esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results: Quantitative Analysis and Most Important Publications
### Temporal Analysis  
This section present the temporal analysis on all datasets discussed in the above section. Both trends and notes will be discussed in detail.  
Figure 4 shows the trend in the number of publications across the three periods. Pay attention to the scale used. The y-axis in Figure 3(a) is 500, in Figure 3(b) it is 1000, and in Figure 3(c) it is 3500. This demonstrates an increasingly rapid growth in the number of publications over time. The number of publications per year has tripled from one period to the next. The trend within each period is almost always upward, except for a few sporadic years in the first period. This indicates a growing interest in the applications of _LLMs_ in medicine.  
The same analysis conducted on the _Review_ dataset reveals a growth in the number of papers per year by an order of magnitude when moving from \(Review_{Medium}\) to \(Review_{New}\). The strong demand for \"reviews\" may be significant because reviews play a key role in unifying approaches from different disciplines, fostering collaboration and knowledge exchange. In particular, the increased use of _LLM_s in medicine in recent years may have caused this surge in reviews (see Table 4).  
Figure 4: Distribution of the _All_ dataset.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Database** & **Period** & **\#Papers** & **\#Papers Yearly** \\ \hline Main & 1995–2024 & 636 & 21.2 \\ \(MainEarly\) & 1995–2017 & 227 & 9.87 \\ \(MainMain_{Medium}\) & 2018–2021 & 138 & 33.5 \\ \(MainNew\) & 2022–2024 & 287 & 95.67 \\ \hline \(MainReview\) & 1995–2024 & 20 & 0.67 \\ \(MainReviewEarly\) & 1995–2017 & 6 & 0.26 \\ \(MainReview_{Medium}\) & 2018–2021 & 2 & 0.5 \\ \(MainReview_{New}\) & 2022–2024 & 14 & 4.67 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main statistics for the databases Main and _MainReview_.  
Figure 4 illustrates publication count trend while Figure 5 deals with publications classified as \"Review\".  
During the first period, shown in Figure 4(a), the number of review publications remained below six per year, with the notable exception of 2017, which experienced a sudden surge to 16 publications. This indicates that 2017 marks the initial significant increase in interest surrounding _LLM_s.  
Figure 4(b) highlights the trend in the intermediate period, beginning with 7 reviews in 2018 and exhibiting a steady rise, reaching 21 publications by the end of this phase.  
A more pronounced shift is observed in the final period, as shown in Figure 4(c). Likely driven by the release of products like ChatGPT, the number of annual review publications reflects a significant growth in interest, climbing from approximately 20 papers to over 200 in 2024. This underscores the accelerating engagement with and practical relevance of _LLM_s in recent years.  
It should be noted that the data for 2024 are not complete, as the data extraction was performed in November 2024.
## 2 Materials and Methods
### Methods
#### 2.3.3 Extraction of Texture Features  
The multispectral bands or spectral indices of satellite imagery are often used to identify infested plants with a classification technique [47]. Most of these techniques, however, only look at the spectral values of individual pixels and do not take the spatial context of pixels into consideration. Texture can be defined as the various measures of smoothness, coarseness, and regularity of an image region [26]. When crops are infested by disease, changes in pigment, water content of the plants result in spectral changes, as well as changes in the canopy structure and the morphology, causing textural changes. Previous studies have shown that it is feasible to identify the crop diseases based on texture features [32]. The popular GLCM texture model [48] has been widely used to extract texture features of images, for this study, the following eight widely used GLCM texture features were selected: contrast, dissimilarity, homogeneity, angular second moment, entropy, mean, variance, and correlation.  
In order to reduce the data redundancy of multispectral bands without significantly reducing the information content in the image, principal component analysis (PCA) [49] was performed on the Sentinel-2A multispectral data in ENVI 5.3 and the first two principal component (PC) images (with more than 98% of the cumulative variance) were chosen. The 16 texture features of the first two PC images were extracted by GLCM. Considering the accuracy and efficiency, the processing window was set to \(3\times 3\), the co-occurrence shift X and Y were set to 1 and 1, and the greyscale quantization levels was set to 64. Then, the 16 texture features were abbreviated as the texture features and numbers. For example, the mean of the first PC images is abbreviated as \"Mean 1\", the variance of the second PC images is abbreviated as \"Variance 1\".
###### Abstract  
Microbial-induced calcium carbonate precipitation (MICP) is a new technology used for reinforcing soils through microbial mineralization. However, the existing MICP treatment technology is more suitTable for deeper soils due to its high permeability. In this study, HPMC, a cohesive material combined with _Sporosarcina pasteurii_-induced calcium carbonate precipitation was used to improve the surface layer of the soil. It was also tested in different contents of bacterial solutions and cementation solutions and with a different number of MICP treatments, and was analyzed and discussed by measuring the surface layer strength, calcium carbonate content generated in the crust, rainfall erosion resistance, wind erosion resistance, and ammonia retention rate of the specimens. The microstructure of the samples was investigated by XRD and SEM. It was shown that the addition of HPMC and increasing the number of MICP treatments were effective in the increase in the calcium carbonate content in the surface crust, but had no effect on the total amount of induced calcium carbonate. The combination of HPMC addition and MICP technology can effectively reduce rainwater scour loss and wind erosion loss, and increase its rainfall erosion resistance and wind erosion resistance. Rainfall losses were reduced by up to 30% in specimens treated with HPMC for MICP technology. When the wind speed was 12 m/s, the maximum mass loss rate of the specimens treated with HPMC for MICP was only 0.828%. The addition of HPMC can effectively improve the ammonia absorption rate and reduce the release of ammonia in the process of MICP technology, which is of great significance for environmental protection. The microstructure showed that the addition of HPMC and the increase in the number of treatments using MICP technology can make the surface structure of the specimens more compact, and the calcium carbonate can more effectively fill the pores and cement the soil particles, while the addition of HPMC may not change the calcium carbonate crystal type.  
MICP; calcium carbonate; HPMC; surface strength; ammonia retention rate; wind erosion resistance +
Footnote †: journal:
## 7 Experimental Results
The main reason that the accuracy of the fault diagnosis in the AMRXU-VSPMI and AMRXU-PMI algorithms is less than that of the proposed AMRXU-AVSPMI is due to the nature of the linear observation (AMRXU-VSPMI and AMRXU-PMI) algorithms. When these procedures are applied to the nonlinear and non-stationary such as bearing signals, the estimation error is developed compared to the nonlinear AMRXU-AVSPMI observation method. However, according to the concept of robustness, the performance of AMRXU-VSPMI is better than the AMRXU-PMI. Subsequently, the proposed hybrid framework is suitable for accurate fault diagnosis of the bearing in different crack sizes and torque loads in comparison with the other referenced methods. From the experimental results, it is obvious that the combination of the AVSPMI observation technique with the AMRXU \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method/Crack Size** & **0.007 in.** & **0.014 in.** & **0.021 in.** & **Average** \\ \hline
**AMRXU-AVSPMI+SVM** & 99\% & 99\% & 100\% & 99.33\% \\
**AMRXU-VSPMI+SVM** & 93\% & 94\% & 96\% & 94.33\% \\
**AMRXU-PMI+SVM** & 92\% & 91\% & 89\% & 90.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The average accuracy in roller crack size detection using the proposed AMRXU-AVSPMI+SVM compared to AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method/Crack Size** & **0.007 in.** & **0.014 in.** & **0.021 in.** & **Average** \\ \hline
**AMRXU-AVSPMI+SVM** & 94\% & 97\% & 96\% & 95.67\% \\
**AMRXU-VSPMI+SVM** & 92\% & 92\% & 93\% & 92.33\% \\
**AMRXU-PMI+SVM** & 89\% & 87\% & 84\% & 86.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The average accuracy for inner crack size detection using the proposed AMRXU-AVSPMI+SVM compared to AMRXU-VSPMI+SVM and AMRXU-PMI+SVM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method/Fault** & **NORM** & **ROLF** & **INRF** & **OURF** \\ \hline
**AMRXU-AVSPMI+SVM** & 100\% & 99.34\% & 95.67\% & 96.33\% \\
**AMRXU-VSPMI+SVM** & 100\% & 94.34\% & 92.33\% & 92.67\% \\
**AMRXU-PMI+SVM** & 100\% & 90.67\% & 86.67\% & 86.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The average accuracy in fault pattern identification from the proposed AMRXU-AVSPMI+SVM compared to AMRXU-VSPMI+SVM and AMRXU-PMI+SVM. modelling approach can improve the performance of the fault diagnosis in comparison with the linear observation technique. However, from Tables 9-12, we can see that the inner and outer performance for fault pattern identification and crack size detection should be improved. Moreover, the preprocessing unit in the presence of the noisy signal must be improved by using filter techniques.
###### Abstract  
Modal parameter monitoring is a widely used structural health monitoring method. However, among other limitations, this method cannot effectively identify slight damage under ambient conditions. This study proposed a novel strain expansion-reduction approach for identifying damage. To verify the feasibility of the proposed method, we numerically and experimentally tested the method using a rigid acrylic frame. The frame was artificially damaged at various depths to reflect various damage scenarios. The increase in the damage index provided an accurate estimation of damage severity. For the case with merely 0.5% damage zone in one slat, the index is increased by 259% of the intact case. When the damage zone was doubled, the index increases significantly by 467% of the intact case, demonstrating excellent sensitivity of the proposed method. To guarantee practical use, the numerical model of the proposed method was applied to an offshore wind turbine jacket substructure and successfully identified multiple damage sites and the damage severity with extremely high (>10) damage index.  
damage identification; jacket substructure; multiple instances of damage; offshore wind turbine; strain expansion-reduction approach; structural health monitoring
## 3 Fault Diagnosis  
The main function of FD is to detect, separate, and identify the faults in MSAE. Meanwhile, judge whether the fault occurs, determine the location and type and diagnose fault size and time. FD exhibits an essential role in demonstrating the relationship between the acquisition data and the health condition, which has got wide attention in MSAE health management, especially for ship O&M. Based on the way or extent they use a priori knowledge, FD methods could be classified into four categories, such as physical model-based method, knowledge-based method, data-driven method, and hybrid method.  
Figure 4: Visualization tools for condition monitoring: (**a**) Ship lubricating oil temperature, (**b**) The SOM graph of marine diesel engine cylinders, and (**c**) Radar chart of ship shafting.
## 2 Materials and Methods
### Sun Vector Calculat Model  
The sun vector in the orbital instant root broadcast was the unit vector in the orbital coordinate system. The sun vector was defined using the J2000 coordinate system [13; 14]. After a series of coordinate transformations from the orbit coordinate system to the unit vector of the optical axis of the guide mirror, the transformation matrix of the satellite attitude had to be considered as shown in Figure 1. Many error factors were difficult to determine, setting all the installation errors was ignored, and the satellite attitude was ignored so the ideal value of the turntable motion could be calculated.  
The ideal position value of the pitch axis can be calculated through the sun vector. It is shown in Equations (1) and (2):  
Sun vector:  
\[S_{0}=[Xs(t)\;\;\;Xs(t)\;\;\;Xs(t)]^{T} \tag{1}\]  
We calculated the ideal value of the pitch axis of the turntable as:  
\[\theta_{pitch}=\text{atan}\bigg{[}\frac{Zs(t)}{Ys(t)}\bigg{]}\times D_{pitch} \tag{2}\]  
where \(\text{S}_{0}\) is the sun vector, \(\theta_{pitch}\) is the pitch axis angle, and \(D_{pitch}\) is the error matrix, which is currently set as the unit matrix. According to the above formula, the initial position of the pitch angle of the turntable could be calculated in advance through the sun vector data broadcasted by the satellite platform. In the actual operation of the turntable, the platform attitude factor and the position error should also be considered. In addition, it was also affected by the operating speed and control current of the turntable itself.
## 4 WEAMR Overview
### Multipath Discovery Process
Figure 5: Overview of the multipath discovery process  
\[C_{p}=\ \alpha H+\beta L+\ \frac{1}{\gamma E}\]  
where \(C_{p}\) is the cost of the path, \(H\) represents the hop count, \(L\) represents the latency, \(E\) is the minimum energy of the path. \(\alpha\), \(\beta\) and \(\gamma\) are the coefficients that assign weight to the associated factors. Depending upon the requirement of the application, we can assign different weights to these factors. The value of these coefficients varies from 0 to 1. Moreover, hop count and the latency values are directly proportional to the path cost, whereas the energy value is inversely proportional to the path cost. WEAMR selects two best paths discovered based on the lowest calculated cost. In case of less dense networks where packet loss and latency is usually less we found out that hop \(\alpha\) and latency \(\beta\) weightages should be kept under 0.10 and the energy coefficient \(\gamma\) should be kept over 0.80 so paths with more energy are used more frequently. In case of densely populated clusters with 1,000 nodes we found out that setting hop \(\alpha\) and latency \(\beta\) coefficients to 0.35 and energy coefficient \(\gamma\) at 0.30  was giving us better throughput and reliability.
# Boosted Reptile Search Algorithm for Engineering and Optimization Problems
## 4 Experimental Results and Discussion
### Series of Analysis 1: Engineering Problems
#### 4.1.1 Welded Beam Design Problem  
Determining the welding beam's settings that lower the fabrication costs are the major goal of solving welded beam design (WBD) problem (Figure 2). These parameters are the bar's height (\(t\)), the length of an attached portion (\(l\)), the weld's thickness (\(l\)), and the bar's thickness (\(b\)).  
The definition of WBD is formulated as  
\[\begin{array}{ll}\textit{Consider}&\vec{u}=[u_{1}\;u_{2}\;u_{3}\;u_{4}]=[h\; l\;t\;b],\\ \textit{Minimize}&f(\vec{u})=1.10471u_{1}^{2}u_{2}+0.04811u_{3}u_{4}(14.0+u_{ 2}),\\ \textit{Subject to}&g_{1}(\vec{u})=\tau(\vec{u})-\tau_{max}\leqslant 0,\\ &g_{2}(\vec{u})=\delta(\vec{u})-\delta_{max}\leqslant 0,\\ &g_{3}(\vec{u})=\sigma(\vec{u})-\sigma_{max}\leqslant 0,\\ &g_{4}(\vec{u})=P-P_{c}(\vec{u})\leqslant 0,\\ &g_{5}(\vec{u})=u_{1}-u_{4}\leqslant 0,\\ &g_{6}(\vec{u})=1.10471u_{1}^{2}+0.04811u_{3}u_{4}(14.0+u_{2})-5.0\leqslant 0,\\ &g^{\gamma}(\vec{u})=0.125-u_{1}\leqslant 0\\ \textit{Variables}^{\prime}\;\textit{range}&0.1\leqslant u_{1}\leqslant 2,\\ &0.1\leqslant u_{2}\leqslant 10,\\ &0.1\leqslant u_{3}\leqslant 10,\\ &0.1\leqslant u_{4}\leqslant 2\\ \textit{where}&\tau(\vec{u})=\sqrt{(\tau^{\prime})^{2}+2\tau^{\prime}\tau^{ \prime\prime}\frac{u_{2}}{2R}+(\tau^{\prime})^{2}},\\ &\tau^{{}^{\prime}}=\frac{p}{\sqrt{2u_{1}u_{2}}},\;\;\tau^{{}^{\prime\prime}} =\frac{MR}{J},\\ &M=P(L+\frac{u_{2}}{2}),\\ &R=\sqrt{\frac{u_{2}^{2}}{4}+(\frac{u_{1}+u_{3}}{2})^{2}},\\ &J=2\Big{\{}\sqrt{2u_{1}u_{2}}\Big{[}\frac{u_{2}^{2}}{4}+(\frac{u_{1}+u_{3}}{ 2})^{2}\Big{]}\Big{\}},\\ &\sigma(\vec{u})=\frac{6 PL}{u_{4}u_{3}^{2}},\;\;\delta(\vec{u})=\frac{6 PL^{3}} {Eu_{3}^{2}u_{4}}\\ &P_{c}(\vec{u})=\frac{\sqrt{\frac{480\pi}{36}}(1-\frac{u_{1}u_{2}^{2}}{2L})}(1 -\frac{u_{3}}{2L}\sqrt{\frac{E}{4G}}),\\ &P=6000\;lb,L=14\;in.,\;\;\delta_{max}=0.25\;in.,\\ &E=30\times 1^{6}\;\text{psi},\;\;G=12\times 10^{6}\;\text{psi},\\ &\tau_{max}=13600\;\text{psi},\;\;\sigma_{max}=30000\;\text{psi}\end{array}\]RSRFT was compared to several optimization techniques, such as the whale optimization algorithm (WOA) [41], MVO [12], evolutionary particle swarm optimization (CPSO) [42], LSHcEpS [43], co-evolutionary differential evolution (CSCA) [44], the simplex method (SIMPLEX) [45], Davidon-Fletcher-Powell (DAVID) [45], the gravitational search algorithm (GSA) [46], the GA [47], harmony search (HS) [48], and LSHADE_SPACMA (we renamed it as LSHSPCM) [49]. The developed RSRFT was conducted for 25 independent runs with \(t_{max}=500\) and \(N=25\).  
Table 1 contains the results of RSRFT and the compared optimization techniques. The RSRFT technique had the lowest cost (as given in the column Optimal Objective), followed by LSHSPCM, while SIMPLEX had the highest cost. As a result, the design parameters obtained by RSRFT were more suitable for WBD.
## 4 Results and Discussion
### New Pictogram Events
#### 4.2.2 What Is the Average Length of Newly Created Pictograms for Each Category?  
As with the generated sentences, by studying the average length of the **name** of the pictograms created for each category (see Table 5), we can deduce the complexity of the communication. Table 17 shows the average length, in characters, of the written texts of the created pictograms.  
Categories such as health, nutrition, and leisure opt for longer constructions (e.g., \"_Dolor de garganta_\", sore throat), while categories like care and social interaction prefer more direct expressions (e.g., \"_Dormir_\", sleep, or \"_Hola_\", hello). It is noticeable that compared with the results obtained in Test 1 (see Section 4.1.1), there is not much difference in the average length of the sentences and the pictograms alone for categories like health, emotions, and social interaction (less than double the length). We see the opposite situation for categories such as care and nutrition, where the average length of sentences triples the length of their pictograms. This seems to back up the interpretation that categories such as health or social interaction prioritize immediacy, while nutrition and care communications prioritize specificity.
# An Algebraic Approach to Identifiability
## 2 Preliminaries  
Let \(\Theta\subseteq\mathbb{R}^{s}\) be a semi-algebraic set of parameter-tuples, i.e., a subset of \(\mathbb{R}^{s}\) described by a boolean combination of polynomial equations and inequalities. For each parameter-tuple \(p\in\Theta\), consider a real-analytic manifold \(\mathcal{M}_{p}\subseteq\mathbb{R}^{n}\) and the dynamic system  
\[\dot{x} =f_{0}(x;p)+\sum_{k=1}^{m}f_{k}(x;p)u_{k} \tag{1a}\] \[y_{k} =h_{k}(x;p),\;k=1,\ldots,l \tag{1b}\]  
with vector fields \(f_{0},f_{k}:\mathcal{M}_{p}\rightarrow\mathbb{T}\mathcal{M}_{p}\) and real-analytic scalar fields \(h_{k}:\mathcal{M}_{p}\rightarrow\mathbb{R}\). Herein, \(x\), \(u\), and \(y\) denote the system state, input, and output, respectively. Furthermore, the parametric manifold \(\mathcal{M}_{p}\) is the real variety of a polynomial ideal \(\left\langle c_{1},\ldots,c_{j}\right\rangle\subset\mathbb{R}[x,p]\), and the components of \(f_{0}\), \(f_{k}\) as well as \(h_{k}\) are polynomials in \(\mathbb{R}[x,p]\).  
In the context of parameter identification, the system state is usually extended to contain the parameters too, which obey the simple differential equation:  
\[\dot{p}=0. \tag{1c}\]One has to ensure that the initial value \(p(0)\in\Theta\) is chosen such that \(\mathcal{M}_{p}\) is a manifold. Note that \(\mathcal{M}_{p}\) may be singular for some \(p\
otin\Theta\). This allows to consider identifiability as a special case of observability, where a subset \(p\) of the extended system state \((x;p)\) is to be determined from the input and output trajectories.  
For nonlinear systems, observability is defined using the concept of the indistinguishability of states: Two system states \(x,\tilde{x}\in\mathcal{M}_{p}\) are said to be _distinguishable_ if and only if there exits an interval \([0,T[\) with \(T>0\) and a piecewise constant input \(u:[0,T[\to\mathbb{R}^{m}\) such that the output trajectories \(h(x(t);p)\) of Equation (1) corresponding to initial conditions \(x(0)=x\) and \(x(0)=\tilde{x}\), respectively, differ in that interval. Contrarily, if the output trajectories are identical, the states are called _indistinguishable_.  
This can also be applied to the extended state \((x;p)\) or formulated for distinguishability in the parameters only, if the initial condition \(x(0)\) is known, as opposed to the observability case, where the parameters are supposed to be known.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 3 Model Architecture  
To address the challenges of retrieving few-shot, small-target, and weak-feature images, this paper proposes a multi-granularity retrieval algorithm based on unsupervised image augmentation. The framework consists of a feature extraction module and an image retrieval module. In the feature extraction stage, this paper designs the AugODNet_BRA algorithm, which efficiently captures high-level semantic features of images through unsupervised learning. The core idea of AugODNet_BRA is image augmentation, where the embedding space is optimized so that enhanced representations of the same source image cluster closely while those of different source images remain distant, thereby enhancing feature discriminability.  
To improve the model's adaptability in complex scenes and its ability to capture small-target details and multi-scale features, this study employed a Deep Convolutional Neural Network (DCNN) as the foundational architecture, with ResNet18 serving as the backbone network. ResNet18 was chosen for its relatively shallow network depth and robust feature extraction capabilities, which excel in few-shot learning scenarios. To adapt to the processing needs of small targets and weak features, this paper introduces targeted improvements to ResNet18. Firstly, Omni-Dimensional Dynamic Convolution modules are incorporated into the second residual stage (Res2) and the third residual stage (Res3) of ResNet18, replacing traditional convolutional layers to enhance the model's adaptive feature extraction capabilities for targets of varying scales. Secondly, a Bi-Level Routing Attention (BRA) mechanism is added after Stages Res2 and Res3 to strengthen the association between mid-level and high-level features, thereby increasing the model's focus on key areas. Additionally, to optimize the robustness and discriminability of feature representation, this research designed a loss function that integrates boundary margins, class center regularization, and dynamic loss weights. The training process of the model adopts an unsupervised learning approach, learning feature embeddings from image pairs generated through data augmentation without reliance on class labels.  
The overall framework of the multi-granularity retrieval algorithm based on unsupervised image enhancement designed in this study is shown in Figure 1.