esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Injection at the Valve and Reactivity Fuels on the Emission Attributes
#### 3.3.1 \(\mathrm{CO_{2}}\) Emission  
CO\({}_{2}\) remained an important emission component in this study because the simulated biogas used composed of 25% \(\mathrm{CO_{2}}\) by mole. As shown in Figure 15a, the \(\mathrm{CO_{2}}\) emission increased as the engine load increased during combustion of B5 and biogas injected at the valve, because of the rise in the amount of the non-combustible \(\mathrm{CO_{2}}\) concentration in the fuel burned at the high IMEP. When the biogas was premixed, the \(\mathrm{CO_{2}}\) emission was insignificantly higher from 4.5 to 6 bar IMEP and showed otherwise at 6.5 bar IMEP. Although, the \(\mathrm{CO_{2}}\) discharge increased with the increase in the engine load but found less at every load level compared to the outcome of the conventional diesel in reactivity with the premixed biogas. The burning of diesel and premixed biogas resulted in low in-cylinder temperature, which influenced a slow conversion process, hence high \(\mathrm{CO_{2}}\) production in a thermochemical process involving gaseous fuel [45]. However, the \(\mathrm{CO_{2}}\) emission for the combustion of diesel and biogas injected at the valve remained higher, at all the load conditions, compared to the usual premixed approach as depicted in Figure 15b. This trend signifies that the \(\mathrm{CO_{2}}\) emission could be independent of the injection method, somewhat might be influenced by other factors such as high CO oxidation, high biogas quantity injected, and mixing rate, among others.  
The effect of the engine speed was examined, for B5 and premixed biogas, to have a better understanding of the causes of some patterns of the \(\mathrm{CO_{2}}\) emission. Figure 15c shows the variation of the \(\mathrm{CO_{2}}\) radiation in response to the change in the engine speed. The \(\mathrm{CO_{2}}\) emission was found less at a rate of 1600 rpm compared to a speed of 1800 rpm for all the load capacities. The difference in the \(\mathrm{CO_{2}}\) radiation was more significant at a load of 5.5-6.5 bar IMEP. The difference could be an influence of the noncombustible \(\mathrm{CO_{2}}\) concentration, which increased with the increase in the engine load or less in-cylinder temperature at 1600 rpm resulting in a reduced rate of CO oxidation to form more of the \(\mathrm{CO_{2}}\) compound. However, the emission of the \(\mathrm{CO_{2}}\) at a speed of 1800 rpm was relatively at par with the use of 2000 rpm, though, the emission at 1800 rpm was found to be insignificantly higher for light load condition. The trends signify that the in-cylinder temperature development at light load was strictly similar; thus, generating relatively similar \(\mathrm{CO_{2}}\) emission.  
Figure 15: Engine-out \(\mathrm{CO_{2}}\) emission for (**a**) B5/biogas RCCI for premixed and port injection at the valve, (**b**) diesel/biogas in a premixed and port injection at the valve, and (**c**) B5 and premixed biogas RCCI combustion at various engine speeds.
## 2 Materials and Methods
### Model Evaluation and Statistical Analysis  
An objective metric that measures how closely the model's bounding box is to the ground truth bounding box is required in order to evaluate the model's performance in detecting teeth. The Jaccard index, also known as the intersection over union (IoU), was used in order to accomplish this. The IoU was calculated by dividing the overlap between the ground truth box (A) and the model-predicted box (B) by the total area of the two boxes' coverage.  
\[\text{IoU}=\frac{\text{A}\ \cap\ \text{B}}{\text{A}\ \cup\ \text{B}}\]  
Model precision (AP) and recall (AR) were determined at different values of the IoU threshold.  
Figure 2: Illustration of molar (boxes) and keypoint (mesial, distal, and apical) settings. Boundary boxes were set according to the polygons drawn by two dentists with more than three years of experience in dental radiograph examinations. The whole panoramic radiographic image was used as the image input. The shown figure was limited to the region of interest for better visualization.  
An object keypoint similarity (OKS) measure was used to assess the model's performance in detecting keypoints [27]. A similar concept to IoU was used here. For each object, an OKS value was calculated. The OKS value ranged between 0 and 1. In general, OKS values tend to be closer to 1 as the model's prediction is closer to the ground truth. As with IoU, this metric was used for assessing tasks related to object detection. In keypoint  
Figure 3: The architecture of keypoint R-CNN. The architecture of keypoint RCNN resembles the Mask-RCNN and only differ in the output size and the way the keypoints are encoded in the keypoint mask.  
detection, OKS serves as a threshold to determine precision and recall. In the case of keypoint detection, only those values whose OKS value were higher than the OKS threshold were considered as true positives. It was possible to obtain AP and AR curves as well as precision-recall curves by adjusting the OKS thresholds. Additionally, we integrated a code snippet to indicate a possible automatic measurement (e.g., in mm for documentation purposes) if a reference line was available on the radiograph image by visualizing the connection line between the mesial keypoint and the apical keypoint.  
AP and AR on the detection of molars were obtained by varying the threshold for the model's confidence scores on bounding box regression using different IoU thresholds (IoU threshold 0.50-0.95, increased by 0.05). AP and AR were also calculated using different thresholds for the model's confidence scores on keypoint detection (OKS threshold of 0.50-0.95, increased by 0.05). In this study, algorithms were constructed and evaluated using the Python (Python version: 3.10.4 (64-bit)) libraries OpenCV, NumPy, Pillow, Seaborn, Matplotlib, TensorFlow, Keras, and scikit-learn. Experiments were performed on a computer (Windows 10 OS) with an AMD Ryzen 9 5950X 16-Core Processor CPU, 64 GB RAM, and an NVIDIA Geforce RTX 3090 GPU.
## 3 Underwater Wireless Optical Communication System
### Hardware Design of Optical Communications System  
In general, LEDs have shorter communication distances than laser diodes, but they consume less power and are more reliable [24], making them ideal for real-time short-range communication in UUVs. In this study, LED was used as an optical communication transmission light source and installed in a planar arrangement based on the horizontal operation of the AUV. Additionally, a green LED chip with a high transmittance in the 520 nm wavelength band was used to allow visible light to reach deep water [25]. Table 3 shows the specifications of LEDs used in the AUV.  
Figure 7 shows the developed PCB with LEDs mounted on the AUV's UWOC system. The UWOC system functions to transmit the AUV's real-time camera and attitude data and to receive commands from the operator. The LED chip for projection was mounted to reduce the size, and eight LEDs were arranged in a circular form to increase the output power. In the UWOC system, a light-receiving sensor was placed in the board center to enable two-way communication, and a driver and MCU for high-power on/off keying (OOK) and size reduction were mounted on the back of the transmission board to control the LEDs. The LED driver was designed to provide a stable current supply using a chip with a continuous 2A, pulsed 30A push-pull drive structure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Color & Green \\ Luminous Flux & 500 Lm \\ Current & 1.4 A \\ Voltage & 3.05 V \\ Dimension & 3.09 \(\times\) 3.65 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of LEDs.  
Figure 8 shows the UWOC system developed for above-surface deployment. This system is integrated into the ground control system (GCS) to relay the operator's commands to the AUV and to receive and transmit signals from the AUV back to the operator. To extend the AUV's operational range, the optical transceiver is coupled with a widely distributed array of high-power, wide-angle LEDs. This configuration enables reliable two-way optical communication across a large area.
## 2 FRCCs-Based Electrical Impedance Acquisition System and Crack Localization Algorithm
### Crack Localization Algorithm  
Figure 2 describes the overall procedure of the crack localization algorithm. The details of each step are as follows. First, baseline datasets including resistance (\(R\)) and reactance (\(X\)) are collected at the multiple sensing regions using the developed instantaneous multi-channel electrical impedance acquisition system. It is assumed that the baseline data set is collected from the pristine condition of FRCCs. Second, the test data set is measured from the current state of FRCCs at the same sensing regions. Then, the corresponding Nyquist plots are made using both \(R\) and \(X\). Next, the Euclidean distances of the Nyquist plots between baseline and test data sets are computed, and damage index (DI) is then calculated as the sum of the Euclidean distances from starting to ending frequencies using Equation (2).  
\[\text{DI }=\sum_{f_{s}}^{f_{e}}\sqrt{\left(x_{n}^{i}-x_{0}^{i}\right)^{2}+ \left(y_{n}^{i}-y_{0}^{i}\right)^{2}}, \tag{2}\]  
where \(x_{n}^{i}\) and \(y_{n}^{i}\) means the \(R\) and \(X\) in the \(n\)th step of \(i\) Hz. The subscript 0 denotes the pristine condition. The \(f_{s}\) and \(f_{e}\) are the starting and ending frequencies, respectively. In case of environmental conditions change in FRCCs, DIs' in the multiple sensing regions will be simultaneously increased. On the other hand, if cracks are initiated at a certain sensing region, only DI at the corresponding sensing region is straightforwardly increased.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water (kg/m\({}^{3}\))** & **Cement (kg/m\({}^{3}\))** & **Sand (kg/m\({}^{3}\))** & **Superplasticizer (kg/m\({}^{3}\))** & **Steel Fiber** \\ \hline
390 & 867.5 & 694 & 1.74 & 108.44 & 108.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix proportions of FRCCs.
## 7 Security  
Given the increasing prevalence of vehicles, it is crucial to understand the risks involved. Connected vehicles are vulnerable to attacks, which can be exploited by individuals to gain control. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles pose a concern, as they have the potential to transport cargo and put human lives and property at risk [127]. In order for someone to use a UAV or unmanned ground vehicle for their purposes, they would first need to gain control over it.  
To prevent attacks, it is important to ensure that both UAVs and unmanned ground vehicles are properly secured. This involves implementing security measures such as encryption, regular password updates, installing software updates, and securing the network [128]. Apart from the risk of misuse, operating both UAVs and unmanned ground vehicles raises safety concerns for people and property as considerations of privacy.  
To tackle these concerns, regulatory authorities worldwide are establishing standards and regulations governing the operation of UAVs and unmanned ground vehicles. These guidelines differ from country to country [129]. They aim to define uses while minimizing the risks of injury or property damage associated with these vehicles. Complying with these regulations is essential.  
Securing communication channels for both UAVs and unmanned ground vehicles requires implementing an AAA process. This approach guarantees the security, authentication, and privacy of communication channels, thereby promoting a more accountable utilization of these vehicles [130]. Security involves the following protocols:  
Figure 7: An example of computation in vehicular networks [126].  
* Authentication is the verification of a user's identity so that the person or system trying to access resources is actually who they say they are.
* Authorization is the granting of permission to access resources. If a person is authenticated, the authorization process determines which parts of the system and resources are accessible to that person and what actions he or she can perform.
* Auditing is the process of recording information about who logged into the system and when, which resources were used, and what actions were performed. It is therefore about monitoring user activity and preventing illegal activities.
## 5 Conclusions and Future Directions  
In this study, a compact wearable flexible dual-band planar meander line monopole was designed for a wide bandwidth (2.2-3.0 GHz and 5.6-6.0 GHz). The proposed antenna was evaluated to validate its performance in various scenarios such as in free-space and on the body with different sizes and under different bending radii and wet conditions. The proposed design was fabricated, and the measurement results agreed well with the simulation results. The proposed antenna provided wide bandwidth and omnidirectional radiation patterns with an acceptable gain values in both free-space and on-body cases. The variation in the impedance matching and operating frequency bands were bounded by 0.5 and 100 MHz, respectively, in both bands, while placed in the vicinity of the human body at an optimum distance of 10 mm from the skin layer. Moreover, the antenna was examined, so that the maximum current of the monopole strip was concentrated on the meander line. In the future, the resilience of the proposed design will be further studied for longer distances in the lossy environment using highly flexible, stretchable 2D high-conductive transition materials such as MXene (\(Ti_{3}C_{2}T_{X}\)) [38], and niobium diselenide (\(NbSe_{2}\)) [39], as well as flexible substrate materials such as embedded Polydimethylsiloxane PDMS [40].  
[PERSON] prepared original research article, and reviewed and edited by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and C.S. The research work was supervised, and the manuscript was finalized by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Universiti Teknologi PETRONAS for supporting this research work. This work is funded by YUTP, through the research grant number 015 LC0-320.  
The authors declare that there is no conflict of interest.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Bayesian and Uncertainty Modeling
#### 6.3.2 Datasets  
The datasets listed in Table 15 reflect the interdisciplinary applicability of Bayesian and uncertainty-aware methods for drift interpretability. Notably, the INSECT dataset emerges as a versatile benchmark, employed across three studies to evaluate drift in dynamic environments, likely due to its temporal granularity and natural concept shifts. Domain-specific datasets like NDBC (marine dissolved oxygen) and City Learn Challenge 2022 (energy management) highlight how Bayesian frameworks excel in uncertainty-rich, real-time domains--typhoon disruptions and grid load variability, respectively-- where posterior distributions naturally encode actionable explanations. However, the predominance of structured time-series data (e.g., Bike Sharing, Keystroke) may underrepresent unstructured data challenges, such as image or text drift, limiting insights into modern multimodal contexts. Additionally, industrial datasets like WWTP (wastewater treatment) and BFIP (biochemical processes) underscore the value of Bayesian methods in safety-critical systems, where uncertainty quantification directly informs operational trust.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Fruit Stem Detection Experiments  
#### 3.2.1 Fruit Stem Detection Model Evaluation Metrics  
This study used common evaluation metrics for detection tasks to assess the detection performance of the fruit stem detection model, including Precision (P), Recall (R), mean Average Precision (mAP), Average Precision (AP), model computational cost (GFLOPs), and FPS (frames per second). The specific calculations are shown in Equations (11)-(14).  
\[P=\frac{TP}{TP+FP} \tag{11}\]  
\[R=\frac{TP}{TP+FN} \tag{12}\]  
\[AP=\int_{0}^{1}P(r)dr \tag{13}\]  
\[mAP=\frac{1}{n}\sum_{i=0}^{n}AP_{i} \tag{14}\]  
where n refers to the number of categories, and in the reported experiment there was only one category: fruit stem. mAP refers to the average precision calculated for the fruit stem category, and [EMAIL_ADDRESS] represents the detection performance of the model when the IoU (Intersection over Union) threshold is set to 0.5. In contrast, [EMAIL_ADDRESS]:0.95 is the average mAP calculated across multiple IoU thresholds ranging from 0.5 to 0.95 with a step size of 0.05, providing a comprehensive evaluation of the model's performance under different IoU thresholds. P indicates the proportion of instances detected as fruit stems that are actually fruit stems, measuring the model's false detection rate. R indicates the proportion of actual fruit stem instances that were correctly detected, measuring the model's misdetection rate. TP represents the number of fruit stems that the model correctly predicted, FP represents the number of background instances the model incorrectly detected as fruit stems, and FN represents the actual number of fruit stems that the model failed to detect.  
Furthermore, FPS and GFLOPs were introduced as metrics for evaluating the inference performance of the models. FPS denotes the number of image frames a model can process per second. In the field of object detection, a model capable of running at frame rates of 30 FPS or above is considered to possess real-time detection capabilities. A higher FPS indicates that the model can be deployed on hardware with lower computational power, thus enhancing its applicability in environments with limited computing resources. GFLOPs, on the other hand, is a metric that measures the computational complexity of an algorithm, typically related to the model's number of parameters and operations.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 4 Experiments
### Baselines  
To validate the effectiveness of StructSim, we selected seven representative approaches as baselines, which included PathCount (the number of path instances) [9], PathSim (a standardized version of PathCount) [9], PCRW (the sum of probability based on random walk of all path instances) [10], StructCount (the number of instances of meta structure) [14], SCSE (the probability of subgraph expansion) [14] and MGP [21] (the shared characteristic meta-graphs).
## 3 Results
### Reference Evaptranspiration (ETo) Estimates  
Estimating ETo is essential for understanding the water requirements of the avocado orchard, particularly for developing irrigation strategies that are responsive to seasonal  
Figure 9: Kc values calculated using the Kc-NDVI relation for the three indices.  
changes in climate. ETo estimates were derived using three meteorological models: the Hargreaves-Samani, Blaney-Criddle, and Priestley-Taylor models. These models provided a range of estimates, each with strengths and limitations, making it crucial to analyze their seasonal trends and the variability in ETo values across the different methods.  
The seasonal trends in ETo calculated using the three different models for the 2021 and 2022 growing seasons are shown in Figure 10. The upper panels show the seasonal variation in ETo estimates derived from the Hargreaves-Samani model, which shows considerable fluctuations across both years. The middle panels display the forecast from the Blaney-Criddle model, which presents a more consistent seasonal trend with lower variability. The lower panels depict the estimates from the Priestley-Taylor model, which, similarly to the Blaney-Criddle model, shows a smooth seasonal curve with minimal fluctuation.  
* Hargreaves-Samani Model: This model, which relies primarily on temperature data, showed higher variability in its ETo estimates than the other models. The fluctuations in the estimates were more pronounced, especially in response to temperature changes. While this model is often used in data-scarce regions, its performance under tropical conditions showed limitations, particularly in capturing the nuances of water demand during the wetter and drier phases of the growing season. Despite this, the Hargreaves-Samani model remains helpful in regions with scarce high-quality wind speed and humidity data.
* Blaney-Criddle Model: The Blaney-Criddle model provided a more consistent estimate of ETo, with lower variability than the Hargreaves-Samani model. This model,  
Figure 10: Seasonal evolution of ETo using three models (2021–2022).  
relying on temperature and daylight data, showed a smooth seasonal curve, offering stability in estimating ETo across both years. However, it may not capture rapid changes in water demand driven by short-term fluctuations in temperature or other environmental factors.
* Priestley-Taylor Model: Like the Blaney-Criddle model, the Priestley-Taylor model demonstrated stable estimates with lesser fluctuation. It is particularly effective in humid regions, where its assumptions about the proportionality between energy availability and evapotranspiration hold more accurately. However, it showed some limitations during the dry periods of the seasons when its estimates did not reflect the sharp fluctuations observed in the field data.
## 2 Proposed Method
### Model Architecture
When the depth of the network model gradually deepens, the model is more sensitive to changes in hyperparameters, and the model becomes more challenging to train. Figure 1: Illustration of our proposed Side-output U-Net architecture. Figure 2: Illustration of a side-output module. However, the BN operation enables the model to be trained with a large learning rate. It reduces the requirements of parameter initialization, decays the oscillation of the loss function, and accelerates the training process. The ReLU function is used as the activation function in each convolution block of the middle architecture. The network was constructed in the order of convolution layer, BN layer, and ReLU layer. By observing the pixel value distribution of the original crack image and the labeled image, it was observed that most cracks were composed of internal and edge areas. The inspectors captured photographs of pavement cracks with a monocular camera, which was mounted at the rear of the detection vehicle and had a fixed shooting distance and angle. Therefore, image quality is easily affected by the pavement environment. Identifying the crack width and length for road sections with limited daylighting conditions and rough surfaces is difficult. The crack gradually transits from the edge area to the internal area of the image. This means that the crack is composed of the inside and the edge. As shown in Figure 3, the cracks affected by environmental factors are divided into two distinct areas. SoUNet can output both segmentation results and edge line results for the input image. The linear fusion of the two results can effectively improve image segmentation accuracy. Output 1 is the crack segmentation image, and Outputs 2-7 are the crack edge line images. The detection accuracy can be improved by linear fusion of Output 1 and Outputs 2-7, respectively. In addition, the refinement method of guided filtering can improve the identification accuracy of the network [41; 43]. Output 1 and Output 3 are the input image and guide image, respectively, and it sets the parameters of the guide filter as the kernel radius \(\mathrm{r}=5\) and the penalty \(\varepsilon=1\times 10^{-6}\). Therefore, the following three methods must be considered. These are (1) adding a BN layer to the network, (2) linear fusion of output results, and (3) processing the output results via guided filtering. We compared the segmentation accuracy of these methods in Section 4.2.