esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Ultrafiltration and Diafiltration Processing  
The changes of relative normalized P flux during both processes is presented in Figure 2. \(J//J_{0}\)-time curves were divided in three sections. At the beginning, until 23 min, a rapid initial P flux decline was observed. According to [PERSON] et al. [27], during the initial stage of UF, solutes with a size range much smaller than that of the membrane pores pass through these pores, where may be adsorbed and deposited due to various forces, leading to the formation of a colloidal film on the internal pore surface, and as a consequence, the reduction of pore size. Moreover, the sharp decrease at the beginning of UF can be attributed with the presence of pectin, adsorption of colloidal species and the build-up of a concentration polarization layer [16; 28]. Next, from 23 to 120 min, relatively smoother and slower decline of P flux took place. It resulted from the accumulation of solutes and colloids on the external membrane surface. In these two ranges the performance of UF and DF, described by the changes of \(J//J_{0}\), was surprisingly almost the same. Only after 120 min was there a difference between UF and DF--during DF \(J//J_{0}\) was quasi-stable until 250 min, while during UF the P flow stopped at 140 min due to fouling caused by pore blocking and cake build-up. According to [PERSON] [29], a modified filtration step, namely diafiltration, in which make up water is added to the reentate, is carried out to maintain P flux. [PERSON] et al. [24] observed that \(J\) declined immediately after starting the UF process (for phenolic compounds separation from apple juice), but due to the diluting of R by adding pure water it could be recovered. However, in the case of apple-cranberry cloudy juice, water addition did not affect the changes in P flux until 120 min of the DF process, the dynamic of \(J//J_{0}\) changes was the same as during UF. The DF approach was successful later in the process, after 120 min further decline of P flux was prevented as a result of water addition. Thus, by this method it would be possible, during a prolonged process, to completely separate sugars, salts and acids from juice containing pectin and other macromolecules.  
Figure 2: The changes of \(J//J_{0}\) during ultrafiltration (UF) and diafiltration (DF) of cloudy apple-cranberry juice.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Acquisition
#### 2.2.3 Experiment Two: Individual Bunches in Field Conditions  
Images of the same 21 individual bunches were captured in in-situ conditions under both canopy treatments with the same RGB and RGB-D proximal sensors that were used in experiment one. Here, individual bunches were still attached to the respective vines:  
1. _RGB imagery:_ The camera captured images of the individual bunches for both FC (Figure 4a) and LR (Figure 4b) treatments. The camera was positioned approximately 40 cm from the bunch being imaged, maintaining the reference length (ruler) in each image. Image acquisition occurred between 12H00 and 13H00, under natural solar illumination.
2. _RGB-D (Kinect) imagery:_ The same software setup from experiment one was used, which allowed the Kinect to capture an individual mesh per bunch for FC (Figure 4c) and LR (Figure 4d) treatments. The imagery was captured after sunset (approximately 20H00) with artificial illumination. The Kinect was held approximately 60 cm from the bunch and was moved around the bunch axis by hand. For the LR treatment, a board was placed behind the bunches, thereby creating an artificial background to improve volume extraction (Figure 4d).
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
## 4 Results and Discussion
### Adsorption Isotherm Studies  
In general, adsorption isotherms play a fundamental role in the optimization of adsorbent consumption and explains the reaction between the pollutants and the adsorbent material [58]. Hence, the choice of best isotherm fit was conductedby linear regression analyses and a comparison of their correlation coefficients (R\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Characteristics**} & \multicolumn{3}{c}{**Temperature K**} \\ \cline{3-5}  & & **288.15 K** & **298.15 K** & **318.15 K** \\ \hline Pseudo-first-order (PFO) & q\({}_{\text{e-exp}}\) [mg g\({}^{-1}\)] & 223.44 & 236.74 & 243.94 \\  & k\({}_{1}\) [min\({}^{-1}\)] & 0.0524 & 0.0523 & 0.0523 \\ model & q\({}_{\text{e-cal}}\) [mg g\({}^{-1}\)] & 241.29 & 240.44 & 240.44 \\  & R\({}^{2}\) & 0.9949 & 0.9949 & 0.9949 \\ \hline Pseudo-second-order (PSO) & k\({}_{2}\) [min\({}^{-1}\)] & 09.57 & 12.51 & 14.72 \\  & q\({}_{\text{e-cal}}\) [mg g\({}^{-1}\)] & 384.61 & 344.83 & 333.33 \\ model & R\({}^{2}\) & 0.9419 & 0.9903 & 0.9961 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of pseudo-first-order (PFO) and pseudo-second-order (PSO) adsorption kinetic models.  
Figure 7: Adsorption kinetics of Az on Ni/Zn LDH nanohybrid: (**a**) pseudo-first-order, (**b**) pseudo-second-order (PSO) kinetics model.  
Equilibrium studies which express the dye adsorbent capacity are explained through the adsorption isotherms. The kinetic curves of Az adsorption onto Ni/Zn LDH nanohybrid are shown in Figure 8, and their parameters were obtained by regression analysis and listed in Table 3.  
The values of the correlation coefficient R\({}^{2}\) = 0.9952, 0.9934 and 0.9871 at 288, 298 and 318 K, respectively, of the Az adsorption process on our nanoribbrid, demonstrate good linearity with the Langmuir expression compared to the Freundlich model. This result reveals the monolayer coverage of Az molecules on the NiZn/LDH nanohybrid accompanied byan homogeneous adsorption process. Moreover, the equilibrium parameter \(R_{\text{L}}\) (\(\frac{1}{1+R_{\text{C}}^{2}}\)) is in the range of [0-1], indicating that implying the Az adsorption process is favorable. Moreover, the higher values of K\({}_{\text{F}}\) (71.58, 72.35 and 43.12 mg g\({}^{-1}\)) and N (3.128, 3.123 and 2.097) at 288, 298 and 318 K temperatures, respectively, indicate that this Ni/Zn LDH nanohybrid has a higher adsorption capacity and affinity for the azo dye molecules, revealing the favorability of this adsorption [59].  
Moreover, the theoretical maximum adsorption capacity of Ni/Zn LDH mesoporous nanoadsorbent calculated depending on the Langmuir model is 227.27 mg g\({}^{-1}\), which is four times more superior than that of other analogical or low-cost adsorbents such as the 3D magnetic hollow porous Ni-ferrites (3D-MHP-NiFe\({}_{2}\)O\({}_{4}\)) with qm = 53.13 mg g\({}^{-1}\)[60] and copper oxide with qm = 1.59 mg g\({}^{-1}\)[59].
## 2 Materials and Methods
### Physical Modeling and Characterization of the Gas Kick in Fractured Reservoirs
#### 2.1.3 Gas Kick Model  
During the gas reservoir drilling, when the bottomhole pressure is lower than the formation pore pressure, the formation gas will enter the wellbore annulus under their differential pressure. Compared with the extraction of gas wells, the differential pressure between the formation pressure and the bottomhole pressure during the gas kick, is much lower than that during the gas extraction, and its isothermal expansion effect is weak, which can be approximated as a Darcy linear flow of the formation of the gas seepage during the gas kick. In addition, since the bottomhole pressure is underbalanced, the influence of the drilling fluid on the permeability of the near-well zone can be ignored. It is known from the literature [11] that the rate of the gas influx into the wellbore can be expressed as Equation (5).  
\[Q_{\text{SC}}=\frac{774.6 Kh\left(P_{e}^{2}-P_{\text{w}}f^{2}\right)}{TZ\mu ln \left(\frac{r_{e}}{r_{w}}\right)} \tag{5}\]  
where \(Q_{\text{SC}}\) is the rate of the gas influx into the wellbore at standard conditions, m\({}^{3}\)/d; \(Q_{g}\) is the gas flow rate at the temperature and the pressure at the bottom of the well, m\({}^{3}\)/d; \(K\) is the permeability of the formation, mD; \(h\) is the thickness of the drilled reservoir, m; \(P_{e}\) is the formation of the pore pressure, MPa; \(P_{\text{w}}\) is the bottomhole pressure, MPa; \(r_{e}\) is the radius of the supply boundary, m; \(r_{w}\) is the radius of the wellbore, m; \(\mu\) is the gas viscosity, mPa \(\cdot\) s; \(Z\) is the gas compressibility factor; \(T\) is the formation temperature, \(K\).  
When drilling in the fractured formations under overbalanced or near balanced conditions, a gravity displacement will occur between the drilling fluid in the annulus and the gas in the formation fractures, due to the density difference between them. The drilling fluid flows into the fracture along the lower wall of the fracture, while the gas influxes into the wellbore along the upper wall, and this process is known as the gravity displacement gas kick. The driving force for the gravity displacement gas kick is the density difference between the drilling fluid and gas. The gas influx rate is affected by parameters, such as the formation porosity, the formation permeability and the wellbore size, as shown in Equation (6). During the gas kick, the formation parameters and the wellbore size remain almost constant, and although the bottomhole pressure decreases continuously with the gas kick, the change in the bottomhole gas density is small in the early stage of the gas kick.  
Therefore, the gas influx rate of the gravity displacement gas kick can be considered as a constant value.  
\[q_{r}=g(K,\phi,r_{w}) \tag{6}\]  
where \(\phi\) is the formation porosity.
## 5 Results and Discussion
### Structure 3  
In this part, a linear pin-jointed assembly was tested, containing six joints and ten bars, as shown in Figure 8. The bars had \(EA=4\times 10^{5}\) N. Joints 3 and 5 were loaded downward with 3000 and 9000 N, respectively, in 100 steps; the reason for applying the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Joint** & **SM (mm)** & **Current Study (mm)** & **[PERSON] (1998) (mm)** & **[PERSON] et al., (1984) (mm)** \\ \hline Downward displacement (mm) & 2 & \(-\)36.4770 & \(-\)12.9887 & \(-\)11.1120 & \(-\)11.1168 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The displacement of y-coordinate of joint 2 of Structure 1 using the current technique and the quoted methods.  
Figure 7: Geometry and loading of a three-dimensional Structure, z-coordinate of joint 2 is 10 mm below the other joints.  
massive loading is to get noticeable displacement. Table 4 gives the displacements of the two end joints (Joints 5 and 6) by the linear SM, the proposed approach and the quoted methods. The results prove that the maximum discrepancy of the referenced non-linear techniques with the proposed approach was only 5%, while the dissimilarity between non-linear techniques and the linear SM is 88%. The results demonstrate that the method is also pragmatic and accurate for linear structures with massive loading.  
Furthermore, Figure 9 clearly shows that by increasing the iteration numbers, the proposed approach results get close to the outcomes of the non-linear (DRM) method. It can be seen that from the figure, in 15 iterations, that the approach gives results reasonably close to DRM ones.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \multirow{2}{*}{**Joint**} & \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{**Methods**} & & \\ \cline{3-6}  & & **SM (mm)** & **Current Study (mm)** & **[PERSON] (1998) (mm)** & **[PERSON] et al.** (1984) (mm)** \\ \hline \multirow{2}{*}{5} & x & 3.75 & 1.989 & 1.99 & 1.988 \\  & y & \(-\)27.857 & \(-\)28.144 & \(-\)27.61 & \(-\)28.221 \\  & x & \(-\)6 & \(-\)7.601 & \(-\)7.54 & \(-\)7.812 \\  & y & \(-\)25.607 & \(-\)25.487 & \(-\)24.95 & \(-\)25.510 \\ \hline \end{tabular}
\end{table}
Table 4: Vertical displacements in mm of Joints 5 and 6 of Structure 3, using the proposed approach and the quoted methods.  
Figure 8: The pin-jointed assembly with ten members (Structure 3) (adapted from [31,32]).  
Figure 9: Geometry and loading of a three-dimensional Structure, z-coordinate of joint 2 is 10 mm below the other joints.  
In summary, on the basis of the results of Tables 2-4 of the preceding three examined structures, it can be concluded that the results of the proposed approach are in good agreement with the non-linear techniques in literature, while the results of the dependent linear technique are far from those results.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 3 Storage and Access of Remote Sensing Data in MongoDB
### Storage and Access of Image Data Based on GridFS  
When storing data in the form of document in the MongoDB database, the volume of each file must be less than sixteen megabytes. However, with the rapid development of earth-observation technologies, the data amount of remote sensing image has already reached hundreds of megabytes, or even bigger, which exceeds the limitation and can never satisfy the storage demand for remote sensing big data [27]. On account that the traditional document-objected method cannot be adopted for the storage of image data, this paper takes advantage of the GridFS file storage mechanism to manage large remote sensing image data.  
Under this circumstance, the data are managed in two collections: rs.files and rs.chunks. The keys in the rs.chunks collection include _id, n, data, and files _id. \"_id\" stands for the unique identifier of the file chunk, \"n\" stands for the relative position in file of the chunk, \"data\" stands for the binary data in the chunk, and \"files_id\" is the same as \"_id\" in the rs.files collection. The keys in the rs.files collection include _id, length, chunksize, uploadDate, and MDS. \"_id\" stands for the unique identifier of the document in the rs.files, \"length\" stands for the number of the bytes, \"chunksize\" stands for the size of every chunk in bytes, \"uploadDate\" stands for the date and time that the file is uploaded to GridFS, and \"MDS\" is the check value of the file, which is calculated by the server.  
Taking one of the Sentinel1 image data as an example, the storage structure of the image data in the rs.files collection is as follows.  
\(\ \\\  
1. Retrieve the image data waiting to be stored according to the specified image name. If the data with the same name exists, then finish the operation; if not, start to store the data with the GridFS mechanism.
2. Store the data in two collections: rs.files and rs.chunks. The \"rs.files\" collection stores the metadata of each image, while \"rs.chunks\" collection stores the binary data of each image.
3. The data in the \"rs.files\" collection usually does not need to be split because its data volume is small, while \"files_id\" and \"n\" are selected as a combined shard key to divide the data into different shard nodes.
4. When accessing the image data, the data is retrieved in the \"rs.files\" collection with the specified query terms, and then the value of \"_id\" is obtained. Owing to the equal relationship between \"_id\" in rs.files and \"files_id\" in rs.chunks, \"files_id\" is also determined accordingly. Then the image data can be read sequentially through the value of \"n\".  
With the GridFS file storage mechanism, the image data is split to small data chunks and stored in the corresponding nodes. Figure 4 shows the storage mechanism of remote sensing image data.  
Figure 3: The flow diagram of storing image data with GridFS.
###### Abstract  
In this study, the influence of fly ash (FA) content (0%, 10%, 20%, and 30%) on the alteration in the physical and mechanical parameters of loess is investigated. The influences of curing time (0, 14, and 28 days) and submergence and non-submergence conditions are analyzed as well. Analysis considers the variation in Atterberg limits (liquid limit, plastic limit, and plasticity index), compaction parameters (optimum moisture content (OMC), and maximum dry density (MDD)), unconfined compressive strength (UCS) stress, UCS strain, California bearing ratio (CBR) value, and swell potential. Results show that the application of FA-stabilized loess (FASL) is effective. Specifically, the MDD decreases and the OMC increases, the UCS stress increases and the UCS strain decreases, the CBR value improves and the swell potential declines, but Atterberg limits are insignificantly changed by the increase in the FA ratio compared with those of untreated loess. The UCS stress and CBR value are improved with the increase in curing time, whereas the UCS strain is negligible. FASL under submergence condition plays an important role in improving the effect of FA on the UCS stress and CBR value compared with that under non-submergence condition. The UCS stress and CBR value are more increased and more decreased than the UCS strain in submerged samples. Therefore, the application of FASL in flood areas is important for obtaining sustainable construction materials and ensuring environmental protection.  
loess; fly ash; stabilization; California bearing ratio (CBR); unconfined compressive strength (UCS); submergence; non-submergence
# Investigations on Factors Affecting 3D-Printed Holes Dimensional Accuracy and Repeatability
## 3 Results and Discussion
### Results of the Experiment 2

Table 6: Test part measurement data for experiment 2 before and after the medical decontamination. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Source**} & \multicolumn{1}{c}{**F-Value**} & \multicolumn{1}{c}{_p-Value_} \\ \hline
2.3 mm holes & 0.16 & 0.692 \\
4.5 mm holes & 0.16 & 0.692 \\
6.5 mm holes & 0.421 & 0.523 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: One-way ANOVA results for holes diameter before and after the medical decontamination. Many studies addressed the accuracy of 3D-printed surgical guide made of different materials and subjected to sterilization [26; 27; 28; 29]. The rationale of these studies relates to the practical observations that some sterilization techniques can determine deformations of the surgical guides with critical impact on accuracy and the functionality of the device [13]. In the current research, by comparing the test parts before and after the medical decontamination, one could notice that this post-processing had no statistically significant impact on the dimensional accuracy of holes. The outcomes of the experiment 2 confirmed the observations of [PERSON] et al. [12] that sterilization by ethylene oxide determined some micro-deformations of the 3D-printed PLA surgical guides, but not that the test parts deformed more when manufactured with a smaller layer thickness. Here, it should be mentioned that the test parts in experiment 2 were subjected not only to sterilization, but also to cleaning and disinfection. The influence of other types of sterilization methods (such as UV-C [30]) on the accuracy of prints needs to be investigated in further research. For evaluating the printing parameters' effect on the holes accuracy in test part 2, \(p\)-values and F-values were calculated using the analysis of variance (Table 8). The confidence interval was 95%. The results indicated that some process parameters have an influence on the 4.5-mm diameter holes, however there was no consistency in the data. A large variability was recorded which can be attributed to the intrinsic variability of the manufacturing process and to the fact the directly 3D-printed holes were manufactured at inclined angles.
## 3 Materials and Methods
### Shadow Index
#### 3.1.3 Gap Filling  
A total of 9 layers were used as the final input for the classification models (Table 1). The layers that were not highly correlated and heavily affected by noise or missing data were selected and downloaded from GEE. Despite the careful selection, the S2 layers contained some missing data. As missing data are not correctly handled by machine learning classifiers, a gap-filling procedure was applied on the six NDVI layers prior to the classification workflow. The missing values were filled by combining the median value over an incremental spatial window with the maximum NDVI value over the 6 bi-monthly composites. The average value resulting from the two approaches was used to fill in the gaps.
###### Abstract  
There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods--based on deep learning--are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods typically depend on large and diverse datasets. Image augmentation can artificially boost dataset diversity, yet its role in building dimension estimation from SVIs remains under-studied. This research presents a methodology that applies eight distinct augmentation techniques--brightness, contrast, perspective, rotation, scale, shearing, translation augmentation, and a combined \"sum of all\" approach--to train models in two tasks: object detection with Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) and instance segmentation with You Only Look Once (YOLO)v10. Comparing the performance with and without augmentation revealed that contrast augmentation consistently provided the greatest improvement in both bounding-box detection and instance segmentation. Using all augmentations at once rarely outperformed the single most effective method, and sometimes degraded the accuracy; shearing augmentation ranked as the second-best approach. Notably, the validation and test findings were closely aligned. These results, alongside the potential applications and the method's current limitations, underscore the importance of carefully selected augmentations for reliable building dimension estimation.  
Keywords:building dimension estimation; computer vision; deep learning; object detection; faster R-CNN; YOLOv10 _Article_
## 3 Latest Applications in Distribution Systems  
The proliferation of affordable and smart measuring instruments enables the collection of high-resolution accurate data. Moreover, the abundance of internet bandwidth and improvements in wireless communications allows for those devices to communicate reliably and effectively with the users and the power system. Furthermore, the widespread deployment of DERs produces bi-directional distribution system power flows. Thus, to fully utilize these benefits and harmoniously integrate cyber and physical components of the distribution system, key grid techniques ought to undergo appropriate improvements. Conventional computational techniques have proven to struggle to deal with challenges in four aspects [6; 7; 8]: (1) considerable computational burdens from the increasing volume and dimension of data; (2) the stochastic nature and intermittency of DER output; (3) dynamic complexity of inverter-connected resources, and (4) scalability in large-scale power systems. Thus, we investigate state-of-the-art AI-enabled algorithms developed in recent years for crucial functionalities in distribution systems, including power flow, state estimation, voltage control, topology identification, and line parameter calibration. Table 1 provides a concise technical summary that classifies these algorithms into various techniques introduced in Section 2.
## 5 Results and Discussion
### Technical Performance
#### 5.1.1 Performance of Scenarios I and III
This analysis assumes that the cold tank exhibits behavior perfectly inverse to that of the hot tank. It is important to note that this is not a measure of actual energy efficiency, as other consumptions--such as electrical tracing in pipelines, pumps, or heliostat movement--are not considered. Nevertheless, it serves as an approximation to quantify the reduction or increase in parasitic electricity consumption in the plant. \[\overline{SOC}(\%)=\frac{1}{N}\sum_{k=1}^{N}\left(\frac{STO(k)-STO_{min}}{ STO_{max}-STO_{min}}\right)\cdot 100 \tag{10}\]  
The average annual state of charge is 28.8% in Scenario I and 35.8% in Scenario III. This indicates reduced use of electric resistances in the cold tank when particles are used, thereby lowering parasitic consumption.