esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the increased need for improved adsorbents for efficient water treatment, sodium alginate (NaAlg) and chitosan (Chi) represent promising platform biopolymers for the preparation of biocomposite adsorbents for the effective removal of waterborne oxyanion (arsenate (Asi) and orthophosphate (Pi)) contaminants. The TMCs were characterized by spectroscopy (infrared (IR), SEM with an energy dispersive X-ray (SEM-EDX)), point-of-zero-charge (PZC) measurements, and dye adsorption by employing \(p\)-nitrophenol at variable pH. Based on dye adsorption results, the adsorbent surface area (SA) was 271 m\({}^{2}\)/g for Al-TMC, 286 m\({}^{2}\)/g for Fe-TMC, and 311 m\({}^{2}\)/g for Cu-TMC. This indicates the role of adsorbent pore structure and swelling in water. Further, the role of either aluminum (Al), copper (Cu), or iron (Fe) for the preparation of TMCs for the selective Asi removal in the presence of Pi as a competitor anion was evaluated. While Al, Fe, and Cu coordinate to the biopolymer framework at C=O sites, only Fe coordinates to -NH2 sites. While Al coordinated via Al-O and interfacial hydroxy groups, Cu showed the formation of Cu2(OH)3 NO3 in contrast to Fe, which observed FeOOH formation. Adsorption of Asi was highest for Al-TMC (80 mg/g), followed by Fe-TMC (77 mg/g) and Cu-TMC (31 mg/g). Adsorption of Pi was highest for Al-TMC (93 mg/g), followed by Fe-TMC (66 mg/g) and Cu-TMC (17 mg/g). While Al-TMC showed the highest adsorption capacity overall, only Fe-TMC (followed by Cu-TMC) showed strong arsenate selectivity over orthophosphate. The selectivity toward Asi in presence of Pi was determined and the binary separation factor (\(a_{tk}\)) and the selectivity coefficient (\(\beta_{t}\)) were calculated, where Cu-TMC (\(a_{tk}=6.1\); \(\beta_{t}=4.4\)) and Fe-TMC (\(a_{tk}=8.3\); \(\beta_{t}=5.0\)) exceeded Al-TMC (\(a_{tk}=1.5\); \(\beta_{t}=1.2\)). This work contributes to the field of oxyanion-selective adsorbents via judicious selection of the metal salt precursor during the synthetic design of the ternary biocomposite systems, as demonstrated herein.  
Keywords:chitosan; alginate; ternary metal composites; arsenate selective adsorption; biopolymers; phosphate removal +
Footnote †: journal: Applied Sciences
# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
###### Abstract  
This study proposes a method of blending visible and near-infrared (NIR) images to enhance their edge details and local contrast based on the Laplacian pyramid and principal component analysis (PCA). In the proposed method, both the Laplacian pyramid and PCA are implemented to generate a radiance map. Using the PCA algorithm, the soft-mixing method and the mask-skipping filter were applied when the images were fused. The color compensation method uses the ratio between the radiance map fused by the Laplacian pyramid and the PCA algorithm and the luminance channel of the visible image to preserve the chrominance of the visible image. The results show that the proposed method improves edge details and local contrast effectively.  
near-infrared; image enhancement; Laplacian pyramid; principal component analysis; soft mixing; stevens effect +
Footnote †: journal: Applied Sciences
## 3 Formation of Heterogeneous Groups
### Cluster Performance Evaluation Method  
Silhouette analysis is a method used to interpret and validate the consistency within clusters of data. The silhouette value is a measure that tells us how similar an object is to its own cluster (called cohesion) compared to other clusters (called separation). It is used to study the separation distance between the clusters found. With the silhouette plot, we can see how close each point in one cluster is to points in the neighboring clusters and this provides a way to evaluate cluster parameters, such as numbers.  
This validation technique calculates the average silhouette index for each cluster, the silhouette index for each sample, and overall average silhouette index for a dataset. In this way, with this approach each cluster could be represented by the Silhouette index, which is based on the comparison of its tightness and separation. The Silhouette Coefficient is measured using the following formula:  
\[S(i)=\frac{b(i)-a(i)}{max\{a,\ b(i)\}}\]  
where  
1. \"\(a(i)\)\"--is the average distance of the point i from all other points in the same cluster;
2. \"\(b(i)\)\"--is the smallest average distance of the point i to all points in the closest cluster. Now, obviously, the range of the silhouette value \(S(i)\) will be between \((-1,1)\).
* If the silhouette value is close to \(1\), the sample is far away from the neighboring clusters. So, the sample is well-clustered and assigned to an appropriate cluster.
* If the silhouette value is about \(0\), the sample is very close to the neighboring clusters and so it could be assigned to another cluster closest to it. And this means it indicates an overlapping cluster.
* If silhouette value is close to \(-1\), the sample is assigned to the wrong cluster and is simply placed somewhere between the clusters.  
So according to this method, we need the coefficients to be as large as possible and close to 1 to have good clusters.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 4 Discussion
Figure 5: A visualization of the tracking results on the MOT20 test dataset. Boxes of the same color and the numbers in the upper-left corner of the boxes indicate the same tracking object. Figure 6: A visualization of the tracking results on the DanceTrack test dataset. Boxes of the same color and the numbers in the upper-left corner of the boxes indicate the same tracking object. The frame number in the lower-left corner shows the frame index of the image in the video. In total, we identified 20,338 objects involved in overlap situations. Among these objects, our model successfully tracked 18,497, achieving a success rate of 91.0%. In comparison, the baseline model only successfully tracked 18,160 objects, with a success rate of 89.2%. This result indicates that our method can more effectively maintain tracking accuracy and robustness in complex object overlap scenarios. Figure 8 further presents the experimental results for specific video sequences from the DanceTrack dataset, where we compare the success tracking rates of our model and the baseline model in handling object overlap scenarios. The charts clearly illustrate the performance differences between the two models in complex environments, highlighting the advantages of our method in object tracking. Figure 8: We present a comparison of the tracking success rates between our method and the baseline model for specific video sequences in the DanceTrack dataset. In the DanceTrack-04 sequence, our method tracked 83.6% of the objects, while the baseline method tracked only 78.1%. In the DanceTrack-26 sequence, our method tracked 97.9% of the objects, while the baseline method tracked 94.1%. In the DanceTrack-63 sequence, our method tracked 94.2% of the objects, while the baseline method tracked only 87.5%. In the DanceTrack-73 sequence, our method tracked 85.1% of the objects, whereas the baseline method tracked 82.3%. Finally, in the DanceTrack-97 sequence, our method tracked 80.7% of the objects, while the baseline method tracked 78.1%. Figure 7: We counted the total number of objects appearing in frames with object overlaps (\(\mathrm{IoU}>0.7\)) in the DanceTrack dataset, totaling 20,338 objects. In these overlapping scenarios, our method successfully tracked 18,497 objects, achieving a success rate of 91.0%; in contrast, the baseline model successfully tracked 18,160 objects, with a success rate of 89.2%.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 4 Materials and Methods
### Data  
#### 4.2.1 Geospatial Data  
Mid-quality/High-quality 3D model data for the city of Coimbra obtained through BLosm plugin were used to create the real scaled model of the city of Coimbra. The area of Coimbra used in the application is a rectangular region of 4.8 \(\times\) 5.4 km\({}^{2}\), rather than the actual real border of the region of the municipality. This region was chosen to be manageable for a mobile application while still being sufficiently large for studying impacted buildings along the river front.  
The model extracted from BLosm was a real-sized scale model, with a terrain resolution of up to 30 m, with buildings from OSM. Most of the buildings were present, and a small part of them were missing as OSM is community-oriented in relation to implementing and adding real buildings' geometries.  
Concerning the dataset from CLMS, a selected area was chosen to acquire the dataset of the Mondego river height, as seen in Figure 3.  
Two dataset points for this area were found for the Mondego river. They were plotted with their location with an OSM map in QGIS, as seen in Figure 4. The higher altitude point is closer to the Coimbra city centre, and therefore, it was chosen for hydrological connectivity. The other point is at a lower altitude and closer to the ocean.  
Figure 3: Platform to acquire the dataset from CLMS. The city of Coimbra is within the red box.
## 4 Motion Planning Application and Analysis
### Platform for HIL Test  
The HIL platform is shown in Figure 7.  
As shown in Figure 7, the HIL is mainly composed of three parts: perception module \"A\"; motion planner \"B\"; and vehicle model \"C\" [36]. These modules communicate with each other via a control area network. The virtual reality simulation software \"PreScan\" runs on \"A\", which is a graphics workstation. It is used to simulate the traffic environment (object, lane etc.) and visualize the vehicle states. The module \"B\" is a rapid prototype controller produced by dSPACE and called \"MicroAutoBox II\" (Quad-core ARM A57 CPU, 2 GHz, and 256-core NVIDIA Pascal GPU). The NMPC algorithm was implemented in it and solved by sequential quadratic programming (SQP) with the real-time iterative framework [37]. The close-loop stability and recursive feasibility were proven in [37]. To obtain a real-time feasibility, the code generation tool, CasADi [38], combined with the quadratic programming solver, qpOASES [39], were used to generate the efficient C-code. The operation cycle was 0.05 s. The module \"C\" is the real-time simulator of dSPACE called \"MicroLabBox\", in which CarSim runs. In module \"C\", the driving force is converted into throttle angle \(\alpha_{thr}\) and braking pressure \(P_{brk}\) through the vehicle longitudinal inverse dynamics model [40], and the steering angle of front tire is converted into steering wheel angle \(\delta_{w}\) through the coefficient of the steering system. The converted throttle, brake and steering wheel angle are used as inputs to CarSim.  
The pseudo-code for the NMPC-SDM algorithm is shown in Algorithm 1.  
Figure 7: Block diagram of the hardware-in-the-loop (HIL).
## 4 Discussion  
The optimisation routine presented in this research can offer an insight in the working principle of dynamic line scan thermography. Understanding the influence of the different parameters on each other helps an inspector select the most appropriate measurement set-up, reducing the time needed to perform inspections. In addition, it enables us to perform measurements without the need of an expert skilled in the art of thermography. It should be mentioned that the presented technique assumes that the defects are sufficiently distant from each other. Defects near one another will influence the contrast shown in the thermograms. The influence is shown in Figure 12. As can be seen bigger defects experience less influence of nearby defects as the reflected energy is greater. Defects starting at different distances beneath the top surface however will minimally influence each other.  
Figure 11: Calculation of the contrast between 9 pixels. The centre pixel is situated in the middle of the defect and the other defects are spaced equally around the centre pixel. The distance between the pixels is 30 pixels. The contrast values are calculated for the images (a–c) of Figure 9. A higher value equals a bigger contrast between the defect and the surroundings.  
Figure 10: Optimal parameter sets found for the holes in the flat bottom hole sample. The ambient room temperature is 22 \({}^{\circ}\)C and the maximum use temperature is chosen at 80 \({}^{\circ}\)C. This is not the maximum use temperature of PVC, but it limits the cooling time the sample needs before performing a consecutive measurement. As the temperature difference is used as a measure of contrast, this limitation of the maximum temperature causes no problems. Next to the optimal parameter values for each parameter, the predicted temperature difference is calculated. This contrast is calculated between the sound area and the area above the defect.
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 3 Diversity Balancing for Two-Stage Collaborative Filtering
### User Interest Distribution
#### 3.2.3 Trust and Interest Weight Aggregation  
In the trust propagation process, the calculation of a similarity weight for two users who are low-trust neighbors must consider not only their trust relations, but also their mutual interests. Therefore, two thresholds are defined in the calculation of aggregation weights:  
\[W_{u,v}=\left\{\begin{array}{ll}TS_{u,v}&TS_{u,v}\geq\theta\\ \gamma TS_{u,v}+(1-\gamma)pref_{u,v}&0<TS_{u,v}<\theta\\ 0&\text{else}\end{array}\right. \tag{7}\]  
**Definition 1**.: _Trust threshold \(\theta\). If the trust between two users has a value greater than \(\theta\), the users are high trust neighbors. One user might accept the recommendation of the other user even if they do not have similar preferences. If the trust between the two users is less than \(\theta\), they are low trust neighbors. Their trust relations and multi-interest preferences should be considered simultaneously in the recommending process._  
**Definition 2**.: _Interest threshold \(\gamma\). If two users are low trust neighbors, they have small overlapped interest fields. It will increase the possibility of finding potential interests for the target user, while the recommendation accuracy is decreased. Therefore, the interest threshold \(\gamma\) is set that two users participate in the weight calculation process only when their interest weight exceeds \(\gamma\)._
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 7 Results and Discussion
### Thermal Properties Parameters of Drilling Fluid  
The expansion coefficient and compression coefficient of the annular fluid vary with different temperatures. Table 3 shows the expansion coefficient and compression coefficient of the annular fluid at different temperatures. As the temperature of the annular fluid increased from 34.42 \({}^{\circ}\)C to 55.71 \({}^{\circ}\)C, both the expansion coefficient and compression coefficient of the fluid increased with temperature, and the increase in the expansion coefficient was greater than that of the compression coefficient. In the process of calculating the pressure, the expansion coefficient and compression coefficient should be selected according to different temperatures. Using a constant coefficient may lead to significant errors. Figure 17 shows the annular pressure calculated using the expansion coefficient and compression coefficient, which vary with temperature, as well as a constant coefficient. The constant coefficient was used for the expansion coefficient and the compression coefficient at 55.71 \({}^{\circ}\)C, and the maximum error of the annular pressure calculated using the constant coefficient was 11.58%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Average Temperature of** & **Annular Temperature Difference** & **Expansion Coefficient** & **Compression Coefficient** \\
**the Annulus (\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\(\times\)10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\))** & **(\(\times\)10\({}^{-6}\) MPa\({}^{-1}\))** \\ \hline
34.42 & 16.08 & 355.15 & 451.03 \\
45.72 & 27.38 & 397.12 & 460.75 \\
48.94 & 30.6 & 409.09 & 463.52 \\
52.39 & 34.05 & 421.90 & 466.49 \\
54.57 & 36.23 & 430.00 & 468.36 \\
55.71 & 37.37 & 434.24 & 469.34 \\ \hline \end{tabular}
\end{table}
Table 3: Fluid expansion coefficient and compression coefficient at different temperatures.  
Figure 16: Annular pressure at different production rates.
## 3 The FQS Algorithm
### Preprocessing Phase
#### 3.1.2 Preprocessing algorithm  
The preprocessing procedure is shown in Algorithm 3. We use an array, \(PrePos\), of length \(|\Sigma|\), to keep the previous position for each symbol, \(c\), where \(c\in\Sigma\). Following the above analysis, we can get \(ES_{j}\) from \(ES_{j}=ES_{j-1}+|\Sigma|-(j-prepos_{j-1}(P[j]))\) (Equation (7)), where \(m-1\geq j\geq 1\). The computation can be done in constant time for each given \(j\).  
```
GetPos(\(P,m,|\Sigma|\))
1 ES \(\leftarrow\) 0, maxES \(\leftarrow\) 0, \(pos\)\(\leftarrow\) 0
2 for(\(i\)\(\leftarrow\) 0 to \(|\Sigma|-1\)) do
3 PrePos[\(i\)] \(\leftarrow\) -1 /*initializing all of \(prepos\)*/
4 endfor
5 for(\(j\)\(\leftarrow\) 0 to \(m-1\)) do
6 ES \(\leftarrow\) ES + \(|\Sigma|-(j-PrePos[P[j]])\);
7 PrePos[P[j]] \(\leftarrow\) \(j\);
8 ifES \(\geq\) maxES then
9 maxES \(\leftarrow\) ES;
10\(pos\gets j\);
11 endif
12 endfor
13 return\(pos\)
```  
**Algorithm 3** Get the maximal expected shift value.  
Algorithm 3 shows the detailed preprocessing steps to compute the maximal expected shift position (\(pos\)) for pattern \(P[0,...,m-1]\) using Equation (4). In Algorithm 3, variable \(ES\) is the expected skip value, which is initialized to zero. In the first step of the loop in Lines 5-12, \(ES_{0}\) will be set to \(|\Sigma|-1\). Variable \(maxES\) is the maximal expected shift value. Additionally, \(pos\), a position in pattern \(P\), is the location where the maximal expected shift value resides in the pattern, \(P\).  
Lines 2-4 initialize the value at each symbol to \"\(-1\)\" for the recent occurrence position array, \(PrePos\) (denoted as \(prepos\) in Equation (3)). Lines 5-12 are a \(for\) loop, which calculates each position'sexpected shift value, \(ES\), and determines the maximal expected value. Line 6 calculates the expected shift value, \(ES\), using the incremental method, as discussed above (Equation (7)). Lines 9-12 search for the maximal expected shift value, \(maxES\). The algorithm finally returns the maximal expected shift position, \(pos\), in Line 13. Note that this preprocessing is only performed once for the pattern, \(P\), using \(\Theta(m+|\Sigma|)\) time.  
Recall that FQS calculates three elements in its preprocessing phase, namely: (1) the maximal expected shift position (\(pos\)) for pattern \(P\); (2) a shift table for pattern \(P\) using the QS algorithm; and (3) a shift table for \(P[0,...,pos-1]\), the pos-length prefix of \(P\), again using the QS algorithm. From the above calculation of the maximal expected shift position (\(pos\)), we know that the time complexity is \(\Theta(m+|\Sigma|)\). For element (2) and (3), the computations are based on the QS algorithm, requiring time in \(\Theta(pos+|\Sigma|)\) and \(\Theta(m+|\Sigma|)\), respectively. Together, the overall preprocessing time complexity for FQS is \(\Theta(m+|\Sigma|)\), since \(pos<m\).
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 4 Discussion  
### Variation in Water Flow Area and Vegetation Cover  
We calibrated the number of white pixels as a percentage of the total number of pixels and in terms of actual area (Table 3). The flow area varied slightly from 39.441% (4437.1 m\({}^{2}\)) to 39.531% (4447.2 m\({}^{2}\)). This slight variation is because of the tide difference, while all the other conditions were similar. An increase of 8.45% of the flow area will lead to overflow conditions. The vegetation growth over the period changed from 19.513% (2195.2 m\({}^{2}\)) to 25.314% (2847.8 m\({}^{2}\)). Malaysia having a tropical climate with year-round rainfall is one of the main reasons for this good amount of vegetation growth [40]. Vegetation significantly influenced the linkages between terrestrial ecosystems and atmospheric processes. It is workable to measure the advantages of ecosystem restoration by identifying the mechanisms through which vegetation dynamics and sensitivities to water resource availability respond at various temporal and geographical scales.
###### Abstract  
Activated carbons were prepared by chemical activation with KOH, FeCl\({}_{3}\) and H\({}_{3}\)PO\({}_{4}\) of the chars obtained via hydrothermal carbonization of grape seeds. The hydrocarbons prepared at temperatures higher than 200 \({}^{\circ}\)C yielded quite similar proximate and ultimate analyses. However, heating value (24.5-31.4 MJ\(\cdot\)kg\({}^{-1}\)) and energy density (1.04-1.33) significantly increased with carbonization temperatures between 180 and 300 \({}^{\circ}\)C. All the hydrocarbons showed negligible BET surface areas, while values between 100 and 845 m\({}^{2}\cdot\)g\({}^{-1}\) were measured by CO\({}_{2}\) adsorption at 273 K. Activation of the hydrocarbons with KOH (activating agent to hydrochar ratio of 3:1 and 750 \({}^{\circ}\)C) led to highly porous carbons with around 2200 m\({}^{2}\cdot\)g\({}^{-1}\) BET surface area. Significantly lower values were obtained with FeCl\({}_{3}\) (321-417 m\({}^{2}\cdot\)g\({}^{-1}\)) and H\({}_{3}\)PO\({}_{4}\) (590-654 m\({}^{2}\cdot\)g\({}^{-1}\)), showing these last activated carbons important contributors to mesopores. The resulting materials were tested in the adsorption of sulfamethoxazole from aqueous solution. The adsorption capacity was determined by the porous texture rather than by the surface composition, and analyzed by FTIR and TPD. The adsorption equilibrium data (20 \({}^{\circ}\)C) fitted the Langmuir equation well. The KOH-activated carbons yielded fairly high saturation capacity reaching up to 650 mg\(\cdot\)g\({}^{-1}\).  
Keywords:g rape seeds; hydrothermal carbonization; hydrochar; activated carbon; adsorption; sulfamethoxazole +
Footnote †: journal: Applied