esa-sceva/satcom-chunk-collection huggingface dataset:
# Crowdsourced Evaluation of Robot Programming Environments: Methodology and Application
## 7 Discussion and Outlook  
Our results suggest that the proposed approach and framework proved to be feasible considering the diverse and valuable input that we received from the mTurk participants obtained by investing a rather small amount of resources. Overall we spent EUR 223.23 and obtained around 117 results from the 29 accepted and 88 rejected participants in the final run. In addition, we obtained valuable feedback from several test runs. Considering the 29 approved results from the final run, the cost of one approved result amounts to EUR 7.69. This is far from the low rates promised when using crowdsourcing tools [43]. Our analysis supports the assumption that this can be attributed to the many trials and errors, and lessons learned, from the initial test runs.  
Concerning the usefulness of crowdsourced usability evaluations of robot programming interfaces, our experience revealed that most usability issues can be identified when following the proposed methodology, with the exception of very specific issues and details.  
Concerning audience diversity, both in terms of demographics and technical expertise, our results indicate that one can obtain a highly diverse group of participants, both in terms of demographics and expertise. In sum, we obtained data from five continents, from a total of nine countries, with ages ranging from 18 to 64. The gender distribution was predominantly male. The educational background was very variable. According to the self-assessment, previous knowledge and experience with programming and familiarity with robotics in general was diversified.  
Concerning the strengths and weaknesses of _Assembly_, we obtained useful feedback. The SUS scores obtained from the mTurk participants were rather low, with 24.79% of workers managing to solve the tasks within the given timeframe of two hours. By contrast, all participants from the student group managed to solve all the tasks. Apart from the SUS score, the most valuable input was obtained through mandatory free-text fields.  
The main takeaways from the participants' feedback were that participants requested tutorials (e.g., videos) that would have helped them to solve the assignment more easily. Whereas the students received a task description, which also contained some screenshots of the _Assembly_ tool, mTurk participants only received textual descriptions.  
The most frequently recurring issues concerned task saving and reusing, the failure of the \"Play button\" after one successful execution, and the error when attempting to connect to the robot. This suggests that more detailed instructions are needed to avoid confusion.  
Considering the participants' diverse backgrounds, it is important to write instructions using a basic vocabulary and unambiguous meaning. One way to guard against this is to introduce a language test before reaching the HIT itself. The same applies in the caseof programming and robotic skills. We did not find English language proficiency to be important enough to justify a language test.
## 1 Introduction
UWSNs are a less explored area and obtains more attention from research communities due to their vast range of applications. In such a resource-constrained environment, sensors are vulnerable to all known attacks. Recognizing the importance of addressing the security concerns, the proposed lightweight and secure Elliptic curve cryptography (ECC) based framework is efficient and computationally feasible as compared to the existing frameworks. The key contribution of the proposed framework is to improve the security of the network to prevent unauthorized access. The framework ensures security goals such as confidentiality, integrity, availability, and non-repudiation. The simulation results show that ECC-based lightweight algorithms reduced the communication and computation overhead to prolong the network lifetime and improve scalability. The proposed framework also opens new possibilities and novel opportunities to explore oceanography, environment monitoring, and military operations. The rest of the paper is organized, Section 2 consists of a detailed literature review; in Section 3 has proposed the methodology of the Lightweight Key Management framework. In Section 4 Simulation results and performance evaluation are presented. In Section 5 the conclusion and future work are presented.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 4 Comparative Analysis of the Results  
### Performance Evaluation of EEMD  
EEMD decomposes the measured signal data into multiple modes, which can reduce the nonlinear variations in the signal and improve the time series prediction accuracy. In the first experiment, four algorithms (i.e., BiGRU, GRU, LSTM, and DNN) were selected for analysis to evaluate the EEMD model's performance. BiGRU, GRU, LSTM, and DNN were the traditional methods for predicting missing data using the original measured acceleration data. Furthermore, EEMD-BiGRU, EEMD-GRU, EEMD-LSTM, and EEMD-DNN were the hybrid prediction methods based on the EEMD proposed in this paper to predict the missing data by inputting the raw measured acceleration decomposition subsequence into the model. To illustrate the effectiveness of the hybrid prediction method, tests were conducted on three different datasets using the above algorithms, and the test results are shown in Table 2, where Point 1 represents one point missing from the original dataset.  
Table 2 shows the results of the prediction performance metrics for three datasets (i.e., white wave, El-Centro wave, and RG wave) using different methods. The evaluation metrics that achieved the best performance on the corresponding datasets are marked in bold. Compared with the single methods of BiGRU, GRU, LSTM, and DNN, the hybrid method based on EEMD had a significant performance improvement with a lower _MSE_, _RMSE_, and _MAE_ and a higher _R\({}^{2}\)_.  
In order to show the effect of EEMD on the prediction effect visually, the Figure 10 compares the prediction results between the traditional single model and the EEMD hybrid model on the white dataset for the actual missing data. Figure 10 shows that at the local peak, the traditional single model was larger than the actual value, and the prediction results of the EEMD hybrid models were closer to the actual values. This indicates that EEMD improves the prediction method performance.  
Taking the white dataset as an example, the results for the _MSE_, _RMSE_, _MSE_, and \(R^{2}\) for the single and hybrid models are shown in Figure 11, which shows that the EEMD-based approach significantly outperformed the traditional single prediction models in terms of performance. Compared with LSTM, GRU, BiGRU and DNN, the EMD method improved the \(R^{2}\) values by 9.15%, 8.80%, 7.11%, and 8.74%, respectively; reduced the _MSE_ values by 82.01%, 80.28%, 75.71%, and 68.92%; reduced the _RMSE_ values by 57.50%, 55.73%, 50.73%, and 44.09%; reduced the _MAE_ values by 59.42%, 57.19%, 52.94%, and 44.34%, respectively. It can be inferred that EEMD can improve the accuracy of deep learning methods for missing data completion. This is consistent with the conclusions reached in the literature [21; 24; 25; 26] that EEMD can improve the performance of individual deep learning model approaches.
# Streamlining Visual UI Design: Mining UI Design Patterns for Top App Bars
## 3 The Proposed Approach
### Preprocessing  
**Dataset.** We propose a pipeline to obtain TAB images and corresponding layout codes from the RICO repository [23]. RICO is a large open repository (http://www. interactionmining.org/rico.html, accessed on 18 January 2025), with data from over 9.3k free mobile applications across 27 application categories on Google Play. Each dataset entry includes a screenshot paired with its corresponding view hierarchy.  
The view hierarchy represents the UI layout code structure during rendering, depicted as a tree. Each node in this tree encapsulates various attributes of UI elements, such as resource ID, implementation class, inheritance relationship, bounding box, and state attributes like visibility and clickability.  
**Crop.** Utilizing a breadth-first traversal strategy, we navigate this tree employing heuristic rules to pinpoint the TAB within the UI page. Specifically, we identify UI elements instantiated from _android.support.v7.widget.Toolbar_ and eliminate those intersected by other elements. Furthermore, UIs lacking a TAB are omitted from our dataset. Subsequently, we extract and store the subtree representing the TAB from the view hierarchy. Employing the bounding box of this subtree, we crop and extract the TAB image from the UI screenshot.  
**Icon Classification.** The icons within the TAB carry functional significance and communicate extensive semantic details. Based on the research conducted by [PERSON] et al. [24], we employ their suggested Convolutional Neural Network (CNN) to categorize icons into 99 semantic groups. This enables us to depict icons through concise word bags, maintaining a high degree of abstraction.  
**Tokenization.** We organize the leaf nodes within the TAB subtree by arranging them in a sequence based on their \(x\)-coordinate, from smallest to largest. To distinguish between text and images, we employ heuristic rules. Text nodes are identified by checking if their implementation class contains \"Text\" or if they inherit from the _TextView_ class, while image nodes are determined by inspecting if their implementation classes contain \"Image\" or if they inherit something from the _ImageView_ class.  
Figure 4: An overview of our proposed approach.  
Images are processed through the icon classification network to obtain distinct semantic tokens. Text nodes are treated as a single token, without further division, since the text within the TAB typically pertains to application names or page content.  
Additionally, our corpus includes three special tokens, i.e., start of sequence (\(\langle\langle\langle\textit{sos}\rangle\rangle\)), end of sequence (\(\langle\langle\textit{eos}\rangle\)), and padding (\(\langle\textit{pad}\rangle\)), resulting in a total corpus size of 103.
## 3 CFD Numerical Simulation Setup  
### CFD Model  
As shown in Figure 2, the simulation model is based on the emergency diesel storage tank of a representative nuclear power plant. The geometric complexity of internal components was simplified by retaining only the main tank structure. A 1:125 scale physical model was constructed using SolidWorks 2021 3D modeling software. Three monitoring points (A, B, and C) were selected to evaluate flow behavior and additive concentration during the simulation. The model consists of three identical nozzles on the tank top (each capable of functioning as either an inlet or outlet), a sealed tank body, and two internal baffles. All components are made of aluminum alloy. Except for the baffles, the rest of the internal space is defined as a fluid domain available for diesel storage and circulation.  
Although the tank geometry is symmetric, the presence of internal baffles and the need for the Genetic Algorithm to explore asymmetric inlet-outlet configurations may lead the final optimal solution to deviate from symmetry. Therefore, the full-domain model was retained in this study.  
As illustrated in Figure 3, the meshing of the 3D tank geometry was performed in Fluent-Mesh, using polyhedral elements. Local mesh refinement was applied near the inlets and outlets to enhance computational accuracy.  
In this study, we employed a primary mesh containing approximately 124,936 cells, with three inflation layers applied to all wall surfaces. The boundary layer parameters  
Figure 3: External view and three-section mesh profile of the diesel tank.  
Figure 2: Physical model and monitoring point locations of the diesel storage tank (unit: mm).  
included a growth rate of 1.2 and a transition ratio of 0.272, which ensured adequate resolution of velocity gradients and species transport near the walls. With this setup, \(y^{+}\) values were maintained below 30 across all critical regions, satisfying the requirements of the turbulence model used. To evaluate mesh independence, we tested five mesh resolutions: 58,660; approximately 25,000; 57,500; 124,936; and approximately 210,000 cells. The comparison showed that the deviation in conditioning time between the 57,500-cell and 124,936-cell meshes was within a single time step (i.e., 600 s). Furthermore, the difference between the 124,936-cell and -210,000-cell meshes was negligible, with key performance indicators varying by less than 2%. These results confirm that the chosen mesh resolution achieves sufficient numerical convergence and robustness.  
The input data used in our simulation were obtained from an ongoing nuclear engineering project focused on diesel conditioning system optimization. Specifically, the inlet velocity corresponds to the rated flow rate of the pump under standard operating conditions (measured at 20 \({}^{\circ}\)C), and the physical properties of the diesel-antioxidant mixture were derived from laboratory analyses conducted on-site using actual fluid samples.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 3 Proposed Vector Quantizer Based on SOM
### The Arithmetic Block  
As discussed in Section 3.2, the SDU can merely calculate the basis elements, \((x_{j}-w_{ij})^{2}\) and \(\alpha(x_{j}-w_{ij})\), for distance calculating and weight vector updating. In order to obtain the absolute values of SED (\(D_{E}^{2}\)) and the new weight vector (\(W_{s}(t+1)\)), extra calculations must be conducted. In our work, we proposed an optimized circuit named RCBAT to accomplish these operations.  
Figure 2: Block diagram of the squared difference unit and its related data flow.  
Figure 3 shows the basic architecture of RCBAT. Similar to the SDU, the arithmetic block in our design can also be reconfigured either in codebook learning mode or in image encoding mode. In the image encoding mode, \"S\({}_{1}\)\" is set as \"0\". The values of \((x_{j}-w_{ij})^{2}\) are first computed by the SDU and then provided to the RCBAT. Next, the 16 separate squared differences are accumulated by the 4 pipeline stages in the RCBAT and thus 1 partial SED is obtained. As we adopt a mechanism of PVCS in the SDU, we must sum up all of the partial SEDs to get the exact SED. Hence, we add an additional stage following the fourth stage, which is shown at the right bottom of Figure 3. In the last stage, the separation signal \"S\({}_{SEP}\)\" keeps as \"0\" until all of the partial vectors have been processed (after \(m\) clock cycles). As shown in Figure 3, when \"S\({}_{SEP}\)\" is \"0\", the last adder is configured to sum the new partial SED and the intermediate exact SED up. When \"S\({}_{SEP}\)\" turns to \"1\", the exact value of SED, \"DE\({}^{2}\)\", which contains \(m\) partial SEDs, will be obtained and then fed into the minimum distance search circuit for winner-neuron searching. In the codebook learning mode, \"S\({}_{1}\)\" is set to \"1\" once the winner-neuron searching phase is completed. The beginning address of the winner-neuron is first loaded to the read-port of the codebook memory and then the 16-dimensional partial weight vectors of the winner-neuron are read out in sequence. In the meantime, the SDU in Figure 2 is configured to compute the value of \(\alpha(x_{j}-w_{ij})\), and the RCBAT is transformed to 16 individual adders to calculate the value of \(w_{ij}+\alpha(x_{j}-w_{ij})\). The data flow of the RCBAT circuit is highlighted with blue color in Figure 3. In this way, the partial new weight vector can be smoothly obtained in one clock. Once it is written back to the codebook memory, the calculation procedure for the next partial new weight vector will be continued in the same manner. Finally, the entire new weight vector of the winner-neuron can be figured out and updated after \(m\) clocks.  
Benefiting from the characteristic of configurability, all of the arithmetic units in SDU and arithmetic block can be substantially reused for SED accumulating and new weight vector updating. As a consequence, the hardware efficiency of our proposed vector quantizer is improved to a large degree.
# Effect of Heat Treatment on Some Titanium Alloys Used as Biomaterials
## 3 Results and Discussions  
### Heat Treatment Process  
The heat treatment has been made following the diagram from Figure 3, which has been generated by the furnace operating system, based on the input values from Table 3.  
Figure 3: Heating diagram of the heat-treatment process (green—programmed and measured temperature, purple—vacuum, red—nitrogen pressure).  
Figure 2: Hardness tester (**a**) front view (**b**) indented trace measurement.  
The heat treatment for the experiment has been conducted as follows:  
* after locking the door to the furnace, a vacuum was achieved and maintained for 16 min in order to make sure that no residues or other chemicals were on the materials;
* a high-temperature quenching accomplished in three steps (650 \({}^{\circ}\)C--kept for 25 min, 850 \({}^{\circ}\)C--kept for 20 min, and 950 \({}^{\circ}\)C--kept for 20 min) and conducted in a vacuum in order to equalize the temperature between the middle and the core of the samples;
* the cooling process was achieved using N\({}_{2}\) gas, which was introduced in the chamber at a 9-bar pressure for 37 min;
* this was followed by heating to a constant temperature tempering (550 \({}^{\circ}\)C) at 1.5 bar pressure and kept for 2 h and 10 min at 2 bar pressure, as presented in Figure 4;
* after that, it was cooled at room temperature at a 1.5 bar pressure.  
The purpose of such heat treatment can be, decreasing the level of internal tension induced in the metal mass of the products (stress relief annealing); recrystallization of the cross grain after cold plastic deformation or of the structure resulting from casting; finishing of overheated structures; reducing the hardness of the metallic material in order to improve workability.
###### Abstract
The main assumption of LEACH is all nodes having equal energy in polling while HEED assumes energy change within the node to optimize the network lifetime. [PERSON] et al. suggested a clustering protocol for a heterogeneous model called the energy-efficient clustering (EEC) scheme [9]. Based on the initial energy, SNs are categorized into regular nodes (RN), advanced nodes (AN), and super nodes (SN). The RN have the smallest amount of energy, the AN have more energy than the RN, and the SN have the uppermost energy. Energy aware evolutionary routing protocol [10], evolution based clustering routing protocol, stable aware and energy efficient routing protocols with stable sensitivity thresholds use DE, HSA [11] and SMO [12]. [PERSON] and [PERSON] recommended a differential evolution (DE)-based protocol [13] to extend the lifetime considerably by how to balance the lifespan of the CH. Nodes with the minimum fitness value are selected as the CH [14; 15]. Both flame optimization (MF) is inspired by nature's recent algorithm, which is driven by the horizontal point of a moth reference in the wild [16]. Transverse navigation represents the moth's progress towards the moon on a straight line. In this work, the MF algorithm is demoralized to maximum potential to answer the upper load balancing problem in the clustering algorithm. The CH selection is a challenging non deterministic task. To address the CH issue, a variety of evolutionary approaches have been proposed, including genetic algorithms, ant colony optimization, and particle swarm optimization [17; 18]. For the best outcome in WSNs, these evolutionary strategies seek to resolve energy-related restrictions [19; 20]. [PERSON] and [PERSON] [21] implemented hierarchy clustering techniques for impoverished data transmission in a WSN by using the high-density node. In this paper, multi-criteria clustering and bio-inspired routings such as moth flame and salp swarm optimizer are combined to diminish the energy consumption, which will get a better lifetime. The clusters are organized in grids for finding the optimal CH based on the decision-making approach. Once the CH is chosen, the combined routing methods are implemented to find the shortest path for transmission. The flow diagram of the proposedapproach is portrayed in Figure 1. Immediately after the generation of population through the hybrid optimization algorithm; the transmission rate can be estimated.
## 3 Results and Discussion
### Thermal Stability Analysis  
TGA analysis was performed for thermal stability of the coconut oil + _n_-hexadecane Mixed SSPCMs at temperatures of 20 to 600 \({}^{\circ}\)C. Because xGnP is a carbon material that has high thermal durability, it does not decompose at temperatures under 600 \({}^{\circ}\)C. So only the coconut oil and _n_-hexadecane of the Mixed SSPCMs decompose in the test temperature range. Figure 5 and Table 6 show the TGA results of the Mixed SSPCMs. As shown in Figure 5, the TGA graphs of the all Mixed SSPCMs appear 2 degradation curves caused by thermal decomposition. The first degradation curves of the graphs represent the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixed SSPCM (Coconut Oil: _n_-hexadecane)** & **Melting Point (\({}^{\circ}\)C)** & **Freezing Point (\({}^{\circ}\)C)** & \multicolumn{2}{c}{**Latent Heat (J/g)**} \\ \cline{3-5}  & & & **Melting** & **Freezing** \\ \hline
70:30 (wt \%) & 12.53 & 5.55 & 89.06 & 92.82 \\
50:50 (wt \%) & 14.51 & 6.54 & 104.30 & 106.20 \\
30:70 (wt \%) & 16.92 & 10.31 & 124.50 & 123.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DSC analysis of Mixed SSPCMs.  
Figure 4: Differential scanning calorimetry (DSC) graphs of Mixed SSPCMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SSPCM** & **Melting Point (\({}^{\circ}\)C)** & **Freezing Point (\({}^{\circ}\)C)** & \multicolumn{2}{c}{**Latent Heat (J/g)**} \\ \cline{3-5}  & & & **Melting** & **Freezing** \\ \hline Coconut oil/xGnP & 26.93 & 14.95 & 82.34 & 77.64 \\ _n_-hexadecane/xGnP & 21.80 & 14.60 & 96.40 & 94.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: DSC analysis of coconut oil/xGnP and _n_-hexadecane/xGnP SSPCMs [24,28].  
decomposition of \(n\)-hexadecane. The first degradation peaks of the \(n\)-hexadecane occurred at the temperature of 165.14 \({}^{\circ}\)C, 172.62 \({}^{\circ}\)C, and 178.38 \({}^{\circ}\)C. The second degradation curves of the graphs represent the decomposition of coconut oil. The second degradation peaks of the coconut oil occurred at temperatures of 363.68 \({}^{\circ}\)C, 357.68 \({}^{\circ}\)C, and 339.79 \({}^{\circ}\)C. In other words, the coconut oil has a greater thermal durability than the \(n\)-hexadecane.  
It is also possible to compute the percentages of impregnated coconut oil and \(n\)-hexadecane in xGnP by investigating the total decomposition rates. The three Mixed SSPCMs appeared similar values of total decomposition rate. The total decomposition rate were 71.59%, 70.73%, and 68.53%. In the case of the Mixed SSPCMs, xGnP remained in them although the two PCMs were fully decomposed. This means that the two PCMs were impregnated into xGnP, and the Mixed SSPCMs have high thermal durability.