esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships
### Implementation Details  
Intersection over Union (IoU) is a widely used evaluation metric in object detection, which measures the spatial overlap between the predicted bounding box and the groundtruth bounding box of an object. The IoU is computed by dividing the intersection area between the predicted and ground truth bounding boxes by the area of their union. A higher IoU value indicates a better localization accuracy of the predicted bounding box. Typically, a threshold value of 0.5 or 0.7 is used to determine whether the predicted bounding box is a true positive or a false positive. A new loss function called Alpha-IoU is proposed to replace the original Intersection over Union (IoU) loss. The Alpha-IoU loss consists of a Power IoU term and an additional Power regular term, both controlled by a single parameter, \(\alpha\). The experiments conducted in this paper demonstrate that using the Alpha-IoU loss can significantly outperform the current IoU loss.  
The Power IoU term in the Alpha-IoU loss is designed to improve the accuracy of high IoU targets, which are typically objects with tight bounding box annotations. The Power IoU term helps the network to localize objects better with high precision. The Power regular term is an additional regularization term that encourages smooth regression of bounding box coordinates. Together, these two terms in the Alpha-IoU loss help improve the accuracy of gradient adaptive weighted box regression.  
The choice of \(\alpha\), the power parameter in the Alpha-IoU loss, is important. This paper suggests that selecting \(\alpha>1\) can improve the loss of high IoU targets and the robustness of the network against noise, especially in small datasets. The \(\alpha\) parameter can be adjusted to fine-tune the regression accuracy for different bounding box sizes, but it is not highly sensitive to different models and datasets. This paper recommends using \(\alpha=3\) as a parameter, as it performs well in the experiments.  
Overall, the proposed Alpha-IoU loss with a selected \(\alpha\) value of 3 effectively improves object detection accuracy, especially for small datasets and high IoU targets, providing a more robust and accurate loss function for training object detectors. The formula of Alpha-IoU is as follows:  
\[\mathcal{L}_{\alpha-\text{IoU}}=\frac{1-\text{IoU}^{\alpha}}{\alpha},\alpha>0 \tag{1}\]  
According to the experiment and the characteristics of the relative loss weight and relative gradient weight of Alpha-IoU, Alpha-IoU can train a better detector than ordinary IoU. For the specific formula derivation, please refer to ref. [35].  
Due to the insufficient sample size for some categories in our data, we have introduced sample weights to reduce the issue of low detection accuracy for a particular category caused by too few samples.  
In other parts, Mosaic data enhancement, adaptive anchor box calculation, and adaptive image scaling used in YOLOv4 are also used. For other formula improvements and features, please refer to ref. [36].
## 3 Results
### Overview of Evaluation Results  
The results from the objective experiments revealed the limitations of each software tool. The evaluation of software quality according to ISO 25010 uses a five-grade system to rank software tools. Table 6 gives an overview of the software tools that meet most of the ISO requirements. Scores are given in a comparative manner, ranking five software tools from the highest score, 5, to the lowest, 1. The highest score was given to the GreenScenario tool, and the lowest score was given to CitySim. However, there was only a small difference between Ladybug, AST and ENVI-met. Nonetheless, overall scores do not reflect the capabilities and limitations of each software tool. Therefore, Figure 7 presents a comparative analysis of the capabilities and limitations of software tools based on the ISO 25010 framework. The results reveal that functional suitability is the strength of GreenScenario and AST, while ENVI-met is the strongest in information quality. Despite the strength in performance and operability, ENVI-met and AST reveal some limitations regarding incompatibility and reliability. Due to its simplicity, AST can be efficiently used for climate adaptation scenarios; however, the results on information are rather limited. To conclude, these results show that the time invested to produce simulations ensures better information quality.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme  
In the section, the proposed TSR-DARDA scheme is described. As an illustration of the methods, the idea and implementation of TSR-DARDA scheme is presented in detail. Then, how to select parameters to satisfy the optimizing requirements is discussed. At the end of the section, we conduct a theoretical analysis on the delay performance.
## 3 Results
In 2014, [PERSON] and [PERSON] reported their car-mounted measurements in Sweden at a frequency range of 30 MHz to 3 GHz using a three-axis antenna over a driving distance of 115 km [28]. Their results showed that the median E-field strengths in rural and urban areas and in Stockholm were 97.8, 110.1, and 119.9 dB\(\mu\)V/m, respectively. In addition, the E-field strengths from mobile phone base stations were dominant. For comparison with these results, similar areas were extracted in Tokyo, namely, the center area (23 wards), cities (outside the 23 wards except for rural areas), and rural areas (mountains) in Tokyo. The results showed that the median E-field strengths in rural areas, cities, and the center area are 104.0, 113.1, and 119.2 dB\(\mu\)V/m, respectively. It was found that these are of the same order as that shown in Ref. [28]. The interesting point is that in rural areas, our result is higher than that in Ref. [28], whereas the others are almost at the same level. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preference** & \begin{tabular}{c} **Driving Distance** \\ **for Measurement** \\ **[km]** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Driving Speed** \\ **[km/h]** \\ \end{tabular} & \
\begin{tabular}{c} **Mesh Area** \\ **Coverage [\%]** \\ \end{tabular} &
\begin{tabular}{c} **Population** \\ **Coverage [\%]** \\ \end{tabular} \\ \hline Tokyo & 1580 & 19.3 & 31.3 & 84.1 \\ Kanagawa & 1689 & 22.5 & 40.1 & 85.4 \\ Saitama & 2624 & 25.3 & 33.5 & 77.5 \\ Chiba & 1801 & 30.2 & 33.4 & 62.2 \\ Ibaraki & 2315 & 33.3 & 28.8 & 60.6 \\ Tochigi & 1040 & 31.8 & 14.2 & 53.9 \\ Gunma & 1048 & 27.6 & 11.1 & 63.0 \\ Shizuoka & 902 & 30.0 & 5.8 & 17.4 \\ Yamanashi & 813 & 33.7 & 11.0 & 45.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Driving distance and coverage ratios in each prefecture in the measurement. Figure 4: Examples of measurement results in Tokyo. (**a**) Whole area of Tokyo; (**b**) around Nihonbashi; (**c**) Okutama area. Figure 5: Box plot of E-field strength for each prefecture. (**a**) Mobile systems (including 5G); (**b**) 5G; (**c**) FM; (**d**) TV.
## 5 Experimental Evaluation
### Results and Discussion
#### 5.4.5 Performance Evaluation under the Number of Files  
Figure 7 illustrates cache hit rate, latency, and backhaul traffic performances when the number of cacheable contents is varied.  
Figure 6: Performance of Zipf distribution on hit rate, latency, and backhaul traffic.  
Similar to all hit rate comparisons, DPoM shows significant performance gains over the rest of the caching schemes. It utilizes the file splitting technique to store as many files as possible. An increase in the number of files does not cause any damaging impact on the proposed scheme.  
The proposed scheme outperforms the baseline schemes in the latency comparison shown in the second sub-figure of Figure 7. The increase in the number of files had little effect on the caching schemes. It is observed that the increase in the number of files slowly  
Figure 7: Performance of varying number of files on hit rate, latency, and backhaul traffic.  
decreased the latency, which might not be thoroughly experienced during streaming since the change is not drastic.  
The backhaul traffic comparison depicts the same observations as seen in previous comparisons. The proposed scheme is an average-performing scheme when backhaul traffic is considered. The amount of data transferred by DPoM is massive. This led to the recording of high backhaul traffic in the analyses.
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 4 Simulation Error Calibration Experiment
### Results Analysis  
To validate the effectiveness of the simulation results, the compensated errors were applied to the robot, and the position of the end-effector was compared to its position without compensation, as shown in Figure 7. In the figure, the sphere represents the actual surface of the calibration sphere. The blue points indicate the position of the robot's end-effector without error compensation, showing a significant deviation from the surface of the calibration sphere. In contrast, the red points represent the position of the end-effector after error compensation, closely aligning with the surface of the calibration sphere, effectively restoring the original contact positions.  
Figure 6: Robot end-effector positioning error.  
Figure 7: Comparison of the robot end-effector position before and after error compensation.  
As shown in Equation (39), the positioning accuracy of the robot was evaluated by calculating the absolute difference between the distance from the robot's end-effector to the center of the calibration sphere and the sphere's radius.  
\[\varepsilon=\left|\sqrt{(x-x_{0})^{2}+(y-y_{0})^{2}+(z-z_{0})^{2}}-R\right| \tag{39}\]  
Figure 8 presents a comparison of the robot's positioning accuracy before and after error compensation. Following calibration and error compensation, the maximum positional error of the robot's end-effector was reduced from 5.559 mm to 1.104 mm, resulting in an 80.1% improvement in accuracy. Similarly, the average error decreased from 3.657 mm to 0.665 mm, reflecting an 81.8% improvement in accuracy.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
###### Abstract  
With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of [PERSON]'s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.  
search and rescue; SAR; ant algorithms; ant colony optimization; ACO; maze exploration; UAV; UAS; drones; civil security; public safety; smart city +
Footnote †: journal: Physics Letters A
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Earthwork Virtual Model Construction
#### 3.3.3. Voxel-Based As-Planned/As-Built Model Development
For the earthwork scenario, an error of 2-3% is allowable [25]. For earthwork designvolume, \(V_{d}\), and the allowable error range, \(E_{allow}\), the voxel size for volume accuracy can be determined as follows:  
\[\textit{Voxel size for accuracy }=\Delta_{accuracy}=\left(V_{d}\times E_{allow}\right)^{ \frac{1}{3}} \tag{7}\]  
Within the allowable error range, the voxel size is directly correlated with computational efficiency. A voxel size that balances the accuracy and computational burden should be selected. The bucket size also influences voxel resolution. A suitable voxel size is calculated by dividing the bucket dimension by the resolution factor (n). For bucket width B\({}_{w}\) and resolution factor n, the voxel size can be determined as follows:  
\[\textit{Voxel size for bucket width}=\Delta_{bucket size}=\frac{Bucket width \left(Bw\right)}{resolution\ factor\ \left(n\right)} \tag{8}\]  
For larger bucket sizes, coarser voxels provide the required resolution for model-equipment manipulation. In addition, the contact area between the bucket and voxel affects the voxel size because it can affect the penetration depth. Larger buckets have greater widths and penetration depths, and require coarser voxels. The voxel size for penetration depth d\({}_{p}\) is calculated as follows:  
\[\textit{Voxel size for interaction}=\Delta_{interaction}=\frac{Bucket width \left(Bw\right)\times Depth of\ bucket\ teet}{Contact\ resolution\ factor\ \left(n\right)} \tag{9}\]  
The optimal voxel size is selected based on min (\(\Delta\)material, \(\Delta\)accuracy, \(\Delta\)bucketsize, \(\Delta\)interaction). After the voxel size is selected, voxel grids are generated. For voxel grids, a diagonal vector is generated in the bounding box. As the centers of the bounding box and mesh are aligned in the AABB, the diagonal vector passes through the mesh geometry. The diagonal vector is deconstructed with a predefined interval that creates grids in the local x-direction. Voxels are generated over the grids in the x-direction; see Figure 5c,d,f. Each voxel in the bounding box is indexed, containing the center position. Initiating voxelization from the center position enables uniform distribution over the bounding box. After voxelization of the bounding box, a ray-casting algorithm is applied to determine whether the voxel is inside the design mesh, intersects it, or is outside. The outside voxels are detected and discarded since voxels belonging to the design or as-built models are used. Additionally, each voxel is embedded with semantic information, as presented in Figure 5g.
# Insider Threat Detection Using Machine Learning Approach
## 3 Materials and Methods
### Evaluation  
Following the prediction phase, we must use various evaluation metrics to assess the correctness of the model results, such as accuracy, precision, recall, and F1 \(-\) Score. The accuracy metric is a type of evaluation statistic that evaluates how accurate a classifier is. We simply add up the samples that were correctly predicted (true positive and true negative) and divide that amount by the number of samples to determine the accuracy using the confusion matrix; see Equation (1). In our case, we will classify the data into two categories: normal and abnormal, and the accuracy metric will give the percentage of the user's activities that are classified correctly.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
Figure 6: The proposed NN architecture.  
where:  
* TP: predicted abnormal activity is an abnormal activity;
* FP: predicted normal activity is an abnormal activity;
* FN: predicted abnormal activity is a normal activity;
* TN: predicted normal activity is a normal activity.  
We can use these elements as input to calculate additional evaluation metrics, as demonstrated in Equation (2). Precision is a measurement of precision that provides us with a measure of exactness, which determines the number of all true predictions of anomaly (or abnormal activity) on all predictions. We can obtain 100% accurate predictions if the precision value is close to 1, which indicates that FP \(\approx\) 0.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
The recall metric presented in Equation (3) provides an indicator to measure completeness. Thus, we will calculate the fraction of true predictions of abnormal activities out of the actual abnormal activities. If recall equals 1, it means we achieve high accuracy and FN = 0. Precision and recall both provide a metric for determining the efficacy of the proposed models in anomaly detection.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
The F1 \(-\) Score, which is shown in Equation (4) and reflects the accuracy of the models' test set, will be used to determine the harmony between precision and recall measurements. We will reach the best value if the F1 \(-\) Score = 1 and the worst value if the F1 \(-\) Score = 0.  
\[\text{F1}-\text{Score}=\frac{2\times\text{precision}\times\text{recall}}{ \text{precision}+\text{recall}} \tag{4}\]
## 2 **Introduction**
The analysis was conducted for the two emission bands with peaks at 1460 nm (\({}^{3}\)F\({}_{4}\) level) and 1860 nm (\({}^{3}\)H\({}_{4}\) level), and Judd-Ofelt theory was used in the analysis of the experimental data.
## 2 Background and International Context
### International Context
#### 2.2.1 Inter-Agencies Working Groups
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mission** & **Sensor** & **Level** & **Description** & **Reference** \\ \hline \multirow{8}{*}{Proba-V} & \multirow{4}{*}{OLCI + SLSTR} & & S3 Synergy Surface Directional Reflectances1 product (SY\_2 SYN), & \multirow{4}{*}{[25]} \\  & & & atmospherically corrected surface reflectances over land provided in & \\  & & & SLSTR solar reflective bands (except S4 dedicated to cloud & \\  & & & detection) and OLCI channels (except for oxygen absorption bands & \\  & & & Oa13, Oa14, Oa15 and water vapour bands Oa19 and Oa20) & \\ \hline \multirow{8}{*}{Proba-V} & \multirow{4}{*}{Vegetation} & \multirow{2}{*}{L1-C} & TOA reflectances in instrument grid unprojected, but & \multirow{2}{*}{[26]} \\  & & & radiometrically and geometrically corrected. & \\ \cline{1-1} \cline{4-4}  & & & Top-Of-Canopy (TOC) atmospherically corrected reflectances1 & \multirow{2}{*}{[26]} \\ \cline{1-1}  & & & provided as multi days composite products (S1, S5, S10) at different & \\ \cline{1-1}  & & & spatial resolution (1 km, 333 m and 100 m). & \\ \hline \hline \end{tabular}  
* The definition of “surface reflectances” slightly differs among these ESA operational products. For S2, the L2A [18] is an approximate of the HDRF (Hemispherical Directional Reflectance Factor) [27]. The same applies to Proba-V TOC products (provided as multi-days synthesis), while for S3, the Surface Directional Reflectance (SDR) corresponds to the BKF (Bi-directional Reflectance Factor) [25; 27]. In the remainder of the paper, the term surface reflectance (SR) is used for simplicity to discuss the generic validation needs for present and future ESA optical land imaging sensors.  
\end{table} 
Table 1: \(Cont.\)Climate Variables (ECVs). ESA is actively supporting CEOS-LPV activities in the various focus areas and is promoting the series of Land Products Validation Workshops [3; 33], gathering the CEOS-LPV teams for discussing on common issues and collecting recommendations for improvements in Cal/Val best practices for land.
## 2 Literature Review
### Handling Sensor Noise and Data Gaps  
Sensor noise significantly impacts reconstruction accuracy. Bilateral filtering [12] and robust regression [16] have been employed to preprocess data. More recent studies by [PERSON] et al. [17] and [PERSON] et al. [16] utilized deep learning for denoising, achieving notable improvements. Various approaches, such as filter-based, optimization-based, and deep learning-based algorithms, are used to enhance the quality of depth images and 3D point clouds. Filter-based methods aim to reduce noise using principles of image processing. Optimization-based approaches minimize noise by applying optimization algorithms to find an optimal point cloud that best fits the noisy data while satisfying specific criteria. Deep learning-based techniques train models using noisy point cloud data and their corresponding ground truth to improve accuracy.  
Outlier removal techniques, including RANSAC [18] and statistical outlier filtering [19], have been integrated into reconstruction pipelines. Curve approximation methods, such as splines [20] and B-splines [21], offer smooth representations of scattered data. However, they are most commonly used in computer graphic applications. In this study, the curve approximation is applied for terrain surface modeling. The use of robust statistical methods [22] and Gaussian processes [23] has further enhanced data reliability.
# Natural Language Processing in Knowledge-Based Support for Operator Assistance
## 3 Methodology  
In the assembly domain, NLP can be used to improve various aspects of operations, such as quality control, supply chain management, and customer service. Based on the general requirement for building a benchmark in NLP [15; 76], a benchmark in the assembly domain for manufacturing would require consideration of the following factors:  
* Domain-specific language: The manufacturing industry has its own language and terminology that people outside the industry may not be familiar with. A benchmark for NLP in manufacturing would need to use language that is specific to the domain.
* Task-specific questions: The benchmark should be designed to evaluate the performance of NLP models on tasks that are relevant to the manufacturing industry, such as defect detection, predictive maintenance, and inventory management. The questions should be carefully crafted to test the ability of NLP models to understand and process information related to these tasks.
* Data quality: The quality of the data used to create the benchmark is critical to its success. The data should be representative of the types of language and tasks that are encountered in the manufacturing industry, and it should be of high quality to ensure that the benchmark results are accurate and reliable.
* Evaluation metrics: The benchmark should define clear evaluation metrics that can be used to measure the performance of NLP models on task-specific questions. The metrics should be relevant to the manufacturing industry and should provide a meaningful assessment of the model's ability to perform the task.
* Ethical considerations: The manufacturing industry deals with sensitive information, such as product designs and trade secrets. Therefore, the benchmark should take into account ethical considerations, such as intellectual property and confidentiality, to ensure that the data used in the benchmark are handled appropriately.  
Overall, building a benchmark for NLP in assembly (as shown in Figure 1) would require careful consideration of the unique language, tasks, and data encountered in the industry, as well as the ethical considerations that come with handling sensitive information. The benchmark should be designed to evaluate the performance of NLP models on tasks that are relevant to the assembly domain in the manufacturing industry and provide a meaningful assessment of the model's ability to perform these tasks.  
In this research, we adopt a methodology similar to that described in [67], encompassing the definition of the task, the introduction of the dataset within the assembly domain, and the establishment of evaluation metrics. First, we outline the specific task. Following that, we present the proposed dataset designed for the assembly domain. Finally, we introduce the evaluation metrics utilized to assess task performance. In essence, the benchmark's purpose lies in the assessment of task performance by contextualizing it within a specific text domain, furnishing it with a tangible dataset, and employing well-defined evaluation metrics.