^{\circ}\)C)) \\ \(d\) & diameter of the U-tube (m) \\ \(D\) & diameter of insulation (m) \\ \(E\) & compressor work (kWh) \\ \(F\) & fin efficiency of straight fin \\ \(F^{\prime}\) & collector efficient factor \\ \(F_{R}\) & heat removal factor \\ \(h\) & heat transfer coefficient (W/(m \({}^{\circ}\)C)), specific enthalpy (kJ/kg) \\ \(h_{g\cdot g}\) & convection heat transfer coefficient (W/(m\({}^{2}\) \({}^{\circ}\)C)) \\ \(h_{p\cdot g\cdot r}\) & radiation heat transfer coefficient (W/(m\({}^{2}\) \({}^{\circ}\)C)) \\ \(h_{p\cdot g\cdot c}\) & conduction heat transfer coefficient (W/(m\({}^{2}\) \({}^{\circ}\)C)) \\ \(I\) & solar irradiance (W/m\({}^{2}\)) \\ \(LMTD\) & logarithmic mean temperature difference (\({}^{\circ}\)C) \\ \(L_{T}\) & length of the header pipe per one U-tube (m) \\ \(m\) & mass flow rate (kg/s) \\ \(Q\) & heat load (kW) \\ \(s\) & tot thermal conductance of the installed UA (kW\({}^{\circ}\)C) \\ \(t\) & time \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(UA\) & product of heat transfer coefficient and area (W\({}^{\circ}\)C) \\ \(U_{e}\) & edge loss coefficient (W/(m\({}^{2}\) \({}^{\circ}\)C)) \\ \(U_{L}\) & overall heat loss coefficient (W/(m\({}^{2}\) \({}^{\circ}\)C)) \\ \(U_{t}\) & heat loss coefficient from absorber tube to ambient W/(m\({}^{2}\) \({}^{\circ}\)C) \\ \(V\) & specific volume of storage tank (m\({}^{3}\)) \\ \end{tabular}
## 3 Automatic Feature-Based Approach
### Registration Accuracy Estimation Technique  
A common procedure to assess the quality of the registered image is to compute the error in the location of a few control points. As it has been commented, this procedure is subject to problems of availability and representativity of such points as well as uncertainty in their interactive location. To overcome these problems, a new method has been developed based on the use of all the image available information. The procedure has two parts:  
\(\bullet\)Error field generation: A distance map is generated computing those pixels which are at a given distance from the reference contours. In this implementation, distances are computed by successive dilations of the reference contours. Figure 5 shows an example of an error field obtained from a reference image consisting on the coastlines extracted from a geographic database. Distance values greater than 3 are not computed because errors of such magnitude do not appear after the geometric correction of the residual errors.  
* Extraction of sea-land contours: The reliable outer sea-land contours are extracted from sensed image and transformed by the obtained correction function, to superimpose them on the distance map. Contours are extracted from the gradient map of the relevant areas. In this application, we have used a thresholding approach given its simplicity. Since gradients are smooth, the resulting contours have to be thinned up to one pixel width.  
Finally, the distance map (DM(.)) is evaluated at all points (p) belonging to the binary representation of the transformed contours (C). The final RMS error is computed by adding up these values and normalizing them by the total number of contour points in C (N = \(\left|\text{C}\right|\)):  
\[RMSE=\sqrt{\frac{\sum\limits_{p\subset C}DM\left(p\right)}{N}} \tag{11}\]  
As it will be shown in the following section, this method enables the use of a large number of points in the RMS error computation, making the measure more robust.  
Figure 5: Registration Accuracy Estimation Technique: **(a)** Example of a reference image (coastlines) extracted from a geographic database (27-33\({}^{\circ}\) N, 8-19\({}^{\circ}\) W and, **(b)** error field generated for this reference image.
# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 2 Materials and Methods  
### Selection of Stressors, Growth Conditions, and Data Sets  
We investigated the _E. coli_ K-12 MG1655 transcriptome under five categories of different stresses: heat, cold, oxidative stress, nitrosative stress, and antibiotic treatment. Microarray data sets were obtained from the Gene Expression Omnibus [11]. All available data sets for stress conditions in _E. coli_ K-12 MG1655 were evaluated, and 14 data sets were chosen to identify genes with altered transcription under the aforementioned stressors: GSE11041(heat, cold), GSE20305 (heat, cold, oxidative), GSE61736 (cold, oxidative), GSE56133 (oxidative, antibiotics), GSE15534 (heat), GSE40557 (heat), GSE42675 (heat), GSE60522 (nitroastive stress), GSE58176 (oxidative stress), GSE19370 (oxidative stress), GSE57084 (antibiotics), GSE47221(antibiotics), GSE37026 (antibiotics), and GSE10160 (antibiotics). Details of data sets accession numbers, stressor subtypes, and different conditions included in the study are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stressor** & **Subtype** & **Media** \\ \hline \multirow{6}{*}{**Heat**} & GSE15534 (45 \({}^{\circ}\)C 10 min) & M9 complete medium \\ \cline{2-3}  & GSE40557 (58 \({}^{\circ}\)C f = 2, 58 \({}^{\circ}\)C f = 3, 60 \({}^{\circ}\)C f = 3, 71 \({}^{\circ}\)C) & Brain Heart Infusion (BHI) \\ \cline{2-3}  & GSE42675 (42 \({}^{\circ}\)C 10 min) & M9 minimal medium \\ \cline{2-3}  & GSE11041 (46 \({}^{\circ}\)C till 1 \(\times\) 10 \({}^{7}\) CFU/mL) & Tryptone soya broth (TSB) \\ \cline{2-3}  & GSE20305 (45 \({}^{\circ}\)C 10 min) & Modified morpholinopropane sulfonate (MOPS) minimal medium \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of stress conditions used in each data set.
## 1 Introduction
### Definitions  
Throughout this paper, we assume \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}\). Let \(x_{\text{best}}^{k}\) denote the best solution found by the method at iteration \(k\) and \(f_{\text{best}}^{k}\) denote the corresponding function value.  
We present the definitions that are used to approximate gradient and Hessian by the DQL method as follows. We begin with the Moore-Penrose pseudoinverse.  
**Definition 1** (Moore-Penrose pseudoinverse).: _Let \(A\in\mathbb{R}^{n\times m}\). The Moore-Penrose pseudoinverse of \(A\), denoted by \(A^{\dagger}\), is the unique matrix in \(\mathbb{R}^{m\times n}\) that satisfies the following four equations:_  
\[AA^{\dagger}A=A, \tag{2}\]  
\[A^{\dagger}AA^{\dagger}=A, \tag{3}\]  
\[(AA^{\dagger})^{\top}=AA^{\dagger}, \tag{4}\]  
\[(A^{\dagger}A)^{\top}=A^{\dagger}A. \tag{5}\]  
The generalized centred simplex gradient and generalized simplex Hessian are studied in [17; 18], respectively.  
**Definition 2** (Generalized centred simplex gradient [17]).: _Let \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}\), \(x_{0}\in\mathbb{R}^{n}\) be the point of interest, and \(D=\begin{bmatrix}d^{1}&d^{2}&\cdots&d^{k}\end{bmatrix}\in\mathbb{R}^{n\times k}\). The generalized centred simplex gradient of \(f\) at \(x_{0}\) over \(D\) is denoted by \(\
abla_{c}f(x_{0};D)\) and defined by,_  
\[\
abla_{c}f(x_{0};D)=(D^{\top})^{\dagger}\delta_{f}^{c}(x_{0};D)\in\mathbb{R} ^{n}, \tag{6}\]  
_where_  
\[\delta_{f}^{c}(x_{0};D)=\frac{1}{2}\big{[}f(x_{0}+d^{1})-f(x_{0}-d^{1}),\ \ \ \cdots,\ \ \ f(x_{0}+d^{k})-f(x_{0}-d^{k})\big{]}^{\top}. \tag{7}\]  
**Definition 3** (Generalized simplex Hessian).: _Let \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}\) and \(x_{0}\) be the point of interest. Let \(S=\begin{bmatrix}s^{1}&s^{2}&\cdots&s^{m}\end{bmatrix}\in\mathbb{R}^{n\times m}\) and \(\{D^{i}:D^{i}\in\mathbb{R}^{n\times k},i=0,1,2,\cdots,m\}\) be the set of direction matrices used to approximate the gradients at \(x_{0},x_{0}+s^{1},\  
In Section 3, in order to prove convergence of our method, we make use of the cosine measure as defined in [9].  
**Definition 4** (Cosine measure).: _Let \(D=\begin{bmatrix}d^{1}&d^{2},\cdots,d^{m}\end{bmatrix}\in\mathbb{R}^{n\times m}\) form a positive basis.We say \(D\) forms a positive basis if \(\{x:x=\sum_{i=1}^{m}\lambda d^{i},\lambda\geq 0\}=R^{n}\) but no proper subset of \(D\) has the same property. The cosine measure of \(D\) is defined by,_  
\[cm(D)=\min_{\omega\in\mathbb{R}^{n}}\Biggl{\{}\max_{d\in D}\Biggl{\{}\frac{ \omega^{\top}d}{\|\omega\|\|d\|}\Biggr{\}}:\|\omega\|=1\Biggr{\}}. \tag{10}\]
# Fractional Order Processing of Satellite Images
## 1 Introduction  
### Objective and Contribution  
Image processing has always been an essential tool in remote sensing. Processed images taken by satellites allow extracting relevant information in fields like land monitoring, routine mapping and surveillance [1].  
Edge detection represents an important role in remote sensing. The conventional edge detectors are based on integer derivatives. With the introduction of fractional calculus, some new non-integer edge detectors were created and the existent integer ones adapted [2].  
The aforementioned detectors were developed for grey-scale images. Since colored images are often available, a simple solution is to convert them to grey-scale, but this may compromise the performance of edge detection and discards potentially relevant information. Thus, color-based edge detection detectors were created [3, 4].  
Algorithms for edge detection methods often involve first or second order derivatives, which can be generalised using a fractional order derivative instead [5]. The goal of this paper is to apply fractional edge detection to satellite images, verifying how appropriate these algorithms are for the job. To assess this, results obtained with fractional order algorithms applied to color images are compared with those of conventional integer order algorithms, for both color and grey-scale images. Another important point to assess performance is whether or not it is possible to use fixed parameters to treat all the images. Experiences with medical images, for which optimal parameters have very disparate values, and are hardly predictable in advance, suggest that this may be an issue [6]. So the contribution of this paper is to show that fractional derivatives achieve good results in image treatment of satellite images with parameters automatically set.
## 4 Results
### Log and RFT Data Fluid Contacts Comparison  
The integration and comparison of the log and RFT results were performed to adequately predict/locate the fluid contacts in the studied intervals. The comparison was successfully performed for E-AJ1 and E-CB1. For E-BK1, the resistivity, density, and neutron porosity logs identified a gas-water contact at 2967.5 m with a 25.5 m gas column. However, due to missing RFT data, an adequate comparison could not be achieved. Below, we present a detailed comparison of E-AJ1 and E-CB1.  
Figure 12 compares and integrates the results obtained from well logs with RFT within the E-AJ1 studied reservoir. The oil-water contact was identified at 2723 m, while the gas-oil contact was encountered at 2699 m using the well log. The well log and RFT-identified contacts are in agreement with each other. The well logs analysis shows a 6 m gas column, a 24 m high oil production column, and a 12 m water column. A gas-oil contact from 2699 m and 3870 psi formation pressure were observed, while an oil-water contact from 2723 m and 3907 psi formation pressure were also observed.  
Figure 12: Well E-A-J1 comparison of log and pressure data (RFT).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Well** & \begin{tabular}{c} **Water** \\ **Gradient** \\ \end{tabular} & **Density (g/cc)** & \begin{tabular}{c} **Oil** \\ **Gradient** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **(g/cc)** \\ \end{tabular} & \
\begin{tabular}{c} **Gas** \\ **Gradient** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **(g/cc)** \\ \end{tabular} \\ \hline E-BK1 & – & – & – & – & 0.14 psi/ft & 0.32 \\  & & & & 0.45 psi/m & \\ E-AJ1 & 0.44 psi/ft & 1.01 & 0.30 psi/ft & 0.69 & 0.1 psi/ft & 0.23 \\  & 1.44 psi/m & & 0.98 psi/m & & 0.33 psi/m & \\ E-CB1 & 0.42 psi/ft & 0.97 & 0.26 psi/ft & 1.00 & – & – \\  & 1.37 psi/m & & 0.85 psi/m & & & \\ Average & 0.43 psi/ft & 0.99 & 0.28 psi/ft & 0.85 & 0.12 psi/ft & 0.28 \\  & 1.41 psi/m & & 0.92 psi/m & & 0.39 psi/m & \\ \hline \end{tabular}
\end{table}
Table 3: Summary of pressure gradients and densities for all wells.  
Figure 13 compares the pressure data and log with the identified oil-water contact for well E-CB1. At a depth of 2523 m, the resistivity curve did, however, show an oil-water contact. The contact occurred at 2528.5 m depth; therefore, it differs from the one determined by the pressure data. The log and pressure data used to establish the oil-water contact do not concur because the well log observations showed a shallower depth compared to RFT data, with a depth difference of 5.5 m. The water and oil gradient results--1.37 psi/m (equivalent to 0.97 g/cc) and 0.85 psi/m (equivalent to 1.00 g/cc)--suggest that the sandstone reservoir seems to be hydraulically connected. The oil-water contact formation pressure observed at 2528.5 m was 3642 psi.