esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Transfer Learning and Pretrained Language Modeling  
Transfer learning improves performance by sharing the parameters of a model that has been trained on similar task data in advance, unlike conventional machine learning that performs isolated and single-task learning. Furthermore, in transfer learning, previously learned tasks are required when learning a new task; therefore, it is more accurate, requires less training data, and its learning process is faster than that of isolated machine learning.  
Transfer learning was first presented and actively applied in image classification, where pretrained network models such as VGG [10], Inception [11], and ResNet [12] were used. In natural language processing (NLP), PLMs such as GPT [13], ELMo [1], and BERT [3] were released in 2018 and achieved state-of-the-art performances in most NLP tasks. In particular, BERT and ELMo overcame the critical constraint of previous language models such as GPT, which is a unidirectional learning method. This unidirectional learning method does not consider the \"context\", which is one of the most important factors of language models for a wide range of NLP tasks; therefore, it restricts the capability of the pretrained representations. To address this issue, bidirectional learning methods that are well-contextualized have been proposed in BERT and ELMo. Unlike ELMo, which is based on long short-term memory (LSTM) and applies a pretrained model to downstream tasks using a feature-based method, BERT solves the vanishing gradient problem constraint caused by recurrent neural network layers by replacing transformer-based modeling with a layer. BERT can be applied to downstream tasks by fine-tuning all pretrained parameters, as shown in Figure 1.  
Figure 1: Architecture of fine-tuning with pretrained BERT in single-sentence classification. For classification, only final hidden vector C is used to predict labels and the other vectors are disregarded.
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 2 Literature Review  
### Current Status of Cost and Schedule Integration  
In construction projects, cost and schedule control is one of the most critical management items. It can be used as a quantitative performance indicator that allows evaluating the success of projects [5]. The common informatization process for managing projects, particularly data associated with cost and schedule, is as follows. First, cost reports are prepared by applying unit cost for each construction activity to quantity-takeoff on building elements. Based on the quantity of construction part, schedule management is performed according to materials, equipment, and work productivity. Measuring progress to plan over time allows operating construction projects in line with progress rate assessment and performance payment. It indicates that quantity, payment, schedule, productivity, progress, and resource management (i.e., labor, material, equipment) have strong correlations and need to be updated as projects proceed. Despite the correlations, however, the vast amount of data associated with project cost and schedule shows a lack of interoperability. It is because the data are produced by individual systems for different purposes, such as quantity take-off, schedule management, cost management.  
Current practice generally uses (1) various documents for construction management, (2) software for independent functions to produce the documents, and (3) relational database management system (RDBMS) to link data in the documents (see Figure 1). Consequently, data associated with cost and schedule are addressed in individual documents with different templates in diverse file formats at various information levels. This fragmented data storage and management causes difficulties in processing, synchronizing, and updating data across the documents with the lack of data connectivity. It brings redundant data input, repeated data management processes, and discrepancies in data during construction management. For instance, quantity-takeoff has been conducted at least six times during construction projects [6]; the same data has been generated seven times on average due to differences in formats [7]. According to the authors' interview, most construction project managers consider information management as cumbersome work and tend to conduct the management at a minimum level.
# The Health-Promoting Potential of Fruit Pomace and Its Application in the Confectionery Industry
## 3 Fruit Pomace: A Nutrient-Rich By-Product with Potential in Food Technology
### Characteristics of Selected Fruit Pomace--Ingredients and Properties
Freeze drying and spray drying are especially effective for preserving heat-sensitive compounds, making them suitable for heat-treated candies or bakery applications. The relevance of fruit pomace in confectionery innovation lies not only in its nutritional contribution but also in its multifunctional roles as a fiber source, colorant, emulsifier, antioxidant, and texture modifier. By selecting appropriate forms--such as dried powders, concentrated extracts, or fermented pastes--producers can tailor pomace incorporation to a wide range of confectionery products, including cookies, candies, marshmallows, and health-oriented chocolate products. Continued research is needed to optimize matrix compatibility, enhance flavor masking when necessary, and evaluate long-term stability of bioactives under storage and processing conditions. All pomaces are excellent sources of dietary fiber and polyphenols. The content of phenolic compounds varies depending on the fruit: grape pomace is dominated by catechins and anthocyanins, while chokeberry and blueberry pomace are rich in anthocyanins. Citrus pomace provides flavanones (hesperidin, eriocitrin) that are rare in other fruits [54; 55]. These compounds correlate with the bioactivity of pomace. Polyphenolrich pomace exhibits potent antioxidant and anti-inflammatory effects. Specific phenolic compounds have targeted benefits, e.g., phlorizin in apple pomace has antidiabetic effects, resveratrol in grapes has cardioprotective effects, and citrus limonoids have anticancer potential. Many studies have confirmed apple, grape, or raspberry pomace extracts' antioxidant, anti-inflammatory, cardioprotective, and antimicrobial activities, and citrus flavonoids' ability to reduce chronic-disease risk. In functional food applications (e.g., fiber-enriched confectionery), these pomaces can thus impart health-promoting phytochemicals such as antioxidants, anti-inflammatory, anticancer, and antimicrobial agents [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 45; 46; 47; 48; 49; 50; 51; 56].
## 3 Methods
### Script Parsing and AR Display Software
#### 3.3.1 AR Registration Based on QR Code  
This study uses QR codes for AR registration. The image of the real scene is captured by the camera, and the QR code in the image is positioned and decoded. According to the 3D model display script generated in the AR configuration module, AR technology is employed to display the 3D animation model on the QR code.  
To superimpose information content on the QR code, AR registration is required, i.e., to locate the pose relationship between the QR code and the camera. AR registration method based on QR code can be regarded as a Perspective-n-Point (PnP) problem. PnP is an algorithm for estimating camera pose by the known coordinates of several 3D feature points and their 2D projection coordinates. As shown in Figure 9, the center point of the QR code is the origin of the world coordinate system; \(P_{i}\) is the corner point coordinates of the QR code in the world coordinate system; \(P^{\prime}_{i}\) is the pixel coordinates of the projection of this point in the image. According to the camera imaging model, the following formula is obtained:  
\[P^{\prime}_{i}=K[R|t]P_{i}\Leftrightarrow s\begin{bmatrix}u_{i}\\ v_{i}\\ 1\end{bmatrix}=\begin{bmatrix}f_{u}&0&u_{0}\\ 0&f_{v}&v_{0}\\ 0&0&1\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{12}&t_{x}\\ r_{21}&r_{22}&r_{23}&t_{y}\\ r_{31}&r_{32}&r_{33}&t_{z}\end{bmatrix}\begin{bmatrix}X_{i}\\ Y_{i}\\ Z_{i}\\ 1\end{bmatrix} \tag{1}\]  
where \(P_{i}\) is the point in the world coordinate system; \(P^{\prime}_{i}\) is the projection of \(P_{i}\) on the image plane; \(K\) is the internal parameter matrix of the camera (where \(f_{u}\) and \(f_{v}\) are the zoomed focal length; \((u_{0},v_{0})\) is the optical center); \(s\) is the scale factor of the image point; \(R\) and \(t\) are camera external parameters, i.e., the rotation and translation of the camera coordinate system relative to the world coordinate system. In this study, the PnP solution is performed on the four corner points of the QR code to obtain the 6-DOF pose of the camera in the QR code coordinate system.  
Figure 9: Schematic diagram of AR registration based on QR code.  
In the script parsing and AR display software, the pose relationship between the QR code and the camera is determined by the camera recognizing the QR code in the real scene. Meanwhile, the 3D model to be displayed and the relative pose between the 3D model and the QR code are determined according to the 3D model display information generated by the parsing script file, thereby realizing AR display based on the QR code. If the assembly disassembly animation is displayed, the motion process between different parts of the assembly is displayed according to the disassembly animation motion parameters, and the user can click the 'Previous' or 'Next' button to realize the animation demonstration. If the industrial virtual instrument is displayed, the instrument interface file can be imported with the instrument display script, and data can be requested from the data forwarding interface through the data tag.
# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 4 Experiments
### Ablation Study
#### 4.5.1 The Impact of Top-k Responses  
The selection of the top-k responses is crucial for optimizing the predictive performance of our methods, yet it is highly dependent on the specific question being analyzed. The optimal top-k setting can vary significantly due to factors such as the number of answer choices and the complexity of the question. For instance, more complex questions or those with a greater number of answer choices may require a higher top-k to capture the necessary context for accurate predictions.  
Our experiments indicate that, on average, F1-Scores tend to increase as top-k approaches 25. Beyond this point, although there are fluctuations, the F1-Scores generally stabilize with no significant improvements. This suggests that while increasing top-k initially enhances the model's ability to capture relevant information, there is a threshold after which additional context provides diminishing returns.  
Figure 3a shows the trend of F1-Scores across different top-k values, clearly demonstrating the plateau effect as top-k exceeds 25. Given this trend, we recommend starting with a top-k value of 5 and incrementally increasing it until the improvements in F1-Score become negligible. Once the performance stabilizes, top-k can be fixed as a hyperparameter for the model. This approach allows for efficient optimization without the need for exhaustive search, balancing the trade-off between computational cost and predictive accuracy.  
Future research could focus on developing methods to automatically determine the optimal top-k for different types of questions, potentially leading to even more refined and efficient survey analysis models.
## 3 RS Applications in Ocean
### Ocean Tide (OT)
#### 3.5.3 SAR
[293] presented the possibility of using high-resolution ALOS, RADARSAT-2, and TSX to characterize the extent of the OT flushing zone. They also provided quantitative constraints for detailed coastal wetland flow models using wetland InSAR techniques.
## 8 Implementation and Performance Evaluation
### Implementation Considerations
#### 8.2.2 Architectural Motivation for Combining DBSM and Point Halving in GN-AK  
Unlike protocols such as ECDH or ECIES, where scalar multiplication typically serves a single pseudorandom key generation role, the GN-AK protocol tightly integrates scalarcomputations within its mutual authentication flow. Both participants repeatedly compute pseudorandom scalar multiplications linked to digital signature commitments and verification steps.  
Applying DBSM and point halving in this context therefore achieves compounded benefits: it reduces not only the core key agreement computation but also the cryptographic overhead associated with session-specific proofs of possession. This dual efficiency is less pronounced in protocols like ECDH, where pseudorandom public values may be precomputed or reused in optimization frameworks and where point halving would primarily impact the one-time key derivation phase.  
By embedding both DBSM and point halving directly into the multi-stage authenticated exchanges of GN-AK, we enable a protocol-level reduction in computational and energy costs that holistically improves handshake performance--especially critical for resource-constrained environments such as medical IoT and embedded authentication systems.  
The next section compares the GN-AK enhancement with other contemporary key exchange protocols to highlight relative strengths and trade-offs.
## 2 Materials and Methods  
The CSC model consists of three main parts as illustrated in the red dashed boxes in Figure 1. The first part is the vertical motion estimation that depends on PS-InSAR surface velocity estimation. The second part is the subsidence causality inputs, which are varied based on the characteristics of the study area. The third part is the spatial integration of vertical velocity and subsidence causality factors. In this part, different ranks and weights are given to extract the best-fitting model to map the complex subsidence causality in the study area.  
Surface velocity is estimated in the LOS direction for ascending and descending geometries. Two LOS measures are integrated to decompose the vertical velocity. Potential subsidence causes are assessed based on literature and initial field investigations. Once confirmed, these spatially distributed factors are used as subsidence causality inputs. Potential causal factors for subsidence are correlated to the vertical velocity map in order to define weights for their classes, based on the mean vertical displacement. Different models are initiated with ranked causality factors, and correlated to a vertical motion map, to extract a best-fit model for CSC mapping in the study area. Detailed steps of the CSC model and subsidence causality inputs for Alexandria illustrated by the blue shapes in Figure 1, the details are presented in the following sub-sections.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 3 An Energy Consumption Optimization for ICN
### Optimal Stopping Theory Is Used to Select Cache Placement Nodes  
The caching placement strategy of this article is proposed by considering a content distribution in a network. The energy consumption sequence can be obtained according to the distance sequence of random variables. Suppose the independent and identically distributed random variable \(P_{u\to v}\) is composed of the energy consumed by the user node \(u_{x}\) to the network node \(v_{y}\). Compared with the content being obtained at the first randomly selected node, the energy savings of the content obtained at the other nodes are represented by \(Y_{N}\). In order to make the model closer to the actual situation, it is assumed that each time the cache node is re-selected, it will consume a certain amount of energy, which is defined as \(P_{c}\). Then \(Y_{N}\) can be expressed as:  
\[Y_{N}=\sum_{x=1}^{m}P_{u_{x}\to v}-\sum_{x=1}^{m}P_{u_{x}\to v_{N}}-NmP_{c} \tag{9}\]  
Among them, the \(mP_{c}\) is used to detect the energy consumed at one time in the case of multiple users, and the number of user nodes is \(m\).  
For the selection of cache nodes, the purpose of maximization (9) is to find the optimal stopping time \(N^{*}\) that can get the best expected reward. The optimal stopping rule is derived using the energy consumption sequence \(P_{u\to v}\) calculated for each observation and the resulting reward sequence \(Y_{N}\). That is, the choice of a time \(N^{*}\), so that:  
\[N^{*}=\arg\frac{\max}{N=0,1,2,...}E(Y_{N}) \tag{10}\]  
Here,  
\[Y_{N}=\sum_{x=1}^{m}P_{u_{x}\to v}-\sum_{x=1}^{m}P_{u_{x}\to v_{N}}-NmP_{c}=X_{N }-NmP_{c} \tag{11}\]  
Formula (9) is brought into Formula (11). For simplicity, the user base is viewed as a whole, so we can get:  
\[\begin{array}{l}Y_{N}=m(c_{k}\tau(G_{E}+\frac{fgd_{u_{x}\to v_{N}}^{2}}{e^{2}}+ G_{R})-c_{k}\tau(G_{E}+\frac{fgd_{u_{x}\to v_{N}}^{2}}{e^{2}}+G_{R})-NP_{c})\\ =m(\frac{c_{k}\tau fg}{e^{2}}\left(d_{u_{x}\to v}^{2}-d_{u_{x}\to v_{N}}^{2} \right)-NP_{c})\\ =m\ \left(\frac{c_{k}\tau fg}{e^{2}}\left(d_{u_{x}\to v}^{2}-d_{u_{x}\to v_{N}}^{2 }\right)-NP_{c}\right)\\ =my_{N}\end{array} \tag{12}\]  
Here,  
\[y_{N}=\frac{c_{k}\tau fg}{e^{2}}\left(d_{u_{x}\to v}^{2}-d_{u_{x}\to v_{N}}^{2 }\right)-NP_{c}=\frac{c_{k}\tau fg}{e^{2}}x_{N}-NP_{c} \tag{13}\]  
The reward function for the problem of maximizing energy savings is shown in Formula (13); \(d_{u\to v1}\) is a fixed value and \(d_{u\to v}\) is a random variable, assuming that it obeys uniform distribution.  
In summary, the problem of selecting the cache placement nodes in ICN has been transformed into the problem of maximizing energy savings, and a corresponding mathematical model has been established. Before solving the problem, it is proved that there is an optimal solution to the proposed problem.  
Figure 1: Optimal stopping elements in cache node selection.
## 2 Nanomaterials in Endodontics
### Metallic Nanoparticles
#### 2.4.2 Metal Oxide Nanoparticles
The biofilm that can accumulate on implants is a complex structure of microorganisms, including bacteria, that can lead to the infection and inflammation of the tissues around the implant. Iron oxide nanoparticles (FeO, Fe\({}_{2}\)O\({}_{3}\)), due to their antibacterial properties, can be an effective tool for disinfecting implant surfaces [155]. They have the ability to destroy the biofilm structure and eliminate bacteria, which may contribute to maintaining the cleanliness and hygiene of dental implants. Moreover, iron oxide nanoparticles can be applied in the form of solutions or nanometric coatings to the surface of implants, which enables their effective penetration and antibacterial effect even in hard-to-reach places [156]. Due to their antibacterial properties, iron oxide nanoparticles have the potential to improve the long-term durability and clinical success of dental implants by reducing the risk of infection and inflammation of peridental tissues [157]. MgO (magnesium oxide) and CaO (calcium oxide) nanoparticles have antibacterial properties against both Gram-positive and Gram-negative microorganisms. These nanoparticles have the ability to damage their cell membranes, which prevents them from surviving and multiplying. This mechanism is widely accepted and used in the fight against bacterial infections, mainly in dental materials, aimed at preventing infections and improving oral hygiene [158; 159]. The potential use of these nanoparticles in endodontics was proposed by [PERSON] et al. They designed an experiment to compare the long-term effectiveness of NaOCI, MgO and chitosan nanoparticles in the eradication of _E. faecalis_[160]. Based on the conducted expert opinions, scientists showed that all these substances were more effective than sodium hypochlorite, commonly used to disinfect root canals during endodontics [160]. CuO nanoparticles have antibacterial potential against both Gram-positive and Gram-negative bacteria by penetrating the bacterial cell membrane and damaging important intracellular enzymes, which leads to the inhibition of bacterial growth and reproduction. Additionally, CuO nanoparticles may also have some antifungal properties, making them a promising candidate for use in various fields, including endodontics. However, the number of published studies on the use of CuO nanoparticles in endodontics is limited, which indicates the need for further long-term studies to assess their effectiveness, safety and potential side effects [161; 162].
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 4 Mechatronics Design of AUH Prototype
### Positioning and Navigation System  
The positioning and navigation system is important for the highly maneuverable AUH, because accurate positioning is necessary for accurate motion control.  
The positioning system used on S-AUH is the inverse ultra-short baseline system (iUSBL). The theory and composition of iUSBL system is similar to the ultra-short baseline (USBL) system. The difference is that one end of iUSBL, which has a hydrophone array, is installed on the AUH instead of on a surface vessel that can be localized by GPS. In addition, the other end of the iUSBL system is installed on an underwater base station. iUSBL can output positioning data directly to the vehicle, while USBL should transfer data from the surface vessel to underwater vehicle through water, which is time-consuming [21].
## 5 Results and Analysis
### Sensitivity Analysis  
Two further investigations explore the impact of learning rate and batch size on the convergence behavior during training. A range of learning rates and batch sizes are tested to determine their effects on the convergence and predictive performance of the CeT model. As shown in Figure 11, the results demonstrate that CeT achieves optimal predictive performance when the learning rate is set to 0.001. This learning rate strikes a balance between convergence speed and stability; a learning rate that is too high leads to divergence or oscillations in the loss function, while a learning rate that is too low results in excessively slow convergence, potentially causing the model to get stuck in suboptimal local minima. Similarly, the experiments reveal that a batch size of 32 is most suitable for CeT when paired with the optimal learning rate. A batch size that is too large leads to inefficient memory usage and slower updates, as it requires more computational resources and time per iteration. Conversely, a batch size that is too small introduces excessive noise in the gradient estimates, leading to unstable convergence and potentially poorer generalization. Therefore, selecting an appropriate batch size is crucial for achieving efficient and effective training. These findings highlight the importance of carefully tuning the learning rate and batch size to ensure robust model performance and efficient training dynamics.  
Figure 10: Analysis of the impact of hyperparameters on predictive performance in Scenario 2.