esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Comparison Between FastSLAM and FastSLAM-PSO
#### 5.2.2 Medium-Scale Scenario  
In the analysis of the medium-scale map experiment, we observed distinct trends among the various SLAM algorithms, highlighting the strengths of multi-objective optimization approaches, particularly FastSLAM-MO-PSO. The results are shown in Figure 5.  
Considering the \(x\)-axis, all algorithms exhibited noticeable deviations during the initial 0-150 time steps, indicating the common challenges faced in accurately navigating the environment's initial layout. Between 250 and 400 time steps, a trend of increasing deviation was observed across all algorithms except for the two multi-objective algorithms, FastSLAM-MO-DE and FastSLAM-MO-PSO. This suggests that non-multi-objective algorithms struggled to maintain accuracy over time, likely due to the accumulation of estimation errors and the complexity of dynamically updating the map and trajectory consistent with actual movements. This trend underscores the advantage of multi-objective algorithms in sustaining performance. Overall, FastSLAM-MO-PSO demonstrated consistent performance in map building, without significant fluctuations, while other algorithms showed considerable variation. This highlights the stability and robustness of the FastSLAM-MO-PSO algorithm. Concurrently, the medium-scale map experiment also revealed the superiority of PSO-based algorithms, with FastSLAM-MO-PSO leading in performance among all contenders.  
On the \(y\)-axis, the performance trends were similar across all algorithms, indicating that they encountered comparable difficulties in the medium-scale map environment. FastSLAM-MO-PSO maintained a stable performance, whereas others exhibited significant instability with large fluctuations. This consistency in the latter part of the experiment can be attributed to the algorithm's ability to leverage accumulated information effectively. However, the performance of FastSLAM-MO-DE deteriorated significantly in the later stages, deviating substantially from the endpoint. This substantial error is likely due to the inherent limitations of the DE algorithm in fine-tuning the trajectory accurately, especially in complex environments. In summary, the medium-scale map experiment reaffirms the resilience and reliability of the FastSLAM-MO-PSO algorithm. Its stable performance and ability to adapt to the evolving map with minimal deviation highlight the effectiveness of integrating multi-objective optimization with PSO. While all algorithms faced similar challenges, the multi-objective algorithms, especially FastSLAM-MO-PSO, show-cased their superiority by maintaining a higher level of accuracy and stability throughout the experiment.
## 2 State of the Art
### Data Collection
#### 2.1.2 Data Retrieval  
During the last two decades, numerous digital image archives containing vast numbers of photographs have been set up [7; 73]. These comprise collections of user-generated contemporary photographs, historic photo collections and image databases with geographic coverage, e.g., Street View. For the 3D world, recently, large-scale datasets, such as Objav- verse, including 10.2 million 3D models [74], and ShapeNet, including 50 k 3D models [75], and repositories such as Sketchfab, hosting several hundreds of thousands of heritage items [76], have been compiled. As an overlapping area, there are several automated 3D model creation processes that utilize extant imagery [77; 78; 79; 80]. A major task is the provision of sufficient metadata to spatialize and temporalize this material [81].
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Analyzing Experimental Outcomes
#### 3.3.3 Comparison of the Proposed Algorithm with Three Existing Algorithms
The _FoM_ is calculated as follows:  
\[FoM=\left|\frac{BD-BR}{\Delta T_{avg}}\right|\times 100 \tag{12}\]  
the _FoM_ can be used to compare the strengths and weaknesses of different algorithms and to find the optimal balance between low RD performance loss and high complexity reduction (i.e., the better the performance of the method, the smaller the _FoM_ value, and vice versa). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Algorithm [11]**} & \multicolumn{3}{c}{**Algorithm [21]**} & \multicolumn{3}{c}{**Algorithm [28]**} & \multicolumn{3}{c}{**Proposed Algorithm**} \\ \cline{2-13}
**Sequence** & **BD-BR** & **BD-PSNR** & \(\Delta\bm  
Table 6 presents the FoMs of the different approaches including the proposed one and the existing methods in [11; 21; 28]. A lower value for FoM is desirable as it indicates a better compromise between RD efficiency and reduced computational complexity. Table 6 shows that the proposed method's FoM was 3.0312, which was significantly lower than the other methods in [11; 21; 28]. The result demonstrates that the proposed CNN-CBAM attained a favorable balance between the low encoding complexity and high RD performance.
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 1 Introduction
At any rate, in all the cited articles, the computational effort has not been properly discussed. Here, we consider the most recent fuzzy-rule-based variants of GAs, i.e., EFGA, along with GFGA, with the latter conceived in a very different way with respect to the other FGAs. We compare their performance on a set of benchmarks, noticing that there is no comparative analysis of the two in the literature. In fact, these kinds of FGAs were originally introduced as part of a learning algorithm of some type of NN. Here, the aim is not to replicate such applications but to explore the use and possible enhancement of the EFGA and GFGA for more general optimization problems. We discuss a possible strategy to speed up the fuzzy inference in these FGA variants. Such a strategy is inspired by nearest-neighbor caching (NNC), which is well known in the context of web applications [17] and wireless networks [18], but, to the best of our knowledge, was never applied to any specific case. Some preliminary experiments on some well-known benchmarks were performed. The outcome of such experiments is that the results obtained by using the NNC are sufficiently accurate without any losses in performance and give a speed-up of over 90%, thus making it a viable alternative. For a fair comparison against former variants of FGAs, some case studies were retrieved from the literature, which include Dejong's functions [19] and the traveling salesman problem (TSP) [11], thus confirming the good performance of the approach. Additionally, a real-world application problem related to rice production was investigated following the model and the GA results discussed in [20]. The contribution of the paper can be summarized as follows:  
* An NNC strategy is discussed as a way to significantly speed up the fuzzy inference in two promising FGAs, from the explainability perspective, i.e., GFGA and EFGA, without any performance losses;
* Variants of the original GFGA and EFGA, in terms of fuzzy logic settings, are explored;
* A comparison against traditional and state-of-the-art techniques, which are both fuzzy and non-fuzzy, is presented over benchmark cases and a real-world application problem. The paper is structured as follows. The next section is devoted to recalling some notions on fuzzy sets and the two considered FGA variants, i.e., EFGA and GFGA. In Section 3, the NNC strategy is described. Section 4 deals with the numerical experiments and discussion. Finally, some conclusions close the paper.
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 4 Discussion  
### 3-D Measurement Method and a Comparison of Image Processing Methods  
In this experiment, the \"high accuracy, high efficiency, and large scale\" measurement of rapeseed pods was realized using the image processing method. The key link was to use the 3-D measurement method to measure the \"actual surface area\" of a small number of rapeseed pods and use this value as the basis for correcting the measured value of the image processing method. A key question is: why not directly use the 3-D measurement method to measure the surface area of rapeseed pods and develop this image processing method? This is primarily because of the limitations and shortcomings of the 3-D measurement method itself. First, although the 3-D measurement method is highly accurate, many preparations are necessary before the values can be measured, and the operation is more complicated. It requires special measurement equipment and software, which has high requirements for the surveyor and is difficult for general staff to master. Secondly, the 3-D measurement is relatively inefficient. According to the 3-D measurement reported in this experiment as an example, it is necessary to pack the pods, scan with the 3-D scanning camera, and perform calculations with platform-specific software. Among them, only the scanning link takes a long time. For example, this experiment takes 7 or 8 min to scan 12 pods at a time, so it is difficult to scan thousands of pods in a short time. In addition, freshly harvested pods generally stay fresh for approximately 3 days, which does not allow adequate time for their measurement. Third, the 3-D measurement equipment and platform are expensive, and the construction needed to use them is very expensive [44]. The ultra-precision and high-speed 3-D laser scanning system LDI-SurveyorZS (LDI-SLP250) used in this experiment is worth more than one million RMB. The image processing method is typically more convenient to operate. The only equipment needed is a digital camera, tripod, and other conventional equipment. It is also highly efficient at measuring. Because the correction link for the measurement result is designed, the measurement accuracy can also meet the needs of crop science research. Therefore, combining the image processing method and the 3-D measurement method for the \"actual area\" measurement of a few rape pods establishes a correction model of the image processing method, which is easier to operate, more efficient, and economical for measuring the surface area of several rapeseed pods.
## 3 Results and Discussions
### Oil Spill Transport Behavior under Various Influencing Factors
#### 3.2.1 River Flow  
To study the effects of flow rate on oil spill behavior, the model only changes the flow rate data, and the wind and leakage parameters are kept fixed at 0 m/s and fracture leakage-2600 t (0.5 h), respectively. Figure 7 shows the variation of **v**, **a**, and **I** with river flow, with all three increasing with flow. As a carrier of oil spill migration, the higher the flow rate of the river, the faster the water velocity, the stronger the turbulence of the water flow, the faster the migration of the oil spill, and the more obvious the behavior of the oil spill in terms of diffusion and longitudinal extension. The increase of **a** is accelerated because the water level of the river will rise with the increase of flow; the increase of the basin area makes the oil spill diffusion space bigger, so the oil spill pollution area increases faster; **I** is slowed down, the increase of the flow, the water will overcome more resistance of the river bank to the migration of the oil spill, so the increase of the length of the oil spill pollution zone will be slowed down.  
To take emergency response measures as soon as possible after the occurrence of a sudden oil spill accident, the pipeline's subsidiary units have set up three oil spill interception and recovery points along the river and stored the required emergency materials, the specific location of which is shown in Figure 2. After the accident, it would take three hours for emergency teams to reach the various interception points and commence interception operations. For sudden oil spill accidents under the condition of static wind, take the flow rate of 1000 m\({}^{3}\)/s and 5000 m\({}^{3}\)/s as an example; when the flow rate Q = 1000 m\({}^{3}\)/s, the time of oil spill migrating to the three oil spill interception points is 3.5 h, 6.5 h, and 15 h respectively; and when the Q = 5000 m\({}^{3}\)/s, it is 1.2 h, 2.2 h, and 3.6 h. Under the two flow rates, the rescue team should be rushed to the 1# and 3# interception points to carry out interception and recovery operations, respectively. In addition, the higher the river flow, the shorter it takes for the oil spill pollution zone to pass through the interception point. When Q = 1000 m\({}^{3}\)/s, the time for the oil spill pollution zone to pass through the 1# interception point is 1.3 h; when Q = 5000 m\({}^{3}\)/s, the time for the oil spill pollution zone to pass through the 3# interception point is 0.8 h. When the flow of the river is higher, the impact of the water on the boom is greater, and the interception efficiency of the boom will  
Figure 7: Variation of **v**, **a**, and **I** with river flow.  
be reduced, requiring more human and material resources to be invested in emergency operations. Therefore, the size of river flow directly affects the difficulty of emergency response to oil spill accidents. Managers should select oil spill interception and recovery points according to the river flow and reasonably equip personnel and emergency supplies.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Fractional Vegetation Cover  
Utilizing Landsat 8 data (refer to Table 2), the fractional vegetation cover (FVC) was calculated using the normalized difference vegetation index (NDVI) in conjunction with the image dichotomy method [26]. The calculations involved the following Equations (3) and (4):  
\[NDVI=(NIR-R)/(NIR+R) \tag{3}\]  
\[FVC=(NDVI-NDVI_{\text{soil}})/\left(NDVI_{\text{seg}}-NDVI_{\text{soil}}\right) \tag{4}\]  
where NDVI is the normalized vegetation index of a mixed image element; NIR is the near-infrared band reflectance; R is the red band reflectance. NDVI\({}_{\text{soil}}\) and NDVI\({}_{\text{seg}}\) represent a pure soil and pure vegetation image element value, respectively [26; 27]. NDVI\({}_{\text{soil}}\) and NDVI\({}_{\text{seg}}\) vary in time and space due to different environmental backgrounds, photographic conditions, and image quality. In the actual calculation process, 5% and 95% confidence levels were used to select the maximum and minimum thresholds for NDVI data for the corresponding periods [27; 28; 29].
## 4 Survey Results and Discussion  
We conducted a survey to evaluate exam accessibility for blind and BVI students, focusing on their experiences with current systems, impressions of a proposed system, and expectations for greater autonomy. Administered through the Gali Center at Umm Al Qura University and a WhatsApp group for special needs students across the kingdom, the survey offered valuable insights into existing challenges and potential improvements.  
Participants highlighted several issues with current exam methods, including heavy reliance on external assistance, difficulty accessing materials independently, and disruptions caused by audio in computer-based exams, which affected question flow and time management. They also stressed the need for a quiet testing environment and prompt technical support.  
Respondents emphasized the importance of features that allow independent access to exam content to enhance autonomy. Recommendations included integrating existing assistive technologies, streamlining setup processes, improving system responsiveness, and expanding compatibility with various exam formats. These enhancements are vital for reducing reliance on external aids and fostering inclusivity.  
Feedback on the proposed system was largely positive. Students were optimistic about features like the specialized answer sheet and voice control with comprehensive audio playback, which they believe will significantly improve their ability to navigate exam questions independently. This innovation addresses existing barriers by simplifying access and resolving technical issues. Additionally, using controlled earphones removes the need for a specific testing environment.  
Further suggestions included incorporating a feature to pause audio during questions to improve comprehension and offering training sessions to help students become familiar with the answer sheet. These recommendations aim to address practical challenges and ensure that students are fully prepared.  
Overall, the survey highlighted the necessity for customized accessibility solutions and demonstrated how our innovative technology is poised to significantly improve the exam experience for BVI students. The feedback collected will guide future enhancements to ensure the system effectively addresses its users' diverse needs.
# Neighborhood Deviation Attack Against In-Context Learning
## 3 Preliminaries
### In-Context Learning  
ICL is a learning method that does not require additional training. Introducing a small number of examples into the model's input enables the model to adapt quickly and complete specific tasks. This ability is primarily attributed to the powerful memory and reasoning capabilities of LLMs, which allow them to capture the patterns and regularities of tasks within a limited context [10].  
During the ICL process, the model first analyzes the input context examples and extracts features and rules relevant to the task [42]. These examples often include a detailed description of the task, the structure of inputs and outputs, and several specific instances. With this information, the model can flexibly adjust its reasoning mechanism without modifying its internal parameters, thereby making accurate predictions or creative generations for newly received inputs.  
For example, when an LLM faces the task of determining whether the sentiment of a new text query is positive or negative, the ICL process is shown in Figure 1 and can be described as follows.  
First, a template is defined to format the input data for the model. For sentiment analysis, the template could be \"Review: [text] Sentiment: [label]\", where [text] represents the review text, and [label] is the sentiment label (either \"Positive\" or \"Negative\"). In this example, label 1 means \"Positive\", and label 0 means \"Negative\".  
Next, demonstration examples are provided. These are labeled examples of text and sentiment, filled into the defined template. The following are examples:  
* \"Review: Delicious food! Sentiment: Positive\".
* \"Review: The food is awful. Sentiment: Negative\".  
Then, a new query is presented, which is the target text the model needs to evaluate. In this case, the new query might be \"Review: Good meal!\" with the sentiment label still undetermined.  
Finally, the large language model receives both the new query and the demonstration examples as input. The model's parameters are frozen, meaning no updates are made to the model's internal weights for the current task. The model processes the input, leveraging its pre-trained knowledge and the context provided by the examples to predict the sentiment label for the new query. In this case, the model predicts that the sentiment of \"Good meal!\" is \"Positive\".  
By following these steps, the model can accurately determine the sentiment of new text inputs without needing to retrain or adjust its internal parameters, relying instead on the context and prior knowledge acquired during pre-training.
## Experimental Setup  
### AR Frameworks Used for Comparison  
For performance comparison, we selected three well-known commercial AR frameworks (Vuforia, ARCore, and MAXST) to support the Android platform. Vuforia is the industry's most advanced and widely adopted AR framework. ARCore is the AR framework by Google. MAXST is the AR framework provided by a leading AR software vendor in Korea.
## 3 Methodology
### Dataset Collection and Annotation  
We prepared an extensive dataset of offensive language used in Roman Urdu using comments scraped from a variety of YouTube news channels to make a high-class distinction between offensive content in Roman Urdu. YouTube is a real-world database of varied comments showing the use of natural language in online conversations, containing examples of offensive language patterns.  
The pre-processed dataset consists of 46,025 Roman Urdu comments. Of those, 24,026 are marked \"offensive\" and 21,999 are \"not offensive.\" This leads to an approximate 52.2% offensive and 47.8% non-offensive split. It should be noted that the dataset is not perfectly balanced, but it is barely unbalanced and certainly does not suffer from a severe class imbalance problem. All models were trained and validated using stratified sampling, so this moderate skew does not influence classification ability.  
Roman Urdu does not have an agreed-upon verbal structure, so we performed a manual annotation process for labeling offensive and non-offensive comments. This annotation was performed by native Roman Urdu speakers to ensure the authenticity of the data contextually. To ensure that we label in an ethical and unbiased way, we implemented a structured annotation process.  
* Annotator Agreement: A document was provided to each annotator for clear guidelines regarding the definition of offensive language (e.g., hate speech, derogatory statements, abusive language, and foul language). To ensure the same standard for the dataset, the annotators officially consented to these guidelines.
* Multiple Annotation Rounds: At a minimum, at least three independent annotators reviewed each comment to reduce subjectivity and improve reliability. A consensus-based approach helped to resolve disagreements, leading to high-quality labeled data.
* Data Privacy and Ethical Considerations: Annotators were made aware of data privacy policies and anonymized all of the collected comments to ensure users' identities were protected. The dataset acts as a typical research dataset for text classification, with fully ethical use of data until October 2023.  
Figure 1: Proposed methodology.  
To quantify annotation consistency, we computed Cohen's Kappa on a randomly selected 10% subset of the comments, obtaining an average score of 0.83, indicating strong agreement among the three annotators based on the interpretation scale.  
The final dataset was balanced, having equal representation of offensive and non-offensive comments, which made it suitable for robust training and evaluation of machine learning and deep learning models.