esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Comparisons
### Criteria for Model Comparisons  
Once the analytical expression for the mean value function m(t) is derived, the model parameters to be estimated in the mean value function can then be obtained with the help of a developed Matlab program based on the least-squares estimate (LSE) method. Five common criteria [27; 28], namely the mean squared error (MSE), the sum absolute error (SAE), the predictive ratio risk (PRR), the predictive power (PP), and Akaike's information criterion (AIC), will be used as criteria for the model estimation of the goodness of fit and to compare the proposed model and other existing models as listed in Table 1. Table 1 summarizes the proposed model and several existing well-known NHPP models with different mean value functions. Note that models 9 and 10 in Table 1 did consider environmental uncertainty. The mean squared error is given by  
\[\text{MSE}=\frac{\sum_{i=0}^{n}\left(\text{m(t}_{i})-\text{y}_{i}\right)^{2}}{n-m}.\]  
The sum absolute error is given by  
\[\text{SAE}=\sum_{i=0}^{n}|\text{m(t}_{i})-\text{y}_{i}|.\]  
The predictive ratio risk and the predictive power are given as follows:  
\[\text{PRR}=\sum_{i=0}^{n}\left(\frac{\hat{\text{m}}(\text{t}_{i})-\text{y}_{i} }{\hat{\text{m}}(\text{t}_{i})}\right)^{2},\text{PP}=\sum_{i=0}^{n}\left(\frac {\hat{\text{m}}(\text{t}_{i})-\text{y}_{i}}{\text{y}_{i}}\right)^{2}.\]  
To compare the all model's ability in terms of maximizing the likelihood function (MLF) while considering the degrees of freedom, Akaike's information criterion (AIC) is applied:  
\[\text{AIC}=-2\log|\text{MLF}|+2\text{m}\]  
where \(\text{y}_{i}\) is the total number of failures observed at time \(\text{t}_{i}\); m is the number of unknown parameters in the model; and \(\text{m(t}_{i})\) is the estimated cumulative number of failures at \(\text{t}_{i}\) for \(\text{i}=1,2,\cdots,n\). The mean squared error measures the distance of a model estimate from the actual data with the consideration of the number of observations, n, and the number of unknown parameters in the model, m. The sum absolute error is similar to the sum squared error, but the way of measuring the deviation is by the use of absolute values, and sums the absolute value of the deviation between the actual data and the estimated curve. The predictive ratio risk measures the distance of model estimates from the actual data against the model estimate. The predictive power measures the distance of model estimates from the actual data against the actual data. MSE, SAE, PRR, and PP are the criterion to measure the difference between the actual and predicted values. AIC is a measure of goodness of fit of an estimated statistical model, and considered to be a measure which can be used to rank the models, and it gives a penalty to a model with more number of parameters. For all five of these criteria--MSE,  
\begin{table}
\begin{tabular}{c l l} \hline
**No.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 2 Traffic Data Collection and Status Prediction Modeling Methods
### Setup of Models Using Regression Analysis  
Regression analysis is a statistical method that uses mathematical computations on large volumes of data to explain and predict relationships between variables. To determine the correlation between dependent and independent variables, polynomial regression involves deriving suitable regression coefficients, establishing regression equations, and calculating confidence intervals for predicted values. Greenshields models for highway traffic can be developed through regression analysis for time-series forecasting by fitting polynomial equations to the collected traffic data. A polynomial curve of degree \(n\), obtained through regression analysis, can be expressed as [43]  
\[Y=\alpha_{0}+\alpha_{1}X+\alpha_{2}X^{2}+\dots+\alpha_{n}X^{n}+\epsilon, \tag{4}\]  
where \(Y\) is the dependent variable, \(X\) is the independent variable, \(\alpha_{0}\) is the intercept, \(\alpha_{1},\alpha_{2},\dots,\alpha_{n}\) are the polynomial coefficients, and \(\epsilon\) is the error term. The curvature of the model and its ability to fit the data depend on the chosen polynomial degree \(n\). Polynomial regression fits a polynomial equation to the data, enabling an estimation of the dependent variable for any given independent variable within the data range. Although this method is straightforward, selecting the appropriate polynomial degree is critical and should be based on the data characteristics and the desired accuracy. If the degree is too high, the model may overfit the data, capturing noise and providing a poor representation of the true relationship.  
Figure 4: Vehicle speed vs. traffic flow curve of the Greenshields model.  
For irregularly spaced or highly variable data, nonlinear regression analysis using a specified response function can offer a more suitable alternative. The nonlinear regression equation for establishing such a model can be expressed as [27; 44]  
\[Y=f(X,\ \mu)+\epsilon, \tag{5}\]  
where \(f(X,\mu)\) is the response function, \(\mu\) represents the parameters, and \(\epsilon\) is the error term. The parameters \(\mu\) in the regression equation can be determined using the least squares estimation (LSE) method. This approach involves multiple iterations to test different parameter values. The key objective is to find the values of \(\mu\) that minimize the cost function based on the sample data, thereby optimizing the model's fit to the data.
## 2 Materials and Methods
### SIF-GPP Relationships  
We first used the universal relationship between SIF and GPP across a wide variety of biomes that we previously found [17] for GPP mapping. We then used the biome-specific relationships that we established [17] for GPP mapping to assess whether the SIF-GPP relationship is universal or biome-specific significantly influences GPP estimates. Besides the universal and biome-specific SIF-GPP relationships based on SIF soundings from OCO-2 and GPP data from 64 EC sites (referred to as site-level relationship hereafter) in our recent studies [17; 36], here we also established the SIF-GPP relationships (referred to as grid cell level relationships hereafter) based on 0.05\({}^{\circ}\) SIF data from GOSIF and tower GPP. Grid cell level SIF-GPP relationships were established using tower GPP from 91 FLUXNET Tier 1 sites and GOSIF. We first identified a total of 116 Tier 1 flux sites that have no fewer than three years of flux data, and removed 25 sites having high land cover heterogeneity within the corresponding 0.05\({}^{\circ}\) grid cells. More details on site selection and quality control of GPP data are described in our recent study [31]. We finally obtained a total of 27,434 GPP data points at the 8-day time step from 91 sites. More information on these flux sites (e.g., site name, latitude/longitude, biome type) is given in Table S1. We used one third of the GPP data to establish the grid cell level SIF-GPP relationships and the remaining data to validate the resulting eight 8-day GPP estimates and the ensemble mean.  
The site-level relationships were directly derived based on the original OCO-2 SIF measurements, and therefore are likely to be more credible than the grid cell level relationships. In addition, since the ground area of the OCO-2 SIF soundings is close to the footprint of an EC flux tower, the site-level relationships were less affected by the heterogeneity compared to the grid cell level relationships based on 0.05\({}^{\circ}\) grid cells. On the other hand, the grid cell level relationships were derived based on a large number of samples (more than 10, 000) and are likely to be more robust than the site-level ones derived from a limited number of samples (hundreds). For each type of SIF-GPP relationship, we also examined whether the regression line was forced to pass through the origin or not influences GPP estimates. The GPP estimates based on these SIF-GPP relationships could help us examine how the results differed from those solely based on the site-level, universal relationship.
## 3 Results  
### Heavy Metal Content in Soil from American-Ginseng Sampling Area  
The heavy metal content in soil at the sampling points is given in Table 1. Among all tested heavy metals, Mn exhibited the highest content in the planting soil. The heavy metal content of the planting soil was in the order Mn \(>\) Cr \(>\) Pb \(>\) Cu \(>\) Ni \(>\) As \(>\) Cd. Their average content was approximately 963.36, 57.13, 22.88, 15.73, 13.42, 8.14, and 0.35 mg/kg, respectively. Considerable difference existed in the heavy metal content among sampling points in different villages. The ranges of Cu, Mn, As, Pb, Cd, Cr, and Ni content in soil was 10.04-18.60, 815.75-1261.77, 5.64-10.17, 21.23-26.53, 0.24-0.45, 36.98-83.64, and 9.64-22.23 mg/kg, respectively. The heavy metal content in soil at the sampling points didnot exceed the national standard of soil quality value (SQV) of grade two [25], except Cd content, which was slightly higher than this standard. This indicated that the planting soil contained a moderately high content of Cd at some planting bases. Moreover, the heavy metal content in soil at Yaojia village sampling site was higher than that in soil at other places.
## 1 Introduction
Finally, it is possible to find studies that have utilized fuzzy logic for automatic scaling of cloud solutions, such as the work conducted by [18], who aimed to dynamically adapt thresholds and cluster size according to the workload in order to reduce cloud resource usage and minimize Service Level Agreement (SLA) violations. However, their work applied to scaling VMs. On the other hand, the work developed by [19] proposed a self-scaling cluster architecture based on containers for lightweight edge devices. The architecture is centered around a serverless solution, where the auto-scaler is a key component that uses fuzzy logic to address the challenges of uncertain environments. In this paper, we propose the use of fuzzy logic as an alternative approach for horizontal scaling in K8s clusters. Fuzzy logic allows for modeling the uncertainty and imprecision present in real systems, making it suitable for dealing with vague information and decision-making under uncertain conditions [20]. Additionally, fuzzy-based systems outperform artificial neural networks in the field of resource scheduling [21]. Thus, this work proposes a system called Fuzzy-based Horizontal Scaling (FHS) that uses a Mamdani-type fuzzy system with three inputs: CPU load, packet reception rate, and current number of pods, and an output that controls the increment or decrement of the number of replicas (pods) of a given application in the K8s cluster. This article addresses a primary research gap, namely, the limited exploration of fuzzy logic systems for horizontal scaling in Kubernetes clusters within edge computing environments. While previous studies have extensively explored approaches based on machine learning, reinforcement learning, and deep learning, there needs to be more focus on the adaptability and interpretability of fuzzy logic systems. Fuzzy logic, particularly of the Mamdani type, can effectively handle uncertainty and resource demand variability. The proposed Fuzzy-based Horizontal Scaling (FHS) system addresses this by dynamically managing resources using real-time metrics such as the CPU load, packet rate, and pod count, thereby enhancing operational efficiency while maintaining service quality. Comparing the results obtained by FHS with the Horizontal Pod Autoscaler (HPA) of K8s, FHS can provide better performance in terms of request response time and cluster resilience. The highlights of this study can be summarized as follows:  
* Use of fuzzy logic for automatic horizontal scaling in Kubernetes. * Enhanced performance in resource usage and replica distribution compared to Kubernetes' HPA. * The experimental validation uses a Kubernetes setup, employing jMeter to generate traffic and Prometheus to gather metrics. * Improved resource utilization while maintaining service quality (SLA).
## 4 Results and Discussion
### Excessive Acceleration Level One Operational Limitation
The argument of using regional and seasonal WSTs for vulnerability criteria assessment rather than the standard WST can be sidelined given that ships are allowed to carry out operational assessments using region and time of operation weather data under the operational measure pathways. However, a ship successfully validated using the standard WST during the design stage is not required to have operational limitation or operational guidance estimation during the design or operation phases. Technically, an assessment using the standard WST and vulnerability criteria is a case of operational limitation for sea areas 8, 9, 15 and 16, and the operational limitation achieved for sea areas 8, 9, 15 and 16 is not adequate for season 4 of the same region. From the safety perspective, the safety limit estimated by the standard WST should be more conservative than regional and seasonal WSTs in order to give unrestricted operation. The current study shows for season 4 that operational limitation is required beyond the standard WST safety estimation. \begin{table}
\begin{tabular}{c c} \hline
**Natural Roll Period, T\({}_{r}\), (s)** & **Wave Steepness Factor, s** \\ \hline \(\leq\)6.6 & 0.100 \\
7.7 & 0.098 \\
8.8 & 0.093 \\
13.2 & 0.065 \\
15.4 & 0.053 \\
17.6 & 0.044 \\
19.8 & 0.038 \\
22.0 & 0.032 \\
24.2 & 0.028 \\
26.4 & 0.025 \\
28.6 & 0.023 \\
30.8 & 0.021 \\ \(\geq\)33.0 & 0.020 \\ \hline \end{tabular}
\end{table} 
Table 11: Wave steepness table AHD data (season 4). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Data** & **BGW** & \multicolumn{5}{c}{**AHD**} \\ \hline
**WST** & **Annual** & **Annual** & **Season 1** & **Season 2** & **Season 3** & **Season 4** \\ \hline Sea area S & 8.0099 & 7.9109 & 7.5485 & 4.9814 & 7.5485 & 8.6104 \\ Sea area 8 & 7.9412 & 7.9109 & 7.59 & 5.0493 & 7.6997 & 9.1549 \\ Sea area 9 & 8.0099 & 8.4068 & 7.8068 & 5.4341 & 7.8405 & 9.1549 \\ Sea area 15 & 7.6622 & 7.0663 & 7.0663 & 4.257 & 6.6088 & 7.7345 \\ Sea area 16 & 7.9109 & 7.8068 & 7.3522 & 4.8456 & 7.3522 & 8.4707 \\ \hline \end{tabular}
\end{table} 
Table 12: Excessive acceleration (m/s\({}^{2}\)) of C11 class container ship with modified WST.
## 3 Results
### Experiment 3: Impact of Image Set Size  
Figure 7a shows _Accuracy_ in different simulations where image set sizes vary. _Accuracy_ has been used in this experiment since it is a normalized parameter and thus easier to interpret graphically, but similar conclusions have been reached with the _Error index_ in this case. Figure 7b shows how simulations with image set sizes lower than 297 images (99 images per class) generate a significant dispersion in performance. Dispersion shows a decreasing trend up to 99 images per class and from that point on there is no clear trend,  
Figure 6: (**a**) Accuracy vs. Image class ratio. (**b**) Error ratio vs. Image class ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline
0—clean water & 0.89 & 0.94 & 0.91 \\
1—spill & 0.95 & 0.86 & 0.90 \\
2—waste & 0.93 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance results for Experiment 1.  
remaining at moderate values. The number of 99 images per class is also the closest among those used to the benchmark of 100 images per class usually recommended for training Image Classification algorithms [27]. With datasets that have a number of images over this amount, Accuracy becomes stable and increases in a linear manner with increasing images provided to train and validate the algorithm. When carrying out a regression in datasets with over 99 images per class (297 total images), both linear and quadratics fits have been considered. Finally, a linear regression, shown in Figure 7a, was selected because the quadratic fit is only marginally better than the linear one and because the linear fit showed significantly more robustness. Robustness was here evaluated as the change in fit parameters when random datapoints are removed from the set of results. Thus, in the range of image set sizes considered in the study, also the relevant range for the application at hand, the _Accuracy_ presents a linear tendency with increasing image set size after a certain number of images have been achieved.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms  
Routing is the process of data transfer between entities in the form of packets. The entities can be base stations, link layers, end devices, or all of these. There are various routing protocols being used in industry, some are complicated, whereas others are relatively simpler. The following discussion addresses some of the complicated routing algorithms in a simple manner.  
In inter-UAV communications, the link quality differs because of the mobility of UAVs, dynamic change in topology, and communication distance changes between UAV nodes [53]. Different types of routing protocols for IoD are explained in the following sections.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods
### Normalized Difference Dust Index (NDDI)  
Using the VIS and NIR bands, dust storms over low-reflectivity surfaces can be well monitored. Qu et al. [24] proposed the NDDI using MODIS reflectance and found that dust had the lowest reflectance in the 0.469 \(\upmu\)m band and the highest reflectance in the 2.13 \(\upmu\)m band. This is opposite to clouds. This difference allows for a good distinction of dust from the water/ice clouds and ground features. NDDI has been successfully applied to dust detection in Asia and Africa [55]. For the FY-4A/AGRI data, the NDDI can be calculated as follows:  
\[\text{NDDI}=(\text{R}_{\text{NOMChannel06}}-\text{R}_{\text{NOMChannel01}})/( \text{R}_{\text{NOMChannel06}}+\text{R}_{\text{NOMChannel01}}) \tag{2}\]  
where \(\text{R}_{\text{NOMChannel06}}\) and \(\text{R}_{\text{NOMChannel01}}\) denote the top of atmosphere reflectance at NOMChannel06 and NOMChannel01. Depending on the area, different NDDI thresholds are used for dust monitoring [56; 57].  
For dusty pixels, the NDDI threshold was set to 0.05-0.35 in this study. Figure 2b shows the frequency distribution map of NDDI values for the selected dust, cloud, and bare ground pixels. Clouds can be effectively screened using negative NDDI values. However, the NDDI values were higher for both dust and bare ground, so it is difficult to distinguish dust from bare ground.
## 2 Materials and Methods
### Deep Learning Model Design
The bottleneck blocks in the ResNet-50 architecture are composed of three convolutional layers with filters of size \(1\times 1\), \(3\times 3\), and \(1\times 1\), respectively. The convolutional layers with a stride of 2 perform the downsampling (conv3_1 and conv4_1), and batch normalization is carried out after each convolution before ReLU activation. The ResNet-50 has two variants of bottleneck blocks: the identity shortcut and the projection shortcut. The convolutional layers in the identity shortcut are connected to skip connection input; this block can be directly used when the input and output feature maps have the same dimension. The projection shortcut has an extra convolutional layer with a filter of size \(1\times 1\) present on the skip connection. When the dimensions change, there are two options: first, the shortcut still performs identity mapping, with extra zero entries padded for increasing the depth, or second, an extra \(1\times 1\) convolutional layer is used to match the dimensions. The deeper layers of the ResNet-50 feature extraction network (fifth stack, average pooling layer, fully connected layer, and softmax layer) were replaced with the YOLOv2 detection subnetwork. Thus, in this case, the ResNet-50 outputs feature maps that downsample the input image by a factor of 16 (224/16 = 14 \(\times\) 14). YOLOv2 is a state-of-the-art real-time object detection system providing a smooth trade-off between speed and accuracy in different scenarios [7]. It is a small convolutional network consisting of serially connected convolutions, ReLU, and batch normalization layers, followed by a transform layer and an output layer. The first two convolutional layers have filters of size \(3\times 3\) with a number of filters matching the number of channels in the ResNet-50 feature extraction layer output. The last convolution layer has a filter of size \(1\times 1\) with a number of filters depending on the number of anchor boxes, classes, and predictions per anchor box. The transform layer extracts activations of the last convolutional layer and transforms the bounding box predictions to fall within the bounds of the ground truth. The output layer provides the refined bounding box locations of the target objects. Figure 2 outlines the layers and operations of the ResNet-50 feature extraction network with the YOLOv2 detector. The upper part of the scheme depicts the feature maps obtained by different layers of the ResNet-50 and the final output of the YOLOv2 detector. The middle part schematically illustrates the representative layers of the network. The effect of the convolution, batch normalization, activation, and maximal pooling produced by the first layer of ResNet-50 from the input image is shown in the lower part of the scheme.
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 3 Experimental Validation
### Experimental Results and Analysis  
Figure 3 shows the movement trajectory of a shearer in the face's coordinate system. It compares the trajectory of the shearer in the traditional INS/ODO scheme and the positioning method based on non-holonomic constraints which is proposed in this paper, and adds the coordinates of the marker points as a reference. From Figure 3, it can be seen that the traditional INS/ODO scheme has rapid error divergence because only the odometer can restrict the forward speed of the shearer. After adding the non-holonomic constraint, the positioning error does not diverge with time due to the increase in the motion-constraint information in the lateral and vertical direction of the shearer, which has good tracking performance for the actual track.  
The absolute values of the positioning errors in the three directions of the two schemes were counted. The results are shown in Figure 4, and Table 2 records the maximum positioning error in all directions. Through comparison, it can be found that the max errors in all three axes decrease significantly after adding the non-holonomic constraint, and the error of the positioning results in the forward direction is reduced by 66%, from 0.88 to 0.30; the lateral direction is reduced by 62%, from 0.08 to 0.03; and the vertical direction is reduced by 67%, from 0.12 to 0.04.  
In Table 2, the data show that although the algorithm based on the non-holonomic constraint can greatly improve the accuracy of the shearer positioning in three directions, the deviation of the positioning result on the x-axis is still much greater than that in the other two directions. The reason is that although there is an odometer to take concerted action with the INS to constrain the shearer's forward speed, the odometer itself also has errors, such as the scale factor, which accumulate through the continuous iteration of the  
Figure 3: Diagram of shearer running trajectory: (**a**) Travel paths in the x–y plane; (**b**) travel paths in the x–z plane.  
filter and are then superimposed in the direction of the shearer. The output of the odometer can be corrected by using a more accurate odometer or adding a new sensor, thereby reducing the impact of the accumulated errors of the odometer [27].