esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Landmark Localization for Medical Images Using Perturbations
## 3 Materials and Methods
### Perturbation Operations
#### 3.3.3 Binarization  
Binarization converts pixels in p into black (0) or white (1). The threshold value is obtained from the Otsu algorithm [37], The threshold value in Otsu algorithm is determined such that the value maximizes inter-class variance by separating pixels into two classes, background and foreground. In our perturbation, a value generated from the normal distribution N(\(\mu\), \(\sigma^{2}\)), where \(\mu\) = 0.2, \(\sigma\) = 0.15 is added to the threshold. The resulting threshold is applied to the patch p to binarize. Figure 2e illustrates an example image perturbed by binarization. As was the case for smoothing, the network should learn the representation using both the reduced local information in patch p, and the global information outside p.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Perturbation Method** & **Term** & **Value** \\ \hline \multirow{2}{*}{Smoothing} & \(\mu\) & 0.2 \\ \cline{2-3}  & \(\sigma\) & 0.15 \\ \hline \multirow{2}{*}{Binarization} & \(\mu\) & 0.2 \\ \cline{2-3}  & \(\sigma\) & 0.15 \\ \hline \multirow{2}{*}{Edge detection} & \(\mu\) & 3.5 \\ \cline{2-3}  & \(\Sigma\) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of parameters used in our experiments.
###### Abstract  
Offshore Wind Turbines (OWT) with increasingly higher energy output are being developed to meet energy demands, posing greater challenges for their foundation design. Several foundation types are used to support these turbines, with monopiles (MPs) accounting for 80% of the installed capacity. In this study, three-dimensional (3D) nonlinear finite element models (FEM) were employed to investigate the behaviour of a monopile foundation supporting a 5 MW wind turbine subjected to lateral loading. The results indicate that the MP behaviour depends on the pile length to diameter (L/D) ratio and the soil shear strength. Inspection of the bending-moment profiles at the lateral ultimate capacity indicated that the monopiles can behave in a flexible manner, even with low L/D ratios. The L/D ratio affected the MP normalized lateral ultimate capacity to varying degrees, and the biggest effect was for soft clays, amounting to an approximately five-fold increase for L/D values of 3.33 to 13.33. Lesser effects were found for stiff clays.  
monopile; offshore wind turbines (OWT); lateral ultimate capacity; green energy +
Footnote †: journal: Applied Sciences  
1  
[PERSON], J.A.  
1 Department Civil Engineering, College of Engineering, Qassim University, King Abdulaziz rd., Qassim 52571, Saudi Arabia 1  
2 Civil and Environmental Engineering Department, Western University, London, ON N6A 3K7, Canada; [EMAIL_ADDRESS] (T.N.); [EMAIL_ADDRESS] (M.H.E.N.) 2  
3 Civil and Environmental Engineering Department, Queen's University at Belfast, Belfast BT7 1 NN, UK; [EMAIL_ADDRESS] 2
## 3 Results and Discussion
### Optimization Parameters for Two HDR Dampers  
To evaluate the damping efficiency of two HDR dampers, the three-dimension relationship of the first modal damping ratio, loss factor, and installation position of dampers are explored in this section. Figure 11 presents the relationship between loss factors of two HDR dampers with the damping ratio. In this analysis, the installation position of two HDR dampers is fixed at 0.03. From the figure, it is evident that loss factors of dampers have a significant influence on the damping ratio of the dampers. As the loss factors of the dampers increases, the damping ratio increases as well. The highest value of damping ratio is obtained for a loss factor value of 12. On the other hand, the relationship between the installation positions of two HDR dampers with the damping ratio is presented in Figure 12. As the magnitude of installation position increases from the cable ends, the higher damping ratio of the cable increases as well. Furthermore, the peak value of the damping ratio is found for an installation position (\(l\)/\(L\)) of 0.1 from the cable ends.  
Figure 11: Loss factors optimization for two HDR dampers.  
Figure 10: Relationship between \(\Delta\) and \(\xi_{1}\)% with different \(l_{1}/L\).
# Multimodal AI and Large Language Models for Orthopantomography Radiology Report Generation and Q&A
## 1 Introduction
### Large Language Models  
LLMs are a subset of deep learning architectures designed to understand, generate, and manipulate natural language by training on vast corpora of text data. Unlike traditional natural language processing approaches that rely on rule-based systems or statistical methods with limited parameters, LLMs utilize transformer-based neural network architectures with billions of parameters to capture complex linguistic patterns and contextual relationships [11; 12]. These models learn language representations through self-supervised learning on diverse textual data, enabling them to perform a wide range of language tasks without task-specific training. LLMs have demonstrated remarkable proficiency in tasks such as text summarization, machine translation, question-answering, and conversational AI, making them highly versatile across various domains, including healthcare [13; 14].  
Recent years have introduced vision-language models (VLMs), which enable AI to understand both images and text, crucial for tasks like image captioning intelligence [15],They comprehend the connections between images and descriptions as they were trained on image-text pairs. VLMs combine computer vision and natural language processing techniques to bridge the gap between visual and textual understanding. Hence, they are able to carry out tasks like image captioning and visual question-answering, improving AI's capacity to perceive multimodal data.  
Pre-training and fine-tuning are the two stages of the training process for these models. Pre-training entails self-supervised learning on large text datasets, where LLMs gain a thorough grasp of linguistic structures by predicting masked words or following tokens, thereby acquiring broad language patterns and world knowledge. Then, using smaller, task-specific datasets along with model fine-tuning techniques such as low-rank adaptation (LoRA) [16] and instruct fine-tuning [17], these previously trained models are adapted to particular tasks. LLM answers can be improved by methods like supervised fine-tuning, parameter-efficient fine-tuning, and reinforcement learning from human feedback (RLHF) [18], bringing them into line with human preferences and enhancing safety.  
These training approaches enable LLMs to acquire a broad understanding of linguistics, facilitating their application in diverse fields, such as research, education, and healthcare. While LLMs have automated numerous linguistic tasks previously requiring human expertise, challenges such as bias, hallucinations, and ethical dilemmas remain significant concerns for LLM users. This work explores the application of LLMs, specifically the VLMs concept in medical applications, investigating their ability to automate the process of radiology report generation and user interaction in the Sinhala Language. By leveraging the power of LLMs and VLMs, we aim to demonstrate their potential to enhance patient care in dental health care, while acknowledging and addressing the inherent limitations outlined in contemporary research [19].
## 1 Introduction
GF5B-AHSI has successfully identified point sources with emissions below 1000 kg/h across multiple locations in Shanxi [22]. In addition, EMIT has advanced the quantification and attribution of fine-scale methane emissions, covering a range from 300 to 73,000 kg/h [23]. The design of satellite instruments involves several parameters that are inherently subject to various constraints. Spatial resolution, a key parameter, determines the level of detail observable in the images. Spectral resolution reflects the sensor's capacity to distinguish subtle spectral features of a target in different wavelength ranges, serving as an informative feature of images [24]. Enhancing spatial resolution frequently necessitates a corresponding reduction in spectral resolution to maintain a signal with adequate strength. Additionally, SNR significantly influences image classification, target recognition, and other processes. Practical applications mandate a comprehensive assessment that balances the advantages and constraints of these three key parameters within defined requirements. Furthermore, major existing point source imagers were not specifically designed for methane plume detection, meaning their key parameters may not be optimally aligned with the requirements for such tasks. Recent studies have investigated the influence of spectral resolution and SNR on the performance parameters of point source imagers. [PERSON] et al. (2019), for instance, conducted a study on the enhancement of point source emissions, affirming that precision increases concomitantly with improvements in spectral resolution and SNR [25]. Likewise, [PERSON] et al. (2019) found that spatial resolution had a greater effect on the results than SNR [26]. [PERSON] et al. (2021) explored the impact of varying spectral resolutions, instrument optical characteristics, and detector exposure times on precision error and bias [27]. Additionally, [PERSON] et al. (2021) investigated the impact of SNR on XCH\({}_{4}\) mapping, emphasizing the importance of SNR in resolving narrow CH\({}_{4}\) absorption features in the SWIR spectrum [28]. However, these studies often focused on individual factors without comprehensively addressing the combined influence of spatial resolution, spectral resolution, and SNR on instrument performance. This paper comprehensively explores the interactions among these parameters in methane plume detection, and investigates their trade-offs for plume quantification. Figure 1 provides a schematic overview of the study. Through simulations of various parameter settings, this study provides crucial insights for the future design and manufacture of satellite instruments. This paper is organized as follows. An overview of the simulation and retrieval of methane plumes is presented in Section 2. The influence of individual parameters and the coupling effects are discussed in Section 3. Finally, discussions and conclusions are given in Sections 4 and 5, respectively.
## 4 Discussion  
The collected EEG data were processed and analyzed. PSD in the alpha band was calculated using the Welch method, allowing for a comparison of alpha band activity with activity in other frequency bands. Additionally, the IAF of each participant was measured using a custom algorithm to detect their individual alpha wave frequency. The IAF values were used to assess changes in alpha wave activity, indicating concentration levels. The proposed IAF and FAA algorithms only process information from the alpha EEG waves. Although alpha waves are associated with certain mental states, such as relaxation or focused attention, the brain is a highly integrated system, and limiting the analysis solely to alpha waves may restrict a comprehensive understanding of cognitive functions and their relationship with other brain areas. For future work, if one desires to explore cognitive processes different from concentration, our proposed algorithms would not be able to provide results due to the lack of information from other EEG waves. Therefore, it is essential to consider incorporating data from other EEG waves or using more comprehensive algorithms to investigate a broader range of cognitive processes effectively.  
Our observations during this research revealed that the Exergames category elicited the most significant level of participant concentration during the gaming session. This information acquisition holds significant value, demonstrating participants' ability to sustain focus and highlighting the simultaneous engagement in physical activity while playing. The convergence of attention and physical exertion within video games presents a substantial advantage, extending its potential benefits even to emotional aspects.  
The experimental results have demonstrated a strong focus on the gameplay of Beat Saber. This finding holds significant implications for future research work. Furthermore, it is worth noting that the video game Beat Saber has earned widespread acclaim as one of the best virtual reality games ever released. It has received prestigious awards, including the Best Virtual Reality Game of 2019 and the Fan Favorite Virtual Reality Game of 2018, among others [34]. These accolades underscore the remarkable reception of Beat Saber among users, which correlates with our results as the Exergames category exhibits high levels of user concentration during gameplay.
## 4 Result and Discussion
### Interpretation of Uncertainty Analysis  
Both the EVI and NDVI showed the least uncertainties compared to the other vegetation parameters in respect to the PDSI for all five years (Figure 4). The RMSE values for both NDVI and EVI were found to range from 2.39% to 3.01% during 2015-2018, which is, overall, the lowest among the others. However, in 2019, the NDVI showed slightly lesser RMSE values than EVI, but in that year, it was fAPAR that actually showed the optimum sensitive condition to the PDSI with a RMSE of 1.89%. Overall, the estimated uncertainties (RMSE) between the PDSI and EVI and and PDSI and NDVI were found to be 1% to 2% lower compared to the others, showing better sensitivity than the other four vegetation parameters in a long-term context.
## 3 Results and Discussion  
### Capillary Water Absorption Test Analysis  
The relationship between the cumulative absorption per unit area and the square root of the capillary absorption time was obtained from the results of the capillary water absorption test. The results are illustrated in Figure 2.  
As shown in Figure 3, the linear correlation between the water absorption mass per unit area and the time square root of each test group was strong, with a correlation coefficient above 0.999. This result agrees with the theoretical derivation. The capillary water absorption coefficient of group K was slightly smaller than that of group J. The difference between the two groups was discernible, which indicated that the waterproof effect of the KIM waterproof agent during the initial stage of capillary water absorption is not obvious. The capillary water absorption coefficient of group KS was significantly lower than that of groups J and K. This indicated that nano-SiO\({}_{2}\) causes crystal nucleation and micro-aggregation, which considerably improved the compactness of the test samples. The modification effect of nano-SiO\({}_{2}\) in the cement mortar with waterproof agent was significant, and the capillary water absorption rate was slower.  
Figure 2: Capillary water absorption test  
According to the linear expression obtained from the fitted curve, the capillary water absorption coefficients of the J, K, and KS groups were 1.0245, 1.0216, and 0.7678 (kg/m\({}^{2}\))/h\({}^{1/2}\), respectively.
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 3 Methodology for 5G NR Link-to-System Mapping
### Preliminary Phase for 5G NR L2S Mapping
Accordingly, the variable \(m\) can be replaced to a constant 1 with the meaning of the first layer, and then it can be removed. Consequently, Equation (4) can be expressed to Equation (5) as follows:  
\[SINR_{k}=\frac{|\left[\mathbf{G}_{k}\mathbf{H}_{k}\mathbf{W}_{k}\right](1,1)|^{2 }}{\sum\limits_{i=2}^{N_{L}}|\left[\mathbf{G}_{k}\mathbf{H}_{k}\mathbf{W}_{k} \right](1,i)|^{2}+\sigma^{2}\sum\limits_{i=1}^{N_{R}}|\left[\mathbf{G}_{k} \right](1,i)|^{2}} \tag{5}\]  
In a SISO case, \(m\), \(N_{T}\), \(N_{R}\), and \(N_{L}\) set to be 1, and there is no inter-stream interference. Thus, the post-processing SINR at the \(k\)th sub-carrier is expressed as follows:  
\[SINR_{k}=\frac{|\mathbf{H}_{k}|^{2}}{\sigma^{2}} \tag{6}\]  
For convenience of expression, post-processing SINRs (\(SINR_{k}\)) is denoted by \(\gamma_{k}\). One thousand TBs are repeatedly transmitted for a given _input SNR_ under the same channel condition. CRC error checking is applied to determine whether the TB is successfully decoded. UE transmits _ACK_ by successful TB decoding among transmitted TBs, whereas it transmits _NACK_ by failed decoding. Therefore, BLER is calculated as follows:  
\[BLER=\frac{\text{The total number of received NACKs}}{\text{The total number of transmitted TBs}}\times 100\ \ \ [\%]. \tag{7}\]  
At the next _input SNR_ under the same channel, 1000 TBs are transmitted recurrently. Correspondingly, the data of (\(\gamma_{1}\), \(\cdots\), \(\gamma_{N_{RE}}\), \(BLER\)) is collected for a given _input SNR_ and \(N_{RE}\) REs where \(N_{RE}\) is the number of allocated sub-carriers per symbol, i.e., 300. Thus, raw data of (_input SNR_, \(\gamma_{1}\), \(\cdots\), \(\gamma_{N_{RE}}\), \(BLER\)) is gathered for L2S mapping. We describe LLS procedure to extract raw data as shown in Algorithm 1. Especially, the condition of channel must not be changed during the generation of 1000 TBs over all SNRs, while the condition of channel is changed at every simulation. The input SNR should be adjusted so that values of BLER are measured within the range of 0.01 and 0.9. When the value of measured BLER with 0 is used, \(log_{10}\)(measured BLER) is to be negative infinite, as shown in Equation (9). BLER with 1 is meaningless data since all TBs failed. Since MSE is calculated from measured BLERs, MSE is dependent by measured BLERs. The exact value of L2S mapping can not be derived if the BLER value does not appear evenly in BLER range from 0.01 to 0.9. Therefore, equally distributed BLERs within the range from 0.01 to 0.9 should be measured to find the values of L2S mapping parameters.
## 1 Introduction
[23] used Edgeboxes algorithm to generate region proposals, and then used CNN to perform feature extraction and classification. Similarly, [PERSON] et al. [4] proposed a \"region proposal-classification-accurate object localization\" framework for detecting objects in remote sensing images. However, all the above methods have the problem of redundancy in candidate regions. In order to solve those problems, [PERSON] et al. [5] proposed an aircraft detection method based on corner clustering and CNN, which used the mean-shift clustering algorithm to generate candidate regions for the corner points detected in the binary image, and then utilized CNN to determine the target category in the candidate region. Compared to traditional methods, these approaches based on CNN models could learn the essential features of the object from a large amount of data, without the need to design the features manually, and the detection accuracy has been significantly improved. However, aircraft detection in remote sensing images faces emerging challenges because of the diversity of orientation, scales, and appearance (color, texture, size, shape, etc.). These problems are very serious in remote sensing images. First, similar to the objects in natural images, the aircraft in remote sensing images also have a multi-scale problem. Huge difference is caused by two aspects, one is that the same objects in remote sensing images may have different sizes due to the varying spatial resolutions of sensors, and the other is the diverse scales of aircrafts [24]. In summary, aircraft in remote sensing images vary greatly in scale and orientation, resulting in a low detection accuracy (high rate of missing detection or false alarm) and region redundancy, especially nesting and overlap in densely arranged region. To address these challenges mentioned above, an aircraft detection framework based on a multi-feature fusion module and a rotating anchor generation mechanism is proposed in this paper. First, multi-scale features and local corner features for enhancing the ability of feature expression are considered in our multi-feature fusion module. Among them, multi-scale features are extracted by the feature pyramid network, and local corner features that can well express the salience of aircraft are extracted on every layer output of each layer of FPN, finally the feature map group is constructed. Then, the rotating anchors are generated by multiple aspect ratios, angles and scales. Finally, aircraft detection with accurate location and size information is achieved by classification and regression. The rest of this paper is organized as follows. The proposed method is described in Section 2. Section 3 illustrates the datasets, details of implementation, evaluation criteria, and experiment results. Finally, Section 4 concludes this paper with a discussion of the experimental results.
## 3 Problem Formulation and Modeling
### Modeling  
Figure 2 shows the complete workflow of the system. The control strategy for the model is structured in the following steps:  
1. State acquisition: Buses dynamically gather essential information about the road section and intersection using onboard sensors and communication devices. The acquired data includes speed, position, rank, arrival time, lane occupancy, traffic light status, and duration.
2. State update: Buses continuously update their own state based on the acquired status information and calculate their current state value \(S\).
3. Action selection: Buses select the optimal action \(A\) based on their current state \(S\) by combining reinforcement-learning algorithms, which involves controlling the maximum \(a^{\max}\) and minimum \(a^{\min}\) green time of the traffic light to minimize waiting time and reduce energy consumption.
4. Action execution: Buses execute the selected action \(A\), which involves sending the maximum green time \(a^{\max}\) and minimum green time \(a^{\min}\) control signals to the traffic control center for scheduling, and continuously updating the state based on the new status information.
5. Reward feedback: Based on the action \(A\) executed by the bus and its current state \(S\), the corresponding reward \(R\) is calculated and fed back to the reinforcement-learning algorithm to update the value function.
6. Policy update: Based on the reward \(R\), the reinforcement-learning algorithm updates the policy to gradually converge on the optimal action, thereby achieving the control goals of minimizing waiting time and reducing energy consumption.  
Figure 2: DQN model training flowchart.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 4 Text Similarity Detection  
Text similarity detection [22] plays a crucial role in our blockchain recipe copyright protection system. First, by analyzing the similarity of newly submitted recipes, highly similar or even duplicated recipes can be detected, preventing the uploading of duplicate content and ensuring the diversity and originality of recipe content in the system. Similarity detection can also help us make preliminary judgments on the issue of suspected copyright conflicts. For cases with a large number of similar passages or even a simple rewrite of existing recipes, we can detect and deal with these problems as early as possible through high similarity judgment to protect the rights and interests of the original creators.