esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Model
### Convective Boundary Modelling  
In order to more accurately simulate the overall thermal and phase change processes within the PCM, it was imperative to capture as closely as possible the order of magnitude of the convection coefficient related to the HTF on the inside surface of the finned tube. For the various experimental trials used as the basis for this numerical study, the internal flow in the tube was in the transition region, with Reynolds numbers varying from 1000 to 4600, based mainly on flow rates and the number of finned tubes in the parallel configuration and calculated at the final HTF temperature. Therefore, using correlations from Chapter 5 of [PERSON], et al. [38] defined for \(2100\leq\text{Re}\leq 10^{6}\) and the assumption of uniform wall temperature:  
\[\text{Nu}_{t}=\text{Nu}_{0}+\frac{\left(0.079(f/2)^{0.5}\text{RePr}\right)}{ \left(1+\text{Pr}^{4/5}\right)^{5/6}} \tag{5}\]  
with \(\text{Nu}_{0}=4.8,\text{ Re}_{D}=\frac{eUD_{h}}{\mu}\), and \(D_{h}=\frac{4A_{c}}{P}\). The required friction factor (\(f\)) for this Nusselt number correlation is defined as:  
\[f=A+\frac{B}{\text{Re}^{\frac{1}{n}}}\begin{cases}\text{Re}<2100;\,A=0,\,\,B=1 6,\,\,m=1\\ 2100<\text{Re}\leq 4000;\,A=0.0054,\,\,B=2.3\times 10^{-8},\,\,m=-2/3\\ \text{Re}>4000;\,A=1.28\times 10^{-3},\,B=0.1143,\,\,m=3.2154\end{cases} \tag{6}\]  
Considering the effect of entrance length on the convection coefficient, it was determined that a weighted average approach was the most physically meaningful and accurate. Equation (7) presents the entrance length correction to be applied to \(\text{Nu}_{t}\) (=\(\text{Nu}_{\infty}\)) determined from Equation (5). The constants \(C\) and \(n\) were selected for the \(90^{\circ}\)_round bend_ case presented in [PERSON], et al. [38].  
\[\frac{\text{Nu}_{m}}{\text{Nu}_{\infty}}=1+\frac{C}{(x/D_{h})^{n}}\text{ with }C=1.0517\text{ and }n=0.629 \tag{7}\]  
Finally, since this entrance region correction is dependent on the distance from the \(90^{\circ}\) bend, a weighted average (\(\overline{\text{Nu}}_{m}\)) was taken. Local corrected \(\text{Nu}_{m}\) values were calculated at every centimeter over the 30 cm length of the finned tube, integrated over the last 29 cm using Riemann's sums, then divided by 29 slices. The average convection coefficient used in a given simulation was calculated from the standard definition of the Nusselt number.  
\[\overline{h}=\overline{\text{Nu}}_{m}(k_{water}/D_{h}) \tag{8}\]  
As mentioned earlier, values of Re for the experimental trials varied from 1000 to 4600, with the correlation in Equation (5) being valid for \(\text{Re}\geq 2100\). It is important to mention that convection coefficients coming from Equation (5) were found to be more accurate and therefore led to simulation results matching experimental validation data better than the purely laminar correlation from \(\text{Re}<2100\). Therefore, the correlation of Equation (5) was used for all numerical studies. The calculated convection coefficients for each validated experiment will be presented in Section 3.7.
## 3 Approach
### Missing Seeding Detection
#### 3.1.3 Missing Seedling Detection  
The detection of missing seedlings is determined by calculating whether the centroid distance of seedlings falls within the maximum plant spacing range of adjacent seedlings. Taking 673 seedling images from Wujiaohu Village, Fanhe Town, Tieling City, Liaoning Province, as an example, the average normal plant spacing in the image processing is 581.68 mm. When there is a floating seedling situation, the distance between adjacent seedlings is greater than the average plant spacing, and the maximum adjacent plant spacing is taken, which is 719.97 mm. Therefore, let d represent the maximum adjacent plant spacing, and D represents the sum of the adjacent centroid distances and the distances from the first and last seedlings to the boundaries. When D > d, it indicates a missing seedling phenomenon [21]. The detection results are shown in Figure 6.  
Simultaneously, during the operation of the transplanting machine, there may be continuous missing and floating seedling situations, including the occurrence of missing seedlings at the beginning and end of the collected images. Therefore, dividing D by the maximum plant spacing \(d\) yields the result \(n\), as shown in Formula (1). As illustrated in Figure 7, the centroids' connecting line is divided into \(n+1\) segments, and the breakpoints are considered as the coordinates of the missing seedlings.  
\[n=D\mid d \tag{1}\]  
Figure 6: Detection of missing seedling centroids.  
Figure 7: Detection of missing seedlings in different scenarios. (**a**) Original image; (**b**) missing seedling detection.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 3 Recognition Model
### Loss Functions  
#### 3.3.1 Distance-Based Cross-Entropy Loss (DCEL)  
In prototypical networks, distance is used to measure the similarity between samples and prototypes [30]. Therefore, the distance between the sample \((x,y)\) and the prototype \(a_{i}\) can measure the probability of the sample belonging to the prototype, where \(x\) is the sample and \(y\) is the label corresponding to \(x\). Based on the analysis in [31], the DCEL can be defined as:  
\[l((x,y);\theta,A)=-\frac{1}{K}\sum_{k=1}^{K}\sum_{n=1}^{N}q(y)\log p(y|x) \tag{6}\]  
where \(A=\{a_{i}|i=1,2,\cdots,N\}\) represents the set of prototypes, \(q(y)\) represents the distribution of sample labels, \(p(y|x)\) represents the probability that sample \(x\) belongs to class \(y\), and \(K\) represents the number of samples in a batch.
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.3.3 Linking the Perceptual and the Acoustic Results  
To further explore the relationship between the perceptual evaluation results and the acoustic features of the L2 Cantonese tone production, we first quantified the tone production data. Following [28; 43], we converted the 20 normalised F0 points for each syllable into three coefficients using the discrete cosine transform (\(DCT\)) approach, with \(DCT1\), \(DCT2\) and \(DCT3\) representing mean F0 height, F0 linear slope and curvature, respectively. To provide a reference for comparison, the \(DCT\)_values_ of each token produced by the native speakers were averaged. After that, we calculated the tonal distance between each token produced by the L2 speakers and the averaged \(DCT\)_values_ of the same token produced by the native speakers with the following equation:  
\[d=\sqrt{\left(DCT1m-DCT1c\right)^{2}+\left(DCT2m-DCT2c\right)^{2}+\left(DCT3m -DCT3c\right)^{2}} \tag{2}\]  
where \(d\) is the calculated tonal distance, \(DCT1m\), \(DCT2m\) and \(DCT3m\) represent the \(DCT\)_values_ of a particular trial produced by Mandarin speakers, and \(DCT1c\), \(DCT2c\) and \(DCT3c\)stand for the averaged _DCT values_ of the same token produced by native Cantonese speakers. For example, for our first Mandarin speaker, the three _DCT_ coefficients for the Cantonese T1 production in a specific situation (syllable: /fu/; repetition: first production; context: in isolation) are 6.02, 0.16, and \(-\)0.39, respectively. The three _DCT_ coefficients of the same Cantonese T1 token from the Cantonese reference group are 6.70, 0.13 and 0.05, respectively. The Euclidean distance between the two points in the three-dimensional space is 0.81.  
To explore the relationship between the perceptual and the acoustic results, GLMMs were employed to investigate whether the acoustic results could predict the perceptual evaluations (correct or incorrect) of native-speaking listeners, with the _DCT values_ (_DCT1_, _DCT2 and DCT3_) and the _tonal distance_ each included as the predictor for separate models. We first fitted models with data of all six tones, and then fitted models with each tone separately. Again, we included by-speaker and by-listener random intercepts.
## 4 Experiment
### Results and Analyses
At the same time, the experiments show that the proposed algorithm has a larger and more significant area in the activated region. This indicates that our algorithm performs well at extracting all relevant activations in the input image and fully extracts their information by using a small number of parameters and FLOPs. This further demonstrates that the proposed algorithm is effective. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **FPS** & **Parameters** & **FLOPs** & **Weights** \\  & **(\%)** & **(\%)** & **(\%)** & **(M)** & **(G)** & **(MB)** \\ \hline Faster R-CNN & 98.4 & 49.2 & 46.5 & 43.98 & 90.9 & 335.0 \\ EfficientNet & 92.9 & 52.1 & 31.9 & 18.36 & 54.4 & 230.7 \\ MASK R-CNN & 91.6 & 54.2 & 41.0 & 43.98 & 142.0 & 356.5 \\ Swin Transformer & 92.6 & 55.2 & 34.7 & 47.38 & 145.0 & 575.0 \\ CDNet & 91.8 & 53.5 & 59.2 & 21.61 & 51.6 & 43.6 \\ YOLOv5 & 98.2 & 72.2 & 136.6 & 7.02 & 15.8 & 14.4 \\ YOLOv7 & 86.3 & 47.6 & 135.1 & 6.01 & 13.0 & 12.2 \\ DETR & 85.6 & 48.7 & 40.7 & 41.56 & 38.82 & 569.8 \\ RT-DETR & 95.4 & 64.8 & 29.8 & 31.99 & 108.0 & 66.1 \\ CGADNet (Ours) & 98.3 & **73.1** & **137** & **4.03** & **8.5** & **8.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with other models. FPS were tested on NVIDIA GeForce RTX 3090 GPU with a batch size of 1. The input images size are 640 \(\times\) 640.
# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 4 Results
### Evaluation Metrics  
In this paper, we mainly used mIoU and mFl scores to evaluate the performance of the model in the negative obstacle detection task. mIoU is the key metric that measures the  
Figure 4: Comparison of feature splicing before and after: (**a1–a4**) represents the original image and (**b1–b4**) represents the enhanced image. The red in the Label picture represents patholes and the green represents cracks.  
accuracy of the model in recognizing obstacles and non-obstacle regions. Its calculation formula is shown in Equation (10):  
\[IoU_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{10}\]  
where \(i\) denotes the detection performance of obstacles in category \(i\), \(TP_{i}\) denotes the number of pixels correctly recognized as negative obstacles, \(FP_{i}\) is the number of pixels incorrectly recognized as negative obstacles, and \(FN_{i}\) is the number of unrecognized negative obstacle pixels. As shown in Equation (11), the final mIoU is the average of all class IoUs:  
\[mIoU=\frac{1}{C}\sum_{i=1}^{C}IoU_{i} \tag{11}\]  
Here, \(C\) is the total number of categories. \(mIoU\) can effectively evaluate the model's detection performance for negative obstacles, especially when the scene is complex or the obstacle types are diverse. On the other hand, the \(mF1\) score integrates precision and recall, which are calculated as shown in Equations (12) to (14):  
\[F1=2\times\frac{precision\times recall}{precision+recall} \tag{12}\]  
\[precision=\frac{TP}{TP+FP} \tag{13}\]  
\[recall=\frac{TP}{TP+FN} \tag{14}\]  
\(TP\) represents the number of true cases, \(FP\) is the number of false-positive cases, and \(FN\) is the number of false-negative cases. \(mF1\) scores are averaged and calculated as shown in Equation (15):  
\[mF1=\frac{1}{C}\sum_{i=1}^{C}F1_{i} \tag{15}\]  
In negative obstacle detection, a high \(mF1\) score indicates that the model is not only able to accurately identify negative obstacles but also effectively captures all relevant targets and reduces missed detections. Through the combination of these two metrics, we are able to comprehensively assess the performance of the model in negative obstacle detection to ensure its effectiveness and reliability in practical applications.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results  
The results presented in Section 7 show that FFR-based techniques efficiently mitigate ICI, improving throughput and SINR. It also shows that no algorithm performs best in every situation, but the Strict FR has a good compromise between performance and complexity. Moreover, not all of its parameters have a significant impact on throughput and SINR, according to Section 8.  
Results from Section 9 indicate that hotspots can have a significant impact on system performance, and different states of the system may require different configurations of the Strict FR parameters to achieve improved performance. Furthermore, Section 2 discusses challenges related to hotspot scenarios and highly dynamic urban areas. In such scenarios, techniques that allow the system to adapt dynamically may increase performance.  
Therefore, this section presents our proposed solution for scenarios where hotspots appear unexpectedly. This solution applies Machine Learning (ML) techniques to dynamically regulate the _RsrqThreshold_ of the Strict FR.  
Figure 16: Simulation results in terms of the 10 th percentile of throughput. The dashed lines represent the scenario with hotspots.
## 3 Materials and Methods
### Risk Evaluation
#### 3.3.2 Human Health Risk Assessment  
To support policy change on fluoride standards in rural drinking water in Malawi, a 'human health risk assessment' was undertaken on each of the water points sampled for geochemistry. The method was based upon the approach introduced by the United States Environmental Protection Agency (USEPA) as a tool to \"assess the nature and possibility of adverse health effects in humans who consume highly contaminated water\" [36; 37]. Similar studies have been conducted since in India and Iran [36; 38; 39]. The method involves the calculation of a non-carcinogenic risk index, also known as a 'hazard quotient' (HQ) (dimensionless) detailed below [40; 41] (in [38]): chronic daily intake (CDI) is calculated (1) [40; 41] (in [38]) and defined by the following parameters:  
\[CDI=\frac{C*DI*F*ED}{BW*AT} \tag{1}\]  
C--Fluoride content in drinking water (mg/L)  
DI--Daily Water Intake (l/day)  
F--Exposure Frequency (days/year)  
ED--Exposure Duration (years)  
BW--Average Body Weight (kg)  
AT--Averaging Time for non-carcinogens (days)  
The HQ is then defined as follows (2) [40; 41] (in [38]):  
\[HQ=\frac{CDI}{RfD} \tag{2}\]  
CDI--Chronic Daily Intake (mg/kg/day)  
RfD--Oral Reference Dose (mg/kg/day)  
The HQ value indicates the \"ratio of the potential exposure to a substance and the level at which no adverse effects are expected as a result of exposure. If HQ > 1, adverse effects are possible\" [39]. HQ represents an indication of potential risk from fluoride at any water point. Three risk categories were applied: children (aged 6 years old), children (aged 12 years old) and adults (>19 years old) (adapted from [PERSON] et al. [38]). The values used for Equations (1) and (2) were from previous studies [38; 39] (Table 1). BW (body weight) and DI (daily water intake) values were based on averages for the age groups chosen and broadly represent the study area population. Once calculated, HQ values were further analysed to explore statistical relationships with geology. This was achieved by comparing calculated proportions of HQ values > 1 for each lithology.
# Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
## 1 Introduction
However, CycleCut cannot improve the stabilization of manifold learning algorithms, which means that CycleCut would also fail when the data have large amounts of small noises. To our best knowledge, however, there is no extension of HLLE to address the outlier or noise problem. In this paper, we investigate the robust manifold learning problem in the context of HLLE and propose a robust version of HLLE algorithm called RHLE. It is worthwhile to highlight our main contributions here:  
1. We reformulate the original HLLE algorithm through the truncation function from differential manifold [24]. As far as we know, we are the first to explicitly relate the truncation function from differential manifold to a classical manifold learning method, _i.e._, HLLE, to explain the connection between the local geometric object and the global geometric object of the manifold. With the help of this reformulation, we explicitly propose a weighted global functional to reduce the influence of outliers and noises. 2. We propose a fast outlier detection method based on robust statistics [25]. In contrast to the traditional RPCA-based algorithms, our method is much more efficient for high-dimensional datasets. Our proposed method can be successfully applied to the existing manifold learning algorithms to improve their robustness. 3. As reviewed in this section, the related works are suitable for either the outlier or noise problem. In contrast, our RHLE algorithm is robust against both outliers and noises. The rest of the paper is organized as follows: we first briefly review the original HLLE algorithm and reformulate it through the truncation function from the differential manifold in Section 2. The sensitivity of HLLE to outliers and noises is illustrated in Section 3. Section 4 gives the details of our proposed RHLE algorithm. Experimental results are reported in Sections 5 and 6 concludes.
## 2 A Method for Assessing the Maturity Level of Key Areas in Manufacturing Companies  
### The Main Obstacles to the Robotization of Production Processes  
Robotizing production is complex and requires careful planning, purchasing, and integration. Fundamental issues may include:  
* Analysis of the factors determining the feasibility of implementing a robot;
* Selection of a robot for a specific application--it is essential to consider various factors such as kinematic structure, load capacity, degrees of freedom, reach, mounting type, speed, motion range of joints, motion repeatability, IP class, operating temperature range, built-in/optional communication ports and IO interfaces, programming method and environment, tooling, and installation requirements;
* Estimation of implementation costs, including the purchase and maintenance expenses, in comparison to the costs of other solutions available in the market;
* Selection of sensors and additional instrumentation (grippers with tactile sensors, accelerometers, 3D vision systems, force and torque sensors, glue or paint guns, welding guns, etc.);
* The need to meet requirements for the operating environment in which humans and robots interact and the safety measures that need to be considered during operation;
* Integration of IIoT collaborative robots, including connectivity technologies such as Ethernet, Wi-Fi, Bluetooth, GSM systems, 5G, and software integration;
* The need to provide necessary additional instrumentation (e.g., utilities, gas extractors, etc.) and adaptation of infrastructure;
* Training of employees; hiring or outsourcing qualified staff.  
Based on initial surveys, the most frequently used criteria (C1-C5) by entrepreneurs have been identified in the form of:  
* C1--Relocating time-consuming processes to robotic workstations while transferring operators to higher-value tasks;
* C2--Elimination of routine quality problems (frequent need for scrap or rework) and areas of inconsistent quality;
* C3--Elimination of the manipulation of heavy tools or parts, workplaces, or locations with potential for dangerous accidents;
* C4--Handling monotonous and rule-based operations (e.g., sorting, simple logic);
* C5--Moving workers away from hazardous areas (exposition to gases, dust, or by-products of the production process) and eliminating manual activities (with repetitive movements and ergonomic problems and those requiring prolonged, intensive concentration or constant change of attention).  
The criteria that have been indicated include issues related to technology, ergonomics, and process reorganization. However, key requirements such as energy savings, reduced adverse environmental impacts [55; 56], and continuous improvement, including the possibility of integrating robots into digitized complex enterprise management systems [57; 58; 59; 60; 61; 62; 63], must also be included.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 4 Results
### Vortex Structures  
The total power is almost the same when turbine spacing is 7D and 8D, which is consistent with the study of [PERSON] et al. [32]. Considering the cost of power transmission and the site utilization of wind farms, 7D is recommended. The vortices structure of the turbine at 7D is analyzed here in detail.  
The evolution of the wake vortices is illustrated through the vortex structures depicted in Figure 16. The wake vortices are identified by the Q criterion. The blade root-bound and tip vortices are clearly visible for WT1, and it moves downward in a spiral shape after shedding from the turbine. With the increase in spacing, the wake vortices gradually diffuse and break up, and they are further disturbed by the blades after reaching WT2, and the complexity of the wake vortices structure increases.  
As yaw-based control is adopted, the results show that the vortices shift obviously and have an asymmetric distribution. The vortices shedding from WT1 are crushed when reaching WT2. The tip vortices of WT1, which quickly break down and mix with each other, do not remain well compared with the blade root vortices of WT1. However, it can be observed that there are relatively clear tip vortices in the non-overlapping part of WT2 when the yaw angle of WT1 becomes large. The influence of vibration and uneven inflow caused by asymmetric turbine wake on the operation of downstream turbines should be considered in the actual wind field design.