esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Limitations
### Gaps in Interpretability and Engineering Applicability  
Although heatmap visualizations aid in analyzing leakage regions, the decision-making process of the model lacks transparency. For example, the mechanism by which SE-Blocks adjust weights across specific channels is not directly linked to physical parameters such as concrete porosity or moisture gradients. This limits engineers' ability to interpret the model's reasoning in detecting concealed leakages. Furthermore, the model currently lacks integrated leakage severity assessment functions, such as water volume estimation or trend prediction. Future work should aim to build an end-to-end damage management system by incorporating fluid dynamics models or Internet of Things (IoT) sensor data.
## 3 Results and Discussion
### Organic Dye Removal Efficiency  
Figure 9 shows the dye adsorption curves for both the bare Al\({}_{2}\)O\({}_{3}\) membrane and TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane. In the absence of UV illumination, the TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane shows a higher adsorption capacity than the bare Al\({}_{2}\)O\({}_{3}\) membrane, which was almost saturated after 9 h of filtration. The TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane presents a higher dye organic removal efficiency (\(\sim\)20%) than the bare Al\({}_{2}\)O\({}_{3}\) membrane after the same filtration time. The higher adsorption capacity of the TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane arises mainly from complementary cavities and rebinding sites on the membrane assisted by the POEM template [26]. The advantages of the presence of such a well-organized TiO\({}_{2}\) film were further observed upon carrying out the membrane filtration under UV illumination (Figure 9b). About 96% of dye removal efficiency was observed with the TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane after 24 h of filtration. The catalytic active sites offered by the POEM template favor the membrane photocatalytic activity. In contrast, only \(\sim\)40% of the organic dye was removed by the bare Al\({}_{2}\)O\({}_{3}\) membrane under UV illumination [15]. However, \(\sim\)89% of organic removal efficiency was achieved after 6.5 h membrane filtration, probably due to photolysis phenomena occurring on the membrane surface [25]. Moreover, the formed cake layer may act as a secondary membrane, thus improving the organic dye removal by membrane filtration [15].
## 3 The Method
### The Corresponding Discretised Model by Means of the Graph Theory
One of the advantages is that the regularisation is directly expressed in a discrete setting, and involved processing methods are computed by simple and efficient iterative algorithms without solving any partial differential equations (PDEs). The idea of using functions on weighted graphs in a regularisation process has been proposed in other contexts, such as semi-supervised data learning [16]. However, in our paper, we adopted a digital approach contrary to the numerical approaches used in [13], for instance. From this, the optimal problem in (26) becomes  
\[\underset{f\in\mathcal{H}(V)}{\min}\left\{\sum_{v\in V}\frac{1}{p(v)}\left( \sum_{v\sim u}\omega(u,v)(f(v)-f(u))^{2}\right)^{\frac{p(v)}{2}}+\frac{\lambda} {2}\sum_{v\in V}|f(v)-f_{0}(v)|^{2}\right\}. \tag{27}\]  
The models with \(p(\cdot\,)=1\) or \(p(\cdot\,)=2\) have been treated (for more details, the reader can consult [4]). In our work, we treat the general case \(1<p(x)<2\). The next proposition is the following. Proposition 2: _The discretised minimising problem in (27) has a unique solution._  
Proof: By using the elementaries tools of convex analysis applied to the functional  
\[f\longmapsto\sum_{v\in V}\frac{1}{p(v)}\left(\sum_{v\sim u}\omega(u,v)(f(v)-f (u))^{2}\right)^{\frac{p(v)}{2}}+\frac{\lambda}{2}\sum_{v\in V}|f(v)-f_{0}(v) |^{2},\]  
\(p(\cdot\,)\) and \(\lambda\) are defined as in the previous case. Obviously, we note that the problem in (27) has a unique global minimum. Indeed, we define the functions \(\varphi_{1}\) and \(\varphi_{2}\) as follows  
\[f\longmapsto\overset{\varphi_{1}}{\longmapsto}\sum_{v\in V}\frac{1}{p(v)} \left(\sum_{v\sim u}\omega(u,v)(f(v)-f(u))^{2}\right)^{\frac{p(v)}{2}}\text{ and }f\longmapsto\overset{\varphi_{2}}{\longrightarrow}\frac{\lambda}{2}\sum_{v \in V}|f(v)-f_{0}(v)|^{2}.\]  
It is clear that each function is convex in \(V\). Moreover, \(\varphi_{2}\) is coercive in \(V\), which means that \(|\varphi_{2}(f)|\) tends to \(+\infty\), when \(\|f\|_{2}\) goes to \(+\infty\). This fact is easily obtained using the triangular inequality. Because of this, it could be argued that the problem described by (27) has a unique solution in \(V\). In our work, the weight \(\omega\) considered is  
\[\omega(u,v)=exp\left(-\frac{|f(u)-f(v)|^{2}}{\sigma^{2}}\right)\]
## 3 Ensemble Soft Sensor Model Construction Based on Diversity Enhancement and Ensemble Pruning
### Diversified Base Model Construction Method Based on Multi-Kernel Latent Variable Space  
The effective generation mechanism of diversified base models is an important factor to ensure the generalization performance of the ensemble soft sensor model. In this study, a base model generation method based on the most relevant variable selection and adaptive multi-kernel supervised latent variable subspace is proposed for homogeneous integration. The basic idea is as follows.  
First, a set of subsample sets \(\{\textbf{X}_{i},\textbf{Y}_{i}\}_{i=1}^{M}\) is obtained by bootstrapping random resampling, where \(M\) is the times of resampling, representing the number of sample subsets. Each subsample set will form a modeling subspace; for example, the \(k\)th subspace is \(S_{k}=\{\textbf{X}_{k},\textbf{Y}_{k}\}\), \(k\in(1,M)\), and \(X=[x_{1},x_{2},\ldots,x_{m}]\in R^{n\times m}\) is the set of process variables, where \(m\) is the number of input variables. \(Y=[y_{1},y_{2},\ldots,y_{m}]\in R^{n\times l}\) is the target variable, where \(l\) is the number of output variables. This is the most common diversity mechanism used in the sample set generation phase.  
Next, delete the process variables whose MIC results are less than the threshold \(\sigma\) on each subsample set, and keep only the process variables with the strongest correlation with the target variables to build the initial modeling variable subspace \(S_{in}=\{\ X_{in},\ Y\}\), where in each initial variable subspace, \(X_{in}\in R^{n\times d}\) means selected process variables, and \(d\) is the number of variables most relevant to the output variable \(Y\). The relationships between variables in each subsample set are not the same, which means the initial constructed variable subspaces will be different. This is the diversity perturbation mechanism incorporated in the variable stage.  
Then, multi-kernel supervised LVS are constructed using the KPLS algorithm based on different types of kernel functions with an adaptive kernel parameter. The number of initial subspaces \(S_{in}\) equal to the number of subsample sets is \(M\), and the types of kernel functions are \(k_{n}\). After that, the number of LVS MS\({}_{LVM}\) is \(M\times k_{n}\). For each LVS, the SOGWO algorithm is used to optimize the parameters of each kernel function with the fitting error as the accuracy evaluation index. This is the diversity perturbation mechanism incorporated in the modeling variable space and model parameters.
# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 4 Experimental Results  
To demonstrate the performance of the proposed RPs matching method, real-world experiments have been carried out in a practical environment. The room is 9.64 \(\times\) 7.04 \(\times\) 2.95 m\({}^{3}\), where the noise is about 40 dB and the walls are not insulated. The simple scene and complex scene of the experiments are shown in Figure 5. The positioning area is a rectangular plane with a length of about 6 m and a width of about 5 m. The 4-channel microphone array is composed of the MPA201 microphones produced by the BSWA Technology Co., Ltd., Beijing, China. The microphones are installed at four vertices of the positioning area with a height of about 1.35 m above the floor. The type of the acquisition card is known as NI9215A from NI company. The sampling frequency is set as 100 kHz, and the sampling period is set as 1 s. The sound source is a bluetooth speaker with a height of 0.20 m embedded on a mobile robot. Its shape is approximately cubic, and the sound unit is composed of three identical speakers on three sides. In view of its small size and horizontal symmetry, its directivity is not considered in this paper.  
As shown in Figures 5a and 2(a1), in the simple positioning scene, the RPs for the positioning database are uniformly distributed in the location service area by grid division, and  
Figure 5: The experimental environment: (**a**) simple positioning scene; (**b**) complex positioning scene.  
the distance between each RPs is 0.593 m. The total number of the RPs is 72, and there are another 13 test points used for target point estimation. As shown in Figures 5b and 2(b1), in the complex positioning scene, the uniformly distributed RPs in local areas are divided by the obstacles (desks) in the location service area, and the distance between each RPs in a local area is 0.593 m. The total number of RPs is 64, and there are another 18 test points used for target point estimation.
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 4 Discussion and Conclusions
Its information may be overwritten by other variables, resulting in a model that is still less accurate after using this method. The specific reasons for this merit further exploration. In contrast, the Artificial Neural Network method achieved a more satisfactory modeling accuracy with an R\({}^{2}\) value of 0.729 and a K-fold cross-validation accuracy of 0.702. The Random Forest method, implemented using Python and relevant packages, resulted in the highest modeling accuracy with an R\({}^{2}\) value of 0.946 and a K-fold cross-validation accuracy of 0.897. The cross-validation produced an RMSE value of 869.8 kg/hm\({}^{-2}\), signifying a highly accurate model and an ideal yield estimation effect. This is consistent with the research results of [PERSON] [29]. The use of Principal Component Analysis resulted in a moderate improvement in the accuracy of the yield estimation models derived from Multiple Linear Regression and Stepwise Multiple Linear Regression when compared to models without Principal Component Analysis. However, the overall results remained suboptimal. In contrast, models built using the neural network and Random Forest methods achieved significantly improved accuracy, with Random Forest proving to be the most effective in yield estimation. This is consistent with the conclusion of [PERSON] [30]. The high correlation between these vegetation indices (NDII, NDWI, NDMI and VDI) and wheat yield is mainly due to their ability to reflect the moisture status of soil and vegetation. High NDII [27] values reflect high vegetation moisture content, while NDWI, NDMI and VDI [24] reflect soil and vegetation moisture status. This is consistent with the research results of [PERSON] [27]. Since water is a key factor affecting wheat growth and yield, these indices show a high correlation with wheat yield. The low correlation between vegetation index NDRE and wheat yield may be affected by growth stage, soil type, accuracy of remote sensing data and other environmental factors, which leads to the limitation of its prediction ability, climatic conditions and thus reduces the correlation with wheat yield. Future studies can explore a new vegetation index, adjust the modeling algorithm or utilize emerging remote sensing technology to improve the accuracy of the wheat yield estimation model.
## 3 Results and Discussion
### Stability of Selenium Species in Beetroot juices
The only exception is the result for organic beetroot juice with DTT addition, where this loss of SeMet was only small and not significantly different in [PERSON]'s test (\(p<0.05\)) for all used thiol additions. The increase in SeMetO peaks was also observed in the samples with thiols; however, there are no clear trends regarding the type of thiol and its concentration. In the chromatogram for organic juice in the presence of two \(\beta\)ME concentrations, peak splitting for SeMetO can be observed (Figure 2). Figure 2: Chromatograms of selenium species in (**a**) organic and (**b**) squeezed beetroot juices after the addition of different concentration of DTT or \(\beta\)ME thiols. Storage temperature 20 \({}^{\circ}\)C. The ZIC-HILIC column with eluent—MeOH/H\({}_{2}\)O (85/15) (\(v/v\)). The data and the relevant statistical analysis are presented in Table S1. This bimodal phenomenon, while other peaks in the samples did not show similar distortion, may be due to the presence of two SeMetO diastereomers [37; 42]. The MeSeCys peaks were also decreased after the addition of thiols, but not so much as in the case of selenomethionine. Its losses were bigger in organic beetroot juices than in squeezed juice samples. The degradation of MeSeCys was also observed in the standard solutions [15]. A significant decrease in Se(IV) concentration was most likely caused by their reduction by thiols to elemental selenium. Among various approaches for the synthesis of selenium nanoparticles, the methods involving naturally occurring substances in plant extracts are preferred, as they could act both as reducing agents and stabilizers [43]. Thus, quantification of Se(IV) and SeMet/SeMetO in the presence of the recommended thiols (added for the reduction of SeMetO) is not advisable. Generally, the addition of DDT or \(\beta\)ME does not increase the concentration of selenomethionine with a possible decrease in SeMetO concentration, as was expected. Moreover, the interpretation of the chromatographic profiles becomes more complicated. To the best of our knowledge, there is no exact explanation regarding the effect of the used thiols, especially in the presence of a sample matrix. Betalains and polyphenols, the main compounds present in beetroots, also exhibit reducing properties [44]. However, their mutual interactions with selenium species require further research. The interactions of polyphenols with other food components are of great interest considering the beneficial effects on human health [45; 46]. These interactions, which may be synergistic or antagonistic, can take place during the processing and storage of food, affecting their stability, enzymatic activity, and nutritional value.
# An Improved Small Target Segmentation Model Based on Mask Dino
## 2 Related Methods  
### Segmentation Methods  
The semantic segmentation [10] task involves assigning a corresponding semantic category to each pixel in an image, providing precise pixel-level annotations. Early methods include techniques based on thresholds, edges, and regions. With the development of deep learning, [PERSON] et al. introduced the Fully Convolutional Network (FCN), revolutionizing the approach to semantic segmentation. Subsequently, [PERSON] applied Transformers to semantic segmentation, using positional encoding and self-attention mechanisms to efficiently capture global information from images, thereby improving segmentation accuracy.  
Instance segmentation [11] not only performs pixel-level classification but also separates and labels each object instance in the image. [PERSON] et al. proposed a Mask R-CNN [12] in 2017, which introduced an additional mask branch to unify the processing of object detection and instance segmentation. The instance segmentation method in YOLOv8 uses the largest feature map as the input for the mask branch, further optimizing the performance of instance segmentation.  
Panoptic segmentation combines semantic and instance segmentation, aiming to assign a unique identifier to each object instance while performing semantic classification for every pixel in the image. FCN achieves end-to-end segmentation of images of any size by converting fully connected layers into convolutional layers. U-Net [13] employs an encoder-Decoder architecture, while the DeepLab series enhances perception of structures at different scales through atrous convolution and context aggregation.
## 4 Results and Discussion
### Performance of Packet Loss Rate  
The suggested ECTSA and element model of the existing method was utilized to test the efficiency of the PLR. When compared to conventional techniques, the ECTSA yields better results. In comparison to the existing CEELBRP [24] and EECRP-PSO [25], the ECTSA has a lower PLR. The results demonstrates that the suggested ECTSA has a better PLR of 0.02% than conventional approaches, which have PLRs of 0.07% and 0.08%, correspondingly. Figure 9 shows the performance analysis of PLR. The performance of the proposed ECTSA, CEELBRP [24], and EECRP-PSO [25] is depicted in Figure 10.
# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
###### Abstract
By integrating three interaction methods, it incorporates interactive information on similarity and dissimilarity, enabling focus on key semantics and local interaction information at different granularity levels. The sentence's overall representation is obtained through max pooling, with the pre-trained language model's CLS vector serving as the coarse-grained semantics of the entire sentence pair. By combining these components, the model comprehensively captures the semantic information of the entire sentence pair. In Chinese, a language lacking natural word delimiters, semantic information is conveyed through lexical units. Additionally, the polysemy of Chinese words can introduce ambiguity in various contexts. Phrases formed by word combinations carry specific underlying semantics, and Chinese sentences with different word orders can exhibit entirely different meanings. Approaches that rely on single-granularity text semantic matching can only analyze semantics at a single level and may not accurately capture the semantic relationship between texts. Furthermore, they often overlook the fusion of features from different granularities. In contrast, MCKI explores key semantic information at the levels of words, phrases, and sentences, while fully considering the interaction and fusion of semantics across different granularities. This enables MCKI to achieve superior performance. The main contributions of this paper can be summarized as follows:  
1. Introduction of the multi-granularity contextual key semantic interaction model (MCKI) for sentence matching, which combines multi-granularity feature extraction and key semantic interaction fusion. This enables the model to capture comprehensive semantic information in the text. 2. Development of a semantic feature extraction mechanism that combines multi-scale CNN and multi-level CNN to extract contextual semantic information at different granularities and levels. 3. Construction of a key semantic alignment mechanism by integrating multi-head self-attention and cross-attention, incorporating interaction information at different levels from low level to high level. 4. The proposed model demonstrates superior matching performance compared to existing baseline models in the LCQMC and BQ datasets. Additionally, it achieves comparable results to large-scale pre-trained language models while using a lightweight encoder.
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 3 Proposed Model  
### Overall Architecture  
To utilize high-order feature interactions for representation learning of the sequential user behaviors, we propose a Feature-Interaction-Enhanced Sequential model (FESeq). The overall architecture of the model is shown in Figure 1, which consists of five components:  
* _Embedding layer_: It is used to represent the input feature as a dense embedding representation. We use the input feature embedding layer and the output item embedding layer to represent all original features in the input data and the target item features, respectively, as is shown in Figure 1. The original features include non-sequential features (the user profile and context) and sequential features (the positions, time intervals, user behaviors, and interacted items). The target item features are all the last element of the interacted item feature sequences.
* _Interacting layer_: It is proposed to extract high-order feature interactions from all the original feature fields by using the self-attention mechanism. The details of this layer and how we view feature interaction as a form of an automatic feature engineering step for the input of the sequential model are in Section 3.2.
* _Sequence-refiner layer_: It refines the user's behavior sequence using the Transformer layer. Unlike the vanilla Transformer, we add the positional embedding and the linear time interval embedding to the input of the Transformer layer. More details on this layer are presented in Section 3.3.
* _Sequence pooling layer_: It is designed to pool the output sequence of the Transformer model based on the relevance of each user behavior to the target item. The details on the pooling methods and the attention score calculations are introduced in Section 3.4.  
Figure 1: The architecture of FESeq. The embedding layer embeds the input features into a low-dimensional fixed-length embedding. Then, the interacting layer extracts high-order feature interactions from all feature fields. The sequence-refiner layer further refines the user’s behavior sequence through a Transformer layer. At the sequence pooling layer, scaled bilinear attention pools the Transformer output through explicit user–item similarity calculation. An MLP network in the output layer predicts the CTR probability.  
* _Output layer_: In this layer, we concatenate the output of the sequence pooling layer with the embeddings of non-sequential features and utilize three MLP layers with LeakyReLU hidden activation to reduce the dimension of the output features. Since the task of CTR prediction is a binary classification problem, a sigmoid function will project the output of the MLP layers into CTR probability.