esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Second Evaluation
### Questionnaire Results  
In this evaluation, the users also experienced no performance problems or dizziness problems ( 50% experienced a little discomfort and nobody got dizzy). This allowed them to properly evaluate the game in technical and educational terms. Table 2 presents the quantitative results obtained through the evaluation of students for each item, the mean (M) and the Standard Deviation (SD) are shown. The overall opinion provided by the surveyed students about the educative VR serious game is very positive. It can be seen that students reported values above 4.25 out of 5, and most of them are around 4.8 out of 5. The students considered that the main elements of the application (e.g., explanations, artistic design, interaction, etc.) are adequate. Almost all questions related to the evaluation of the serious game were rated above 4.25 out of 5. The students also positively valued aspects related to locomotion (4.25) and, especially, the interaction mechanisms (4.83). Once again, the results reported in this evaluation indicated that the experience is correctly designed from the narrative and technical point of view, and it would allow the fulfillment of the educational objectives pursued. Similar responses can be observed in the group of questions related to the key aspects covered by Scrum VR. The students rated all items above 4.33 out of 5. Furthermore, students were asked to select the part of the game they found most interesting. The six key aspects of the serious game presented in the second section of Table 2 were offered as options. Approximately 60% of the students selected the estimation of user stories through the PokerPlanning technique presented in Chapter 4 as the most interesting part of the serious game. The highest scores were reported in the following groups of questions. Regarding the potential academic impact of the serious game, the students rated every item of this dimension above 4.80 out of 5. On the other hand, in the case of the evaluation of this type of VR application, they rated every item above 4.90 out of 5. Based on these promising results, the use of this application in a real environment is of interest, as it could be easily accepted by students. Finally, several comments were gathered from the students through the open questions posed in the questionnaire. Some of these comments emphasized the immersion feeling, for example, _\"It was great to feel like you are in a real company\"_, _\"The immersion helps a lot with the introduction and familiarization with how Scrum works\"_ and _\"The story is very good, it's great to feel part of a real development case and to be able to participate in it\"_. Many comments remarked on the interaction possibilities of the serious game: _\"I loved being able to interact with the environment and that depending on the choice I made, the application would have an appropriate response\"_ and _\"I found very useful the discussion with the poker game and how the others interacted with me depending on what I had done\"_. Finally, other comments like
## 3 Results and Discussion
### Performance Prediction  
Based on the above physical field reconstruction results, the power and efficiency of the S-CO\({}_{2}\) turbine were predicted under off-design conditions, as shown in Figure 13. The abscissa in the figure is the actual power and efficiency data calculated by numerical simulation. The ordinate is the power and efficiency data predicted by the model. The blue scattered points are the predicted sample points and the red line indicates that the prediction is completely correct at the ideal situation. The gray area indicates the distribution interval of the prediction error within 5%. The results show that basically all the prediction results of this model are within the distribution interval of 5%. The scattered points with poor prediction results are mostly in the low efficiency area.  
The detailed distribution density of power and efficiency in the range of \(\pm\)5% relative error is shown in Figure 14. The relative error of power and efficiency are basically between \(-\)4% and 4%. The prediction of efficiency has a better effect, and the relative errors are concentrated in the \(\pm\)1% range. It can be proved that the model in this research has high prediction accuracy.  
Figure 14: The distribution density of relative error: (**a**) power; (**b**) efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Physical Memory** & **Graphics Memory** & **Train Time** & **Evaluation Time** \\ \hline CFD solver & 730–1730 Mb & / & / & 3.5 h \\ Our study—CPU & 1975–2975 Mb & / & 24 h & 0.24 s \\ Our study—GPU & 2787–3787 Mb & 1785–2385 Mb & 4–5 h & 0.04 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computation costs with different methods.  
Figure 13: True-pre performance curve: (**a**) power; (**b**) efficiency.  
In this study, five classic data prediction methods of XGboost, KNN, RF, SVR, and MLP were compared with this model, as shown in Table 4 and Figure 15. The training and verification set of the above models are consistent. The evaluation index is the R\({}^{2}\), MAE, and RMSE of the power and efficiency prediction result. The comparison of square values shows that the prediction efficiency of our model is the best.
## 4 Experiments and Results
### Experimental Conditions  
We train our model using 64,000 training images. The training dataset consists of non-defected images. We train RED30 with the Adam optimizer [19], Xavier initialization [20], a learning rate of 0.0001, a weight decay of 0.0005, and a noise level of 0.25. The number of filters is 64 and the filter size of the convolution and deconvolution layers is \(3\times 3\).
## 1 Introduction
Figure 2: NASA UAS Traffic Management (UTM) Technical Capability Level (TCL) capabilities [15] (p. 11). The FAA UTM flight campaigns, titled the UTM Pilot Program (UPP) provided realistic scenarios for expected UAS/manned aircraft operations to determine the appropriate next steps toward the development and deployment of prototype enterprise services into the FAA framework [16] (p. 1). \"UTM services demonstrated in UPP Phase One included: (1) the exchange of flight intent among operators, (2) the generation of notifications to UAS Operators regarding air and ground activities, known as UAS Volume Reservations (UVRs), and (3) the ability to share UVRs with stakeholders, including other UAS Service Suppliers (USS) and the Flight Information Management System (FIMS)\". UPP2 objectives include testing of Remote Identification (RID) technologies and operations with increasing volumes and density in cooperation with NASA, FAA UAS Test Sites, industry stakeholders, and UAS IPP participants [17] (p. 1). UAF organizational interest in UTM: In December 2013, UAF's Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) was selected as one of the six FAA UAS test sites established by the 2012 FAA Modernization and Reform Act. The Pan Pacific UAS Test Range Complex (PPUTRC) reports to ACUASI, but also includes principal partners in Oregon and Hawaii as well as 56 non-state partners located across US and internationally. Figure 3 shows locations of the PPUTRC major ranges and partners. In May 2018, UAF was named as one of the 10 Integration Pilot Program (IPP) teams by the Department of Transportation (DoT) and Federal Aviation Administration (FAA). The team's 21 partners include UAS manufacturers, technology developers, operators and public and private end users from Alaska and the rest of the US. The team's charter focuses primarily on monitoring pipelines, roads and other linear infrastructure, and also includes areas such as delivering medical devices to remote areas, helping search and rescue (SAR) efforts, and surveying fish and wildlife. In support of these missions, UAF has been granted authority by the FAA to conduct true BVLOS operations. UAF was selected for UTM participation and to support the development, design, and testing of the TCL1, TCL2, and TCL3 campaigns. This paper describes the UTM program goals and accomplishments integrating UAS into NAS in Western US accomplished by the ACUASI team.
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 4 Control Design  
The primary objective of the WEC control system is to maximize power capture while maintaining stability across varying sea states. In this study, a PI controller is implemented, where the gains \(K_{P}\) and \(K_{I}\) are computed based on the mechanical impedance of the system rather than an optimization-based tuning approach.  
The PI controller regulates the response of the WEC to ocean waves, ensuring optimal energy harvesting. The controller transfer function is given by  
\[C(\omega;K_{P},K_{I})=K_{P}-i\frac{K_{I}}{\omega}, \tag{9}\]  
where \(K_{P}\) and \(K_{I}\) represent the proportional and integral gains, respectively, and \(\omega=2\pi f\) is the angular frequency.  
Instead of determining \(K_{P}\) and \(K_{I}\) through numerical optimization, the new approach computes the gains directly from the system's mechanical impedance. The state-space representation of the WEC device is given by  
\[\mathbf{x}[k+1]=A\mathbf{x}[k]+B\mathbf{u}[k], \tag{10}\]  
\[\mathbf{y}[k]=C\mathbf{x}[k]+D\mathbf{u}[k], \tag{11}\]  
where \(A,B,C,\) and \(D\) are the state-space matrices representing the system dynamics.  
The mechanical impedance of the WEC is derived from the system's transfer function. Given a wave period \(T\), the corresponding frequency is \(\omega=\frac{2\pi}{T}\).  
Using a discrete-time representation, the transfer function \(H(z)\) is computed as  
\[H(z)=C(zI-A)^{-1}B+D, \tag{12}\]  
where \(z=e^{i\omega T_{s}}\) is the discrete-time frequency response variable, and \(T_{s}\) is the sampling time:  
\[K_{P}=\text{Re}(Z_{\text{mech}}^{*}), \tag{13}\]  
\[K_{I}=-\omega\cdot\text{Im}(Z_{\text{mech}}^{*}). \tag{14}\]  
To ensure stability and prevent extreme values, \(K_{P}\) and \(K_{I}\) are constrained within predefined limits: \(K_{P}\in[0,5\cdot 10^{3}],\quad K_{I}\in[-2\cdot 10^{4},1\cdot 10^{6}]\). Details on the impedance-based WEC control design, and performance and stability considerations can be found in [6; 18].  
Figure 2: System diagram for sea-state predictive control.  
This method allows for the real-time adaptation of control gains based on sea state variations without requiring iterative numerical optimization, improving computational efficiency and robustness. The implementation is carried out in MATLAB, where the PI gains are computed dynamically based on the measured wave period \(T_{p}\).
## 4 Discussion  
Techniques that aim at characterizing FESEM and BET were followed in order to investigate the structural features and surface areas of the RH and RHAC. Figure 1a demonstrates that the outer epidermis of the RH sample was wrapped with a uniform fluted texture. The sample of RHAC had a microporous structure with a large surface area and many adsorption active spots (Figure 1b). The analysis of EDX revealed that the RH and RHAC samples had comparable C and O elemental configurations; nonetheless, they had dissimilar ratios by weight (Figure 1c,d). The elemental composition of RH showed a higher carbon content, which makes it a convenient raw material for activated carbon preparations. Carbon represents the largest part of RHAC, whereas a complete absence of silica was observed within the RHAC, and existed merely in the RH. The carbon content of the RHAC was 94.16%, which was higher than that of the RH (49.15%), while the RHAC surface area was 586.59 m\({}^{2}\cdot\)g\({}^{-1}\). The larger RHAC surface area observed was due to the increase in interspaces among the layers of carbon of the RHAC as a result of the ZnCl\({}_{2}\) action; microspores existed in the adsorbent [6]. The nature of the boost in the RHAC microporosity was, likewise, due to the areas left by ZnCl\({}_{2}\) during carbon activation process. This occurred as a result of the frequent washing process, which aimed to eliminate the surplus ZnCl\({}_{2}\) linked to the RHAC. Additionally, ZnCl\({}_{2}\) brings about swelling, considering cellulose as a major compound in RH; ZnCl\({}_{2}\) imposes electrolytic action to the cellulose molecular structure. The swelling action caused by ZnCl\({}_{2}\) on the sidelong can break the bonds of the molecules of cellulose, which leads to an intensification in intermicelle and intram micelle voids of RHAC [6]. Therefore, a large surface area was observed for RHAC.  
Figure 11: Lines of bed depth service time for 0.10, 0.60, and 0.88 breakthrough for diverse bed heights (Co given as 1000 mg/L; Q given as 10 mL/min).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(C_{\mathit{eff}}C_{\mathbf{0}}\) & \(K\) (L-mg\({}^{-1}\).h\({}^{-1}\)) & \(N_{0}^{\prime}\)\(\times\) 10\({}^{-3}\) (mg\(\cdot\)L\({}^{-1}\)) & \(Z_{\mathbf{0}}\) (cm) & \(R^{2}\) \\ \hline
0.10 & 0.0108 & 62.048 & 4.0842 & 0.9975 \\
0.60 & 0.0164 & 62.048 & 2.7089 & 0.9299 \\
0.88 & 0.0171 & 74.673 & 2.1481 & 0.9665 \\ \hline \end{tabular}
\end{table}
Table 5: Service time model parameters for bed depth used for phenol adsorption (\(C_{0}\) given as 1000 mg/L; Q given as 10 mL/min).  
[PERSON] et al. [6] reported related findings with a greater surface area of activated carbon treated with ZnCl\({}_{2}\).
## 3 Results and Discussion
### Mineral Analysis
The present result demonstrated that all analysed minerals were present in the control noodles made from wheat flour, but the addition of WEM powder increased the prevalent macro and microelements, such as potassium, calcium, magnesium, iron, and zinc. Despite that, no increment was observed for manganese, copper, chromium, and selenium, in the incorporated noodles because of the low levels recorded in the WEM powder. Sodium was the highest constituent in both instant noodles due to salt addition in the noodle-making process. According to [PERSON] et al., the increment of minerals in the incorporated food was attributed to the high contents of mineral salts in the mushroom [60]. In addition, the current potassium, calcium, and iron content were higher than the previous finding by [PERSON] et al. that included oyster mushrooms in instant noodles (Z705.5 mg/kg potassium, 275.8 mg/kg calcium, and 52.6 mg/kg iron) [15]. Therefore, the positive outcomes of this study could be an alternative solution for nutrient deficiencies [3]. Different mushroom species may accumulate minerals at different capacities from the substratum, and various analytical methods could be the factors that explained the variation of minerals [46; 57]. Furthermore, high mineral values suggested that WEM is a potential source of good-quality ingredients [44]. The essential elements in WEM are comparable to other edible mushrooms and are required in varying amounts for the proper functioning of the body (biochemical reactions, metabolic growth, and enzymatic activities) [61]. Due to bioaccumulation ability of edible mushrooms, consumer should aware of the excessive mineral intake which could harm the body due to association with increasing cardiovascular risk factor and heart diseases; thus, the guideline of recommended daily intake of minerals and trace elements should be followed for safe consumption [62]. For instance, the safe limit of copper, iron, manganese, and zinc per kg of human body weight set by the World Health Organization are 40 mg/kg, 15 mg/kg, 400-1000 mg/kg, and 60 mg/kg, respectively [46]. However, the recommendation for daily intake of dietary minerals also depends on factors such as sexes, dietary practices, food preparation and processing, bioavailability, mineral interactions, and the chemical forms of the minerals [63].
# Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend
## 6 Multimodal Benchmark
### Text-to-Image Generation  
COCO is also widely used in text-to-image generation tasks. There are also several datasets containing only one broad category. Caltech-UCSD Birds (CUB) [207] has 11,788 images of 200 different categories of birds. Oxford-102 Flower [208] is a 102-category dataset consisting of 102 flower categories. The flowers are those commonly occurring in the United Kingdom. The images have large scale, pose and light variations. CelebAText-HQ [209] is a large-scale face image dataset with facial attributes, designed for text-to-face generation. In state-of-the-art models that generate images from text, large-scale pretraining is a key factor. GANs have shown extraordinary potential in text generation tasks. As for the evaluation metrics of text-to-image generation, IS [210] and FID [211] are two of the most used. **IS** is inception score. It considers both the quality and diversity of the generated images. To be specific, for a single image, the category probability distribution should be the focus, but for a set of images, the category probability distribution should be diverse. IS is calculated by the formula:  
\[IS=\exp{E_{x\sim p_{G}}KL(p(y|x)||p(y))}. \tag{5}\]  
where \(x\) denotes the generated image, \(y\) denotes the probability of different categories, \(G\) denotes the image generator, and \(KL\) denotes the KL-divergence. **FID** is Frechet inception distance score. Unlike IS, FID compares the generated image with the real image by computing a kind of similarity. FID is calculated by the formula:  
\[FID(r,G)=||\mu_{r}-\mu_{g}||+T_{r}(\Sigma_{r}+\Sigma_{g}-2\sqrt{\Sigma_{r} \Sigma_{g}}) \tag{6}\]  
where \(\mu\) and \(\Sigma\) denote means and covariances, \(r\) and \(g\) denote the real images and generated images, \(T_{r}\) is the trace of matrix. As the state-of-the-art model, [PERSON] et al. [2020] propose the first work to leverage the well-aligned multimodal semantic space of the powerful pretrained CLIP model without any text data. [PERSON] et al. [2020] consider this task as a two-stage problem including low-resolution image generation and image super-resolution. They argue that the model distribution generated from low-resolution images has a better probability of overlapping with the natural image distribution, but at the same time the need for high-resolution images is necessary. In conclusion, existing multimodal datasets are still relatively limited due to the fact that labeled and aligned multimodal data are more difficult to obtain. We hope that more real-world-based multimodal tasks, evaluation metrics, and datasets are released.
## 6 Conclusions  
The recognition of aircraft activity at airports is of great significance in both civil and military fields. In this paper, an efficient aircraft activity recognition algorithm, which supports deploying video micro-satellites, was proposed. Firstly, the aircraft in the start frame was detected with a robust rotation detector. Then, aircraft tracking was achieved using a neighborhood search and an improved rotating image matching method. Combining the tracking result and geospatial information, the speed of the aircraft was obtained. Finally, aircraft activity was classified as parking, taxiing, or flying according to the speed of the aircraft. We experimentally verified the algorithm on real remote sensing videos. The experimental results demonstrated the effectiveness and efficiency of the algorithm. When deployed on video micro-satellites with limited computing and memory resources, the algorithm can still effectively support applications where short delays are acceptable, such as monitoring whether an airport is operating. The scheme in this paper also provides a general framework for on-orbit target tracking and event recognition.  
Conceptualization, R.Z. and X.Z.; methodology, R.Z., X.Z. and L.X.; software, R.Z. and J.Q.; validation, R.Z. and L.X.; writing--original draft preparation, L.X. and J.Q.; writing--review and editing, R.Z. and X.Z.; visualization, J.Q.; funding acquisition, R.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We are grateful to all reviewers for their thoughtful comments and suggestions.  
The authors declare no conflict of interest.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 2 Materials and Methods
### Fusion Positioning Module
#### 2.3.1 Classical Particle Filter
This phenomenon is particle degradation which can affect the performance of PF and waste computing resources. Therefore, ensuring the diversity of samples and optimizing particle degradation are the key factors in determining the performance of PF. We concentrate on these problems and design the optimization mechanism for particle degradation in our research work.
# Improved Sliding Mode Control for Permanent Magnet Synchronous Motor Speed Regulation System
###### Abstract  
Due to advantages such as high speed, high accuracy, low maintenance and high reliability, permanent magnet synchronous motor (PMSM) servo systems have been employed in many fields. In some cases, for example, speed fluctuations caused by load mutation would restrict the control stability, thereby limiting the usefulness of PMSM in high-precision applications. The speed regulation problem of PMSM servo control systems is discussed in this paper. A sliding mode disturbance control is developed in the vector control system to improve tracking performance of the PMSM system in order to suppress the speed fluctuations. The integration of sliding mode control and the proportional plus integral (PI) control can improve the performance of the closed-loop system and attenuate disturbances to a great extent. The proposed method can effectively improve the robustness and response speed of the system. Simulation and experimental analyses are conducted to demonstrate the superior properties of the proposed control method.  
permanent magnet synchronous motor; sliding mode control; repetitive control; speed fluctuation; speed regulation +
Footnote †: journal: Applied Physics Letters
###### Abstract  
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario in the test script. At this time, debugging information can be obtained in HiL tests, using memory-updated information, without the source code or the debugging tool. However, this method does not know when the fault occurred, and it is difficult to select the starting point of debugging if the execution flow of the software is not known. In this paper, we propose a fault-localization method using a pattern in which each memory address is updated in the HiL test. Via a sequential pattern-mining algorithm in the memory-updated information of the transferred unit tests, memory-updated patterns are extracted, and the system learns using a convolutional neural network. Applying the learned pattern in the memory-updated information of the integration test can determine the fault point from the normal pattern. The point of departure from the normal pattern is highlighted as a fault-occurrence time, and updated addresses are presented as fault candidates. We applied the proposed method to an HiL test of an OSEK/VDX-based electronic control unit. Through fault-injection testing, we could find the cause of faults by checking the average memory address of 3.28%, and we could present the point of fault occurrence with an average accuracy of 80%.  
automotive software; fault localization; hardware-in-the-loop (HiL); sequential pattern mining; convolutional neural network (CNN) +
Footnote †: journal: Applied Sciences