esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study analyzed the influence of different ammonia stripping parameters on ammonia removal efficiency and mass transfer rate. Ammonia stripping was performed on two devices, a column and a packed tower, with artificial ammonium hydroxide wastewater. First, ammonia concentration and pH were varied in a column without liquid circulation. At the same pH, the removal efficiency and mass transfer rate were constant, irrespective of initial ammonia concentration. When pH was increased, the ammonia fraction also increased, resulting in higher removal efficiency and mass transfer rate. Second, the effects of stripping were assessed using a packed tower with fluid circulation. The ammonium hydroxide concentration did not affect the removal efficiency or mass transfer rate. Furthermore, at apparatus liquid-gas ratios of 26.8-107.2 L/m3, a lower liquid-gas ratio led to increased ammonia removal efficiency and mass transfer rate. Conversely, the lower the liquid-gas ratio, the greater the air consumption. In conclusion, considering the removal rate and volume of air supply, the range of optimal liquid-gas ratio was determined as 26.8-53.6 L/m3. In particular, the 26.8 L/m3 condition achieved the best ammonia removal rate of 63.0% through only 6 h of stripping at 70 \({}^{\circ}\)C and pH 8.5.  
wastewater treatment; nitrogen removal; ammonia stripping; air stripping; stripping tower +
Footnote †: journal: Applied Sciences  
10 November 2020  
13 December 2020  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**[PERSON]**  
Center for Plant Engineering, Institute for Advanced Engineering, Yongin-si 17180, Korea; [EMAIL_ADDRESS] (E.J.K.); kimb@[EMAIL_ADDRESS] (H.K.)  
**Correspondence: [EMAIL_ADDRESS]**
# Recent Advances in Extraction and Stirring Integrated Techniques
## 2 Stir Bar Sorptive Extraction  
Stir bar sorptive extraction (SBSE) was the first technique based on the integration of the extraction and stirring elements in the same device. It was proposed in 1999 as an alternative to solid phase microextraction (SPME) with which it shares some extraction principles [3]. The basic SBSE device consists of a stir bar coated with a polymeric phase that acts as the extractant. The stir bar is introduced and stirred into the sample and the analytes are extracted in an enhanced diffusion medium. The similarities between SPME and SBSE are based on the use of almost the same coatings which makes the application field similar. However, both techniques operate with different workflows and also differ based on the sorptive phase thickness. The last fact is of paramount importance and has thermodynamic and kinetics connotations. On the one hand, the extraction recoveries in SBSE are higher than in SPME, as the volume of the sorptive phase is 50-250 times higher in the first technique. However, the extraction kinetics are somewhat affected by the thickness since the diffusion of the analytes in the polymeric coating is hindered [4].  
Polydimethylsiloxane (PDMS), a classic SPME coating, is extensively reported in SBSE applications, which are focused, as a direct consequence of the hydrophobic nature of the coating, on the extraction of non-polar compounds [5]. The in-lab synthesis of new coatings to extend the applicability of SBSE has been the main research topic during the last decade. The most salient coatings are presented in the following sections. However, it is necessary to highlight that some of the new coatings are not commercially available since they are not as robust as the classical PDMS one.
## 2 Efficiency Evaluation Tools
### Passage Efficiency Evaluation Tool: Flow-3D  
The most important factor that should be considered concerning passage efficiency is the flow inside the fishway. The flow velocity inside a fishway is not easy to predict because it is highly dependent on external variables such as flow rate and slope. Furthermore, the flow patterns are diverse according to the shape, size, and intervals of structures (e.g., notch, orifice, and weir) installed inside the fishway. Nevertheless, the river design standard [20] in Korea only provides the average flow velocity range (0.5-1.0 m/s) inside the fishway as a design criterion. The flow structure inside the pool-weir type fishway is generally known to be divided into the streaming flow and the plunging flow [21]. This is briefly illustrated in Figure 5.  
As shown, reverse rotational flow occurs along with streaming flow near the fishway bottom; thus, fish swimming near the bottom are highly likely to lose the upstream direction and fail to ascend. However, fish can easily ascend with plunging flow because of the appropriate flow characteristics. In contrast, if the fish are swimming near the surface, the streaming flow is advantageous for ascending. Therefore, the specific internal flow structure (streaming flow/plunging flow) of the fishway will be an indicator of passage efficiency. A three-dimensional hydraulic analysis is required to discriminate this. Among the various 3D hydraulic models, we used the FLOW-3D model which can quickly analyze the fluid behaviors of hydraulic structures using the free surface. The FLOW-3D model applies the VOF (volume of fluid) theory and is employed in the field of hydraulics where the free surface motion is important. In addition, the FAVOR technique for complex shape expression is added to the FVM (finite volume method) using a rectangular grid for three-dimensional grid division, so that accurate shapes can be expressed quickly [22]. The FLOW-3D model uses the Reynolds equation. The equation has time-averaged the Navier-Stokes equation and the continuity equation as governing equations. Moreover, it provides various options including the k-\(\varepsilon\) and LES models as the turbulence model. \[\mathrm{R_{DIF}}=\frac{\partial}{\partial\mathrm{x}}\left(\mathrm{u_{\rho}A_{ x}}\frac{\partial\rho}{\partial\mathrm{x}}\right)+\frac{\partial}{\partial \mathrm{y}}\left(\mathrm{v_{\rho}A_{y}}\frac{\partial\rho}{\partial\mathrm{y} }\right)+\frac{\partial}{\partial\mathrm{x}}\left(\mathrm{w_{\rho}A_{x}} \frac{\partial\rho}{\partial\mathrm{z}}\right) \tag{3}\]  
where \(\mathrm{V_{F}}\) is the volume in contact with the fluid; \(\rho\) is the density of the fluid; \(\mathrm{R_{DIF}}\) is the turbulence diffusion term; \(\mathrm{u_{r}}\), \(\mathrm{v_{r}}\) and \(\mathrm{w}\) are the flow velocities in the \(\mathrm{x}\), \(\mathrm{y}\), and \(\mathrm{z}\) directions;  
Figure 5: Flow pattern in pool/weir fishway [21]. \(\mathrm{A_{x}}\), \(\mathrm{A_{y}}\), and \(\mathrm{A_{z}}\) are the contact areas of the fluid in the \(\mathrm{x}\), \(\mathrm{y}\), and \(\mathrm{z}\) directions of the cell, respectively [23]. \[
# Independent System Calibration of Sentinel-1B
## 2 In-Orbit Calibration Overview
(**a**) DLR C-band “Kalibri” transponder; (**b**) DLR remote controlled corner reflector. A Sentinel-1 SAR image in StripMap mode (SM 5, V polarization on transmit) and acquired over the DLR calibration field is shown in Figure 4. By a zoomed view of the corresponding test site, the impulse response of a corner reflector and a transponder becomes visible: overplotted for the co-(VV) and for the cross-channel (VH). It is clearly visible that the DLR transponder re-transmits both polarization signals simultaneously, whereby the corner reflector, as expected, does not rotate the polarisation of an incidence wave, i.e., only the co-polarized signal (VV) is visible. Figure 4: Sentinel-1 SAR image across the DLR calibration field in StripMap operation (SM5) for the co-polar channel VV. The regions around the deployed reference targets are zoomed out for the corresponding polarization channel (VV, VH). Figure 3: DLR’s calibration site with three 2.8 m corner reflectors (D38, D42, D43) and three C-band transponder (D39, D40, D41) in South Germany with approximate center coordinate 48\({}^{\circ}\)0\({}^{\prime}\)N, 10\({}^{\circ}\)42\({}^{\prime}\)E. This calibration site is optimized for the Sentinel-1 mission, whereby the blue hatched areas indicate the Sentinel-1 coverage of all beams being selected for in-flight measurements (SM1, SM2, IW1, EW3, IW3, SM5, WV1). Based on this calibration site containing six reference targets, measurement campaigns for more than 65 overpasses were successfully executed during the Sentinel-1B commissioning phase. Using the specifically developed analysis tools, the corresponding data were analyzed and evaluated, yielding the results of the different calibration procedures as described in the following sections.
###### Abstract  
In the energy sector, distributed synchronism and a high degree of stability are necessary for all real-time monitoring and control systems. Instantaneous response to critical situations is essential for the integration of renewable energies. The most widely used standards for clock synchronisation, such as Network Time Protocol (NTP) and Precision Time Protocol (PTP), do not allow for achieving synchronised simultaneous sampling in distributed systems. In this work, a novel distributed synchronism system based on the Time-Sensitive Networking (TSN) standard has been validated for its integration in an architecture oriented towards the high-resolution digitisation of photovoltaic (PV) generation systems. This method guarantees a time stamping with an optimal resolution that allows for the analysis of the influence of fast-evolving atmospheric fluctuations in several plants located in the same geographical area. This paper proposes an enhanced micro-phasor measurement unit (\(\mu\)PMU) that acts as a phasor meter and TSN master controlling the monitoring system synchronism. With this technique, the synchronism would be extended to the remaining measurement systems that would be involved in the installation at distances greater than 100 m. Several analyses were carried out with an on-line topology of four acquisition systems capturing simultaneously. The influence of the Ethernet network and the transducers involved in the acquisition process were studied. Tests were performed with Ethernet cable lengths of 2, 10, 50, and 75 m. The results were validated with 24-bit Sigma-Delta converters and high-precision resistor networks specialised in high-voltage monitoring. It was observed that with an appropriate choice of sensors and TSN synchronism, phase errors of less than \(\pm\)1 us can be guaranteed by performing distributed captures up to 50 kS/s. Statistical analysis showed that uncertainties of less than \(\pm\)100 ns were achieved with 16-bit Successive Approximation Register (SAR) converters at a moderate cost. Finally, the requirements of the IEEE C37.118.1-2011 standard for phasor measurement units (PMU) were also satisfied. This standard establishes an uncertainty of \(\pm\)3.1 us for 50 Hz systems. These results demonstrate the feasibility of implementing a simultaneous sampling system for distributed acquisition systems coordinated by a \(\mu\)PMU.  
distributed synchronism; TSN; gPTP; microgrid; \(\mu\)PMU; synthetic models; digital twins; phasor models and EMT models +
Footnote †: journal: Applied Sciences
# Response of Mangrove Carbon Fluxes to Drought Stress Detected by Photochemical Reflectance Index
## 3 Results
### Temporal Variations of PRI and Its Components  
The seasonal patterns of PRI appeared to be hump-shaped with higher values in spring and summer, which was the opposite pattern to the seasonal trend of LUE (Figure 4). The seasonal mean PRI ranged from 0.10 to 0.21 over the four years. On an annual scale, the mean values of PRI in each year reached up to 0.15, 0.18, 0.16, and 0.11. Compared to previous years, annual mean values of PRI in 2020 decreased by 29.0%, presenting an obvious decreasing trend. The decreasing trend of PRI appeared to lag behind the increasing VPD and decreasing rainfall but went ahead of the decreasing carbon sink (Figures 2 and 3).  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{**PRI (nomal = \({}^{-2}\)-\({}^{-1}\))**} & \multicolumn{1}{c}{**Tair**} & \multicolumn{1}{c}{**VPD**} & \multicolumn{1}{c}{**Surface Water**} & \multicolumn{1}{c}{**Rainfall**} & \multicolumn{1}{c}{**Saliency**} & \multicolumn{1}{c}{**PRI**} & \multicolumn{1}{c}{**LUE (nomal = \({}^{-1}\))**} & \multicolumn{1}{c}{**GPP (nomal = \({}^{-2}\)-\({}^{-1}\))**} & \multicolumn{1}{c}{**NEE (nomal = \({}^{-2}\)-\({}^{-1}\))**} \\ \hline \multirow{4}{*}{2017} & Spring & 0.77 & 22.56 & 0.33 & 0.40 & 365.2 & 1.40 & 0.15 & 0.025 & 16.58 \\  & Asmussen & 1.09 & 20.36 & 0.36 & 0.56 & 84.1 & 5.40 & 0.15 & 0.018 & 17.90 & 13.38 \\  & Asmussen & 1.09 & 20.67 & 0.39 & 0.35 & 77.3 & 15.3 & 0.16 & 0.030 & 16.22 & 12.80 \\  & Winter & 0.24 & 17.60 & 0.31 & 0.40 & 94.9 & 13.36 & 0.12 & 0.024 & 16.05 & 13.63 \\ \hline \multirow{4}{*}{2020} & Spring & 1.00 & 24.43 & 0.40 & 0.24 & 186.5 & 14.89 & 0.16 & 0.018 & 17.46 & 13.62 \\  & Swimmer & 1.02 & 29.94 & 0.49 & 0.40 & 774.8 & 14.05 & 0.21 & 0.018 & 16.22 & 12.20 \\  & [PERSON] & 0.86 & 25.78 & 0.62 & 0.45 & 173.9 & 8.65 & 0.19 & 0.020 & 15.30 & 11.92 \\  & Winter & 0.63 & 16.88 & 0.19 & 0.40 & 146.1 & 11.36 & 0.14 & 0.026 & 14.32 & 12.19 \\ \hline \multirow{4}{*}{2019} & Spring & 0.72 & 22.56 & 0.29 & 0.31 & 563.3 & 6.36 & 0.23 & 0.025 & 16.06 & 12.83 \\  & [PERSON] & 1.02 & 30.41 & 0.27 & 0.26 & 511.6 & 3.80 & 0.19 & 0.019 & 18.10 & 13.38 \\  & [PERSON] & 1.00 & 26.48 & 10.6 & 0.43 & 28.7 & 9.4 Seasonal and annual variation patterns of different components of PRI were presented to explore the underlying mechanisms driving PRI variations (Figure 4). The variation patterns of PRI0 and sunlit PRI were similar with PRI0 slightly higher than sunlit PRI. In addition, the peaks of PRI0 and sunlit PRI in 2020 were lower than in previous years. The annual mean values of PRI0 and sunlit PRI in 2020 decreased by 27.8% and 29.6%, respectively. \(\Delta\)PRI tended to be hump-shaped with higher (lower) values in summer (winter) within the year but showed no obvious difference among years.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 4 Evaluation
### Relationship between the Number of Retransmissions and Network Performance  
To evaluate the effect of the number of retransmissions on the performance of the network, a simple network with one sink (gateway) and nine sensor nodes was considered. The link quality of the channel was fixed, while the number of retransmissions varied from 0 to 3, and we examined the packet reception rate at the sink with each value for the number of retransmissions.  
The results in Figure 5 show that since the sensor nodes were not allowed retransmissions, the PRR at the sink achieved a low PRR: 66.3%. When the sensor nodes were allowed retransmissions, the PRR improved since the number of retransmissions increased. It is shown that when the link quality is not very good, the number of retransmissions will decide the network performance. Therefore, to estimate the number of transmissions is very important.
# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 3 Results and Discussion
### Evaluating Topic Coherence Improvement Across Datasets
#### 3.1.2 Improvement of Topic Coherence in TagMyNews Dataset  
Table 6 shows that the refinement method demonstrated varied impacts across different models, with LDA showing the most significant improvement. Specifically, LDA's coherence score increased from 0.336 to 0.431, marking a 28.27% rise, the largest among all tested models. This substantial gain highlights the method's effectiveness in enhancing models that initially exhibit poor coherence. In contrast, BERTopic, an embedding-based model, also benefited from the refinement, though more moderately. Its score improved from 0.539 to 0.572, a 6.12% increase, suggesting that refinement techniques can effectively address challenges in datasets with shorter or noisier text like TagMyNews. Meanwhile, NMF displayed stable performance with its coherence score improving from 0.589 to 0.604, a 2.55% increase. Although modest, this improvement underscores the method's capability to boost coherence even in models that already have moderately strong baseline performance.  
G-BAT exhibited only a marginal improvement in its performance, with its coherence score increasing slightly from 0.646 to 0.650, a mere 0.62% increase. This minimal change suggests that G-BAT may already capture most of the coherence achievable for this dataset, indicating limited scope for further refinement. In a broader context, compared to the AGNews dataset, all models registered lower baseline scores when applied to the TagMyNews dataset. This disparity suggests that TagMyNews presents notable challenges for topic modeling, likely due to factors such as shorter text, noisier content or overlapping topics.
# Potential of Advanced Consolidants for the Application on Sandstone
## 3 Results and Discussion
### Application of the Consolidants on the Sandstone
#### 3.3.3 Mechanical Testing in Macroscale
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sandstone** & **BET Area** & **BET Area** & **DRMS Fresh** & **DRMS Aged** & \\
**Treatment** & **Fresh 1/** & **Aged 2/** & **3/** & **4/** & **\%** \\  & **m\({}^{2}\) g\({}^{-1}\)** & **m\({}^{2}\) g\({}^{-1}\)** & **N** & **N** & **\%** \\ \hline Untreated & 0.56 & - & 9.7 \(\pm\) 0.5 & 3.2 \(\pm\) 0.2 & \(-\)67 \\ SiGel-oa & 0.05 & 5.90 & 11.6 \(\pm\) 0.8 & 9.2 \(\pm\) 1.2 & \(-\)21 \\ SiGel SiO\({}_{2}\)-oa & 0.05 & 16.77 & 15.5 \(\pm\) 0.8 & 12.1 \(\pm\) 0.8 & \(-\)22 \\ SiGel SiO\({}_{2}\)-octyl-oa & 0.15 & 8.41 & 10.5 \(\pm\) 0.7 & 4.0 \(\pm\) 1.0 & \(-\)62 \\ SiGel SiO\({}_{2}\)-methyl-oa & 0.06 & 7.42 & 17.3 \(\pm\) 1.9 & 12.7 \(\pm\) 1.0 & \(-\)27 \\ SiGel TiO\({}_{2}\)-oa & 0.02 & 0.17 & 23.3 \(\pm\) 2.5 & 18.1 \(\pm\) 1.4 & \(-\)22 \\ SiGel ZnO-oa/d & 5.10 \({}^{*}\) & 10.60 \({}^{*}\) & 54.9 \(\pm\) 1.7 & 60.9 \(\pm\) 2.2 & \(+\)11 \\ KSE OH & 0.46 & 2.54 & 25.2 \(\pm\) 1.4 & 18.5 \(\pm\) 1.5 & \(-\)27 \\ SP FX & 2.10 \({}^{*}\) & 3.00 & 22.7 \(\pm\) 1.6 & 16.4 \(\pm\) 1.4 & \(-\)28 \\ HAP & 0.23 & 0.42 & 18.5 \(\pm\) 1.3 & 18.1 \(\pm\) 1.6 & \(-\)2 \\ HAP+Ca(OH)\({}_{2}\) & 0.23 & 0.73 & 17.8 \(\pm\) 1.4 & 17.6 \(\pm\) 1.5 & \(-\)1 \\ \hline \hline \multicolumn{7}{l}{\({}^{*}\) Samples containing micropores; 1 BET surface area before weathering; 2 BET surface area after weathering;} \\ \multicolumn{7}{l}{\({}^{3}\) Drilling resistance force before weathering; 4 Drilling resistance force after weathering; 5 Change in the Drilling resistance force due to weathering.} \\ \end{tabular}
\end{table} 
Table 6: Properties of sandstone treated with various consolidants weathered by 50 cycles (see Table 2). g\({}^{-1}\)), i.e., SiGel TiO\({}_{2}\)-oa, HAP and HAP+Ca(OH)\({}_{2}\), exhibited high durability. The large surface area of the SiGel-oa sample and those containing SiO\({}_{2}\) nanoparticles correlated with their lower durability, with the exception of SiGel SiO\({}_{2}\)-oa. SiGel ZnO-oa/d exhibited unusual surface areas, which might be connected with the structural changes, as suggested above.
# Effect of PEF Treatment on Chosen Properties of Raw and Hot Air- and Freeze-Dried Poultry Meat
## 2 Materials and Methods
### Technological Processing  
#### 2.2.1 Pulsed Electric Field (PEF) Treatment  
The preliminary treatment with a pulsed electric field was carried out in a prototype PEF device, model ERTEC-RI-1B (ERTEC, Wroclaw, Poland), characterized by an output voltage of up to 30 kV and a capacity of 0.25 \(\upmu\)F. The distance between the electrodes was 18 mm, with a surface area of 15.2 cm\({}^{2}\). The device generated monopolar exponential pulses with an average width of 10 \(\upmu\)s and a pulse interval of 1 s. Due to the capacity of the chamber for applying the research material, it was divided into two pieces of 18 g each and flooded with distilled water to improve the dielectric contact between the electrodes. The treatment was conducted by applying 20 and 40 pulses with an electric field intensity of 12 kV/cm. The specific energy intake (Ws) was 15 and 30 kJ/kg and was calculated based on the equation according to [PERSON] et al. [35]:  
\[\text{Ws}=\text{I}\,\text{U}\,\text{t}\,\text{n}/1000\,\text{m} \tag{1}\]  
where I is the current [A], U is the voltage [V], t is the duration of the pulse [s], n is the number of pulses [\(-\)], and m is the total input mass [kg].  
After treatment, the material was gently dried on filter paper to remove excess water and weighed. The treatment was carried out in two repetitions. Table 1 presents mass changes, dry matter content, and water activity for raw material and after the PEF treatment. Higher mass changes and higher dry matter content were observed for material treated with higher specific energy intake. In the case of water activity, PEF treatment resulted in increased water activity compared to raw material.
# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 4 Results
### RSTSRN Results for Natural Images
The test results of some algorithms on the benchmark test dataset are as follows. The results of the LapSRN, AWSRN, and D-DBPN with reconstruction capabilities at three scales of 2\(\times\), 4\(\times\), and 8\(\times\) are presented. In the comparison of 2\(\times\) and 4\(\times\) reconstruction results, a CARN with parameters close to an RSTSRN was selected. In the comparison of 4\(\times\) reconstruction results, the EDSR with the second-best performance and the largest number of parameters was selected. In the comparison of 4\(\times\) and 8\(\times\) reconstruction results, D-DBPN has the second largest number of parameters. In addition, D-DBPN achieved suboptimal results in 8\(\times\) reconstruction and also achieved good results in 4\(\times\) reconstruction. Figure 5 shows the local magnification visual effects of an image named \"barbara\" in the Set14 dataset at 2\(\times\)SR. It is obvious that other algorithms except for the RSTSRN and AWSRN have not been able to fully reconstruct the black and white stripes of the scarf in the image. However, the reconstruction results of the AWSRN at the upper end of the enlarged area did not match the original image, so only the RSTSRN completed the complete reconstruction of the area. Figure 6 shows the visual effect of two local magnifications of an image named \"butterfly\" in the Set5 dataset at 4\(\times\) SR. For the first area on the upper right, the notch shape of the white patch in the original image can only be reflected to a certain extent in the SR reconstruction results of the CARN and RSTSRN, while the results of the RSTSRN are closer to the geometric shape of the original image compared to the CARN. For the second region, the shape of the notch in the original image can be reconstructed by most algorithms, but only the reconstruction effect of the RSTSRN can reflect the sharp angle of the notch in the original image. In summary, the RSTSRN demonstrated the best visual performance in the 4\(\times\) SR reconstruction task of a \"butterfly\". \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Year** & **2\(\times\)** & **4\(\times\)** & **8\(\times\)** & **Multi- Scales** \\ \hline VDSR [45] & 2016 & 665 K & 665 K & 665 K & 1995 K \\ DRCN [46] & 2016 & 1774 K & 1774 K & 1774 K & 5322 K \\ MemNet [49] & 2017 & 677 K & 677 K & - & 1354 K \\ LapSRN [10] & 2017 & 813 K & 813 K & 813 K & 2439 K \\ CARN [73] & 2018 & 1592 K & 1592 K & - & 3184 K \\ AWSRN [77] & 2019 & 1397 K & 1587 K & 2348 K & 5332 K \\ RSTSRN(ours) & - & 1830 K & 1830 K & 1830 K & 1830 K \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison with the number of parameters of lightweight SR algorithms on multi-scale tasks. ’Year’ indicates the publication year of each paper. Figure 5: Visual comparison for 2\(\times\)SR on the barbara. Figure 7 shows the local magnification visual effects of an image named \"img_040\" in the Urban100 dataset at 8\(\times\) SR. It is evident that only the RSTSRN has completed the correct reconstruction of this area.