esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental and Numerical Procedures
### Geotextile Reinforcement  
A woven geotextile that was manufactured from the high strength polyester (PET) was used as the reinforcement in this study. The material properties of geotextile were: mass per unit area = 445 g/m\({}^{2}\), nominal thickness = 0.9 mm, tensile strength = 200 kN/m, and 50 kN/m in machine and cross-machine directions, respectively, and the tensile strain at failure in machine direction = 10%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Soil** & **Total Unit** & **Dry Unit** & **Optimum** & **Liquid** & **Plastic** & **Plasticity** & **Internal** & **Cohesion** \\
**Mixtures** & **Weight** & **Weight** & **Water** & **Limit LL** & **Limit PL** & **Index (PI)** & **Friction Angle** & **(kPa)** \\  & **(kN/m\({}^{3}\))** & **(kN/m\({}^{3}\))** & **Content (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(Degree)** & **(kPa)** \\ \hline B0 & 17.66 & 17.14 & 3.0 & - & - & - & 40 & 2.8 \\ B20 & 18.69 & 16.87 & 10.8 & 49 & 19 & 30 & 30 & 7 \\ B40 & 17.40 & 15.01 & 16.0 & 138 & 20 & 118 & 12 & 21 \\ B60 & 16.17 & 13.32 & 21.4 & 207 & 26 & 181 & 9 & 23 \\ B80 & 15.81 & 12.65 & 25.0 & 290 & 28 & 262 & 5 & 25 \\ B100 & 14.17 & 10.99 & 29.0 & 347 & 33 & 314 & 0 & 33 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and engineering properties of soils.  
Figure 1: (a) Grain size distribution curves of all soil mixtures; (b) liquid limit (LL), plastic limit (PL), and plasticity index (PI) of all soil mixtures.
###### Abstract  
This paper proposes a target vector modification method for the all-transfer deep learning (ATDL) method. Deep neural networks (DNNs) have been used widely in many applications; however, the DNN has been known to be problematic when large amounts of training data are not available. Transfer learning can provide a solution to this problem. Previous methods regularize all layers, including the output layer, by estimating the relation vectors, which are then used instead of one-hot target vectors of the target domain. These vectors are estimated by averaging the target domain data of each target domain label in the output space. This method improves the classification performance, but it does not consider the relation between the relation vectors. From this point of view, we propose a relation vector modification based on constrained pairwise repulsive forces. High pairwise repulsive forces provide large distances between the relation vectors. In addition, the risk of divergence is mitigated by the constraint based on distributions of the output vectors of the target domain data. We apply our method to two simulation experiments and a disease classification using two-dimensional electrophoresis images. The experimental results show that reusing all layers through our estimation method is effective, especially for a significantly small number of the target domain data.  
deep neural network; transfer learning; proteomics; sepsis classification +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Conventional Ultrasound  
Ultrasound of the neck in B-mode (2B) was performed using Hitachi Preirus equipment (Hitachi Medical Corporation, Tokyo, Japan) with a multifrequency Hitachi L64 (5-18 MHz) linear probe, and on a SuperSonic Mach 30 machine (SuperSonic Imagine, France) with a high-resolution linear US probe of 5-18 MHz (L 18-5), by an examiner experienced in this technique (D.S.) [20]. The examination was performed with the patient in the supine position, with hyperextension of the neck, and with the application of a generous amount of coupling gel, evaluating both thyroid lobes and the isthmus morphology.  
In all nodules, a comprehensive, uniform characterization of their US appearance was made before the elastographic evaluation on the same US machine (Hitachi Preirus). The following US features of each nodule were noted for each nodule: maximum dimension (cm), volume (mL), shape (taller-than-wide/wider-than-tall), composition (solid or mixed), echogenicity (iso-, hyper-, hypo-/markedly hypoechoic), homogeneity, margins (regular, irregular/blurred), thyroid capsule integrity, and the presence of calcifications (micro- or macro-, rim or absent calcifications). High-risk ultrasound features in the B-mode evaluation were considered to be: a solid pattern, marked intranodular hypoechogenicity, inhomogeneity, inhomogeneity, a taller-than-wide shape, irregular margins, capsule rupture, the presence of scattered/intranodular microcalcifications [4; 25] and the presence of lymph nodes with a round shape and alteration of the hilium position/aspect [1; 26; 27; 11; 28].  
All nodules were classified into three categories of risk, given by the presence of high-risk features in 2B, according to the European TIRADS (EU-TIRADS) [12]: low risk = solid, isoechoic, or hyperechoic nodule, with no other features of risk (EU-TIRADS 2 and 3); intermediate risk (EU-TIRADS 4) = solid, mildly hypoechoic lesion, without microcalcifications, irregular borders or a taller-than-wide shape; high risk (EU-TIRADS 5) = solid hypoechoic nodule, with a minimum of one very-high-risk feature, such as irregular margins, a taller-than-wide shape, extrathyroidal extension, or microcalcifications.  
The percentage of the change in volume was noted in cases with a previous US evaluation. Color Doppler was used to describe the vascular pattern: predominantly perinodular, intranodular or mixed, but the parameter was not included in the risk assessment strategy [4; 29]. The characteristics in the color Doppler assessment were not considered in our risk-estimation strategy. When more than one nodule was described in the same patient, the ones with the highest risk according to standard US and/or elastography features and that generated the final diagnostic, were included in the final analysis.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Methodology
#### 1.4.2 Variational Membership Mapping Bayesian Models  
In order to derive analytical expressions for the defined privacy leakage, interpretability, and transferability measures, the stochastic inverse models (governing the relationships amongst variables) will be required. In this study, the variational membership mappings are leveraged to build the required stochastic inverse models. Membership mappings [14; 15] have been introduced as an alternative to deep neural networks in order to address the issues such as determining the optimal model structure, smaller training dataset, and iterative time-consuming nature of numerical learning algorithms [16; 17; 18; 19; 20; 21; 22]. A membership mapping represents data through a fuzzy set (characterized by a membership function such that the dimension of the membership function increases with an increasing data size). A remarkable feature of membership mappings is that these allow an analytical approach to the variational learning of a membership-mappings-based data representation model. Our idea is to employ membership mappings for defining a stochastic inverse model, which is then inferred using variational Bayesian methodology.
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity with Direct Simulation Method
#### 4.2.1 Sensitivity Estimation with Directional Sampling
Consequently, an evaluation of the gradient \(\
abla_{\mathbf{x}}g\) is required for each observation \(\mathbf{T}^{(j)}\), in addition to the evaluation of the derivative of \(g\) with respect to \(s_{\ell}\). Therefore, the simulation budget becomes substantially heavier. In [43], the sensitivities with respect to distribution parameters only, \(\mathbf{s}=\boldsymbol{\theta}\), are furthered detailed. Furthermore, a link between the root function \(r\), and the norm \(\beta\) of the design point in the FORM framework is underlined. Indeed, for each observation \(r\Big{(}\mathbf{s},\mathbf{T}^{(j)}\Big{)}\mathbf{T}^{(j)}\) at the intersection between the sampling direction and the limit state surface, a hyperplane can be built as in the FORM context to locally approximate the lsf. There is consequently an equivalence between the derivatives of \(r\) and those of \(\beta\); cf Section 4.1.1. The resulting sensitivity estimate can then be seen as the mean value, with a correction factor, of all the local sensitivities computed on the approximated lsfs [43].
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Technological Evolution of DES in Healthcare
#### 4.5.1 Artificial Intelligence Integration  
Recent advancements in AI have significantly improved DES applications. [PERSON] et al. [55] demonstrated that machine learning algorithms enhance prediction accuracy for patient arrivals, dynamically adjust service time distributions, and automate pattern recognition in patient flow. Moreover, AI-driven models enable real-time optimization of resource allocation, leading to more adaptive and responsive healthcare simulations. Their study in an emergency department setting showed that integrating machine learning improved waiting time predictions by 24% compared to traditional statistical approaches.
# Controllable Image Captioning with Feature Refinement and Multilayer Fusion
## 4 Experiments
### Ablation Experiment
#### 4.4.2 Impact of Each Module on Decoder  
To quantify the impact of each proposed module in the decoder, the ablation experiment is shown in Table 2. All models have MR-WGCN, MA, and RD modules in the encoder. The base model (Row 1 and 5) beginning with the decoder only has semantic content attention (SCA). Then, in Rows 2 and 6, we added a fusion constraint decode (FCD) to the decoder and the performance improved in the accuracy and diversity metrics, except for a b@4 drop of 0.01 in Flickr30k Entities. In particular, CIDEr/Spice/Self-CIDEr improved by 2.1/0.31/0.11 and 2.57/0.04/1.13, respectively. When comparing Row 2 with Row 3, in which a graph update (gupda) was employed for updating node features based on the generated words and currently accessed nodes, there was an improvement in all metrics except s-C. For the performance of Flickr30k Entities, the metrics of B@4, R, NW, s-C slightly decreased, while C, S, and M significantly increased. It shows the networks with gupda tended to generate similar prepositions when the training data were insufficient, and the increased M, R, C, S indicate that the nouns, relations, and so forth were described correctly. Rows 4 and 8 enhance the decoder with graph structure attention (GSA). The graph structure attention captures the structure information in the graph to supplement  
Figure 2: Examples of captions generated by the baseline model, various ablation models mentioned in the Section 4.4.1, as well as the corresponding ground truths (GT).  
semantic content attention. Hence, It outperforms other models in most metrics on two datasets. The metrics of METEOR/CIDEr/Spice/NW/D-1/D-2/Self-CIDEr improved by 0.1/0.98/0.27/0.03/0.52/0.51/2.54 and 0.04/0.42/2.78/0.7/0.63/0.55/0.63/0.11, respectively. The improvement on the small dataset was greater than that on a large dataset. The improvement for visual words was more obvious. Hence, the designed GSA is useful for generating more diverse and accurate sentences.  
Figure 3 shows a few examples of images and the generated captions. From these examples, the proposed modules have advantages in the following two aspects: (1). IANR counts objects more accurately. There are two men/an elephant, not a young boy/some giraffes in the first and second examples. (2). IANR describes the objects more accurately. For example, an elephant, not some giraffes standing in the grass in the second example; a glass and a vase, not a glass and glass on a table in the third example; a teddy bear in front of a car dashboard, not on top of a black car in the fourth example. IANR has these advantages because it makes fuller use of the control signal to generate the correct sentences.
## 3 Results
### Missing Imputation  
As mentioned above, the data used in the study have a lot missing, and in this case, the removal method is usually used. However, the removal method leads to the loss of information. Therefore, in this study, only the dimethyl disulfide variable with a missing rate of 91.2% was removed and the remaining variables were imputed. The suitability of the imputation method was ensured by comparing it to a model that used the removal method. Table 5 shows the top results of the five models when variables with high missing ratios were removed.  
A comparison of the results of the imputation and removal models revealed that R\({}^{2}\) and MAE were superior in the case of imputing. Therefore, it was ascertained that the imputation method was better than the removal method when there were high levels of missing data.  
Figure 9 is a correlation heatmap of the data imputed by multivariate imputation using the Bayesian ridge and KNN imputation used in the upper half of Table 3 among the missing imputation methods. Table 5 shows the correlation results for all the imputation methods used for missing data.  
Table 6 shows that hydrogen sulfide and dimethyl sulfide had a negative correlation with the complex odor in all the results, which means that the complex odor decreased as the two substances increased. Since the other 12 odor substances had a positive correlation, it was inferred that the complex odor increased with the value of the variable.  
The variable that had the most influence on the complex odor evaluated by the correlation coefficient was methyl mercaptan, followed by p-cresol, acetic acid, ammonia, and indole in that order, and the variable with the least influence was hydrogen sulfide. The results of using data preprocessing to improve prediction performance on imputed data are as follows.
## 2 System Model
### Spectrum Efficiency Analysis  
Reasonable configuration of RB allocation and transmission power for the integrated system within a sub-cycle will be provided under the condition of ensuring the QoS requirements of different traffic flows. The goal of the sub-cycle optimization is to maximize the overall network utility, which is the sum of the utility of all users' traffic flows in the integrated system. The users' utility is directly related to their data rate.  
The downlink and uplink SINR of the system can be expressed as:  
\[\gamma_{i}^{n,d}\triangleq\frac{P_{B_{i}}\cdot h_{iB_{i}}}{\sum_{k\in\mathcal{ B}\setminus\{B_{i}\}}P_{k}^{d}h_{k,B_{k}}+\sigma^{2}}; \tag{1}\]  
\[\gamma_{i}^{l,d}=\frac{P_{i}^{l,d}\cdot h_{i,\;B_{i}}}{\sum_{k\in\mathcal{B} \setminus\{B_{i}\}}P_{k}^{d}h_{k,B_{k}}+\sigma^{2}}; \tag{2}\]  
\[\gamma_{i}^{n,u}=\frac{P_{i}^{u}\cdot h_{i,\;B_{i}}}{\sum_{k\in\mathcal{B} \setminus\{B_{i}\}}I_{k,B_{k}}+\sigma^{2}}; \tag{3}\]  
\[\gamma_{i}^{l,u}=\frac{P_{i}^{l,u}\cdot h_{i,\;B_{i}}}{\sum_{k\in\mathcal{B} \setminus\{B_{i}\}}I_{k,B_{k}}+\sigma^{2}}; \tag{4}\]  
where \(\sigma^{2}\) represents noise power, \(h_{i,B_{i}}\) is channel fading from user \(i\) to BS \(B_{i}\):  
\[B_{i}=\operatorname*{argmax}_{j\in\mathcal{B}}\{P_{j}h_{i,j}+\beta\}; \tag{5}\]  
Due to the limitation of user transmission power, the uplink transmission power is  
\[P_{i}^{u}\!\!=\min\{\gamma\sigma^{2}/h_{i,B_{i}},\,P_{i,\max}^{u}\}; \tag{6}\]  
Since the system adopts a dynamic TDD mode, the frequency bands for both the HAPS layer and IMT layer are the same. The interference of uplink transmission depends on the users scheduled together. Therefore, the uplink interferences from the system are different in each time slot. In this paper, average interference is adopted instead of real  
Figure 2: Frame structure design of integrated network.  
time interference in order to avoid the complicated calculation caused by scheduling. The average interference is calculated as follows.  
\[I_{k,j}=\left(\sum_{i\in N_{k}^{u}}P_{i}^{u}\cdot h_{i,j}\right)/|\Gamma_{k}^{u}| \tag{7}\]  
where \(j\) is the number of the interfered cell and \(k\) is the number of the interfering cell. \(\Gamma_{k}^{u}|\) is the number of the uplink only users in the interfering cell. The data rate of the users is:  
\[c_{i}^{d}=\log_{2}(1+\gamma_{i}^{d}) \tag{8}\]  
\[c_{i}^{u}=\log_{2}(1+\gamma_{i}^{u}) \tag{9}\]  
where \(c_{i}^{d}\) represents the data rate of downlink only User \(i\), \(c_{i}^{u}\) represents the data rate of the uplink only user \(i\).
# Developments in Contact Lens Imaging: New Applications of Optical Coherence Tomography
## 2 Clinical Uses in CL Practice  
About 2% of the world's population wears CLs, and in developed countries, this percentage is much higher, reaching 15% in the United States of America [20]. This number is expected to increase gradually given the various benefits that CLs provide in daily activities compared to eyeglasses. According to data from the International Survey of Contact Lens Prescribing in 2018, soft lenses accounted for about 90% of new fits and refits, and the rest used rigid gas permeable (RGP) lenses [21; 22]. Scleral CLs have gained renewed interest since the late 2000s and have become an increasingly important part of RGP fittings in several countries [23].  
Many factors influence lens fitting characteristics, such as the lens material and design, corneal shape, blink rate, lid-lens interactions, and post-lens tear film. Different powers, base curves, edge designs, and diameters affect the correct positioning of the CL on the eye, resulting in measurable lens lag and movement [24]. The RGP lens has a unique fitting method with \"apical touch\" (the lens to the  
Figure 1: Schematic division of OCT systems.  
corneal apex), intermediate clearance, and a band of touch at the junction of the optical zone and the secondary curve. The relationship between the lens edge and the cornea is another significant factor influencing optimal lens fitting and determining patient comfort and maximum wearing time [25; 26]. Most studies on ocular surface shape only investigated the central cornea, which is the most important factor for vision. However, information from the peripheral part of the cornea is particularly relevant in the design and fitting assessment of CLs [27; 28]. The general health of the patient, ergonomics, and the environment are important for the comfort of CL users.  
At present, during the application of soft CL, the practitioner routinely observes lens centration, coverage, and movement with a slit-lamp biomicroscope. Staining of the ocular tear film with fluorescein is used to evaluate the RGP contact lens fit and to assess the ocular surface after a period of wear. However, both methods provide limited information on the interaction between the lens and the ocular surface, and this examination may be insufficient to understand the cause of CL-induced discomfort and complications [28; 29; 30; 31].
## 3 Materials and Testing Methods  
### Materials  
#### 3.1.1 W-Oil  
W-oil is the byproduct after the extraction of fatty acid from vegetable oil, as shown in Figure 2. At present, the annual output of W-oil is approx. 900,000 tons, and the main treatment measure for waste oil is combustion, which produces a pungent odor and greenhouse gas, which pollutes the environment. The typical physical properties of W-oil are shown in Table 1. As shown in Table 1, the low viscosity of W-oil, as 286.7 mPa\(\cdot\)S, means that at 60 \({}^{\circ}\)C, it can soften aged asphalt effectively. In general, the higher the molecular weight, the less volatile it is. The number-average molecular weight of W-oil is 1067 Daltons, so W-oil is expected to have a good anti-volatile performance in construction as a rejuvenator. Fourier transform infrared spectroscopy (FTIR) is a method of determining the chemical functional groups within a medium. The chemical functional groups are groups of atoms that are responsible for different reactions within a compound [12]. In Figure 3, a comparison of infrared spectrogram is made between virgin asphalt and W-oil. The major differences in the composition of functional groups include: (1) W-oil has a stronger absorption peak at 1150 cm\({}^{-1}\) and 1700 cm\({}^{-1}\), while virgin asphalt basically has no absorption peak, implying that W-oil contains a large number of ester bonds; (2) Virgin asphalt shows absorption peaks at 800 cm\({}^{-1}\) and 1580 cm\({}^{-1}\), where W-oil has no absorption peak, which demonstrates that W-oil does not contain benzene. Therefore, W-oil does not contain strong carcinogen-polycyclic aromatic hydrocarbon (PAH), and the use of W-oil as a rejuvenator can reduce the harm to the construction workers.  
Figure 1: The Test Plan.  
2. W-oil rejuvenated asphalt  
W-oil is mixed into the aged asphalt at proportions (by weight) of 5%, 10%, 15%, and 20% at a recovering temperature of 135 \({}^{\circ}\)C in the stirrer at a speed of 2000 RPM for 15 min. Virgin asphalt, aged asphalt, and 5%, 10%, 15%, and 20% W-oil rejuvenated asphalt are named as Virgin, 0%WRA, 5%WRA, 10%WRA, 15%WRA, and 20%WRA, respectively.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 5 Experimental Results and Analysis
### Quantitative Evaluation Results Comparison of Trained Models  
This section quantitatively compares the performance between the proposed multimodal RAG system and existing QA models (Base model). Evaluation used BERT score, cosine similarity, and ROUGE-L, with experiments conducted on the same maintenance query set.  
Table 1 shows performance changes during the learning process of basic language models without RAG technology applied. The basic model showed steady performance improvement from epochs 1-3 and then converged. Final performance recorded cosine similarity 75.81%, BERT score 75.10%, and ROUGE-L 9.09%.  
Table 2 shows the learning performance of the proposed system with RAG technology combined, with significant performance improvements confirmed in all evaluation metrics. The RAG-applied system achieved final performance of 78.11% in both cosine similarity and BERT score, and showed considerable improvement to 27.12% in ROUGE-L compared to the basic model.  
The introduction of RAG technology shows meaningful performance improvements in all aspects of semantic similarity and lexical accuracy. Cosine similarity improved from 75.81% to 78.11% (2.30%p improvement) and BERT score improved from 75.10%  
\begin{table}
\begin{tabular}{c c c c} \hline
**Epoch** & **Cosine Similarity (\%)** & **BERT Score (\%)** & **ROUGE-L (\%)** \\ \hline
1 & 65.23 & 66.12 & 5.45 \\
2 & 71.45 & 72.33 & 7.23 \\
3 & 75.81 & 75.10 & 9.09 \\ \hline \end{tabular}
\end{table}
Table 1: Base model performance (without RAG).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Epoch** & **Cosine Similarity (\%)** & **BERT Score (\%)** & **ROUGE-L (\%)** \\ \hline
1 & 55.32 & 58.21 & 15.67 \\
2 & 76.45 & 76.89 & 25.34 \\
3 & 78.11 & 78.11 & 27.12 \\ \hline \end{tabular}
\end{table}
Table 2: RAG model performance.  
to 78.11% (3.01%p improvement). Particularly in the ROUGE-L metric, an improvement range of 18.03%p was observed (9.09% \(\rightarrow\) 27.12%), indicating significant improvements in domain technical term accuracy and sentence structure consistency in maintenance manual responses. Procedural accuracy also increased to 89.7%, confirming performance improvements supporting actual maintenance feasibility.