esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Since the proposed method focuses on generating solar radiation data to improve forecasting performance, this work also paves the way for future studies in a wide array of areas by expanding and embracing the modern technique proposed to augment data, whereas most studies of photovoltaic power forecasting, wind power forecasting, and energy consumption concentrate on the short-term forecast, disregarding medium-term and long-term forecasting because of a lack of training data for forecasting models. The following are the significant contributions of our current work:  
1. Convolutional GAN (Conv-GAN) that combines GAN and CNN will be enhanced by replacing the fully connected CNN layer with a more superficial linear SVR layer; this linear SVR assists in restricting the deviation of generated specimens, is robust in discarding outliers, and has excellent generalization capability; and the model is trained using a Multi-Objective loss function that combines Mean Square Error (MSE) and Binary Cross Entropy (BCE). The MSE loss function was used to determine how similar the produced samples were to the original samples, and the BCE loss function was used to stabilize the training process and confirm that the generated samples were structurally fairly similar to the training data, which led to obtaining data which were identical to the original data. This model has indeed been trained to create meteorological data that include both spatial and temporal data, which lead to better forecasting. 2. The new augmented solar radiation dataset via the GAN-CSVR model is evaluated by two effective indices: the standard deviation (STD) and the cumulative distribution function (CDF). 3. To validate the impact of augmented data on the accuracy of forecasting models, solar radiation forecasting is rigorously evaluated on the original datasets and augmented datasets of three different locations. The materials and methods are described in Section 2. In Section 3, the experimental design is presented. The evaluation and discussion are presented in Section 4. Section 5 gives the conclusion.
## 4 Main Issues and Challenges
### Open Issues
#### 4.2.4 Tightly coupling of the mission management to the specific vehicles  
In general, the reviewed architectures generate mission plans that are tightly coupled to the vehicles that are being used in their respective projects, or are too generic to delegate the responsibility of matching the vehicles capabilities to the specific implementation for each scenario. However, to improve the reusability of the proposed architectures, a virtualization approach should be considered. By using a virtual representation of the vehicles within the scope of the architecture, it is possible to integrate new and legacy vehicles by just implementing an adaptor that on the on the one side matches to the specifics of the vehicle, and on other side matches the ones of the virtual model defined for the architecture.
## 2 Materials and Methods
### Test Methods
#### 2.4.4 Tensile-Recovery Shape Memory Test  
The dumbbell-shaped specimens were used to evaluate the shape-memory performance of the shape memory hydrogenated epoxy resin used for the asphalt mixtures. The specimens with five different formulations were measured under each condition. The tensile-recovery shape memory test was performed according to the following steps: (i) The original length of the specimen was recorded as \(L_{0}\). The dumbbell-shaped specimen was setup in the clamp of the tensile instrument we prepared. Then, a constant temperature system was heated up to a certain temperature by circulating hot water; the hot water was then put into the tensile instrument equipped with the specimen. The specimen was held for 5 min for full heating. The temperatures used were set to \(T_{\rm g}-10\), \(T_{\rm g}\), \(T_{\rm g}+10\), and \(T_{\rm g}+20\)\({}^{\circ}\)C; (ii) The specimen was stretched to a certain length. The stretch rate was 5 cm/min and the length was recorded as \(L_{1}\). The stretched specimen was then quickly cooled with a constant external force and the tensile deformation was frozen. Afterwards, the stretched specimen was dipped into a cold water bottle (\(-10\)\({}^{\circ}\)C) in the refrigerator for 10 min. The length was recorded as \(L_{2}\) at this moment; (iii) To quantify the shape-memory performance, the stretched specimen was placed into an electric-heated thermostatic water bath set to the temperature at which the specimen was deformed, and the shape recovery process was observed. The recovery time was recorded as the point at which the specimen did not change. Five parallel specimens for each of the SM-HEP-AM were measured at the designated temperature to obtain the average recovery time. This recovery time was designated as the shape recovery time for that temperature. The length was then recorded as \(L_{3}\). The shape fixity ratio (\(R_{\text{f}}\)) and shape recovery ratio (\(R_{\text{r}}\)) were calculated using Equations (1) and (2).  
\[R_{\text{f}}=\frac{(L_{2}-L_{0})}{(L_{1}-L_{0})}\times 100\% \tag{1}\]  
\[R_{\text{r}}=\frac{(L_{2}-L_{3})}{(L_{2}-L_{0})}\times 100\% \tag{2}\]
## 5 Conclusions  
This study comprehensively elucidated the complex regulatory mechanisms governing aroma formation in crabapple flowers and successfully classified crabapple aroma types through a novel predictive model that integrates VOC and AA data. Employing chemometric methods, this research unveiled intricate relationships between aroma compounds and AAs. Volatile aroma components from 18 crabapple flowers were extracted and analyzed using HS-SPME-GC/MS, while AA profiles were determined via UPLC-MS/MS technology. The analysis revealed that Glu, Asp, Asn, Ser, and Ala were particularly abundant across the cultivars and played significant roles in the synthesis of aroma compounds. Major aroma compounds identified included benzyl alcohol, benzaldehyde, and ethyl benzoate, which possessed notably higher OAVs than other components, underscoring their critical influence on the aroma profile. The correlation analysis identified significant positive associations between Phe and benzaldehyde, Leu and ethyl benzoate, and Cit with fatty acid-derived VOCs, indicating potential synergistic interactions. Future research could expand this methodology to explore the interactions between aroma compounds and AAs in a broader array of crabapple cultivars. This could provide a theoretical basis for the molecular mechanism analysis and varietal improvement of crabapple flower aromas. Additionally, the detection of timonacic, noted for its potential antioxidant and anti-tumor properties, underscores its promising applications as a functional ingredient for both medicinal and edible uses.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/horticulturalae11070845/s1](https://www.mdpi.com/article/10.3390/horticulturalae11070845/s1)]([https://www.mdpi.com/article/10.3390/horticulturalae11070845/s1](https://www.mdpi.com/article/10.3390/horticulturalae11070845/s1)), Table S1. Sensory evaluation scores of 18 crabapple cultivars. Table S2. Fifty-one compounds from 18 crabapple cultivars. Table S3. Compounds with a relative content greater than 0.1 mg/L among 18 cultivars. Table S4. Descriptions and threshold values of major scent compounds in crabapple cultivars. Table S5. Contents of twenty-one amino acids in 18 crabapple cultivars.  
Writing--original draft preparation, J.H. and Y.Y.; conceptualization, W.K. and J.L.; methodology, J.H.; validation, W.K. and Y.W.; data curation, J.H. and Y.Y.; writing--review and editing, H.W. and L.Q.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shijiazhuang Key Research and Development and SME Innovation Program (No.241290212A).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 5 Experimental Analysis And Results
### Limitations  
As noticeable from the images of MonitorView in Figures 6 and 7, the visual impact is different for the different visualisation metaphors. Data gathered in the experiments show that, although every student accessed the first assignment's material and the project for the second assignment, not everyone accessed the other course resources. The interpretation and impact of this information on course learning design and strategy can vary depending on the context in which the instructor operates. For instance, a straightforward strategy is that material that has not been accessed is not attractive, too complicated or too easy to understand, thus useless if not reviewed. An alternative interpretation of the data takes into account the characteristics of the courses, i.e., the blended teaching mode, where students can share the course material through other channels, such as Facebook pages or exchanging handouts.  
Another noticeable event is that students that accessed the system reviewed part of the older lessons, leaving some of the recent course learning objects almost untouched. The latter is particularly relevant for the learning object named _PDDL Language slides_ of the Computer Science course. The educator should investigate the event because the slides were included in the second assignment project, but the students did not access the material. The access to older material can be related to a general course review made by some of the students; this event can be analysed in-depth, looking at the single student behaviour instead of the aggregated data.  
The feedback provided from the instructors also suggests that there is space for a great improvement of the _Tag cloud_ metaphor by retaining some bounds to the original structure, either by introducing a visual connection to original elements or by visually animating the morphing interface, i.e., the transformation from the standard interface to the _Tag cloud_.
## 2 Materials and Methods  
### Selection of Data Set  
The key to vehicle detection is to train a good classification model, which requires the use of a large training data set. At present, there are several excellent data sets in the field of image recognition, such as the CALTECH 101 dataset, PascalVOC dataset, MSCOCO dataset, and CIFAR-10 dataset, which have made great contributions to the field of deep learning object detection [15]. In vehicle tracking, the main object of identification is a vehicle on an urban road. Therefore, a variety of vehicle images were used to create the dataset, and the PascalVOC dataset format was used to support the training and testing of the YOLO network.  
PascalVOC is a commonly used data set in object recognition. VOC2012 is one of the more complete versions, which contains 11,530 images and 20 marked object types, and can complete object classification, object detection, object segmentation, and other tasks. The data storage rule of each file is shown in Figure 2.  
Figure 1: Overall structure of the algorithm in this work.  
VOC2012 contains rich types of object detection, but, on average, a small amount of data are available for each object, and the image scenes in the data set are not rich enough. Therefore, a dataset that contains more vehicle images of urban road scenes or high-speed road scenes are needed to better detect vehicles on urban or high-speed roads. In this paper, the VisDrone data set was selected to train the vehicle detection model. The data set is introduced and processed as follows.
## 3 Designing Virtualization of Wireless Sensor Network
### Challenges behind Designing VSNs  
In the recent past it was a general thinking that a tiny sensor node with its little processing capability could be applicable in very specific areas and very specific purposes, but with the rapid advancement of micro electro mechanical systems (MEMS), the perceptions of researchers have been dramatically changed. Nowadays IP-based WSNs and implementation of 6 LoWPAN sensor nodes have been used in different fields such as health care, facility management, building and home automation, personal sports and entertainment, asset management, environmental monitoring, security and safety and industrial automation. In these entire applications the sensor network virtualization concept can be a challenging issue. To the best of our knowledge true virtualization concepts have not yet been introduced. A few research papers have been published, but these focused on the gateway-based virtualization among different sensor networks and the public or private internet, but the implementation of a true virtualization concept in a sensor network has many challenging issues, a few of which are discussed here.
## 4 Results
### Evaluation
#### 4.1.2 Comparative Evaluation
[13] attempted classification of the current dataset using traditional augmentation methods and achieved an accuracy of 92.8%, sensitivity of 93.2%, and specificity of 90.1%. Our proposed model outperformed the classification metrics, achieving an accuracy of 95.5%, a precision of 96.2%, and a recall (sensitivity) of 97.7%.
## 3 Numerical Analysis and Verification
### Numerical Results and Validation
To further verify the reliability of the numerical model, the monitoring values of the other four measuring points \(P_{1}\), \(P_{2}\), \(P_{3}\), and \(P_{4}\) on the longitudinal symmetric plane of the test model were selected for comparison with the calculated values, as shown in Figure 13. It can be clearly seen from Figure 13 that the temperature decreases first and  
Figure 12: Comparison of the filled soil temperature in the Y direction: (**a**) at measuring points \(Z_{1}\)–\(Z_{9}\) and (**b**) at measuring point \(Z_{5}\). then increases linearly with time. The temperature of each point showed a decreasing trend because the burial depths of the four measuring points were 0.1 and 0.2 m, which were close to the surface of the model, so they were greatly affected by the external environment. The variation trend of the calculated temperature is consistent with that of the monitored temperature, and the difference between them is only 0.2 \({}^{\circ}\)C-0.5 \({}^{\circ}\)C with a 2.9% error. In conclusion, the above analysis shows that the numerical model proposed in this paper can accurately calculate the temperature distribution in the filled soil, thus verifying the reliability of the numerical model.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 2 Materials and Methods  
### Composition and Structure of the Robot  
The proposed control scheme is performed through experiment verification by an omnidirectional WMR robot. The size of the robot is 600 mm in length, 400 mm in width, and height is 850 mm, as shown in Figure 1a. The robot has six MX motors. The arms have two MX-64 motors in the shoulders, two MX-64 motors in the elbows, and two MX-28 motors at wrists on both sides. The MX-28 is 35.6 mm in length, 35.5 mm in width, and 50.6 mm in height. Its static torque is 2.5 N.m. The MX-64 is 40.2 mm in length, 41 mm in width, and 61.1 mm in height. Its static torque is 6.0 N.m. The robot has two mechanical arms and fingers that can grip objects, the length is 470 mm and 150 mm, respectively. The StarGazer is on top of the robot's head for indoor navigation and position. The omnidirectional WMR's chassis size is 240 mm radius. Three omnidirectional wheels are configured in separated 120\({}^{\circ}\) from each other, and three 12V-DC motors are installed to provide rated torque 700 g-cm. The WMR has two 12V batteries (12V1.3 AH/20 HR lead-acid batteries) and a UPS uninterruptible power system on the first and second layer. The WMR has six ultrasonic sensors in front of the carrier of the second layer, which is used to measure the distance of the obstacle. The WMR also has a SICK LMS100 2D laser scanner in front of the carrier of the fourth layer, which is used for long-distance measurements. Both sensors are used to detect an obstacle as well as to an avoid obstacle. The WMR has a GNSS RTK positioning system receiver in the back of the fourth layer, which is used for outdoor navigation and positioning. The GNSS RTK system consists of two parts, one is GNSS receiver, the other is GNSS antenna, as shown in Figure 1b. The system uses GPS/BDS dual satellite systems. In addition, it uses a low-cost GNSS chip, and the price is about one-tenth of the traditional RTK system. Because the number of useful satellites becomes more than twice the number of a single satellite system, it can provide high accuracy of the observations and can very effectively solve the problem of satellite signals which are clouded when the WMR is moving.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 4 Simulation and Experiment
### Experiment
#### 4.2.1 Setting  
Figure 4 presents the configuration of the hardware and communication for the robot manipulator. The system uses a Windows operating system based PC for computation, IntervalZero RTX64 real-time extension to provide the real-time operation environment, two controller area network(CAN)-bus boards (each providing four channels) for the encoder data communication, and an eight-channel analog output board to provide the control input.  
Considering the limited communication speed of CAN-Bus and the data volumes of seven encoders, the system sampling time for receiving the rotary encoder data and transmitting the control input was set to 2 ms. According to (A12) in previous research [13], the parameter \(\eta_{j}\) in SPO should be selected near 33.3. Thus, the controller parameters (27) for our proposed scheme are selected as \(\lambda_{1j}=\eta_{j}=\{21,14,35.5,20,100,34,31\}\), other parameters \(\text{k}_{1j}=\{10,7,8,7.5,8,9.5,10\}\), \(\text{k}_{2j}=\{0.14,0.15,0.21,0.165,0.4,0.25,0.31\}\), \(\text{k}_{3j}=\{0.01,0.03,0.01,0.02,0.01,0.02,0.1\}\), \(\  
Figure 4: Robot arm configuration.  
Figure 3: Simulation result.  
\(\{\,0.9,0.9\,,0.9\,,0.89,0.9,0.89,0.9\,,0.9\,\}\), \(\beta_{j}=\{\,0.9,0.93,0.92,0.93,0.9,0.92,0.9\,\}\), where \(j=1,2\ldots 7\). For FOTSMCSPO, SMCSPO, TSMCSPOP, and FOSMCSPO, the values of \(\beta_{j}\), \(\alpha_{j}\), \(r_{1j}\), and \(r_{2j}\) are set as the same as the simulations, which determine whether the controllers use the fraction-order derivative and TSM or not (Table 1).  
The desired trajectories are simulated assembly work on the end effector. They contain two stages of movement, the first stage simulated grabbing, picking up and homing; the second one simulated the movement that included moving to the assembled position and homing. The desired movement is shown in Figure 4(a),b in joint space and the machine coordinate space (MCS), respectively (the base of the MCS is shown in Figure 1).
###### Abstract  
The main focus of this paper is the integration of an integrated function modeling (IFM) framework in an engineering framework based on graph-based design languages (GBDLs). Over the last decade, GBDLs have received increasing attention as they offer a promising approach for addressing several important challenges in engineering, such as the frequent and time-consuming transfer of data between different computer aided engineering (CAE) tools. This absorbs significant amounts of manual labor in engineering design projects. GBDLs create digital system models at a meta level, encompassing all relevant information concerning a certain product design and feeding this into the relevant simulation tools needed for evaluating the impact of possible design variations on the performance of the resulting products/parts. It is possible to automate this process using digital compilers. Because of this, it is also possible to realize systematic design variations for a very large number of parameters and topological variants. Therefore, these kinds of graph-based languages are a powerful means for creating a large number of viable design alternatives and for permitting fast evaluation processes against the given specifications. While, thus far, such analyses tend to be based on a more or less fully defined system, this paper proposes an expansion of the applicability of GBDLs into the domain of product functions to cohesively link conceptual with embodiment design stages. This will also help with early systematic, automated generation and the validation of design alternatives through relevant simulation tools during embodiment design. Further, it will permit the automated exploration of function paths and enable extended analysis possibilities, such as the detection of functional bottlenecks, while enhancing the traceability of the design over the development process. For these extended analysis possibilities, a function analysis tool was developed that adopts core ideas of the failure mode and effects analysis (FMEA). In this, the functional distinction between function carriers and function-related processes allows the goal-directed assessment of component reliabilities and the detectability and importance of processes in a technical system. In the paper, the graph-based modeling of functions and the function analysis tools are demonstrated on the example of a multicopter.  
function modeling; systems engineering; digital engineering; graph-based design +
Footnote †: journal: Applied Sciences
## 5 Verification Case Study and Discussions  
In this section, the predicted reflection coefficients of a planar monopole antenna obtained by the improved PSO-RBFNN model are given and compared with those obtained by other surrogate models. Then, a planar miniaturized multiband antenna design is presented to verify the proposed multi-objective antenna optimization framework. The experiments are running in an environment equipped with 64-bit operating systems, 8 GB RAM, and Intel(R) Core(TM) i5-4590 CPU.
## 2 Method
### Design of Rock-Breaking Mechanism and Parameters
#### 2.2.2 Spacing Between Deep and Shallow Holes  
The unfilled portion of the deep hole is equivalent to the empty hole near the shallow hole. According to the stress concentration effect of the empty hole, the load formed by the detonation of the cylindrical charge in engineering blasting can be approximated as a cylindrical wave. Given the proximity of the blast hole to the empty hole and the assumption that stress differences on curved surfaces are negligible, it can be approximated that the blast side of the hollow hole is subjected to uniformly distributed loads and produces stress concentration.  
According to the first strength theory, maximum tensile stress occurs in the direction of the connecting line between the central axis of adjacent boreholes. The peak tangential tensile stress at the intersection of the borehole wall edge and the connecting line can be expressed as follows [22]:  
\[\sigma_{max}=(1+3\lambda)P_{0}\left(\frac{r}{R-r}\right)^{\alpha} \tag{4}\]  
where \(\lambda=\mu_{d}/(1-\mu_{d})\) is the lateral stress coefficient of the borehole wall; \(\alpha=2-\mu_{d}/(1-\mu_{d})\) is the stress wave attenuation coefficient; \(\mu_{d}\) is the dynamic Poisson's ratio of the rock mass; \(P_{0}\) is the initial pressure penetrating into the borehole wall; \(R\) is the spacing of deep and shallow boreholes; and \(r\) is the radius of the borehole.  
To ensure that rock fragments fall in the deep hole without a charge section, it should meet the requirement that \(\sigma_{max}\) is greater than the tensile strength of the rock mass \([\sigma_{t}]\), so it can be determined that the deep and shallow hole spacing should be as follows:  
\[r<R\leq\left\{1+\left[\frac{(1+3\lambda)P_{0}}{[\sigma_{t}]}\right]^{\frac{1 }{2}}\right\}r \tag{5}\]