esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.2 Autoencoder Architecture  
Each encoder within this architecture consists of a convolutional layer followed by batch normalisation, and max-pooling. Stacking these encoder blocks together forms a Stacked Autoencoder (SAE). The convolutional layers use a 3 \(\times\) 3 kernel and ReLU for activation. At the end of the encoder is a dense layer for the conversion of feature maps to vectors enabling extraction of the latent space variables. The decoder combines upsampling within the convolutional layer. The decoder is mirrored in the number of, \(N\), units stacked to create the full decoder allowing the network to generate high-resolution feature maps, see Figure 2. The encoder and decoder are independent models and therefore share no information apart from the loss incurred during reconstruction whilst training.  
The encoder consists of 5 blocks. The initial layer starts with 256 filters, halving for each subsequent layer. Each max-pooling layer scales the image by a factor of 2. The latent space, represented by the dense layer, has 1024 units. Training data are categorized into 10 groups based on the water content in each image, where to handle the significant difference in reflectance between water and land features, we weight the training batches based on the water content. We calculate the water content using the McFeeters Normalized Difference Water Index (NDWI) [7 ], which measures the difference between the green and near-infrared bands. As we aim to introduce an unsupervised pipeline we chose to not utilise the labels for water in the SWED dataset. The difference between ground truth and NDWI labels when binned was similar apart from noise introduced by clouds. The model is trained on mostly land content and validated on a set equally balanced between water and land. Data augmentation adds further variation using rotated images and adding salt  
Figure 1: Proposed pipeline.  
and pepper noise, adjusting random pixels either to 0 or 1, to improve model generalisation. The optimal point in training is reached when the test loss matches the validation loss for 10 epochs, with the validation loss typically being lower due to more uniform textural information being present in water. The latent space vector provided by the autoencoder of size 1024 is passed on to subsequent stages of our pipeline.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 3 Our Perceptual Hash Algorithm Design
### Band-Clustering  
We employ AP clustering to divide the original MS image \(I\) into several band groups, and we do not need to set the clustering number. Firstly, the similarity matrix \(S\) is constructed, where each matrix element is the mutual information between \(\text{In}R_{k}\) and \(\text{In}R_{i}\) and the matrix size is determined by the number of bands. Then, the messages of similarity matrix \(S\) are updated repeatedly until some stop after a fixed number of iterations, at which point we need to set the damping factor \(\lambda\) and the maximum number of iterations in advance.  
After the step of band-clustering, the bands are divided into band clusters (band subsets). That is to say, the \(L\) bands are divided into \(N\) clusters, and each cluster is denoted as \(IC_{n}\) (\(n<N\)). For each band cluster \(IC_{n}\):  
\[IC_{n}=\left\{IC_{n}^{1},IC_{n}^{2},...IC_{n}^{i}\right\} \tag{3}\]  
where \(IC_{n}^{i}\in I\); therefore, the original MS image can also be expressed as:  
\[I=\left\{IC_{1},IC_{2},...,IC_{N}\right\} \tag{4}\]
# A UML Activity Flow Graph-Based Regression Testing Approach
## 2 Our Testing Approach
### Associating UML Sequence Diagrams and Activity Diagrams  
In this section, we present a method to associate activity and sequence diagrams for workflow modeling, which will enhance our ability to generate test cases from activity models. The approach is based on the concept that activity diagrams represent all control and data flows, while sequence diagrams represent method invocations or inter-object communication. Specifically, for each activity in an activity diagram, a sequence diagram can be utilized to provide a detailed explanation. This association between sequence and activity diagrams allows us to easily add more information to the activity diagram. Figure 1 depicts an example of this association, where multiple sequence diagrams are linked to a single activity diagram. The association between multiple sequence diagrams and a single activity diagram is typically established through the use of activity partitions. An activity partition is a vertical region within an activity diagram that represents a specific element or component of the system. Each sequence diagram is linked to a specific activity partition within the activity diagram, indicating that the interactions depicted in the sequence diagram are related to the activities taking place within that partition. For example, consider a system for processing customer orders. An activity diagram for this system might include partitions for receiving the order, verifying payment, and shipping the order. Multiple sequence diagrams could be created to show the interactions between the customer, the order processing system, and the payment processing system. Each sequence diagram would be associated with the appropriate activity partition within the activity diagram, providing a detailed view of how each interaction fits into the overall process. The association between sequence diagrams and activity diagrams in this way can be very useful for understanding the behavior of a system, particularly in complex systems with many interacting components. It can help developers to identify potential issues or inefficiencies in the system, and can also be used to validate the design of the system and ensure that it meets the requirements of the stakeholders. In this process, each activity node in the activity diagram generates a method declaration and a series of method calls, which define the method.  
In this section, we provide a formal definition of activity diagrams and sequence diagrams and clarify their connection. We explain which elements of activity diagrams and sequence diagrams are relevant for workflow modeling and collaboration modeling, respectively. This definition will help us better understand the relationship between activity diagrams and sequence diagrams and their roles in the software development process.
## 3 Results and Discussion
### Essential Oil Yield  
The yields obtained of the essential oils of _Artemisia hero-alba_ harvested in March, June, and September are 0.49, 1.74, and 1.30% (mL/100 g), respectively. The difference shown could be explained by the influence of both important parameters during the harvest periods: vegetative stage and the effect of climatic conditions.  
Our results corroborate those found in the essential oils of Algeria (0.62-1.19%) [57; 58; 59; 60] and Tunisia (0.1-1.86%) [61; 62; 63; 64; 65]. However, those of Jordan (1.3-3%) [11; 66] and Spain (0.41-2.30%) [67] present the highest percentage, while those originating from Egypt showed the lowest value, 0.11% [68].
## 3 Results and Discussion
### Additional Experimental Validation of WD-YOLO
#### 3.4.2 Robustness to Noise  
To evaluate the robustness of the WD-YOLO model, multiple types of noise were added to the input images from the PlantDoc dataset, and the model's performance under these degraded inputs was visually analyzed. The noise types applied included Gaussian noise with variances of 1 and 5, as well as salt-and-pepper noise with salt_prob and pepper_prob set at 0.01 and 0.05, respectively. Figure 12 illustrates how these noise types affected the appearance of input images.  
Despite these visual impairments, the results presented in Table 5 demonstrate that WD-YOLO maintains relative stability in performance. Specifically, under Gaussian noise with a variance of 1, the mAP50 decreased slightly to 64.6%, with a minor reduction in precision and recall to 59.8% and 59.2%, respectively. At a higher variance of 5, the mAP50 dropped to 62.9%, with precision and recall decreasing to 58.2% and 57.4%. For salt-and-pepper noise, the mAP50 remained relatively stable, decreasing marginally to 65.0% at the low noise level (salt_prob and pepper_prob = 0.01), with precision and recall at 58.6% and 59.2%. At the higher noise level (salt_prob and pepper_prob = 0.05), the mAP50 decreased to 63.4%, with precision and recall dropping to 55.9% and 55.7%.  
The results demonstrate that WD-YOLO maintains robust performance against external noise, with only slight declines observed even under intense noise interference. As detailed in Table 1, this robustness to noise is a significant benefit for real-world deployment, where environmental factors often cause unpredictable noise in input data. The model's consistent accuracy under such adverse conditions underscores its dependability, highlighting its practical value for plant disease detection across diverse environments.  
Figure 12: Noise impact visualization: (**a**) original image, (**b**) Gaussian noise (\(\sigma^{2}=1\)), (**c**) Gaussian noise (\(\sigma^{2}=5\)), (**d**) salt-and-pepper noise (\(p=0.01\)), (**e**) salt-and-pepper noise (\(p=0.05\)).
## 2 Applications in Chemistry
### Alkene Polymerization
This versatility was explored by the authors via the synthesis of fluorinated polymers bearing hydroxyurethane moieties PFHU **78a-b** and **79a-b** through the ring-opening of the GC unit with isopropylamine in DCM at 50 \({}^{\circ}\)C. Complete conversion of the terpolymers **74**to PFHU 78a-b derivatives was achieved using a large excess (20-30 equivalent) of base, while full conversion of 75 into 79a-b only required 1.2 equivalent of base. [PERSON] and co-workers [110] promoted the reaction of the double bond of polystyrene polymers bearing pendant cyclic carbonate groups BMD 80a and BBD 80b via radical photopolymerization to develop new alternative materials for application in nanocomposites of sodium smectite, such as the one used in saline leachates, keeping the cyclic carbonate unit intact in the polymerization step. The synthesis of BMD 80a was performed via the reaction between GC 1a and 4-vinylbenzyl bromide 81a in the presence of sodium hydride in THF (Scheme 44). The analog BBD 80b was similarly synthesized by reacting 4-(4-hydroxyethyl)-1,3-dioxolan-2-one with 81a. The monomers 80a and 80b were photopolymerized over 24 h under nitrogen atmosphere using AlBN and a 12-cm-high UV lamp. The corresponding polymers were obtained with 93% (PBMD 82a) and 74% yields (PBBD 82b). On treatment of 82a with a saline solution (3 M NaCl), a nanocomposite with disordered intercalation was formed. Alternatively, 82a and 82b were intercalated in the presence of the same saline solution, providing partially disordered intercalates nanocomposites. [PERSON] and co-workers [111] prepared monoglycerides 83 by reacting CG 1a or glycolid 2a with carboxylic acids 61. These intermediates 83 were converted into the corresponding monoglycerie bis-acrylates 84, which underwent polymerization under UV irradiation in the presence of 2-hydroxy-2-methyl-1-phenyl-propan-1-one (Darocur 1173(r) at room temperature (Scheme 45). When GC 1a was employed (step 1), the desired product was isolated with 14% yield only (even after 24 h of reaction) due to side-reactions. Incontrast, when glycolid **2a** was used in excess (carboxylic acid **61**:**2a** = 1:2), the intermediate **83** was obtained in yields above 80% after only 50 min. **Scheme 45. Synthesis of monoglycerides 83 and subsequent conversion to monoglyceride bisacrylates 84. **
## 3 Calibration: Compact Tension Testing
### Calibration Results  
Figure 4 shows the evolution of the MSE (errors in load vs. displacement graphs between experiments and simulations) according to Equation (5). The optimisation algorithm converges after 17 generations. Considering the evaluation of 24 FE simulations in each generation, the optimisation process results in a total of \(17\times 24=408\) FE simulations. Note that the simulations within one generation can be computed in parallel.  
To further illustrate the optimisation process, Figure 5 compares the force vs. displacement data from experiments [6] to the best simulation results obtained from different generations during the execution of the GA optimisation. It can be seen that the simulation results successively approach the target curves from experiments, minimising the MSE until convergence is achieved in the 17 th generation. The input parameters associated with the optimal results in the last generation are listed in Table 2.  
In order to ensure that the parameters yield physically meaningful results, it is important to compare the simulated damage evolution with experimental measurements. Figure 6 compares measurements of the crack length \(\Delta a\) from the initial notch at approximately 1.5 mm displacement during simulated CT tests with the corresponding experimental data [6]. The FE simulation shows damage in the \(0^{\circ}\) ply of the \([90/45/0/-45]_{s}\) laminates in vertical loading direction, indicated by the white lines in the top right corner of the FE contour plot. It can be seen that FE simulation with the optimal damage input parameters  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\varepsilon_{1}^{i}}\) **(-)** & \(\mathbf{\varepsilon_{2}^{i}}\) **(-)** & \(\mathbf{\varepsilon_{1}^{s}}\) **(-)** & \(\mathbf{\varepsilon_{2}^{s}}\) **(-)** \\ \hline
0.0059 & 0.0065 & 0.084 & 0.081 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal damage input parameters found by GA-based calibration to simulate progressive damage in beech veneer laminates.  
Figure 4: Evolution of mean squared error during GA optimisation to find suitable FE input parameters to simulate progressive damage in wood veneer laminates.  
Figure 5: Force vs. displacement results from GA-based optimisation of damage input parameters compared to target curves obtained from experiments [6] on quasi-isotropic \([90/45/0/-45]_{s}\) beech veneer laminates subjected to compact tension tests.  
can reproduce the extent of damage observed in experiments. Note that Figure 6 shows that damage grows along a single row of elements. This is typically seen in local CDM analyses and will be discussed in detail in later sections.  
With confidence in the FE modelling and the associated optimal damage input parameters obtained from GA, a range of OHT tests will be simulated to investigate how these parameters can predict tension tests other than CT.
###### Abstract  
Hydrogen is a promising zero-carbon fuel for internal combustion engines; however, the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NOx emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs, the injector featuring three holes at a 15.24\({}^{\circ}\) injection angle outperformed the baseline, delivering improved mixture uniformity, reduced knock tendency, and lower NOx emissions. These results demonstrate the potential of geometry-based optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies.  
hydrogen combustion; computational fluid dynamics; combustion modelling; PIV validation; combustion chamber optimization Article
## 2 Architecture of Dima System
### Installation Layer  
The installation layer describes hardware allocation to the anatomy of underlying aircraft. It is powered by mount locations and power network.  
Hardware devices can be installed at different mount locations of the aircraft, such as cockpit, avionics bay, middle, tail, and so forth. The installation location is characterized by its coordinates, volume, cooling capacity, power supply, number of slots and mass. The location characteristics should be considered for the optimization on installation of devices. While placement of some components are fixed (e.g. sensors and actuators), CPMs placement and cable routing are needed to determine for the optimization subject to constraints, such as physical and safety requirements.  
The power network enables the mount locations to be connected with tunnels, which provides power for the devices installed at the location. Such a network is different from the one in the hardware layer that represents communication network among the devices.  
These three layers are interconnected via hardware and software mapping in the DIMA system. Particularly, the hardware and software mapping can include the followings:  
* Device assignment: it maps devices to locations in the aircraft. The assignment should consider connectivity and physical constraints.
* Task assignment: it maps tasks to devices. The assignment should consider resource and segregation constraints.
* Link assignment: it maps cable routes for links to connect devices. The assignment should consider connectivity and resource constraints.
* Signal assignment: it maps signals to links when tasks, devices, and links are assigned. The assignment should consider segregation and resource constraints.  
Such hardware and software mapping should be carefully designed by considering a set of quality measures and aircraft constraints, which calls for a formalization of DIMA optimization presented in Section 3.
## 3 Results and Discussion
### Results
#### 3.1.6 N Dynamics below the Root Zone  
Below root zone dynamics are similar to those simulated in the root zone, with flushing rates of both DON and DIN significantly lower than the rates of internal recycling (Figure 9). However, flushing rates of DON are larger than from within the rooting zone (467.7 mg/m\({}^{2}\)-year root zone compared to 283.6 mg/m\({}^{2}\)-year below the root zone), while flushing of DIN is somewhat higher from below the rooting zone (0.36 mg/m\({}^{2}\)-year root zone compared to 23.61 mg/m\({}^{2}\)-year below the root zone) primarily because vegetation is no longer able to mediate the flux of DIN from below the rooting zone. The model results indicate that the region below the root zone is a moderate nitrogen sink, with a net mobilization value of \(-\)99.40 mg/m\({}^{2}\)-year. No measurements exist within this region of the watershed, and Figure 9 therefore represents only one possible result which is consistent with both simulated inputs from the root zone, and more importantly, measured outputs of water and dissolved nitrogen from the catchment.
# Research on Speech Emotion Recognition Method Based A-CapsNet
## 5 Experimental Results of the Proposed Model A-CapsNet
### Experimental Analysis  
1. The performance of the model A-CapsNet improves as the SNR increases. Under the EIRD data division technique, the recognition performance with an SNR of \(-10\) and \(10\) is \(57.68\) and \(99.26\), respectively, as illustrated in Figure 9. There is a rise of \(40\) percentage points, which is a significant improvement, and it suggests that the SNR has a significant impact on the performance of the model.
2. Using the same database, the performance of the proposed model under different data division techniques varies, but no data division method outperforms the others, as illustrated in Figure 10. For example, when the SNR is \(-10\), the model A-CapsNet performs best when using the data division technique EIRD, and when the SNR is \(10\), it performs best when using the data division method EDRD. With the improvement in the signal-to-noise ratio, no matter which data division method is used, the averages of the accuracies are improved, which is a relatively ideal recognition result. When the signal-to-noise ratio (SNR) is \(10\), there is minimal variation in the experimental performance of the four data division algorithms.  
Figure 9: The boxplot of different SNRs.  
3. Figure 11 shows the outcomes of four data division techniques for varying model validation times using the Multi-SNR-5-EMODB database. It can be demonstrated that the suggested model A-CapsNet's performance was higher than 88.06 percent, which is an important recognition outcome. When compared to other data division techniques, the model A-CapsNet performs better under the data division approach EIRD. At the same time, it is also clear that the model's performance fluctuates significantly when cross-validated at various intervals, suggesting that data partitioning techniques may have an adverse effect on the model's performance. Therefore, it is important to assess the resilience and generalization of the model using various data partitioning techniques. However, no consistent conclusion has been obtained regarding which data partitioning method results in the best performance of the model. It can be said that it is necessary to evaluate the performance of the model by integrating the results of the four data partitioning methods.  
In order to further highlight the advantages of the proposed model, Table 3 summarizes the performance improvement of the model A-CapsNet compared to the related peer methods with the baseline database EMODB. It shows that the suggested model A-CapsNet performs better with the EMODB databases compared to the findings of prior research, indicating that the proposed model M-CapsNet is more efficient. Additionally, in this experiment, the model parameters, feature dimensions, and training time are all relatively small. As a result, it complies with real-time system requirements.  
Figure 11: The results of different model validation times for the database Multi-SNR-5-EMODB using four data division methods.  
Figure 10: The results of four data division methods under different SNRs.