esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Discrimination of Cherry Cultivar Using E-Tongue Potentiometric Profiles  
The E-tongue sensors recorded 40 potentiometric signals during the analysis of the aqueous cherry pastes, which varied between 106.5 and 354.8 mV (coefficients of variation from 0.2 to 7.2%) for the several independent cherry samples evaluated per cultivar studied. The average potentials recorded for the four cultivars (Durona, Lapins, Summit, and Van) were of the same magnitude. resulting in similar potentiometric signal profiles. Nevertheless, the slight differences observed were further explored to test the discrimination power of the E-tongue. These electrochemical devices have been widely used as taste-sensors. For example, potentiometric multisensor devices with lipid membranes (non-specific and cross-sensitivity sensors) were used by the research team for assessing sensory sensations or sensory-related chemical compounds in soft beverages and mineral waters [60; 61], table olives and sweet peppers [23; 24; 25; 26], and olive oil [31; 32; 33; 34; 35]. In this work, for the first time, the capability of applying a lab-made potentiometric E-tongue to discriminate sweet cherries according to the cultivar was investigated. An E-tongue-LDA-SA model, with three discriminant functions (explaining 100% of the data variability), was developed based on signals of 11 non-redundant sensors (1 st array: S1:4, S1:5, S1:6, S1:7, S1:11, S1:13, and S1:17; 2 nd array: S2:2, S2:15, S2:18, and S2:20). The supervised classification had sensitivities of 100%, 92%, and 88 \(\pm\) 7% for the original grouped data (Figure 4), LOO-CV, and repeated K-fold-CV, respectively. In general, Summit and Van cherries were correctly classified. Misclassifications were mainly observed for Durona or Lapins cherries. The results also showed the superiority predictive cultivar classification performance of the E-tongue compared with the biometric-physicochemical data fusion strategy. On the other hand, this simpler, fast, and single-run technique had predictive correct classification rates, similar to those reported in the literature (sensitivities varying from 86.6 to 97.4%) when more extensive physicochemical, spectroscopic, and chromatographic analysis was performed [6; 9; 11].  
Figure 3: Discrimination of sweet cherries according to four cultivars (\(\square\) Durona; \(\blacktriangle\) Lapins; \(\circ\) Summit; or, \(\blacklozenge\) Van) using a LDA-SA model based on nine selected biometric-physicochemical characteristics (L* and a* CIELAB color scales, titratable acidity, penetration force, length, maximum and minimum diameter, stone weight, and pulp/stone mass ratio): (**a**) 2D-plot of the first and second discriminant functions; (**b**) 2D-plot of the first and third discriminant functions. The full ellipses represent the confidence region boundaries determined based on the posterior probabilities calculated for each class membership.
## 2 Numerical Simulations and Wind Tunnel Tests
### Solution Algorithms  
In the numerical simulation, the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm is used to solve the velocity-pressure coupled equations. The nonlinear convection term is discretized using the Second Order Upwind format, while the momentum equation is discretized using the Bounded Central Differencing format. Time discretization is performed using the Second Order Fully Hidden format. The time it takes for the wind to cross the computational domain from inlet to outlet is referred to as a full-basin time. A total of 30 full-basin times are simulated, with results from the latter 24 full-basin times extracted for statistical analysis to ensure the stability of the statistical results.
## 3 Datasets and Evaluation Metrics
### MUSIQ
PSNR (Peak Signal-to-Noise Ratio)  
The Peak Signal-to-Noise Ratio (PSNR) [29] measures the quality of reconstructed or lossy compressed images using the difference between the original and distorted images. It is determined by Equation (7):  
\[PSNR=10\cdot\log_{10}\biggl{(}\frac{L^{2}}{MSE}\biggr{)} \tag{7}\]  
where \(L\) is the maximum possible pixel value (e.g., 255). Higher PSNR values (in dB) indicate better quality.
## 3 Formulation
### Formulations of Constraints
#### Constraints of Operational Zone
As for the departure order for additional trains \(k\) and \(f\) within their operational zone, only one of \(\mu_{kf}^{i}\) and \(\mu_{fk}^{i}\) can be equal to 1, because either train \(k\) comes before train \(f\) or train \(f\) comes before train \(k\). Constraint (15) restricts the departure order for train \(k\) and \(f\) within their operational zones. Meanwhile, if train \(k\) is located before train \(f\), the interval of departure time between train \(k\) and train \(f\) must be greater than the minimum headway \(l_{min}\) at station \(i\) of their operational zones, as defined by Constraint (16). \[\mu_{kf}^{i}+\mu_{fk}^{i}=\sum_{i^{\prime}\leq i}z_{ki^{\prime}}^{I}\ast\sum_{ i\in S,P^{\prime}\geq i}z_{ki^{\prime}}^{T}\ast\sum_{i^{\prime}\leq i}z_{ fi^{\prime}}^{I}\ast\sum_{i\in S,P^{\prime}\geq i}z_{ff^{\prime}}^{T}\ k,f\in K_{2}\ and\ f>k\,i\in S. \tag{15}\]  
\[D_{f,i}^{a}-D_{k,i}^{a}\geq h_{min}\ast\mu_{kf}^{i}\ k,f\in K_{2}\,\ i\in S. \tag{16}\]The other set of headway constraints between existing train \(f\) and additional train \(k\) are written as Constraints (17) and (18). The mathematical expression in Constraint (17) indicates that, if train \(k\) departs earlier than train \(f\) within their operational zone, the interval between the departure time of the two trains must be greater than the minimum headway \(h_{min}\). Otherwise, the situation of train \(f\) being before train \(k\) is formulated in Constraint (18). \[D^{e}_{f,i}-D^{a}_{k,i}\geq h_{min}\cdot\mu^{i}_{kf}\cdot\sum_{i^{\prime}\in S,\;\;r\leq i}z^{I}_{kk^{\prime}},\sum_{i^{\prime}\in S,i^{\prime}\geq i}z^{T}_{ kk^{\prime}}\;k\in K_{2},f\in K_{1}\;\text{and}\;D^{e}_{f,i}>0\;,\;i\in S. \tag{17}\]  
\[D^{a}_{k,i}-D^{e}_{f,i}\geq h_{min}\cdot\left(1-\mu^{i}_{kf}\right)\sum_{i^{ \prime}\in S,i^{\prime}\leq i}z^{I}_{kk^{\prime}},\sum_{i^{\prime}\in S,i^{ \prime}\geq i}z^{T}_{ki^{\prime}}\;k\in K_{2},f\in K_{1}\text{and}\;D^{e}_{f, i}>0\;,\;i\in S. \tag{18}\]
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 4 Recent Advancements in WGA Algorithms  
The field of whole-genome alignment has seen significant progress over the years. With the rapid advancements in sequencing technologies and the ever-increasing amount of genomic data, the development of efficient and accurate WGA algorithms has become more critical than ever [67].  
One of the major advancements in WGA algorithms has been the development of methods that can handle large-scale genomic rearrangements. While traditional alignment algorithms often struggle with these rearrangements, newer tools like Cactus and Progressive Cactus have incorporated mechanisms to accurately align regions with inversions, duplications, and translocations [62]. These tools use graph-based approaches, allowing them to model and align complex genomic structures [68] (Table 1).  
Another significant advancement is the development of algorithms that can align divergent genomes. Traditionally, aligning divergent genomes has been challenging due to the high levels of sequence divergence and the presence of unique genomic elements. However, tools like LASTZ and MULTIZ have been designed to tackle these challenges, enabling the alignment of divergent genomes [25; 69].  
The advent of cloud computing and parallel processing has also influenced the development of WGA algorithms. Tools like Cloud Aligner (alpha version) and ParaGraph have leveraged these technologies to perform alignments on a massive scale, handling large volumes of data and multiple genomes simultaneously [70; 71]. This has significantly improved the speed and scalability of WGA algorithms, allowing researchers to undertake more ambitious projects.  
Incorporating machine learning techniques into WGA algorithms is another promising direction. Through learning from existing alignments, these algorithms can potentially improve their accuracy and efficiency. For instance, tools like Deep Align have demonstrated the potential of machine learning to improve the accuracy of sequence alignments [72].  
Furthermore, the integration of WGA algorithms with other bioinformatics tools has also seen notable progress. For example, pipelines like EAGER2 integrate WGA tools with other bioinformatics software, providing a comprehensive solution for genomic analysis [73]. This not only simplifies the analysis process but also enhances the utility of WGA algorithms in research.  
In conclusion, the recent advancements in WGA algorithms have significantly improved their performance and versatility, catering to a wider range of research applications. As the field continues to progress, it will be exciting to see how these advancements will shape the future of whole-genome alignment [74].
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 3 Proposed Method
### Enhanced Feature Extraction and Sequence Decoupling
Moreover, the prediction module is designed to forecast the future positions of target objects using refined feature representations extracted from video sequences. The discriminator is trained using a modified binary cross-entropy loss function, formulated as follows:  
\[l=-h\log\left(h^{\prime}\right)-\left(1.0-h\right)\log\left(1.0-h^{\prime}\right) \tag{3}\]  
where \(h\) represents the true feature representation extracted from adjacent frames, and \(h^{\prime}\) denotes the predicted motion feature by the network. These features undergo a normalisation process to ensure their values remain within the range \([0.0,1.0]\), which is a necessary condition for the binary classification framework before computing the loss. Furthermore, ChebyPooling is employed to enhance spatial feature abstraction by leveraging the mathematical properties of Chebyshev polynomials, thereby reducing sensitivity to input variations. The present process captures the dynamic aspects of the video sequence while preserving feature information from content motion. As this work targets the extraction of depth information from colour features in the video sequence, the focus is primarily on motion feature extraction, with the result that noisy information on the time-axis from the frame sequence is discarded. VGGreNet utilises Chebyshev pooling [59, 60] with size \(H\) and \(W\) in the current frame as follows:  
\[\mu =\text{AvgPool}(f) \tag{4a}\] \[\sigma^{2} =\text{AvgPool}\Big{(}f^{2}\Big{)}-\mu^{2}\] (4b) \[t =\text{Softplus}(\text{MaxPool}(f))\] (4c) \[\text{ChebyPool}(f) =\frac{\sigma^{2}}{\sigma^{2}+(t-\mu)^{2}} \tag{4d}\]  
where AvgPool and MaxPool denote the average and maximum pooling, respectively. Such advanced pooling, \(\text{ChebyPool}(f)\in\mathbb{R}^{H\times W}\), is recommended to project the feature into a probability domain that can be used as a weighting feature. It makes use of the Chebyshev depth theory, whereby the output feature can be projected to a stable range without the need for a sigmoid function, which provides a readable probabilistic result for subsequent processing as follows:  
\[f(x)=\frac{1}{1+\exp(-x)} \tag{5}\]  
This activation function ensures that outputs remain within the \([0.0,1.0]\) interval, which is essential for subsequent binary classification tasks.
## 4 Rule-Based Large Neighborhood Search Algorithm (RLNS)
### Heuristic Rules and Various Operators
#### 4.2.1 Construction of the Initial Solution  
Based on the random allocation scheme for satellites and ground stations, the initial solution construction operator is designed according to the start time of the time window and suitable timing, following the precedence rules for scheduling, which is shown in Figure 2.  
Figure 2: Flowchart of the construction of the initial solution.
## 3 Experiments
### Data Augmentation  
This study employs data augmentation techniques to expand the dataset and address the issue of increased parameters and overfitting as the segmentation model deepens. These methods help improve the model's generalization performance. As shown in Table 5, data augmentation includes operations, such as translation, flipping (horizontal and vertical), rotation, random cropping, and adjustments to the HSV saturation of the existing data samples. Figure 6 illustrates the results after applying data augmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resolution** & **MIoU** & **Border IoU** & **FLOPs (G)** & **Inference Time (ms)** \\ \hline
128 \(\times\) 128 & 0.7523 & 0.8625 & 17.3 & 12.5 \\
256 \(\times\) 256 & 0.7952 & 0.9295 & 69.3 & 48.9 \\
512 \(\times\) 512 & 0.8127 & 0.9396 & 277.2 & 195.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison across input resolutions.  
Figure 5: Diversified data set.  
Ablation Study on Augmentation Strategies  
To evaluate the contribution of each augmentation technique, we trained REU-Net(2 EEAM) on the Massachusetts dataset with only one augmentation enabled at a time. The baseline model (no augmentation) achieved an MIoU of 0.6820, while enabling individual augmentations improved performance as shown in Table 6. Combining all augmentations yielded the highest MIoU of 0.7952, demonstrating their synergistic effect.  
The ablation study reveals that each individual data augmentation method provides only limited improvement to the model's training performance. However, applying all five methods collectively enhances the model's MIoU by 0.1132. Therefore, in this study, all five augmentation techniques were applied to the dataset, expanding it to 22,650 images, which sufficiently meets the requirements for model training.  
Figure 6: Diagrams of different data augmentation methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Method** & **Parameter** \\ \hline Translation & \(\pm\)15\% \\ Random Rotation & \(\pm\)15\({}^{\circ}\) \\ Random Horizontal Flip & 50\% \\ Random Vertical Flip & 50\% \\ HSV Saturation & \(\pm\)50\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters of different data augmentation.
## 5 Conclusions  
In this paper, a class of state observers for the linear continuous time systems is discussed, the stability of the state observers, as well as the stability conditions are analyzed and the upper bound of the system error estimation is given. Based on the improvement of the PI state observer, the stability and the stability conditions of the nonlinear state observer are also analyzed, and it is proved that the system state estimation of the nonlinear state observer is better than that of the PI state observer.  
In the simulation, the UAV was taken as the simulation object, and the fault estimation effects of the PI state observer and the nonlinear state observer with different parameters were investigated. From the results of the fault estimation, with a large parameter, the fault estimation results are more stable, but the offset between the estimated fault and the real fault is larger. With a small parameter, the estimated faults are closer to the real fault, but there is a large vibration. On this basis, the adaptive parameter selection was investigated. The fault estimation results have smaller offset, faster response speed and smaller vibration amplitude. To sum up, the nonlinear state observer can achieve better track of the system state, and overcome the contradiction between the response and offset in the traditional PI state observer. At the same time, the algorithm can be used to improve other state observers, which can achieve better expansibility.  
Although our method has achieved good results in the linear model, there are still some problems to be further studied. Firstly, how to apply this method to the fault diagnosis of nonlinear system and whether it can achieve better fault diagnosis results. Secondly, for the nonlinear system, how to design the corresponding nonlinear observer to improve the effect of fault diagnosis.  
[PERSON] proposed the main idea, finished the draft manuscript; [PERSON]. ([PERSON]) conceived of the experiments and drew the figures and tables; [PERSON]. ([PERSON]) analyzed the data; [PERSON] conducted the simulations. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Nature Science Foundation of China (No.62001115, 61903086), the Fundamentals and Basic of Applications Research Foundation of Guangdong Province (No.2019A1515110136) and the Project of Department of Education of Guangdong Province (No.2019 KTSCX193).  
**Institutional Review Board Statement:** Not applicable.  
Figure 6: Fault estimation of nonlinear observer with adaptive parameters (with noise).
## 3 Experimental Results by Unit Testing
### LOS Test and Non-LOS Test  
An LOS test was conducted after verifying the communication function between the anchor and node. Our LOS test installed three anchors in an indoor space without obstacles and we repeatedly measured the coordinates of 16 fixed positions for a tag along the x-, y-axes, as shown in Figure 6. The results of the repeated LOS and NLOS tests are presented in Table 1. All of the errors in indoor spaces were within \(\pm\)200 mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measurement Error [mm]**} & \multicolumn{2}{c}{**LOS**} & \multicolumn{2}{c}{**NLOS**} \\ \cline{2-5}  & **x Axis** & **y Axis** & **x Axis** & **y Axis** \\ \hline Average & 68.01 & \(-\)29.56 & \(-\)43.25 & \(-\)45 \\ Minimum & \(-\)194 & \(-\)194 & 289 & 107 \\ Maximum & 195 & 188 & \(-\)382 & \(-\)237 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indoor LOS and NLOS measurement data.  
Figure 4: Positioning process with extended Kalman filter.  
Figure 5: Communication test in indoor and outdoor spaces with a tag (yellow circle) and 3 anchors (red circles).  
Figure 6: LOS environment test for measuring the coordinates of 16 fixed positions for a tag (yellow circle) based on 3 anchors (red circle; A1, A2, A3) installed in an indoor space without obstacle.  
After the LOS test, an NLOS test was conducted to inspect the positioning performance in an indoor space with obstacles. In this experiment, the position coordinates of four tags in the indoor space were repeatedly measured. From Table 1, the measurement errors in the NLOS environment were slightly higher than those in the LOS environment, as predicted. Average measurement errors of LOS and NLOS tests are out 0 mm. However, a lot of tests for various indoors allows the average errors to approach 0 mm.
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
## 3 Results and Discussion
### Influence of Nanoparticles on the System Performance  
Different concentrations of silica nanofluid were prepared by diluting 30 wt% silica nanofluid in sodium chloride solution (4 wt%). During the experiment, the scraping rate was maintained at 29 rpm and the flow rate was maintained at 1.0 m\({}^{3}\)/h.  
As shown in Figure 15, as the concentration of nanosilica increased, the time required for the formation of ice slurry decreased from 1030 s to 840 s. Based on the heterogeneous nucleation theory [39], if there are solid particles in parent phase, crystal nucleus will be formed firstly on the solid particle surface, which contributes to reducing nucleation work. As we know, heterogeneous nucleation is the main form of nucleation, and the use of nanosilica as a nucleating agent induced heterogeneous nucleation and advance the freezing time. However, the formation temperature of ice slurry fluctuated between \(-2.3\) and \(-2.4\)\({}^{\circ}\)C under different nanosilica concentrations, which indicates that nanosilica has little effect on increasing the freezing point temperature.  
The effect of nanosilica on the COP of the system is shown in Figure 16. Compared with COP of a 4 wt% sodium chloride solution, the COP was significantly increased by 10.47%, 10.76%, 12.65%, 14.77%, 15.30%, and 17.38%, with addition of nanosilica at concentrations of 0.05 wt%, 0.1 wt%, 0.2 wt%, 0.5 wt%, 0.75 wt%, and 1 wt%, respectively. Obviously, as the amount of nanosilica added increased, the COP of the system also increased. Existing researches have shown that the addition of nanoparticles to solutions can significantly increase the thermal conductivity of solutions and improve the heat transfer performance [21; 22; 23; 24; 40; 41; 42]. Therefore, we speculate that the addition of nanosilica also improves the heat transfer performance of the fluid, thereby achieving a higher COP. However, there is no uniform explanation for the mechanism of the increase in thermal conductivity [43; 44].  
Figure 14: Effects of scraping speed on COP.  
Figure 15: Ice slurry formation characteristics at different nanosilica concentrations.
# A Brief Survey of Fixed-Parameter Parallelism
## 5 Implementation Aspects
### Input Queries  
Checking whether some set \(S\) of the input contains some element \(x\) can be done in constant time by checking the corresponding index. In general, we can efficiently check whether some set \(S\) of the input contains as a subset some given set \(T\). The operation is \(FPP\) using \(O(n*m)\) processors, just as in the case of a single element. In fact, for each set of the input, the \(O(m)\) processors assigned to this set will check whether \(T\) is a subset. This can be done in constant time since the universe of elements is of size \(m\), so there exists a processor for every element of the universe that checks whether this element is present in both \(T\) and \(S\). An element not in \(T\) is ignored. The \(m\) processors for a given set forward their results in a binary _reduction tree_ manner in logarithmic time. If no processor has detected an element in \(T\) that is not in \(S\), then the set contains \(T\) as a subset. The results from all sets are then combined in a similar reduction tree approach in logarithmic time.  
This means that finding if some element or set of elements exists in a particular set of the input is \(FPP\). Note that we can also count the occurrences, that is how many sets contain a particular subset, and this helps in a variety of problems that perform reduction or branching rules. For example, the classical \(d\)-Hitting Set kernelization finds elements or subsets of elements that are present above a certain limit and deals with them until the obtained instance is a kernel (i.e., cannot be further reduced by the used kernelization procedure). For \(d=3\) (3-Hitting Set), subsets of elements of size 2 that are present in more than \(k\) sets are included in the solution. Then, elements that are present in more than \(k^{2}\) sets are also dealt with. There will be at most \(m^{2}\) sets of size 2 and \(m\) elements to check, and we have seen that checking the occurrences of each of these sets is \(FPP\). It is just the number of processors needed that has to be multiplied by \(m^{2}\) to achieve the mentioned kernelization. To find a subset of elements of constant size \(d\) that is present in at least \(k\) sets, we can consider every possible combination of \(d\) elements from the universe as an element. This will require additional processors; however, the upper bound of \(O(m^{d}*m*n)\) shows that the query is \(FPP\).  
Finally, computing the number of occurrences of every element in all sets is also clearly \(FPP\). For a given element, the are \(n\) processors, each in a set, allocated for this element, These processors can check for the presence of the element in their sets and combine their results in logarithmic time.