esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### GC-MS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.1.8. Esters  
In Table 1, four unsaturated fatty acid esters (neryl acetate, methyl palmitoleate, methyl linoleate, and methyl linolenate) and one saturated fatty acid ester (methyl palmitate) were detected in dried peel of the two cultivars. Neryl acetate was a major monoterpene ester in most citrus germplasms [17] and showed decreased content in the oleocellosis peel of orange [40]. For both cultivar peels, it can be found from Table 1 that straight-chain fatty acid methyl esters (methyl palmitate, methyl palmitate, methyl linoleate, and methyl linolenate) were only detected in FD peel with saturated fatty acid methyl ester (methyl palmitate) of highest peak area and percentage; while branched monoterpene ester (neryl acetate) was undetected in FD peel. Then, for both cultivars, esters accounted for about 20% for FD peel while it had an almost negligible percentage for AD/SD peel (Table S1 and Figure 1b). Thus, there were few reports on straight-chain fatty acid methyl esters in dried citrus peel since citrus peel was always dried by AD or SD.
###### Abstract  
Complex mechanical systems, such as agricultural combine harvesters, are subjected to dynamic excitations from multiple coupled sources, compromising structural integrity and operational reliability. Disentangling these vibrations to identify dominant sources and quantify their transmission paths remains a significant engineering challenge. This study proposes a robust diagnostic framework to address this issue. We employed a multi-condition vibration test with sequential source activation and an improved Operational Transfer Path Analysis (OTPA) method. Applied to a harvester chassis, the results revealed that vibration energy is predominantly concentrated in the 0-200 Hz frequency band. Path contribution analysis quantified that the \"cutting header \(\rightarrow\) conveyor trough \(\rightarrow\) hydraulic cylinder \(\rightarrow\) chassis frame\" path is the most critical contributor to vertical vibration, with a vibration acceleration level of 117.6 dB. Further analysis identified the engine (29.3 Hz) as the primary source for vertical vibration, while lateral vibration was mainly attributed to a coupled resonance between the threshing cylinder (58 Hz) and the engine's second-order harmonic. This study's theoretical contribution lies in validating a powerful methodology for vibration source apportionment in complex systems. Practically, the findings provide direct, actionable insights for targeted structural optimization and vibration suppression.  
Operational Transfer Path Analysis (OTPA); multi-source vibration; combine harvester; MIMO System (Multiple-Input Multiple-Output); contribution analysis +
Footnote †: journal: Applied Sciences  
Article
## 3 Results and Discussion
### Axis Selection  
According to the calculations, the signals in the \(x\) and \(z\) directions are similar and it is reasonable to use the signals from those two directions as individual trials. This expanded the data set from nine test trials to 18. The signal from the y direction was omitted from further processing.
# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
## 2 Materials and Methods
### Dataset Description
To solve the building detection task, the building footprints were considered as a group of pixels that can be distinguished from the pixels representing any other objects that may appear in an urban environment (roads, paved areas, vegetation, etc.) through discriminatory features extracted from the DSM raster images. Thus, the input to the models and test data were lists of DSM tiles and the corresponding binary maps (Figure 5) containing the ground truth for two semantic classes: building and non-building. For the Vaihingen and Potsdam sets, the binary masks were generated by selecting the building category from the provided ground truth (8-bit RGB tif files with one color per land cover class). For the Toronto set, the 8-bit binary masks were obtained by rasterizing the shapefiles describing the building outlines. Figure 4: Digital surface model (DSM) patches of the Toronto dataset: training (red) and test (green) subsets. Figure 5: Sample of the dataset of digital surface model (DSM) patches and corresponding binary masks (ground truth): (a) Vaihingen test set, (b) Potsdam test set, and (c) Toronto test set. The preprocessing phase consisted of rescaling the raster data with an adequate factor and applying nearest interpolation (Figure 1). The initial mismatching resolutions--9 cm/pixel for the Vaihingen set, 5 cm/pixel for the Potsdam set, and 25 cm/pixel for the Toronto set--were conformed to a unique value of 36 cm/pixel to improve computational speed and performances. This value was obtained by progressively down-scaling a sample until a marked computational time reduction was seen without significant loss of information. The adjusted set of DSM tiles and masks was binned into training and test subsets. Despite representing different and complex urban morphologies, the generated dataset showed a balanced class distribution. The number of pixels belonging to the building class and non-building class was close to the ideal 1:1 ratio (percentage << 0.5%) in every tile. Furthermore, the quality of the original datasets ensured the absence of uncertainty in labeling due to noise, low resolution, no-data pixels, inaccurate object edges, or class overlapping. Thus, the possible biases for the building extraction models were minimized, safeguarding the fairness of the test evaluation and the potential transferability of the learned knowledge.
## 2 Materials and Methods  
Numerical modelling together with the model reduction techniques were employed in this study. Finite element model of the helmet impact test was developed and validated with the experimental tests. Effects of material used for the outer shell and the inner liner was investigated first. Then, the commercial child helmet was slightly modified and used to generate a set of training data to establish a reduced-order model based on the proper orthogonal decomposition and the Kriging methods. The training data were generated from finite element simulations with various inner liner thickness and the head-helmet interface friction coefficients. This reduced-order model was subsequently used to generate a large set of data with a range of liner thickness and head-helmet friction coefficients as input parameters. The head injury responses measured in this study were the peak linear acceleration and the rotational acceleration. The linear acceleration relates to the skull fracture and brain confusion. However, the brain can be injured due to concussion and diffuse axonal injury (DAI) without a skull fracture. The rotational acceleration and the brain principal strain relate to brain injury [19; 20; 21]. In fact, the rotational kinematic outputs are correlated to brain strains [30; 31; 32; 33]. [PERSON] et al. [31] reported that the cumulative strain damage measure (CSDM) and the maximum principal strain (MPS) were the two brain strain measures which were proportional to peak rotational accelerations [31]. The CSDM is based on the brain's cumulative volume fraction calculation, which has experienced a specific level of stretch (the maximum principal strain). They can also be used for predicting risk of brain injuries [31; 32; 33].
## 6 Evaluation
### General Experimental Setup
#### 6.3.1 RNN Specific Setup  
With regards to the RNN model both the Bi-LSTM and attention layers had a 0.3 dropout rate. The encoder layers had a size \(L\) of 200. Other values were also tested, though size 200 yielded best performance. As a way to increase input variability from epoch to epoch, white noise was randomly added to input embeddings with 0.15 probability rate to increase the robustness of the model. Figure 1 shows that some classes have more training examples than others. Hence, to prevent introducing bias in our models, we apply class weights to the loss function, penalizing more the misclassification of under-represented classes. These weights are computed as the inverse frequencies of the classes in the training set.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### New Classification Based on UWSNs
#### 4.2.3 Communication-Aware SLAM Systems  
Communication-aware SLAM systems adapt their algorithms based on communication constraints and network conditions. These systems optimize operations by considering factors such as bandwidth limitations, latency, and the reliability of underwater communication channels. By dynamically adjusting data exchange strategies, communication-aware systems can maintain performance even in challenging communication environments [49].
## 4 Discussion  
### Analysis of the Results  
Orthodontic therapy using intermaximillary elastics achieved substitution of the missing upper lateral incisors with the canines. According to the cephalometric analysis, the class II malocclusion appeared to be resolved at T3. Good control of divergence and the inclination of the upper and lower incisors was maintained, despite the use of class II elastics [20].  
However, the functional analysis indicated that the orthodontic camouflage was associated with a worsening of the apnea-hypopnea index (AHI). In agreement with the literature, it appears that the resolution of class II skeletal malocclusion and the use of elastics did not improve the respiratory status of the patient [21]. The quantitative  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement** & **Before UPP (T0)** & **After UPP (T1)** & \
\begin{tabular}{c} **Before Orthodontic** \\ **Therapy (T2)** \\ \end{tabular} &
\begin{tabular}{c} **After Orthodontic** \\ **Therapy (T3)** \\ \end{tabular} \\ \hline PAS (mm) & 4.95 & 7.97 & 11.21 & 10.96 \\ MPH (mm) & 15.33 & 21.82 & 22.7 & 29.12 \\ AHI (events/hour) & 53.9 & 5.9 & 65.4 & 69.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variations of PAS, MPH, and AHI over time.  
Figure 6: Measurements of perimeter and radius using 3-Matic software in frontal and lateral view. (**A**) Retropalatal volume in N0. (**B**) Retropalatal volume in N1. (**C**) Oropharyngeal volume in N0. (**D**) Oropharyngeal volume in N1.  
evaluation provided the following results: the mandibular plane-hyoid distance (MPH) slightly improved after therapy, in line with the reduction in posterior airway space (PAS). Nevertheless, the perimeter of the retropalatal volume remained stable in N1, with only a slight increase in radius. In contrast, the perimeter and radius of the retropharyngeal volume increased only at the upper border.
###### Abstract  
In order to improve the performance of the cladding layer, this study used the Taguchi orthogonal design to investigate the influence of laser power, scanning speed, gas flow, and SiC powder ratio on the micro-hardness and wear volume of the cladding layer. The results indicate that the SiC powder ratio was the major factor that had the main impact on the micro-hardness and wear volume of the cladding layer. The contribution of SiC powder ratio on the micro-hardness and wear volume are 92.08% and 79.39%, respectively. Through signal to noise ratio conversion and combining grey relational analysis, the multiple objectives optimization was attained. With the target of maximizing the micro-hardness and minimizing the wear volume simultaneously, grey relational analysis was applied to obtain the optimal processing parameters set and predict the corresponding grey relational grade. The error rate was 5.3% between the prediction and experimental validation. This study provides the guidance for optimizing multiple goals at the same time using grey relational analysis about the coating properties deposited by laser cladding in actual industrial applications. It provided theoretical basis for the processing parameters optimization with targeting the micro-hardness and wear resistance.  
laser cladding; composite; micro-hardness; wear volume; grey relational analysis 2020 10 3167 10.3390/app10093167
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Separable Convolutions  
Separable convolutions are a type of smart convolutional operation designed to make convolutional processes simpler and faster as shown in Figure 2. They split the convolution into two steps: depth-wise convolution and point-wise convolution. Depth-wise convolution looks at how things are arranged in the data, but it only focuses on one channel at a time. It does not care about how different channels relate to each other. It is like zooming in on each channel's details separately. Point-wise convolution comes next. It looks at all the channels together at each point, combining the details from each channel. This step brings together the zoomed-in details from the depth-wise convolution to create a bigger picture of what is going on.  
The big advantage of separable convolutions is that they use fewer calculations and fewer numbers to remember. Depth-wise separable convolution can be decomposed into depth-wise convolution and \(1\times 1\) convolution (also known as point-by-point convolution) [24]. This makes them much faster and more efficient than standard convolutions. It is like using a simpler tool that does the same job but faster and with less effort. Because they are faster, separable convolutions are really useful when working with lots of data or if you have only little computing power. And even though they are simpler, they can still do a great job of picking out important details in the data. So, they are handy for tasks like figuring out what is in a picture or spotting objects in a video.  
Another interesting thing about separable convolutions is that they help to prevent the model from becoming too specialized on the training data. With fewer things to remember, the model can be more flexible and better at dealing with new, unseen data. This makes it more reliable and able to handle different situations.  
Standard convolution operations involve a large number of parameters and computations, making them resource-intensive. In contrast, separable convolutions break down the operation into depth-wise and point-wise convolutions, reducing the computational burden and enhancing efficiency. This decomposition is particularly advantageous in scenarios with limited resources, providing a more lightweight alternative to standard convolutions.
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 5 HEAD Metamodel: Development Approach to Access Control in Dynamic and Heterogeneous Environments
### Policy Analysis and Assessment
#### 5.4.2 Assessing Policy Enforceability  
Policy assessment is the process of predicting and evaluating the possible impacts of policy options by, for example, testing policies with respect to a set of scenarios to determine their enforceability. The enforceability of AC policies is a challenging task due to its high dependence on contexts, for example, some policies can be easily enforced within an organization, and the same policy may not be enforceable in the IoT context. Assessing enforceability of policies may require monitoring the intended system and gathering information about some unexpected events, delays, system failures, etc., and based on this information, the AC system could assess the difficulty or impossibility of enforcing certain policies [13, 39]. Moreover, AC systems should also allow identifying constant policy abusers and provide the required evidence to exclude users who do not respect the best practices of an organization. For example, by controlling who goes where in a certain context, to ensure that accessing sensitive areas are limited to users with the needed permission, the AC system should hence be able to detect intruders and prevent them from accessing these areas.  
Assessing policy enforceability is critical for a new networking generation such as IoT computing environments due to the huge number of interconnected devices, millions of users, the various contexts, and other facts. The ability to detect inappropriate or unusual behavior, assess it as a real event, and use the evidence to support additional system training or enforcement is of tremendous value for any organization.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 6 Discussion
As the network expands, all the cost will eventually increase. However, our results show that IACS requires the least cost of computation, communication and energy consumption while maintaining the localization error compared with MSL* and LCC even at a high density of nodes. Based on the results of the simulation, there are small trade-offs between the cost of communication and computation with the accuracy of the localization methods tested. IACS gain approximately 1.4%more estimation error than LCC with 15% less communication and 13% computation tested using the default parameter stated in the experimental setup section (Section 3) and the results in Section 5.5.1. The methods are also tested in different scenarios. As LCC and IACS work well only when the nodes mobility speed are low and different maximum samples of number do not result in a significant improvement of the accuracy. IACS is proven to work better than MSL* and LCC in a higher DOI value environment. When DOI = 0.5, IACS accuracy is 3.13% better than LCC. In term of node density, IACS obtained a low estimation error in a high node density environment like MSL* and LCC with lower costs. From the discussion, it shows that IACS works very well in high node density, high DOI value, and low maximum velocity node's environments.
## 6 Conclusions  
This paper has presented a novel high-efficiency multi-task optimization framework focusing on choosing the most suitable individuals to handle each task, which can improve the optimization accuracy via knowledge transfer. It divides the initial population into different task groups according to individuals' skill membership values. Each task group cannot only do the vertical evolution, but also horizontal evolution that contains knowledge transfer among different tasks. The knowledge transfer includes population information sharing and inter-task learning. To further confirm when the knowledge should transfer, the convergence rate is used to guide horizontal knowledge transfer. When a solution process is trapped into local optima, a knowledge transfer mechanism is triggered. Four representative ingle-objective optimization algorithms are used to combine with IMTO. The experimental results show that the proposed framework can significantly outperform baseline solvers and MFO.  
Our future work aims to extend the proposed framework to solve multi-objective MTO problems [51; 52; 53; 54; 55] and make this kind of individually guided knowledge transfer more general. How to improve the effect of knowledge transfer maximally and avoid negative transfer minimally [57; 58] deserve further research efforts.  
Figure 5: Convergence traces of IMTO and baseline solvers on multitasking problem \(P_{5}\) in test suite 1. (**a**) Convergence traces of IMGA and B-GA, (**b**) Convergence traces of IMPSO and B-PSO, (**c**) Convergence traces of IMDE and B-DE, and (**d**) Convergence traces of IMABC and B-ABC.  
**Author Contributions:** X.W., Method development, Writing and original draft preparation, and Performing experiments and result analysis; Q.K., Idea development, and Review and editing; M.Z., Paper revision and research supervision; Z.F., Data analysis, and Paper revision; A.A., Paper review and revision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (517/5385); in part by the Strategy Research Project of Artificial Intelligence Algorithms of Ministry of Education of China; in part by the Shanghai Industrial Collaborative Science and Technology Innovation Project (2021-cyst42-kj10); in part by Innovation Program of Shanghai Municipal Education Commission (202101070007E00098); and in part by The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia has funded this project, under grant no. (RG-11-135-43). We are also grateful for the efforts from our colleagues in the Sino-German Center of Intelligent Systems, Tongji University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The test problems used in this work come from standard test suites.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### SARA Generally Provides Better Estimation Accuracy Due to Its Intrinsic Stability  
Compared to the helical axis approach, SARA highlighted the best performance in terms of rms distance between the estimated AoR and the exact one, which is the parameter used to assess the accuracy of the AoR estimation. In particular, this result was confirmed for A, B, and C configurations for all the conditions tested, whereas both algorithms performed in a similar way for the tri-planar motion configuration (D, as shown in Figures 11b and 12b). As far as the linear and angular dispersion parameters are concerned, SARA showed the lowest values in most of the conditions observed; however, this result was expected since it is directly connected to its formulation being quite different than the IHA's. As presented in Section 2.2.2, SARA initially considers two separate LCSs, respectively integral with the shank and foot, to evaluate the relative motion between each segment and the fixed GCS. In this calculation, each AoR is considered stationary with respect to the corresponding segment. Then, the functional axis obtained at the end of the procedure is estimated as the average of the AoRs calculated for each segment. Compared to the SARA approach, the helical axis does not consider any of those hypotheses for determining the relative motion between two rigid segments, yet the additional freedom of this methodology plays a key role in the \"stability\" of the results. For this reason, the variability in the IHA sets is expected to be more pronounced than the one observed as the outcome of the SARA algorithm. This aspect must be taken into account when the two methods are compared. For tri-planar motion (see Table 1), the angular dispersion obtained for the IHA approach is quite a good approximation of the actual orientation variability of the joint AoR imposed by the model; however, this is not confirmed for all the ROM/speed tested. On the contrary, linear dispersion is always an overestimation of the actual linear variability of the joint AoR. This confirms the weakness of the helical axis approach in determining the correct positioning of the AoR, already highlighted in the literature [34].