esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Performance of MIPs and NIPs
#### 3.3.4 Selectivity Study  
For visual observation of the selectivity of MIPs, _L. monocytogenes_ and _S. aureus_ were separately absorbed on MIPs and were directly observed via an optical microscope (Figure 6). Many _L. monocytogenes_ were observed, indicating the high binding uptake for template bacteria. By contrast, only a few _S. aureus_ were absorbed onto the MIPs, showing that the spherical _S. aureus_ did not fit into the imprinted sites. Those that were generated were completely fitted with the rod _L. monocytogenes_. The low binding capacity for _S. aureus_ was simply dependent on the non-specific adsorption.  
The interference experiment evaluated selective recognition of the template bacteria. Some commonly foodborne pathogens were selected as competitors, including _E. coli_, _Salmonella_, and _S. aureus_. Figure 5c shows the adsorption capacities of MIPs and NIPs on different bacteria. Under the same conditions, the binding capacities of the MIPs on _L. monocytogenes_ and the other three bacteria were significantly different (_p_ < 0.05). The imprinted polymers expressed the highest specific adsorption for _L. monocytogenes_ with significantly lower adsorption of other bacteria. By contrast, the NIPs absorbed fewer template bacteria than the MIP beads because of the absence of selective imprinted recognition sites. Therefore, other than the electrostatic interactions, the recognition mechanism of the MIP beads was closely related to the complementary shape and size of binding sites that were produced by the template bacteria. The results indicate that the high recognition specificity of the imprinted polymer enabled the MIP beads to bind the target bacteria.
# Incorporating Code Structure and Quality in Deep Code Search
## 4 Model
### Architecture
#### 4.1.1 Description Embedding Module  
Description embedding module (DE-Module) embeds natural language code annotations into description vectors. The first sentence in the code annotation of a code snippet  
Figure 4: The detailed structure of the MD-JEnn.  
Figure 3: The overall structure of the MD-JEnn.  
always represents the summary of the entire code snippet. In order to obtain the embedded description vector, DE-Module processes a natural language description in the following steps:  
Firstly, DE-Module takes a natural language description as input and outputs an embedded feature vector. In DE-Module, BLSTM considers a natural language description \(D=\{w_{1},w_{2},\dots,w_{N_{D}}\}\) as a sequence that contains \(N_{D}\) words. BLSTM takes the description as input and calculates the hidden state according to each time step. BLSTM updates the hidden state \(h_{t}\) at time \(t\) by concentrating input word \(w_{t}\) and preceding hidden state \(h_{t-1}\):  
\[h_{t}=tanh(W^{D}[h_{t-1};\mathbf{w}_{t}]+b^{D}),\forall t=1,2,\dots,N_{D} \tag{12}\]  
where \(h_{t},t=1,2,\dots,N_{D}\) are the hidden states of BLSTM. \([x;y]\in R^{2d}\) is a concentrate operation to integrate \(x\) and \(y\). \(tanh\) is a commonly used activation function. \(\mathbf{w}_{t}\in R^{d}\) represents the embedded representation of natural language word \(w_{t}\). \(W^{D}\) and \(b^{D}\) are the weight matrix and bias matrix of BLSTM (either of the matrices is bi-directional). In this way, a description is embedded into \(N_{D}\)\(d\)-dimensional embedded feature vectors.  
Secondly, since some words in a description are important, it is necessary to assign higher weights to these important words. For example, the words _write_ and _file_ are more important as they express the key semantics of the description _write to a file_. To give higher weights to important words, MD-JEnn introduce attention mechanism to aggregate the embedded vectors of the description into a represented vector by calculating a scalar weight for each vector of the description word. The individual vectors are aggregated to a represented description vector \(v\) via attention:  
\[\alpha_{i}=\frac{exp(\widetilde{h_{i}}^{T}\cdot\alpha)}{\sum_{j=1}^{n}exp( \widetilde{h_{j}}^{T}\cdot\alpha)} \tag{13}\]  
\[v=\sum_{i=1}^{n}\alpha_{i}\cdot\widetilde{h_{i}} \tag{14}\]  
where \(\alpha\in R^{d}\) is initialized in random, \(h_{t},t=1,2,\dots,N_{D}\) are the hidden states of the previous BLSTM layer. \(\alpha_{i}\) is the attention weight of each \(\widetilde{h_{i}}\). The exponents in \(\alpha_{i}\) are the common form of softmax function. The vector \(d\) can be treated as the representation of the input description. The description vector \(d\) considers both lexical information and semantic information. In this way, DE-Module can emphasize the key words in a description.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview  
In this section, existing underwater technologies are described. Figure 1 depicts the architecture of the underwater environments of numerous technologies pertaining to communication. Signal communication in such environments might comprise several aspects, such as relations to buoy or ship, from terrestrial to satellite. It is also possible to exchange information using RF antennas set up on floating equipment and land stations. The exchange of data with underwater stations is made possible by using floating structures that contain communication devices. Inside water environments, it is possible to deploy various kinds of communication nodes containing AUVs, wired networks, and wireless systems in local areas. Some devices may be attached or anchored to the seafloor.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Basic Image Augmentation Methods
#### 2.1.3 Dropout  
Dropout [29] refers to randomly discarding the channel of the image or discarding some areas of the image in different ways to achieve the effect of data augmentation [30]. Discarding the image area sets the pixel value of the area to the background pixel value or zero. It is similar to randomly setting the activation value of some neurons to zero in the neural network, which can realize the regularization of the neural network model and make the network learn more robust features to improve the segmentation ability of the depth network for the image foreground and background. Examples include ChannelDropout, CoarseDropout and GridDropout.
## 6 Discussion  
### Publication of Literature on SM-EMOEAs and MSIMP Based on SM-EMOEAs  
In this work, we searched through databases such as the Science Citation Index (SCI), Scopus Literature Index, and EI Engineering Index (EI); conducted literature research on SM-EMOEAs; and classified the development of SM-EMOEAs into four stages as follows.  
1. Embryonic period (2000-2005): preliminary application and basic research of surrogate models [155; 156; 157].
2. Development period (2006-2012): improvement of surrogate models and optimization of expensive multi-objective evolutionary algorithms [158; 159; 160].
3. Rapid growth period (2012-2019): improvement of surrogate models and application of expensive multi-objective evolutionary algorithms [161; 162; 163].
4. Maturity period (2019-2024): combination of advanced technology leads to methodologically diverse SM-EMOEAs, improving their efficiency and adaptability [164].  
The specifics regarding the development of SM-EMOEAs are shown in Figure 9.  
In Figure 10, the publication of journal papers, conferences, and dissertations involving SM-EMOEAs from 2000 to 2023 is summarized. Figure 10a demonstrates the publication numbers of these three types of papers, and the results show that the research interest and  
Figure 9: Development of SM-EMOEAs.  
attention of domestic and foreign researchers regarding SM-EMOEAs have been increasing, especially in 2015, which showed significant growth. Figure 10b shows the statistics of the literature publication categories based on SM-EMOEAs, in which the three categories of technology, science, and computer science occupy the main share, indicating that SM-EMOEAs have a wide range of application scenarios in these fields.  
In addition, the literature on MSIMP based on SM-EMOEAs was analyzed. The publication of MSIMP studies based on SM-EMOEAs between 2000 and 2023 is shown in Figure 11. Figure 11a shows that the number of MSIMP studies based on SM-EMOEAs exhibited a significant increase between 2000 and 2023. Figure 11b reflects that MSIMP based on SM-EMOEAs provides strong technical support and a good algorithmic foundation in technical fields such as aerospace and computer science.  
Figure 11: Literature published on MSIMP based on SM-EMOEAs. (**a**) MSIMP literature publication based on SM-EMOEAs; (**b**) 2000–2023 MSIMP literature publication category statistics based on SM-EMOEAs.  
Figure 10: Publications on SM-EMOEAs. (**a**) Literature publication based on SM-EMOEAs; (**b**) 2000–2023 literature publication category statistics based on SM-EMOEAs.
## 3 Results
### Maintenance Event Timelines and Workloads
#### 3.2.1 Scheduled Maintenance Event Timeline  
The scheduled maintenance plan provided by the case study company covers two categories: (1) downtimes that lead to production stoppage, and (2) downtimes that have no effect on production. In this context, the scheduled maintenance intervals that lead to production stoppages are especially interesting as they represent opportunistic intervals, as shown in Figure 12. In the figure, the \(x\)-axis represents the simulated time in hours and the \(y\)-axis defines the state of the operation that is either characterized by 0 or 1. Furthermore, 0 defines an operational state when the equipment operates as normal and 1 defines an operational state when the equipment is out of operation due to scheduled maintenance. As highlighted by the figure, there are in total 19 opportunistic maintenance intervals occurring during 20 years of operation, which PdM can be leveraged into in terms of intelligent maintenance.
# From Large Language Models to Large Multimodal Models: A Literature Review
## 2 Large Language Models
### LLM Parameter-Efficient Fine-Tuning  
Distinct from full fine-tuning, parameter-efficient fine-tuning strategies aim to achieve the adaptation of pretrained models with a minimum of parameter updates and computational resources. Prevailing methods for parameter-efficient tuning include:  
**Adapter Tuning [26]:** Adapter-based approaches propose the insertion of compact learnable modules, referred to as adapters. This tuning can be inserted within every layer or a subset of layers in the model. Owing to lower dimensionality, adapters enable fine-tuning that exclusively targets these newly added parameters while preserving the original model weights, which ensures the feasibility of fine-tuning and maintains computational efficiency.  
**Low-Rank Adaptation (LoRA) [27]:** The core idea behind LoRA is to perform updates through low-rank approximations of the model's original weight matrices. This necessitates learning only two smaller matrices that effectively modify the entire model behavior, thereby significantly reducing the number of required parameters while maintaining performance comparable to full fine-tuning.  
**Quantized Low-Rank Adaptation (QLoRA) [28]:** Building upon LoRA [27], QLoRA integrates quantization. It introduces quantization operations to further compress the space of trainable parameters. QLoRA is particularly advantageous in scenarios where deep learning models are deployed and updated in resource-constrained environments, such as edge devices, offering substantial benefits in terms of efficiency under strict limitations.
## 5 Discussion
This is mainly because there is a rapid transition from always irrigated rice fields at the reproductive stage to no longer irrigated status at the ripening stage. We can observe that non-parametric methods (SVR and RF) have better performance over the parametric MLR method for estimating rice parameters (See Tables 3 and 5). Between the SVR and RF models, the RF method obtains slightly better results than the SVR. With the RF method, the correlation coefficient \(R^{2}\) and RMSE were 0.92 and 16% for rice height, 0.90 and 18% for rice dry biomass. For rice height, all methods (MLR, SVR, and RF) yield the best performances with the dual-polarization VV and VH model. For rice biomass, we observe that the combination of VH and VV polarizations does not bring to the good result as a single VH polarization in SVR and RF methods. This can be explained by the fact that the VH backscattering is mainly from the depolarizing part represents a small proportion of biomass, but it is highly correlated with the total biomass [48]. The RMSE was 16% (7.9 cm) for rice height, which is better than the result (13.3 cm) reported in the recent literature [53]. For rice biomass, the RMSE was 18% (162 g\(\cdot\)m\({}^{-2}\)), which is better than the recent results (RMSE = 170.49 g\(\cdot\)m\({}^{-2}\)) reported by [PERSON] et al. [54], who used an Artificial Neural Network (ANN) inversion method on the C-band RADARSAT data. Last but not least, our rice biomass estimation (162 g\(\cdot\)m\({}^{-2}\)) is much better than the performance (200 g\(\cdot\)m\({}^{-2}\)) from L-band ALOS/PALSAR data [55]. In parallel with machine learning algorithms, there is also another approach to estimate rice parameters. This is physical model-based inversion (such as Karam Model [56], Water Cloud Model [57], etc.). However, physical models are complicated and related to certain rice growth models, which require a wide area for the experiment station to measure model parameters. These limitations of physical model-based inversion methods make sense to work on machine learning methods [36; 37]. We demonstrated in this paper that the non-parametric methods (SVR and RF) had a good performance in estimating the rice biophysical parameters. Finally, among agricultural practices in the Camargue region, the rice crop plays a crucial role in the cropping systems. This is mainly because the rice irrigation allows the leaching of salt and consequently leads to the introduction of other species (e.g., wheat, sunflower, etc) into the rotation of crops [39]. However, the rice areas tend to decrease from 16,000 ha in 2011 ([29]) to 10,627 ha ([30]) in 2017. The downward trend of the rice extent can lead to a negative impact on the sustainable agricultural development. Future studies should be considered the remote sensing assimilation, such as Sentinel-1 radar for rice height and biomass as our demonstration, in crop models to better follow farming practices, estimate rice production and provide strategies for the sustainable agricultural development.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 1 Introduction
The remainder of this paper is organized as follows: Section 2 provides some preliminary information about the underlying IPL model and derives its representation (in terms of the PDF and CDF), suitable for communications systems' descriptions, as well as its MGF with its high-SNR asymptotics; Section 3 derives the results for the specific application of the IPL channel in wireless communications (i.e., the exact expressions for the AoF, ABER, OP, and EC); Section 4 delivers an in-depth analysis (theoretical, numerical, and simulation) of the derived expressions for all specific channel and system parameters' values; and conclusions are drawn in Section 5.
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 2 Related Work
### Intensity Transformation-Based Approaches to Gait Recognition  
As mentioned in Section 1, the intensity transformation-based approaches transform intensity values of an original gait feature into more discriminative values to increase the robustness against change of the covariate conditions. For example, [PERSON] et al. [12] computed the GEnI by Shannon entropy of the foreground probability at each pixel (i.e., gait energy in the GEI). The GEnI encodes the randomness of pixel values in the silhouette images over a complete gait cycle, thereby capturing more motion information (dynamic components) rather than static information, which improves robustness against shape changes (e.g., clothing and carrying status). Masked GEI [13] is another intensity transformation-based approach that keeps the dynamic components as their original values, while it zero-pads the static components (i.e., both almost foreground and background parts), which are decided by a certain threshold. In contrast to upper two approaches, recently [PERSON] et al. [22] proposed a joint intensity transformation-based method, which focused on the joint intensity transformation of a pair images instead of a single one. Specifically, a metric on joint intensities between a pair of probe and gallery was learned to mitigate the large intra-subject differences as well as leverage the subtle inter-subject differences. However, similarly to our previous work [20], it does not consider the different effect of the joint intensity metric among different body parts (e.g., more leverage on the motion difference in the legs than the torso part since clothing and carrying status less affect the legs).
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 3 PV Model Architecture  
### Physical Model  
The output power of a PV array can be calculated by (16) [27].  
\[P_{s}=\eta SR[1-e(t_{0}-25)] \tag{16}\]  
where, \(\eta\) is the transform efficiency of the PV array; \(S\) is the area of the PV array (\(m^{2}\)); \(R\) is the solar radiation intensity (\(\mathrm{kW/m^{2}}\)); \(e\) is the loss in efficiency of the array for every degree Celsius of cell temperature increase (always equals to 0.005); and \(t_{0}\) is the ambient temperature (\({}^{\circ}\)C).  
As shown in (16), the output power is affected by several factors, including the transform efficiency, PV array size, solar radiation, and ambient temperature.
## 3 Results of Experiments and Their Discussion  
### XRD Measurements  
Figure 1 shows that the crystallite size \(t\) drops down abruptly when the plastic deformation takes place instead of the elastic one, and the local stresses expressed in terms of micro-deformation grow, followed by the region of plastic strain up to 15% when the alterations of these parameters become quite negligible. As soon as the \(\varepsilon\) becomes more developed and exceeds 15%, a significant decrease of \(t\) and the corresponding growth of micro-deformation can be seen. In this particular case, the crystallite size is mainly affected by the increasing dislocation density and the corresponding hardness [27; 28] (check also the previous study [12]).  
Figure 1: Crystallite size and micro-deformation as a function of \(\varepsilon\).  
The increasing density of lattice imperfections makes the size of the coherently diffracted domains lower and increases the degree of local micro-stresses since the strain hardening in S235 steel is mainly driven by the dislocation motion and its increasing density.  
Pole figures in Figure 2 demonstrate the growing preferential crystallographic orientation of the matrix in the TD direction along with more developed plastic straining.  
For small plastic straining of 2.5%, the weak texture \(\gamma\)-fibre was found. Conversely, strengthening of the _[111]_<123>, _[001]_<110>_ rotational cubic, and _[110]_<001>_ Goss texture components were found with increasing plastic strain (see also Figure 3). This effect is typical for deformed materials [21]. The typical _[111]<112>_ texture component was shifted by approximately 15\({}^{\circ}\) from its ideal location to _[111]<123>_, for all samples. The strengthening of the rotational cubic and _[111]<123>_ texture components, which is evident from the pole figures _[110]_, results in the easy magnetisation axis perpendicular to the RD.
## 3 System Model Description and Problem Statement
### DQN Model for RAN-Level Slice Admission Control
#### 3.4.3 Reward Function  
To ensure optimal decision making, the reward function is designed to maximize efficient resource utilization while maintaining QoS guarantees. At each time step \(t\), the agent receives a positive reward only when a request is correctly processed:  
\[R_{t}=\begin{cases}1,&\text{if the slice request is correctly processed}\\ 0,&\text{otherwise}\end{cases}.\]  
A request is considered correctly processed if the following are met:  
* The assigned PRBs match the requested bandwidth segment.
* The latency and transmission rate requirements are satisfied.
* The slice life cycle constraints are met.
* The system either assigns or rejects the request correctly based on resource availability.  
In this context, \"correctly processed\" implies that the agent successfully allocates resources while meeting QoS constraints or appropriately rejects the request when resources are insufficient.
## 4 Results and Analysis
### Comparative Analysis of ML Models with TextBlob Sentiments Using Balanced Dataset
#### 4.5.2 Performance of Models on Balanced Dataset Using SMOTE Technique  
Table 31 shows the performance of the ML models with respect to the SMOTE balanced dataset. The results show that the performance of machine learning models improved significantly when the SMOTE technique was used. In addition, SVM outperformed all other models in terms of all evaluation metrics.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 87.14\% & 0.872 & 0.873 & 0.872 \\ Bagging & 88.36\% & 0.881 & 0.883 & 0.882 \\ SVM & 87.39\% & 0.873 & 0.873 & 0.873 \\ LR & 84.92\% & 0.849 & 0.851 & 0.850 \\ NB & 82.11\% & 0.821 & 0.821 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 30: Performance results of ML models using the under-sampling dataset and the concatenated features and all lexicons.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Original Dataset Size** & **Under-Sampling** & **Oversampling** \\ \hline Positive Reviews & 39,165 & 9304 & 39,165 \\ Negative Reviews & 9304 & 9304 & 39,165 \\ Total & 48,469 & 18,608 & 78,330 \\ \hline \hline \end{tabular}
\end{table}
Table 29: Number of samples after applying re-sampling.  
By comparing the performance of all ML models before and after balancing the dataset, it is obvious that the NB classifier benefited more when the SMOTE technique was applied. On the other hand, the NB classifier was extremely affected, as shown in Figure 10.  
In addition, the outcomes of the proposed methods (ML models using SMOTE dataset and the concatenated features and all lexicons) were compared with a recent and similar study [47] that used four ML methods for Arabic sentiment analysis of users' opinions of governmental mobile applications. The study [47] was one of the earliest to explore users' opinions about governmental apps in Saudi Arabia. They applied DIree, SVM, KNN, and NB classifiers on a new Arabic dataset that included 7759 reviews collected from Google Play and the App Store. The results of [47] showed that KNN outperformed the other methods with the accuracy of 78.46%. Table 32 shows the comparison results of the best-performing methods in this study and [47]. It is obviously shown that the proposed method obtained superior performance to the ML methods used in the previous recent study [39] using all evaluation criteria.