esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Analysis
#### 3.2.2 Regression Models: Generation, Calibration and Analysis of Variance
In contrast, the coagulation activity showed a convex shape profile at low pH (pH 4) when the bio-coagulant dosage was increased. The coagulation activity was observed to be decreased until a certain point before it started to increase gradually. Another significant observation on coagulation activity was observed between the interaction of deshelled _Carica papaya_ dosage and initial turbidity, at a constant pH of 5.3 (Figure 5b). Based on Figure 5b, different observation was shown upon the increase of initial turbidity from 100 NTU to 500 NTU for both low and high dosage. At low dosage of 50 mg/L, the coagulation activity was slowly decreased until the initial turbidity reached up to 250 NTU. Beyond this concentration, the coagulation activity started to show positive results. Low initial turbidity was corresponded with less colloidal particles in the water to be treated. This reduces the chance of agglomeration especially when low dosage was used. Contrary observation was seen when high dosage of 200 mg/L was used. The  
Figure 5: Response surface of the coagulation activity as a function of (**a**) deshelled _Carica papaya_ seeds dosage and pH, (**b**) deshelled _Carica papaya_ seeds dosage and initial turbidity and (**c**) pH and initial turbidity. coagulation activity showed consistent gradual increment as the initial turbidity increased from 100 NTU to 500 NTU. Figure 5b also shows the increased efficiency of coagulation activity as the coagulant dosage increased up to 150 mg/L at high initial turbidity of 500 NTU. With further increment beyond this threshold value, negative efficiency was obtained. Excessive dosing has only increased the residual turbidity as polymer chain of deshelled _Carica papaya_ seeds were mostly linking with one another due to surface saturation and the overloaded effect [43]. Thus, a reverse effect was observed. Equally, reductions in the bare surface area for attachment of segments following the excess of that bio-coagulant addition would lead to particle restabilization and to a certain extent, steric repulsions may form [44]. Figure 5c shows the response surface plot that illustrates the significant relationship between pH and initial turbidity while the coagulant dosage is constant at 125 mg/L. As the initial turbidity increased from 100 NTU to 500 NTU, the coagulation activity showed gradual increase at various pHs. Besides, this figure also shows that as the pH was decreased from pH 6.5 to pH 4 the turbidity increased, resulting in the slight increase in the coagulation activity. Combination between optimum pH and initial turbidity at pH 4 and 500 NTU, respectively, resulted in 80% coagulation activity.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Multimodal Deepfake Detection  
A key limitation in current video deepfake detection approaches using CNN and LSTM is their heavy reliance on visual cues. These models primarily analyse facial expressions, movements, or pixel-level details in video frames, but deepfakes often manipulate multiple modalities, including audio [37]. As a result, purely visual-based detection systems may overlook critical tampering in the audio stream, such as altered voices or speech patterns that do not match the visual content. This single-modality focus reduces the effectiveness of detection systems in identifying more sophisticated deepfakes that manipulate both video and audio [43]. More research is needed to explore multimodal detection techniques that analyse both audio and video components of deepfakes. Additionally, cross-modal consistency checks could be developed to compare the synchrony and coherence between the audio and visual elements, further enhancing the reliability of detection systems.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms
### The Proposed Improved Equilibrium Optimizer Algorithm
#### 4.3.2 Non-Dominated Sorting with Elite Strategy  
To enable EO to address the multi-objective distribution network reconfiguration problem, this study incorporates Non-Dominated Sorting (NDS) with an Elite Strategy.  
First, evaluate each solution based on all objective functions in the target space and use the NDS mechanism to identify non-dominated solutions. In each iteration, compare the new and old equilibrium concentrations. If the new equilibrium concentration dominates the old one, replace the old concentration with the new one, and store the non-dominated solutions found in that iteration in an external archive.  
An elite strategy is introduced to retain the best individuals from previous generations and incorporate them into the next generation, working alongside the non-dominated sorting. This strategy expands the sampling space, accelerates the algorithm's execution, and, to some extent, prevents the loss of solutions on the Pareto frontier.  
The external archive is updated by comparing the non-dominated solutions with the new candidates. When the number of non-dominated solutions exceeds the archive size, the crowding distance for each objective function is calculated for each individual in the non-dominated layer, as shown in Equation (38). The individual with the smallest crowding distance is then removed iteratively.  
\[i_{d}=\sum_{j=1}^{m}\left(\left|f_{j}^{i+1}-f_{j}^{i-1}\right|\right) \tag{38}\]  
In the proposed IEO framework, the integration of a non-dominated sorting mechanism not only ensures diversity but also effectively guides the search toward the Pareto front. This strategy allows the algorithm to maintain a set of trade-off solutions across multiple objectives.  
Unlike conventional approaches that rely on scalarization through weighted summation, the IEO adopts a Pareto-based multi-objective optimization strategy. Instead of combining objectives such as power loss and voltage deviation into a single composite function with predefined weights, the algorithm directly optimizes these criteria in parallel. During the iterative process, elite non-dominated sorting is employed to evaluate solution dominance and maintain a high-quality and diverse Pareto front.  
This approach eliminates the need for subjective weight selection and preserves the intrinsic trade-off relationships between conflicting objectives. As a result, the optimization process produces a well-distributed set of solutions, each representing a distinct balance between power loss minimization and voltage profile improvement. This not only enhances the fairness and flexibility of the reconfiguration scheme but also improves its interpretability and practical applicability under varying operational priorities.
## 5 Analysis of HFT Results
### Sensitivity Analysis of the Principal Stress Anisotropy  
As mentioned earlier, this reservoir model has a very high \(\sigma_{\rm{Hmax}}/\sigma_{\rm{hmin}}\) anisotropy of about 13.8 MPa, and no rotation occurred in the HFs growth direction. However, in the case of a formation with anisotropy of 2.1 MPa, the fracture propagating direction may be affected depending on the magnitude of SSE. To confirm the effect, HFT was performed again in the reservoir with a lower \(\sigma_{\rm{Hmax}}/\sigma_{\rm{hmin}}\) anisotropy of 2.1 MPa for the SANDY03 well where heavy SSE occurred. In the results, as the fracturing stage continued to 2 nd, 3 rd, and 4 th, the change in the propagating direction was confirmed, as shown in Figure 14. As the total stress in the vicinity of the 2 nd stage HF increased due to SSE induced by the 1 st stage HF, the propagating direction of the 2 nd HF was rotated toward the higher stressed zone (yellow arrow to blue arrow in Figure 14); consequently, the HF was formed closer to the horizontal well. It is reported that for fractures not transverse to a horizontal wellbore, the stress interaction between the adjacent fractures may attract and even connect each other [35; 36; 37]. In other words, depending on the state of principal stresses, HFT could create HFs grown in a direction that is unfavorable for the well productivity, which is L-dir in this study. In particular, when the principal stress anisotropy is relatively small, the HF propagating direction may change due to the occurrence of SSE, and the disadvantage to production may be more severe. Then, the influence of the horizontal well drilling azimuth determination by considering the in-situ stress condition on the efficiency of the HFT becomes more critical.  
Figure 13: Monthly oil production and cumulative oil production history of SANDY03 well for 7 years (October 2012-September 2019).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Actual Well Pattern** & **Proposed Well Pattern** \\ \hline SRV & 99,926,737 m\({}^{3}\) & 116,291,809 ft\({}^{3}\) & 16.4\% \(\uparrow\) \\ OOIP & 2,440,211 m\({}^{3}\) & 2,763,093 m\({}^{3}\) & 13.2\% \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: SRV and original oil in place (OOIP) results for “Actual Well Pattern” and “Proposed Well Pattern”.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## Study Area and Data  
### Study Area  
Most applications using data fusion approaches discussed in the literature focus on vegetated areas--mainly forest and cropland. In this study, two study areas dominated by crop and forest are selected (Figure 1).  
Study area 1 is located in Eastern Nebraska (41\({}^{\circ}\)8\({}^{\prime}\)N, 97\({}^{\circ}\)16\({}^{\prime}\)W), U.S., close to David City in Butler County. This is an agricultural area with a humid continental climate. The average precipitation is about 29 inches per year, less than the U.S. average (39 inches). The major crops in this region (U.S. Corn Belt) are corn and soybeans, and most crop fields in the study area require irrigation.  
Study area 2 is located in a mountain range of the Ouachita Mountains in West Central Arkansas (34\({}^{\circ}\)43\({}^{\prime}\)N, 93\({}^{\circ}\)47\({}^{\prime}\)W), U.S., crossing parts of Montgomery County and Yell County. Arkansas generally has a humid subtropical climate, where summer is hot and humid and winter is slightly dry and mild to cool. Study area 2 is primarily covered by forest, including deciduous, evergreen, and mixed forest types.  
To reduce the impact of clouds and cloud shadows in both Landsat and MODIS images on the pair dates and the assessment (prediction) dates, we subset two small areas in the tests to ensure that most of the images are cloud-free and, thus, the statistics generated from different dates are comparable. The image extents are 9.3 km \(\times\) 9.4 km (310 by 314 Landsat pixels) for site 1 and 13.0 km \(\times\) 38.7 km (436 by 1290 Landsat pixels) for site 2.
## 3 Results and Discussion
### Cloning and Expression of Active spPEP  
An amplicon of 2,153 bp was obtained and successfully ligated into the LIC vector. Figure 4A shows a SDS PAGE of the cell lysate of the soluble and insoluble fractions. Significant amounts of recombinant PEP were found in the soluble fraction of the induced cells, but there was also PEP found in the insoluble fraction (Figure 4A). Figure 4B shows the Coomassieie-stained 80-kD spPEP eluted from the nickel column on a 10% SDS PAGE gel. A total of 18 mg, approximately 95% pure, was obtained from the soluble fraction of 6 L of culture (Figure 4B).  
After the buffer exchange of PEP into the 25 mM ethanolamine buffer, pH 8.5, containing 0.1M NaCl and 1 mM DTT, the PEP enzyme activity was measured in both uninduced and induced protein fractions. The enzyme activity for both the soluble crude extract and the refolded insoluble protein fraction are shown in Figure 5. Both the soluble extract and the refolded insoluble protein fraction exhibited PEP specific activity. The specific activity was approximately the same for the induced samples whether soluble or refolded insoluble at 2.4 \(\upmu\)M's-1'mg-1 of total protein. Within the 9-min assay, the substrate was not depleted for these samples. Enzyme activity was observed in the uninduced refolded insoluble protein as a result of leaky expression; however, the activity was reduced within about 2.5 min, reaching only  
Figure 4: **(A)** SDS polyacrylamide gel of the cell lysates of uninduced and IPTG-induced Bl21(DE3)pLysS cultures expressing spPEP. UI = uninduced, I = induced with 0.4 mM IPTG. The cell lysate of the host without the recombinant enzyme construct is shown for comparison. There was no insoluble fraction for the host alone for comparison. **(B)** SDS PAGE (10%) of recombinant spPEP eluted from a Ni-NTA agarose column. Protein was stained with Coomassieie Blue R250.  
2/3 of the activity of the induced samples. Very little spPEP was observed in this fraction at 80 kD on the SDS PAGE (Figure 4A), which would be the size of the full-length enzyme. No protease inhibitors were added to the cell lysates in order to keep the spPEP from being inhibited, so it is possible that the small amount of activity observed in this fraction might have been due to unstable fragments of the enzyme.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 5 Discussion
### Spatial Distribution and Variation Patterns of Wheat Sowing and Maturity Dates  
The growth period and crop yield are strongly influenced by water deficit. Rainfed wheat may suffer severe water stress and exhibit significant differences compared to irrigated wheat. During  
Figure 8: Sowing dates of rainfed (**a**) and irrigated (**b**) wheat. Maturity dates of rainfed (**c**) and irrigated (**d**) wheat in south central Shanxi Province.  
the growing season, an increase in air temperature may result in a shorter growth period and earlier maturity date under deficient water supply, as shown in rainfed wheat in arid and semi-arid areas in this study. The growth and crop yield of rainfed wheat are severely affected by water deficit, e.g., during the filling stage of winter wheat when photosynthetic products in roots, stems and other organs are transported to grains.  
This study quantified spatial variations and rates changes of the wheat growth period under different water supply conditions based on an enhanced classification method. The results show that the sowing dates of rainfed and irrigated wheat exhibit similar spatial patterns, and both are significantly related to elevation. Irrigated wheat varies faster than rainfed wheat. The maturity dates of rainfed and irrigated wheat are significantly postponed when elevation increases; however, they exhibit distinct spatial patterns.
## 3 Results and Discussion
### Aroma Compounds
#### 3.3.1 Terpenes
These grape-derived aroma compounds could have originated directly from the grape berry or from the release of their glycoside precursors during the fermentation and storage of wine [39]. Furthermore, factors such as cultivar, climatic condition, grape maturity, and winemaking procedure could play a vital role in modulating the volatile terpene concentrations in grapes and wines, finally determining the expression of aroma characteristics [40]. Research focusing on the comparison of the aroma compounds in Cabernet Sauvignon and Merlot wines from four regions in China showed that both varieties from HBCL (a subregion of BHB, plotted in the map shown in Figure S2) contained higher concentrations of citronello than those in HBSC (a synonym of the HZB region, Figure S2) [41]. Additionally, according to the results of our previous studies aimed at investigating the differences in the volatile compounds in Muscat Blanc a Petits Grains grapes between two regions, we found that mature grape berries from CL contained higher concentrations of free and glycosidically bound terpenes than berries from the GT region, which is considered to have a cool continental climate due to its high altitude and is also characterized by high sunshine duration, PAR, and diurnal temperature ranges, with low rainfall [42]. Previous studies concluded that the grape berries exposed to sunlight had increased content of both free and glycosidically bound monoterenes (e.g., linalool, \(\beta\)-citronello1, and geraniol) in Gewurztraminer [43], Riesling [39], and Pinot Noir [44]. However, in this study, conflicting results were found in the comparison of Marselan wines among regions with different climates. The HZB, LP, and XJ regions have relatively high sunshine duration and PAR compared to the JDP and BHB regions. As we know, the environmental factors that affect the accumulation of the aroma compounds in grapes or wines were obtained from many viticultural trials, such as leaf removal, shading, and canopy management, which influence the microclimate conditions of the fruit zone. Until now, the difference of the terpenoid metabolism in grapes under different climatic conditions, especially monsoon-influenced climates in China, has remained largely unstudied.
## 2 Materials and Methods  
### Equipment and Material  
The testing samples were fabricated using a ME polymeric machine, Ultimaker 5S, equipped with a direct drive extruder with a hardened steel nozzle CC0.6 of diameter 0.6 mm, which was the smallest nozzle diameter, limiting the clogging problem. A filament with a diameter of 2.85 mm, provided by BASF (Ultrafuse 316L), was used. This is an innovative filament that is made up of 316L austenitic stainless-steel powders (90 wt%), evenly distributed in a polymeric matrix composed of polyoxymethylene (POM) and polyolefin. The immobilization of the metal particles and the uniform distribution of the metal within the binder matrix allow for safe and simple handling. The parts are built up layer upon layer from a moldable material, with the polymer content of the filament acting as a binder. The main polymer content (primary binder) was removed from the so-called green part through a catalytic and thermal debinding process at 120 \({}^{\circ}\)C with HNO\({}_{3}\) (concentration 98%). The result of this process is the brown part, which consists of pure metal particles and a residual binder (secondary binder). The brown part is characterized by the same volume and a loss of mass compared to that of the green part. The subsequent sintering process removes the secondary binder from the as-built part and causes metal particle coalescence. The sintering cycle was performed in an argon atmosphere and consisted of three thermal ramps:  
* room temperature-5 \({}^{\circ}\)C/min-600 \({}^{\circ}\)C, holding time 1 h;
* 600 \({}^{\circ}\)C-5 \({}^{\circ}\)C/min-1380 \({}^{\circ}\)C, holding time 3 h;
* 1380 \({}^{\circ}\)C-furnace cooling-room temperature.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 4 Experiment
### Experimental Setup  
In the experiment, The parameter settings and evaluation methods of the model are as follows:  
**Network settings.** First, in MemCapsNet, the GloVe model is used in the word embedding layer, and the unit numbers of the input vector layer and the hidden layer are both set to 300 in BiLSTMs. Second, in AgCapsNet, ResNet with RGB three channels is used as the feature mapping network in the feature extracting layer, where the size of the convolution kernel is \(5\times 5\) and the stride is 2. Last, for capsule convolution networks, the number of iterations of EM routing in capsule convolution of text and image is uniformly set as 3. The number of convolution layers in both networks is 2. The one-dimensional convolution kernel of text capsules in two layers are 5 and 3, respectively, and the strides are 3 and 2 respectively. Meanwhile, the two-dimensional convolution kernel of image capsules is \(3\times 3\) and the strides in two layers are 2 and 1, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Positive**} & \multicolumn{2}{c}{**Neutral**} & \multicolumn{2}{c}{**Negative**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline Lap14 & 987 & 341 & 460 & 169 & 866 & 218 \\ Rest14 & 2164 & 728 & 633 & 196 & 805 & 196 \\ Rest15 & 955 & 34 & 272 & 340 & 28 & 195 \\ Rest16 & 1297 & 63 & 466 & 474 & 29 & 127 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the datasets on SemEval.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Star Rating** & **Review Number** & **Percentage** \\ \hline
5 & 10,000 & 23.50\% \\
4 & 10,000 & 23.50\% \\
3 & 10,000 & 23.50\% \\
2 & 7428 & 17.45\% \\
1 & 5115 & 12.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of the stars in MTCom.  
**Model setup and metrics.** In the multimodal experiments, the maximum number of samples used at the same time in training is 8. If only MemCapsNet is used for text sentiment analysis, the maximum number of samples used simultaneously is 64. In our all neural network-based experiments, the Adam optimizer with a learning rate of 0.0005 is used to minimize the interval loss of the model, where the L2 regularization coefficient was 0.0000002. A dropout strategy with a ratio of 0.3 is used in the add and norm layers. The metrics used in our experiment are accuracy and \(F_{1}\)-score (\(F_{1}\)), and the definition of \(F_{1}\)-score is expressed below:  
\[F_{1}=\frac{2\times Prec\times Rec}{Prec+Rec} \tag{36}\]  
where \(Prec\) and \(Rec\) are precision and recall, which are used to calculate \(F_{1}\). The micro-\(F_{1}\) method is employed to evaluate the results of aspect-based fine-grained sentiment analysis in our experiments.
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
## 3 Proposed Adapations of the Model for Serial Distributed Systems  
In this work, some adaptations are proposed to adjust the original model presented in Section 2 not only to purely dedicated link front-haul but also to hybrid serial/dedicated link front-haul and to fully serial front-haul topologies. In order to express that, a modified total power equation (4) is defined considering that for this case, the front-haul power is proportional to the number of serial links (\(N\)) instead of \(M\), which supposes a considerable power saving since normally \(N\ll M\). The new equation is as follows,  
\[P_{\text{Total}}=\sum_{n=1}^{N}\left(P_{n}^{\text{FH, fix}}+B\chi_{n}^{\text{FH }}S_{\text{dl}}\right)+\sum_{m=1}^{M}P_{m}^{\text{AP}}, \tag{4}\]  
where \(P_{n}^{\text{FH, fix}}\) and \(\chi_{n}^{\text{FH}}\) represent the traffic-independent power consumed and the traffic-dependent power consumption coefficient, respectively, for each \(n\)-th link. For the sake of simplicity, in (4), it is assumed that \(P_{n}^{\text{FH, fix}}\) and \(\chi_{n}^{\text{FH}}\) are equal for the \(N\) links and \(P_{m}^{\text{AP}}\) equal for all APs.  
As mentioned before, front-haul technology can also impact power consumption. To cite an example, in [24], different embodiments for antennas for distributed massive MIMO are described. These embodiments consider several connection options between the APs and the CPU, additional modules connected to the antenna arrangement, and different shapes of the body (cable, strip, or film). All of this definitely affects the network power consumption. A detailed comparison of the power consumption for specific front-haul technologies or multiple lengths of the links is out of the scope of this work. Instead, in Section 4, typical values for front-haul power are considered to obtain numerical values for a specific scenario, and as a bonus, the power saving is quantified in terms of fixed power if APs with \(P_{m}^{\text{AP}}\) around 1 W are considered.