esa-sceva/satcom-chunk-collection huggingface dataset:
# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 3 Methods
### Preprocessing
```
1:procedureCalculate Ground Truth Attention Sentence tokens \(t_{1},t_{2},t_{3},\ldots,t_{n}\); Rationale vector after conversion \(r_{1},r_{2},r_{3},\ldots,r_{n}\); \(\text{Hate}/\text{Offensive Attention Vector}\), \(\mathbf{H}=(h_{1},h_{2},h_{3},\ldots,h_{n})\); Sarcasm Attention Vector, \(\mathbf{S}=(s_{1},s_{2},s_{3},\ldots,s_{n})\)
2:for\(i\gets 1\) to \(n\)do
3: Calculate combined attention score for token \(t_{i}\):
4:if\(s_{i}>h_{i}\)then
5:\(r_{i}\leftarrow\dfrac{s_{i}+h_{i}}{2}\)
6:endif
7:if\(s_{i}<h_{i}\)then
8:\(r_{i}\gets h_{i}\)
9:endif
10:if\(s_{i}=h_{i}\)then
11:\(r_{i}\gets h_{i}+\dfrac{s_{i}+h_{i}}{2}\)
12:endif
13:endfor
14:return Rationale Attention Vector \(r\)
15:endprocedure
```  
**Algorithm 1** Calculate ground truth attention vector  
Drawing on the concept of an attention vector, a new attention vector is established for tokens within the rationale that simultaneously convey hateful/offensive and sarcastic elements. This new attention vector is utilized for the calculation of the ground truth attention. Specifically, if, in sarcasm rationale, a vector element \(s_{i}\) surpasses a hate rationale vector element \(h_{i}\), the final newly assigned vector element is \(r_{i}=0.5\). When a sarcasm rationale vector element \(s_{i}\) is less than the hate rationale vector element \(h_{i}\), the final new assigned vector element \(r_{i}=1\). And lastly, when the sarcasm rationale vector element \(s_{i}\) is equal to the hate rationale vector element \(h_{i}\) and if both of them are 1, then the final new assigned vector element \(r_{i}=1.5\), as shown in Figure 2.  
This approach refines the interpretation of attention dynamics in the context of sentences exhibiting both hatespeech/offensive and sarcasm. Following this, the average of the attention vectors is calculated to obtain the ground truth attention for each post, as in the previous process shown in Figure 1. Once the updated ground truth attention is obtained, a  
Figure 2: Attention vector calculation before the average of ground truth attention. softmax function is applied to distribute the concentration of rationale, mirroring the final ground truth attention calculation as previously demonstrated.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
###### Abstract  
Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology to evaluate lesion features such as asymmetry, border, color, and diameter. Our model consists of five distinct blocks, two of which focus on learning general image characteristics, while the remaining three focus on specialized features related to the ABCD rule. The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. Additionally, Grad-CAM analysis is employed to assess how each block in the ensemble focuses on distinct lesion features, further enhancing the interpretability and reliability of the model. Our findings demonstrate that integrating general image features with specific lesion characteristics improves classification performance, and that adjusting the soft voting weights yields optimal results. This novel model offers a reliable tool for early skin lesion diagnosis.  
ensemble; ABCD rule; vision transformer; data balancing Article
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 2 Materials and Methods
### Problem Formulation
#### 2.2.2 Problem Statement  
This paper addresses the problem of selecting surface points \(S_{s}\) and \(S_{f}\), and planning a trajectory for the AUV (3) that starts at \(S_{s}\), traverses a sequence of waypoints to collect data from the \(M\) CHs, and ends at \(S_{f}\), such that the objective function (13) is maximized, subject to constraints (1), (2), and (6).
## 3 Methodology
For the implementation of the proposed architecture and to be based on the ISO 23247 protocol and its layers, the following structure will be followed: (1) realization of the 3D design and visualization of all physical production lines in all of their stages and of the available resources in the cyberspace model, (2) establishment of bidirectional communication with the existing manufacturing system through protocols such as Node network and OPC UA, (3) communication of the physical and virtual environments for obtaining real-time data from all stages of the process and thus establishing monitoring and parameter settings from the physical or virtual environments, and (4) performing simulation, configuration and optimization experiments with the DT in the CP lab and CP Factory from the physical and virtual environments. The proposed architecture of the immersive DT will be implemented in the Industry 4.0 laboratory to extend its functionalities, data, and bidirectional communication. The wireless data will be collected throughout the production cycle to allow for history, simulation, and data analyses for optimized decision-making and thus increase the advantages of the current MES implemented in the laboratory. Within the hardware and software used in the architecture, the following are used: (1) Industry 4.0 Lab, (2) TIA Portal software for PLC programming and configuration, (3) Blender and Solid Works for the design of the 3D virtual environment, (4) Unity which allows for the development of AR and VR applications and is compatible with multiple platforms such as Linux, Android, Windows, IoS, and Android, and (5) the OPC UA protocol that enables bidirectional communication based on client/server and publish/subscribe communication models and provides a semantically enriched information model to represent data. The steps for the proposed architecture are described in detail below.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 3 Advanced Applications of NBs in Precision Surface Cleaning
### Dental Hygiene and Plaque Removal  
The complex shapes of teeth and orthodontic appliances make it difficult to remove plaque and bacterial biofilms using conventional procedures, which makes surface cleaning in dental hygiene a common challenge [72]. Through their incorporation into dental cleaning products such as mouthwash and NB water systems, NBs provide a novel way to improve cleaning effectiveness and show promise in cleaning surfaces without the need for harsh mechanical or chemical techniques [73]. In addition to improving oral hygiene, this novel method offers a mild and effective cleaning option, particularly for patients undergoing orthodontic treatment and those with poor hand dexterity.  
[PERSON] et al. [72] tested an NB generator with a high-density stainless steel mesh nozzle to clean bacterial dentures by delivering NB water--normal water and two kinds of mouthwash--through a tooth tray (Figure 10). Plaque removal rates were 95% or greater for a range of motor speed settings and pore diameters, with some combinations achieving 100% clearance. The findings demonstrated that the flow velocity and NB size had a substantial impact on plaque clearance, with smaller bubbles resulting in better efficacy. According to the findings, plaque removal was improved by NBs with smaller dimensions. Furthermore, the soaking method for plaque removal was less successful than using a mouthwash in the NB form. According to the results, this strategy may aid dental cleanliness, particularly in patients who have been bedridden for a long time. The experiment may be improved in future research to increase the effectiveness of bacterial eradication and to modify the design for actual human oral cavities.  
[PERSON] et al. [73] examined the efficiency of ultrafine bubble water (UFBW) for preventing dental cavities by eliminating plaque during orthodontic treatment. According to an in vitro investigation, high-concentration UFBW (HUFBW) outperformed other mouthwashes in terms of plaque removal. In a clinical investigation, distilled water was not as effective at removing plaque as HUFBW (\(p<0.01\)). UFBW has a biofilm-removal action that is dependent on the bubble concentration and is stable for at least one month  
Figure 10: Experimental procedure for cleaning the denture. Reproduced with permission from [72], CC-BY 4.0 license, Copyright © 2020 Pei-Ju Lin.  
when refrigerated. Areas surrounding multi-bracket appliances that are difficult to reach with mechanical brushing were successfully cleaned using HUFBW. HUFBW can help lower the risk of dental cavities following orthodontic treatment when regularly used for at-home maintenance.
## 4 Results and Discussion
### Comparative Experimental Results
The loss curves on the USIS10K dataset are shown in Figure 11, revealing that the model learns effectively across all tasks, as evidenced by the steady decrease in training losses for box, segmentation, and classification over epochs. This indicates that the model is successfully minimizing errors and improving its performance during training. The validation losses for these components also stabilize after a few epochs, suggesting that the model generalizes well to unseen data. The small gap between training and validation losses indicates minimal overfitting and strong generalization ability. Overall, the model demonstrates good performance on the USIS10K dataset, with effective optimization and minimal overfitting. The analysis of the confusion matrix in Figure 12 shows that the model performs well in distinguishing categories such as \"wrecks/ruins\" and \"human divers\", with high accuracy and minimal confusion between these categories. However, there are misclassifications for the \"background\" category, which is often confused with other classes, particularly \"wrecks/ruins\", \"fish\", and \"sea-floor\". Additionally, some misclassification occurs between \"fish\" and \"reefs\". While the model is effective at differentiating most categories,  
Figure 10: Visual comparison on USIS10K dataset. Each column represents GT, RDPNet [40], OQTR [43], SAM + Mask [44], RSPrompter [45], WaterMask [8], USIS-SAM [10], and AASNet, respectively. Figure 9: Visualization of the USIS10K dataset: the first row contains original images, the second shows ground truth masks, and the third presents the model’s predicted masks. further improvements are needed for categories prone to confusion, such as \"background\" and \"sea-floor\", especially in more complex environments. Notably, the model's performance on the \"fish\" category is better in the USIS10K dataset, with an accuracy of 86%, compared to 60% in the UIIS dataset, demonstrating an overall improvement in the model's classification capabilities on the USIS10K dataset.
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
Based on the above analysis, in this paper, we propose a transformer-based cross-level iterative subtraction network (CISNet) for COD, which is designed to perform difference complementation between cross-level features and then progressively improve the accuracy of segmentation in a top-down manner. In detail, we present a cross-level iterative structure  
Figure 1: Simplified schematics of feature integration approaches in different networks. Yellow blocks, blue blocks, and green blocks denote the encoder blocks, feature integration modules, and decoder modules, respectively. And the solid lines indicate the direction of feature transfer. (CIS), as depicted in Figure 1e, specifically designed for cross-level feature integration. And, within CIS, we adopt a sequential iteration strategy to facilitate the information interaction across multiple levels. This structure incorporates the utilization of multi-scale strip convolution subtraction (MSCSub) to integrate the highest-level features with their relevant lower-level features, leading to more comprehensive and semantically expressive feature representation. Within MSCSub, strip convolution layers with multiple kernel sizes are employed to capture features in different directions and scales, facilitating the improved extraction of local detail information and increasing the adaptability of the model to handle camouflaged objects with varying appearances. And, through a subtraction operation in MSCSub, difference information between different levels can be extracted, and feature complementarity is performed. Moreover, we aggregate the outputs of each MSCSub at the same level to capture expansive horizontal information. To further refine the features at each level, we design enhanced guidance attention (EGA) to improve the sensing of local context information and capture a broader range of feature relationships with greater diversity. Our main contributions can be summarized as follows:  
* We propose a cross-level iterative subtraction network for camouflaged object detection, which focuses on difference complementation between cross-level features to enhance the segmentation performance;
* We present a cross-level iterative structure and multi-scale strip convolution subtraction module for the efficient interaction and integration of features across multiple levels, which captures difference information between cross-level features and guides the network to focus on the details of object;
* We present the enhanced guidance attention module to deeply mine multi-scale local context information and capture the relationships between different features, which aims to improve the segmentation accuracy of camouflaged objects. The rest of this paper is organized as follows. In Section 2, we discuss several related works. Then, in Section 3, we provide an overview of our model and detailed descriptions of each module. Section 4 presents experimental results and related analysis. And Section 5 shows some use cases. Finally, we conclude the paper in Section 6.
## 3 Results
### Creating a New Data Set
As can be seen, the least accurate results were those in the \"three finger\" class. Regarding the background of the training images, the first set of rows in Table 2 shows the results from the testing images using a model trained with flat backgrounds, and the second set of rows in Table 2 are the results from a model trained with textured images that are made by synthetizing objects and background images. Mixing objects with textured backgrounds increases the object detection rate in 3% of the images, similar to the results from the original pet images of the previous experiment. This demonstrates that images with a controlled background are useful for the easy detection of objects and a combination of detected objects with backgrounds increases the performance of the training process. Figure 5: Labelled images using the proposed automatic image-labelling algorithm. First row shows images of hands with a controlled background to easily detect hand silhouettes. Second row shows masks extracted using algorithm described in Section 3. Third row show images where hands are combined with backgrounds to improve the training process. Figure 6: Images to test deep model trained to detect hands with fingers. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{5}{c}{**CLASSES**} \\  & & **ONE** & **TWO** & **THREE** & **FOUR** & **FIVE** \\ \hline  & ONE & 0.7000 & 0.1167 & 0.0100 & 0.0167 & 0.0033 \\  & TWO & 0.1733 & 0.7333 & 0.1600 & 0.0233 & 0.0233 \\ CLASSIFIED AS (\%) & THREE & 0.0767 & 0.1300 & 0.6667 & 0.1100 & 0.0300 \\ (Training data are flat & FOUR & 0.0333 & 0.0167 & 0.1400 & 0.6933 & 0.1333 \\ background images) & FIVE & 0.0167 & 0.0033 & 0.0233 & 0.1567 & 0.8100 \\  & Precision & 0.8268 & 0.6587 & 0.6579 & 0.6820 & 0.8020 \\  & Recall & 0.7000 & 0.7333 & 0.6667 & 0.6933 & 0.8100 \\ \hline  & ONE & 0.7033 & 0.1167 & 0.0100 & 0.0167 & 0.0033 \\ CLASSIFIED AS (\%) & TWO & 0.1767 & 0.7400 & 0.1333 & 0.0300 & 0.0167 \\ (Training data are with texturized & THREE & 0.0633 & 0.1233 & 0.6833 & 0.1200 & 0.0267 \\ background as combination of & FOUR & 0.0400 & 0.0167 & 0.1500 & 0.7000 & 0.1300 \\ objects and images & FIVE & 0.0167 & 0.0033 & 0.0233 & 0.1333 & 0.8233 \\ of backgrounds) & Precision & 0.8275 & 0.6748 & 0.6721 & 0.6752 & 0.8233 \\  & Recall & 0.7033 & 0.7400 & 0.6833 & 0.7000 & 0.8233 \\ \hline \end{tabular}  
* Classes are “one finger”, “two fingers” and so on, which are classified as “one finger”, “two fingers”, etc. Precision is computed with 300 testing images of each class, similar to Figure 5. Recall is computed with the total amount of images that the model classifies under class “one finger”, “two fingers”, etc.  
\end{table} 
Table 2: Detection rates (precision/recall) of deep model FASTER-RCNN-INCEPTION to detect finger hands 1.
## 3 Discussion
Further, this mixing is superficial because information on non-photochemistry is canceled out in the product of (1 + NPQ) and \(\Phi_{\text{P}}/(1-\Phi_{\text{P}})\) as \(\Phi_{\text{P}}\) contains both photochemical and non-photochemical information. We need to consider both the energy-dependent and energy-independent components of NPQ. This is particularly important for this study because it focuses on water stress, which likely induces energy-independent NPQ. The weak response of fluorescence to a water deficit suggests that SIF alone, at least its physiological component, is not able to track drought-induced changes in plant physiology. However, the development of the rMLR model allows for a mechanistic understanding of the drought impact on photosynthetic characteristics using SIF, soil moisture, and two measurable meteorological variables as the inputs. To apply the rMLR model at the canopy scale, that is, by using narrowband \(\text{SIF}_{\text{Ioc}}\) as an input variable for Equation (3), several more steps will need to be performed. First, the contribution of PSII fluorescence to \(\text{SIF}_{\text{Ioc}}\) (\(f_{\text{PSII}}\)) should be determined. Although [PERSON] et al. [48] showed that \(f_{\text{PSII}}\) can be also estimated from \(T_{\text{air}}\) and PAR, more research is needed to assess how \(f_{\text{PSII}}\) varies with species and environmental conditions [44]. Second, the probability of a fluorescence photon escaping from a leaf level to canopy scale (\(f_{\text{esc,L-C}}\)) must be quantified, in addition to \(f_{\text{esc,P-L}}\) (Equation (8)). In the near-infrared (NIR) region, \(f_{\text{esc,L-C}}\) can be estimated from directional reflectance (RNIR) [49]. Note that both \(\text{SIF}_{\text{Ioc}}\) and \(R_{\text{NIR}}\) can be concurrently obtained from measurements of irradiance/radiance [50]. Thus, the estimation of \(f_{\text{esc,L-C}}\) requires no additional observations. Third, a full-band SIF emission should be reconstructed from narrowband SIF. The full SIF spectrum at TOC can be approximated by a linear combination of basis spectra [46; 51; 52]. The Soil-Canopy Observations of Photosynthesis and Energy Balance (SCOPE) [53] model is typically used to generate a dataset that is representative of the majority of actual scenes. Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) techniques are then applied to extract the basis spectra from this simulated dataset. However, the SCOPE model is designed for homogeneous vegetation canopies, such as crops, and its performance may deteriorate for a heterogeneous, structurally complex canopy [54]. Finally, a large-scale, and near-real-time root-zone soil moisture (RZSM) dataset is needed to estimate \(\beta_{\text{S}}\) and \(\beta_{\text{B}}\). Since satellite soil moisture satellite observations are sensitive to surface soil moisture, typically within the first few centimeters, current RZSM datasets are mostly produced by assimilating observations into model simulations [55; 56].
## 5 Results
### The Proposed Mechanisms
#### 5.3.5 The Impact of the Number of Nodes  
We conducted tests on the Portland scenario with fewer network nodes. Accordingly, we removed nodes located on the right side of the river, as indicated in Figure 5. The idea was to evaluate the performance of our mechanisms in less dense scenarios. For this scenario, we considered eight consumers and two producer nodes and increased the number of interest requests from 10 to 50 int/s.  
Figure 11 shows that RECIF-soft outperformed the other mechanisms in terms of the delivery rate. This mechanism reduced the number of interest packets forwarded, but it was less aggressive than PIF02, for example. RECIF-soft delivered more contents than the default NDN when the interest rate increased. For 50 int/s, RECIF-soft delivered 21.9% and 150.6% more contents than the default NDN and LFBL respectively. RECIF-soft also reduced the delivery delay compared with the default NDN and LFBL. Hence, RECIF-soft achieved a better adaptative retransmit threshold to restrict the number of interest packets broadcast on the network. Therefore, RECIF-soft delivered more contents by using an adaptively interest restriction. This restriction can be seen in Figure 11, where RECIF-soft presents less overhead than the default NDN but has a higher overhead than PIF02. More aggressive  
Figure 10: Content packets delivered, average delivery delay, and overhead for two consumers requesting popular content with different cache sizes.  
mechanisms reduce the number of interest packets that reach the producer and therefore, content delivery is reduced. In this less saturated scenario, this restrictive approach is not ideal.  
The PIF, ReCIF, and ReCIF+PIF mechanisms provide (i) a higher content delivery as the network saturation level increases, (ii) less broadcast of interest packets to retrieve contents, and (iii) a lower average delivery delay. Nevertheless, some mechanisms are more suitable for less saturated environments, such as ReCIF, and others, like PIF02, performs better in a saturated environment. The main reason for these behaviors is the interest forwarding criteria used by the mechanisms. As a result, more restrictive criteria tend to be suitable for saturated networks. On the other hand, for less saturated scenarios, the RECIF-soft mechanism presents a higher performance because its restriction criteria adapt better to the scenario. This flexible filter criteria, present in RECIF-soft, allow higher content delivery without the decreased overhead performance present in PIF02. Thus, less restrictive and more flexible criteria are suitable for less saturated networks.
## 5 Simulation and Analysis
### Performance of the Routing Protocols with the Mobility Models
#### 5.2.3 Rgr  
In terms of PDR (Figure 13), the performance of RGR is distinctive from one mobility model to the next. Once again, the best performance is under RWP, followed by EGM, and the worst performance is under RD followed by GM. The PDR here ranges from about 83% to about 92%, thus a range length of about nine percentage points. This range length is between that of AODV and GRP, and it is at a much higher level (from 83%). RWP also brings about a lower delay (Figure 14) compared to the rest of the models. Most of the statistics presented in Table 2 suggest this ranking. The explanation given for the PDR ranking in the case of GRP also holds here. For the delay, we saw with the statistics that RWP presented the highest average number of neighbors and, thus, the shortest path length; which naturally reduces the end-to-end delay.  
Figure 12: Routing overhead of GRP under various mobility models.  
Figure 13: PDR of reactive-geographic hybrid routing (RGR) under various mobility models.