esa-sceva/satcom-chunk-collection huggingface dataset:
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 3 Experiment Results
### Performance Indicators  
In order to validate the effectiveness of the proposed network, five key indicators are selected from the main comparative indicators of object detection, which include precision (P), mean Average Precision (mAP), number of parameters (Params), floating point operations (FLOPs), and F\({}_{1}\) value. Among them, P is the most important indicator since it is closest to the predicted number of people. This is the key to success in emergency rescue and management, the bigger the \(p\) value is, the more people will be saved.  
Taking the pedestrians category in the COP dataset as an example, P describes the ratio of true pedestrians, as expressed in Formula (9).  
\[\text{P}=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) (true positives) refer to the number of pedestrians category, for which both its label and prediction are pedestrians.\(FP\) (false negatives) refer to the number of pedestrians category, for which its label is not pedestrians but prediction is.  
The formula for calculating \(mAP\) is shown as (10).  
\[mAP=\frac{1}{\text{M}}\sum_{i=1}^{M}AP_{i}=\frac{1}{\text{M}}\sum_{i=1}^{M} \int_{0}^{1}P(R)\text{d}\text{r} \tag{10}\]  
where M is the number of all the categories, and here it is five in the COP dataset. \(mAP\) is an average value of all of AP (average precision), which refers to the average precision value of a specific category, for example, the pedestrians. \(mAP50\) refers to the \(mAP\) value when the overlap rate IoU (Intersection over Union) is more than 50%.  
Params refers to the number of parameters that need to be learned in the model; it contains the weights and biases of the model, and is usually used to measure the size and to map inputs to outputs. The larger it is, the greater the storage space will be.  
FLOPs are used to measure the computational complexity of models. Generally, a smaller value indicates less computational resources required, as well as higher inference speed and better real-time performance. The calculation formula is shown in Formula (11).  
\[\text{FLOPs}=2\text{HW}(\text{C}_{\text{in}}\text{K}^{2}+1)\text{C}_{\text{out}} \tag{11}\]  
where H is for the height of the input image, W is the width, \(C_{\text{in}}\) and \(C_{\text{out}}\) represent the number of input and output channels, respectively, and K represent the convolution kernel size.
## 3 Results  
### Climatic Parameters, Development of the Phenological Stages and Grape Ripening  
The monthly average temperatures, precipitation, and GDDs index for both vintages (2020, 2021) and for the two vineyard locations (B and M) are shown in Figure S1. The monthly average temperatures and total precipitation for the period from 1 April to 30 September differ between the two years for both vineyard locations (17.4 \(\pm\) 4.3 \({}^{\circ}\)C vs. 16.9 \(\pm\) 6.1 \({}^{\circ}\)C, 574.4 mm vs. 538.2 mm for location B, and 16.8 \(\pm\) 4.0 \({}^{\circ}\)C vs. 16.1 \(\pm\) 5.7 \({}^{\circ}\)C, 583.9 vs. 528.2 for location M). The 2020 vintage was characterised by higher monthly average temperatures in April, May, and August at both vineyard locations, which was reflected in the higher GDDs values for these months. In contrast, the 2021 vintage was characterised by higher monthly average temperatures in June and July, with higher GDDs values (Figure S1, right graphs). In September, a comparable GDDs summation was observed for both vintages at both vineyard locations. The 2020 vintage was wetter from June to September at both vineyard locations, while the 2021 vintage was wetter in April and May (Figure S1, left graphs). The average monthly temperatures from April to June were comparable between the locations in both years. Location B had higher monthly average temperatures and GDDs values from June to September (Figure S1), which was also reflected in a higher GDDs sum for the period from 1 April to 30 September (1658 (location B) vs. 1521 (location M) in 2020; 1647 (location B) vs. 1469 (location M) in 2021). Location B received more precipitation in the spring months, with a peak in May in both years, while location M received more precipitation in June and August in both years and in September 2021 (Figure S1).  
The differences in the climatic parameters resulted in slight differences in the occurrence of phenological stages between the years. For example, at location B, the phenological stage at which the fruit berries develop (BBCH 71-79) occurred earlier in 2021 compared to 2020 (Supplementary Table S2), and exactly the opposite at location M (Table S1). The differences in the sum of GDDs between the two locations and between the two years within the same location were even more pronounced. The sum of GDDs at harvest was much higher at location B compared to location M, namely 86 units higher in 2020 and 172 units higher in 2021. In 2021, the GDDs value at harvest was 54 units higher at location B compared to 2020, and 32 units lower at location M (Tables S1 and S2). The criteria for harvesting at both locations were that the pH value of the grape should not rise above 3.5 and that the TSS content in the grape should reach at least 20 \({}^{\circ}\)Bx.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### NPs of Other Metals
#### 4.6.7 AuNPs  
Elicitor Effect  
AuNPs (24.2 \(\pm\) 2.4 nm) applied at concentrations of 50 and 10 mg/L, respectively, elicited biosynthesis of secondary metabolites in _Lavandula angustifolia_ in vitro cultures, whereby lower molecular weight compounds of EO (e.g., \(\alpha\)- and \(\beta\)-pinene, camphene, 8-3-carene, \(p\)-cymene, 1.8-cineole) were replaced by compounds showing higher molecular weight, e.g., cadalene, \(\alpha\)-bisabolol, and (_E,E_)-farnesol [130]. Spraying of shoots of 6-year-old ginseng plants with AuNPs, performed 3-fold, increased the content of gingenosides Rg1, Re, Rf, and Rb1; using the steaming process only, the levels of Rd and Rg3 were enhanced [282].  
Considering 3.2-fold enhancement of glaucine in _Nigella arrensis_ L. plants cultivated in hydroponics in the presence of 1000 mg/L NiO NPs, this method seems to be suitable to achieve enhanced levels of this aporphine alkaloid in _N. arrensis_ plants [136] (Table 1).
###### Abstract  
Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0-20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The \(R^{2}\) values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds.  
soil properties; recursive feature elimination; random forest; extreme gradient boosting; spatial distribution +
Footnote †: journal: _Sensors_**2024**, 24, 3784. [[https://doi.org/10.3390/s24123784](https://doi.org/10.3390/s24123784)]([https://doi.org/10.3390/s24123784](https://doi.org/10.3390/s24123784))
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 3 Materials and Methods
### System Components and Modeling
This achieved accuracy is especially required in industrial settings and at concerts [14]. B-weighting is a method designed to better reflect the sensitivity of the human ear at medium to high sound levels. Since it is necessary to regulate the system based on the mid to upper sound levels in setting vehicle sound systems, B-weighting, shown in Figure 3, which presents the frequency response from 20 Hz to 20 kHz, is used. In this context, our study is based on the equal-loudness contour at 1000 Hz, with an amplitude level of 90 dB, as shown in Figure 1. After applying the B-weighting process to the white-noise signal, the input audio used in this study is generated. The frequency response of this input sound is depicted in Figure 4.
# A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus
## 4 Driving Behavior Planning Based on Double-Layer FSM
### Bottom-Layer FSM Design  
Driving scenarios for driverless bus are relatively simple and bus usually travels in a fixed route. In this section, the driving state set is designed as {structured driving (Q1), unstructured driving (Q2), following (Q3), overtaking (Q4), buffer adjustment (Q5), and special path (Q6)). The corresponding external trigger events are devised based on the above states, which mainly divided into the following categories:  
* E1/E2: The moving vehicle in front of the bus enters/disappears in detection area.
* E3/E4: The lane information from perception is valid/invalid.
* E5: The bus enters the buffered road.
* E6: The stationary obstacle in front of the bus enters in detection area.
* E7: The process of overtaking completes.
* E8: The bus enters the special path.
* E9: The bus finishes the special path.
* E10: The bus enters the area of destination.  
According to the states and the trigger events, the bottom-layer FSM possesses six states, as shown in Figure 6. In our system, different behaviors also determine the local reference points which is used for trajectory generation.  
* Structured driving: The bus drives in structured road and the lane recognition is valid. It is an initial state in the bottom-layer FSM. We mainly utilize the results of lane recognition in this stage to guide the bus to drive in this stage. So the local optimal reference points are generated by the results of lane detection.  
Figure 5: Top-layer state transition.  
Figure 6: Bottom-layer state transition block diagram.  
* Following: It is a state that the autonomous bus can follow the vehicle ahead only when the lane detection is valid. The speed varies with the vehicle ahead.
* Overtaking: The autonomous bus overtakes the vehicle ahead which drives slowly (speed of the vehicle does not exceed 2 m/s) in this stage. It is divided into three steps: (a) lane change; (b) overtake; (c) lane return. Safe space is considered to avoid collisions with other vehicles. If \(d_{1}<d_{th}\), the bus changes the lane (\(d_{1}\) represents the distance between two vehicles, \(d_{th}\) is the safe space). At this time, the local reference points are shifted from the current lane to another lane. The bus will return the lane again until it overtakes the vehicle and \(d_{1}>d_{th}\).
* Buffer adjustment: It is an intermediate stage that the bus needs to adjust speed especially when the bus is around a ramp or intersection. The signs of enter and exit can be obtained from the global map.
* Special path: It is a stage after buffer adjustment and used to deal with special terrain such as intersections. The local reference points come from the special path database that is established in Section 3.2.
* Unstructured driving: Lane lines fail to be identified by perception layer in this stage. A check zone is set manually and detect the threaten obstacles in this area. Then check whether a dangerous obstacle exists in the check zone. For instance, in Figure 7, C1 is a threat for the bus while C2 is not.
# Motion Control System of Unmanned Railars Based on Image Recognition
## 3 Experiment System Architecture and Setting  
The integrated system architecture is given in Figure 5. From the bottom, the hardware layer consists of the sensing devices, actuating devices and a PC as the controller. The sensing device is a CMOS camera with a resolution of 270,000 pixels installed on the N scale (1/150) model railcar, which transmits the captured analog images to our main computer through wireless microwave.  
The analog images received by wireless this microwave receiver are converted to digital images through an image converter that is connected to the controller PC with a USB interface. The PC processes the consecutive input image frames, and then properly sends motion commands to the actuating device, this being the Arduino UNO R3 board (Arduino, Somerville, MA, USA) [28] with L298n motor driving module to control the motion of the N scale (1/150) model railcars. Therefore, the device drive programs of both the Vcatch image converter and the Arduino board should be included in the device drive layer of the system. The PC controller uses the Windows 7 32-bit operating system. Numpy [29] is the fundamental package for scientific computing with Python [17], and is imported into the system. Firmata [30] is a protocol for our host computer to communicate with the Arduino UNO R3 microcontroller, while pyFirmata is the Python interface of the Firmata protocol. Both of them are applied in the system. OpenCV (Open Source Computer Vision Library) [17] is an open source  
Figure 5: Integrated system architecture.  
computer vision and machine learning software library. In this paper, image processing and computer vision related tasks use many utilities provided by the OpenCV library. Therefore, the Numpy library, Pyfirmata library, and OpenCV library, together with the Windows 7 32-bit operating system, build the operating system and library layer. The application main program is written in Python. The system provides a video display interface in order to display the images captured by the CMOS camera (RF System lab, Nagano, Japan) installed on the model railcar, such that the user may visualize what is seen by CMOS camera. There is also a man/machine interface for the user to control the motion of the model railcar by entering the proper commands manually and remotely. In this research, we used 32-bit Python 2.7.11, OpenCV2.4.11, Numpy, Arduino1.6.9 and Pyfirmata in our control system.  
The experiment facility is shown in Figure 6.
## 3 Comparison with the Existing Theory  
To verify the validity of the theoretical solution obtained in this paper, we compared the solution presented here with the solution in the literature [26]. In the literature [26], the solution of the free vibration of the general cantilever beam is given. In order to facilitate the comparison, we degraded the problem solved here to the problem of the general cantilever beam, that is, let  
\[d_{31}=0,c=0. \tag{57}\]  
Substituting Equation (57) into Equations (55) and (56), respectively, we may obtain the following  
\[w^{\prime}(x,t)=\frac{[\sin{Dx}-\sinh{Dx}+\frac{\sin{Dl}+\sinh{Dl}}{\cos{Dl}+ \cosh{Dl}}(\cosh{Dx}-\cos{Dx})]}{[\sin{Dl}-\sinh{Dl}+\frac{\sin{Dl}+\sinh{Dl}}{ \cos{Dl}+\cosh{Dl}}(\cosh{Dl}-\cos{Dl})]}w_{0}\cos(\omega_{1}t) \tag{58}\]  
and  
\[\omega^{\prime}=\sqrt{\frac{D^{4}I_{y}}{\overline{m}s_{11}}}. \tag{59}\]  
From Equation (32), \(w^{\prime}(x,t)\) can be transformed into the following  
\[w^{\prime}(x,t)=B_{2}\cos(\omega_{1}t)A_{1}[\sin{Dx}-\sinh{Dx}+\frac{\sin{Dl}+ \sinh{Dl}}{\cos{Dl}+\cosh{Dl}}(\cosh{Dx}-\cos{Dx})]. \tag{60}\]  
For general materials, one has the following:  
\[E=\frac{1}{s_{11}}. \tag{61}\]  
Thus, we can obtain the following:  
\[\omega^{\prime}=\sqrt{\frac{D^{4}E_{y}}{\overline{m}}}. \tag{62}\]  
Comparing Equations (60) and (62) with the expressions of the vibration displacement and frequency presented in the literature [26], it can be found that they are exactly the same. This shows that the solution in this paper, to a certain extent, is effective.  
Equation (56) is the expression of the vibration frequency of the piezoelectric cantilever beam, and Equation (62) is the expression of the vibration frequency of the cantilever beam without piezoelectric properties. Comparing Equations (56) and (62), it can be found that \(\omega^{\prime}=\sqrt{D^{4}E_{y}/\overline{m}}\)\(\omega=\sqrt{D^{4}E^{\prime}I_{y}/\overline{m}}\). This shows that the piezoelectric properties will increase the vibration frequency of the free vibration of the piezoelectric cantilever beams.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Preprocessing of Scenic View Evaluation Data  
The questionnaire, utilizing a Likert scale, explicitly instructs participants to interpret adjacent scores as representing equal psychological distances [65]. To facilitate the subsequent comprehensive analysis of physiological continuous variables (eye movement and EEG) within a unified dimension, statistical verification of isometric continuous variables was conducted. Initially, a data distribution test was performed using SPSS 24.0 (IBM, Armonk, NY, USA) on eight indicators, as shown in Table 1, revealing that the absolute values of skewness and kurtosis for all variables were less than 1, indicating an approximately symmetric distribution [66]. Subsequently, unidimensional verification through factor analysis yielded a Kaiser-Meyer-Olkin (KMO) value of 0.916 and a factor loading of 60.14%, confirming the suitability of the data for factor analysis and demonstrating strong unidimensional properties. This aligns with established practices in multimodal data fusion within the engineering domain [67].
# Map API-Based Evacuation Route Guidance System for Floods
## 4 Implementation and Considerations
### Implementation of Map Output and Evacuation System  
Information regarding the Korean coordinate system was prepared using seven variables of the [PERSON] model of the \"National Coordinate Conversion Factor\" implemented by the National Geographic Information Institute (No. 2002-433) in December 2002 for ellipsoid transformation, among which EPSG:5178 (which is a coordinate system) was selected and used. In the KATEC series, the UTM-K (Bessel) coordinate system representing the entire Korean Peninsula can be used as a new address map. By selecting EPSG5178 as the coordinate system, the map and data were visualized, and the testbed area was printed on the map. The aforementioned public data were classified into four types of terrain data that were selected as testbeds. Instead of a single-file format, it was saved in a shape file in an extended format and included DBF, SHP, and SHX. Data were saved in the SHP format, which is a shape file, and a building layer was created and output. The map API optimized for walking uses the T-Map API by providing an evacuation route for walking to accommodate the characteristics of the recreational forest selected as a testbed. Meanwhile, the route was searched using the route calculation method, which was expressed in JSON form and stored in the database. Figure 12 shows the map output obtained using the map API, and Figure 13 shows the starting point of the shelter.  
Figure 14 shows a map linking the flood prediction models. In a preliminary study, a flood prediction model was implemented and a risk warning was transmitted to the system by selecting a flood risk range. Using the transmitted data, the system output a flood-risk warning on a map and displayed it on the output data. The corresponding terrain data were output, the coordinate system was selected, and the flood range was output on the map via flood prediction. When this range reached an arbitrary value in the water level data, it was displayed on the map as the first warning step, and then the locations of nearby users were identified.  
Figure 12: Map output.  
provided for each location, or the existing route was stopped when an unexpected situation occurred on the moving route. The user's current location was identified, and existing shelters were limited to searching for the nearest shelter. The building of the testbed was selected as a shelter and then saved as a dataset. The saved shelter dataset is displayed on the map as a marker, and the risk ranges predicted based on the model, route search, and provision are shown in Figure 15.  
Figure 16 shows the evacuation route in JSON format, and Figure 17 shows the final result of the proposed system. The map on the system's administration website provides a visualization of the current situation, allowing one to determine whether the correct route is to be provided to the user.  
Figure 16: JSON breadcrumb output.  
Figure 15: Flood prediction and evacuation route detection.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 1 Introduction
The YOLOv5 algorithm can detect apples quite precisely without any additional techniques, and the accuracy rate calculated by the number of apples was 97.20% [20]. [PERSON] et al., proposed an optimized Mask R-CNN method for the separation of overlapping fruits. The method is tested by a random test set with 120 images, and the precision rate reached 97.31% [21]. According to the experimental results, the target apple detection accuracy based on ordinary image processing algorithm is low, while the target fruit detection algorithm based on deep learning has a higher recognition accuracy. However, the training images required for collection and labeling require a large amount of time and human cost input [22]. In order to achieve high recognition accuracy, most of the algorithm models are complex in structure level, time-consuming in processing, and unable to realize fast recognition and location. Moreover, most studies only focus on a certain type of apple pictures, and the test data set is of a single type, so the algorithm is applicable to a small range. In 2020, [PERSON] et al., proposed the DeepSnake algorithm to iteratively deform the initial contour through deep learning so as to obtain more accurate instance segmentation results [23]. Due to the lack of public data sets, the DeepSnake algorithm is real-time and can effectively extract contour features through circular convolution, without the need to use a large number of training data sets, which effectively reduces the time cost. Therefore, the DeepSnake algorithm is introduced in this paper to detect the target fruit. Since the results of the DeepSnake algorithm are affected by the initial detection box, this paper integrates the improved saliency detection algorithm to identify the fruit part. At the same time, a dynamic adaptive target detection algorithm was proposed to separate the overlapping fruits and obtain the initial labeling results. Based on the above detection results of target fruits, the DeepSnake instance segmentation algorithm was used to optimize the detection results. It can be seen from the experimental results that the proposed algorithm can effectively separate the overlapping fruits with a small number of training samples, and is less affected by illumination and foliage occlusion, and can detect the target fruits quickly and accurately with good robustness.