esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies  
Text-to-SQL semantic analysis essentially involves translating natural language into a logical format [10; 11]. Early approaches often utilized sketch-based slot-filling methods, employing various modules to predict the corresponding components of SQL queries. These methods divided the SQL generation task into multiple independent sketches and used different classifiers to predict the respective parts, such as SQLNet [12], SyntaxSQL-Net [13], and RYANSQL [14]. However, most of these methods only handled simple queries and could not generate correct SQL for complex datasets (e.g., Spider).  
In the context of multiple tables and complex SQL settings, utilizing graph structures to encode various intricate relationships has emerged as a significant trend in the text-to-SQL task [15; 16; 17]. Significant efforts have been made to enhance both encoders and decoders. For instance, Global-GNN [18] represents complex database schemas as graphs. RAT-SQL [7] introduces schema encoding and schema linking, assigning a relationship to every pair of input items. [PERSON] et al. [19] proposed DuoRAT, which is based on RAT-SQL. Instead of building increasingly complex encoding structures, the DuoRAT model primarily focuses on simplifying certain aspects of the RAT-SQL model. ShadowGNN [20] first utilizes a graph mapping network to abstract the semantic information from natural language and database schemas, thereby obtaining a simplified structure. It then employs the RAT framework to achieve comprehensive encoding, mitigating the impact of domain-specific information and enhancing the model's cross-domain capabilities.  
LGESQL [8] integrates relational structures by combining graph neural networks, while RASAT [9] introduces relational structures in T5 through attention mechanisms, thereby better utilizing the parameters of pre-trained models. However, both approaches treat the importance of various grammatical dependency relationships in natural language questions equally, without fully considering the significance of fine-grained grammatical information within sentences. This limitation affects syntactic parsing and semantic understanding. Therefore, the proposed approach refines grammatical dependencies by incorporating a grammatical dependency tree, which enhances the understanding of natural language questions.  
Additionally, both methods directly acquire grammatical dependency relationships from the original natural language questions. However, when dealing with long queries, the obtained relationships become too complex, resulting in suboptimal performance. To address this issue, we introduce segmentation processing, dividing long queries based on their structure and syntax, which effectively tackles the challenges posed by long queries.
# Passive Thermal Management of Tablet PCs Using Phase Change Materials: Intermittent Operation
## 1 Introduction
In recent literature, the performance of PCM based thermal storage units under intermittent applications has become an essential part of the overall research approach. Since typical usage power profiles of tablet PCs have not been published in the open literature, nor have they been shared by the various electronics companies, most studies rely on artificially generated temperature profiles for the study of thermal performance of PCM TES units. For a cyclic operation study, [PERSON] et al. [16] applied 10 charging-discharging cycles consisting of 30 min of charging and 150 min of discharging on a 16 Ah LiCoO\({}_{2}\) battery. They used a composite PCM (CPCM), containing paraffin, diffused into a copper foam as a thermal management system. Their thermal management system was reported to provide operating temperature below than the 50 \({}^{\circ}\)C threshold temperature during cyclic operations. [PERSON] et al. [17] utilized paraffin-EG composite encapsulated in an aluminum casing as battery thermal management (BTM) system. They employed three cycles of 60 min of battery discharging, 30 min of rest, and 150 min of charging to study the performance of PCM TES units under cyclic operation of the battery packs. [PERSON] et al. [18] fabricated a form-stable flexible CPCM for the thermal management of Li-ion batteries that utilizes \(n\)-eicosane as PCM. They mimicked a transient working condition by applying 5 min of heating and 10 min of cooling. During the transient study, the CPCM system was able to reduce the operating temperature up to 10 \({}^{\circ}\)C while the Li-ion battery pack was rated to supply 10 W and 15 W. [PERSON] et al. [19] evaluated the thermal performance of a square pin-fin aluminum heat sink (114 mm \(\times\) 114 mm \(\times\) 25 mm) filled with \(n\)-eicosane installed on a mock-up electronic device operated at four different made up intermittent usage profiles. Their study investigated three cycles of heating and cooling where the duration of the heating phase varied from 5 min to 25 min and the duration of cooling phase varies from 10 min to 30 min. Though these studies introduce the pathways to evaluate thermal performance of PCM TES units under intermittent operation, there are no standalone published studies looking at the in-depth intermittent operation of thin PCM TES units incorporated into realistic tablet PCs. The performance of recently developed slim PCM TES units by the current authors [15] has not been reported under any intermittent type usage situations yet. Therefore, in this paper, results from a study using an available PCM-based thermal management system [15] operating intermittently are presented. The effects of power input levels, varying size of TES units, and inclination of the device, on the thermal response of the tablet PC were studied experimentally. These results are an essential complement to the previous continuous operation study and provide a solution to the issues imposed by the practical implementation of PCM-based TES units in tablet PCs.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 2 Network Setup and Architecture  
In this section, the UAV-enabled LoRa system components and the main design choices are presented and described. The main objective of the UAV-enabled LoRa system is to provide network coverage to a set of mobile GNs using a fleet of UAVs, whose purpose is to promptly relay the collected data to a base station through an ad hoc network established between its members, as shown in Figure 1. To meet these requirements, each UAV must be able to automatically adjust its position to reflect the continuously changing topology of the GN and at the same time, it must be able to maintain a communication path with the base station. Disconnections, although difficult to avoid, must be minimized and a recovery technique must be set up to recover the connectivity whenever it is necessary. UAV-enabled LoRa network provides mechanisms to deal with all the aforementioned problems. This kind of system might be useful in a wide variety of situations, especially when there is the need to rapidly deploy a network to collect data generated by moving targets in remote or disrupted areas, where other networks might be unavailable, damaged or highly congested [38; 39].  
The architecture of the UAV-enabled LoRa network consists of a two-layer system, in which the first layer is composed of GNs that transmit data using LoRaWAN and the second layer is composed of a swarm of drones communicating over a WiFi ad hoc network and acting as relays between the GNs and a BS. The main components are listed and described below.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 4 SE-ResNeXt50 Transfer Learning Model
### SE-ResNeXt50  
[PERSON] et al. [28]. proposed the SEnet structure from the channel dimension and obtained the weight of each feature channel through the SEnet structure, with the goal of improving the representation capability of the network by convolution of the interdependence between feature channels. The attention module focuses attention on the region of interest, inhibits unnecessary features, assigns different weights to different features and improves the training efficiency and accuracy of the model.  
Based on SEnet and ResNeXt, this paper constructed SE-ResNeXt50 and adopted a fine-tuning transfer learning strategy. Firstly, the source SE-ResNeXt50 model was trained on the ImageNet dataset [29] to obtain fault feature information. Then, a new SE-ResNeXt50 target model was created by transferring all the designs and parameters of the source model to the target model and adding an output layer. Eventually, the target model was trained on the target dataset.  
The overall structure of the transfer learning model based on GASF and SE-ResNeXt50 is presented in Figure 3. First, the signal was intercepted by overlapping sampling, and then converted into a unique 2D feature map by GASF transformation. Then the feature map was split up into a training set and a test set, and the data of the training set were enhanced. Finally, the training set and test set were introduced into the SE-ResNeXt50 transfer learning model. The parameters of the SE-ResNeXt50 model used in this paper are demonstrated in Table 1.  
\begin{table}
\begin{tabular}{c c c}
## 4 Results
### Review Papers and Application Fields
#### 4.6.5 MCDA and Sustainability  
Sustainability can be defined as the ultimate aim, seeking a balance between social and economic pursuits while safeguarding the environment [139]. Consequently, sustainability-related matters comprise an ever-growing range of areas. [PERSON] et al. [116] conducted a study on MCDA applications involving environmental sustainability. Their review revealed significant progress in the quantity and quality of publications on the topic. The study identified the most frequently studied problems in the reviewed literature which included construction, manufacturing, agriculture, forestry and land use, supply chain and logistics, and transport management. [PERSON] et al. [117] conclude that, despite the widespread use of MCDA methods, most studies neglect the long-term implications and unforeseen events that could impact future project assessments within the context of sustainable development. [PERSON] et al. [56] examined hybrid MCDA and highlighted many benefits of combining different techniques to assist decision-makers in handling valuable information, including stakeholder preferences, connected or conflicting criteria, and uncertain contexts under the context of ensuring sustainability. Meanwhile, [PERSON] et al. [118] and [PERSON] et al. [120] contend that greater use of MCDA in sustainability decision making results in more efficient outcomes. [PERSON] et al. [119] reviewed the application of MCDA to achieve the United Nations Sustainable Development Goals (SDGs) and implement the 2030 Agenda for Sustainable Development in regional, national, or local contexts. They concluded that MCDA is a useful tool for addressing development and climate issues; however, researchers and practitioners must broaden their use to tackle complex multidimensional dilemmas.
# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 2 Related Work
### Dynamic Cost Prediction for Shale Gas Drilling and Development  
The dynamic nature of shale gas drilling and development requires the use of advanced predictive models that can adapt to changing conditions and incorporate a range of factors that influence costs over time.  
[PERSON] et al. [23] proposed a machine learning framework for optimising drilling operations. Their approach integrates real-time sensor data, geological information, and machine learning models to provide decision support for adjusting drilling parameters such as weight on bit and speed. The authors demonstrate the framework's ability to improve ROP and reduce drilling time and costs compared to conventional optimisation methods. Similarly, [PERSON] et al. [24] focused on optimising drilling parameters for target wells using machine learning and data analytics. Their research highlights the importance of adaptive models that can account for the unique characteristics of each well within shale gas fields. By integrating data pre-processing, feature selection and various machine learning algorithms, they effectively optimised drilling parameters in a dynamic context.  
[PERSON] and [PERSON] [25] developed an automated real-time prediction system for geological formation tops during drilling operations. Although their study did not directly address cost prediction, their machine learning solution applied to the Norwegian Continental Shelf illustrates the potential of real-time data integration within dynamic drilling models that can significantly influence cost estimates. Their system achieved high accuracy in predicting formation tops, potentially leading to reduced drilling times and associated costs. In addition, [PERSON] et al. [26] introduced a machine learning approach for predicting the rate of penetration (ROP) based on petrophysical and mud logging data. This method, which is adaptable for real-time predictions, has significant implications for dynamic cost estimation in shale gas drilling. The researchers compared different machine learning algorithms and found that ensemble methods were most effective in addressing the complexities associated with ROP prediction. Together, these studies underscore the growing importance of dynamic, adaptive models capable of real-time cost prediction and optimisation in the rapidly evolving landscape of shale gas drilling and development.
# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 4 NMN Regulates Physiological Metabolism
### Regulation of Immune Cell Function  
Inflammation is considered a key driver of aging and related diseases, as well as an important risk factor for incidence and mortality in chronic diseases [112]. Chronic inflammation affects glucose and lipid metabolism as well as insulin sensitivity by affecting the interaction between immune cells and metabolically active muscle cells, stem cells, and adipocytes in tissues and organs, thus exerting a systemic impact on the whole body's metabolism [113; 114]. Macrophage polarization is present in the visceral tissues of both elderly and obese individuals, accompanied by the enhanced expression of pro-inflammatory cytokines, such as tumor necrosis factor, IL-6, and IL-1\(\beta\), as well as insulin resistance and a decreased rate of lipolysis [115; 116]. The upregulation of NAD\({}^{+}\) biosynthesis or degradation pathways regulates the early activity of macrophages and influences their activation [117]. Pro-inflammatory M1-like macrophage polarization enhances CD38 expression and increases NAD\({}^{+}\) depletion. Conversely, research has linked the polarization of anti-inflammatory M2-like macrophages to elevated NAD\({}^{+}\) levels dependent on NAMPT [118; 119]. Inhibiting the NAM salvage pathway notably decreased the expression of certain genes linked to the distinctive phenotypes of both M1 and M2 macrophages [120]. Remarkably, this impact was counteracted by augmenting NAD\({}^{+}\) levels through supplementation, which bypasses NAMPT inhibition [121]. The important role of NMN in rescuing the biological processes of macrophage activation suggests that as a key metabolite of macrophages, it is involved in the regulation of macrophage polarization and pro-inflammatory responses [122].  
Chronic inflammation is associated with changes in the function of immune cells and a reduction in their ability to respond [119]. Aging is also an important factor, leading to imbalance or skewing of immune cell populations, including decreased levels of T and B cells, as well as a loss of T cell antigen receptor diversity [119]. Studies have shown that intracellular NAD\({}^{+}\) and NAD\({}^{+}\)-depleting enzymes play roles in regulating the biological functions of T cells, including signals that can induce cell death in specific T-cell subsets, affecting T-cell polarization [123]. Metabolic reprogramming of immune cells can affect the age-related dysfunction of adaptive immunity by manipulating the NAD\({}^{+}\)-associated pathways, for example, by inhibiting CD38 to upregulate the NAD\({}^{+}\)-SIRT1-FOXO1 axis and increase T-effector cell function [115]. Thus, targeting NAD\({}^{+}\) metabolic pathways using CD38 inhibitors or NAD\({}^{+}\) precursors, such as NMN, to modulate cellular metabolic reprogramming is a potential therapeutic strategy to improve the function of the adaptive immune system in the elderly in the future.
## 5 Discussion
Computationally, more parameters about vegetation variation can be derived by the BEAST algorithm, such as the probability of being a changepoint, probability distribution of total changepoint number, the estimated harmonic order and the probability of having a positive rate of change in trend. However, the BEAST algorithm has larger computation loads than other algorithms. When it is used to monitor vegetation dynamics over a large scale using high-resolution data, the huge computation load would limit the application. The Google Earth Engine (GEE) platform combines a range of satellite imagery and geospatial datasets with cloud-based computation power for environmental data analysis, which is conducive to implement the proposed method. Besides, BEAST cannot consider both spatial and temporal information simultaneously, and it only uses the information of temporal variation to seek abrupt changes in a pixel. Therefore, the detection accuracy depends on the quality and interval of the time-series images in the temporal domain. Actually, there are spatial correlations between the vegetation change and natural or human factors. Furthermore, the time series decomposition algorithm based on individual pixel cannot characterize a series of vegetation changes in the spatial domain. Developing an object-based time series decomposition algorithm may link the spatial and temporal information of vegetation changes and solve the problem.
## 4 Discussion
### Identified Frequencies
On the one hand, for some lower frequencies, a decrease in the structure's stiffness may be related not only to the change in mass but also as a result of crack development in concrete (including those at the micro-level) due to the previous earthquakes, an increase in the service load and long-term deflections, redistribution of internal forces, and sometimes as a result of moisture shrinkage and thermal strains fluctuations. The structuring is also important since it affects the building's mass distribution and the implementation of non-structural elements, which also contribute to the stiffness differential. Therefore, it can be seen that the higher frequencies in time for both directions increase. On the other hand, the intermediate or low frequencies in the EW direction tend to decrease as time passes. The fact that they increase or decrease may be associated with one of the above factors contributing to the mode of vibrations. Figure 10: Identified frequencies by date for the east-west and north-south directions showing how the frequency varies during the time the building was instrumented. Considering that the design frequencies of the first 6 modes are 0.497, 0.65, 0.88, 2.13, 3.23, and 3.70 Hz [28], it is possible to appreciate that the identified frequencies, in both the east-west and the north-south directions, are similar except for 0.497 Hz.
## 6 Thematic Taxonomy of UWSNs
### Security
#### 6.4.1 Authentication  
The preliminary phase for an UWSN is bootstrapping, in which a sensor alerts to be linked to the network. Each sensor node (as a receiver) is required to authenticate the sender for verification and identification purposes before receiving the message. All nodes connected to the network need to have permission or authorization from the network services during transmission. After the authentication process successful, the nodes will ready to execute any tasks assigned according to encoded procedures. Thus, execution of a robust authorization technique is essential in UWSN. Several security requirements are identified in [96] to protect a wireless sensor network as well as UWSNs from internal and external attacks.
## 3 Results and Discussion
### Method Development
#### 3.1.2 Effect of Pd/Zr Modifier  
Indirect measurement of F in a form of GaF molecule requires the application of palladium as a modifier to enhance the efficiency of the transformation into GaF, to avoid the low-temperature fluorine losses, mainly in the form of volatile HF, and to obtain a sharp molecular absorption signal [2]. Additional use of Zr was also proposed, which usually results in a considerable increase in sensitivity [14]; moreover is known to support suppression of phosphorus monoxide formation (PO) [37]. An attempt was made to use a mixture of Pd/Zr as a modifier, thermally pretreated before each sample injection; mainly to find out the optimum modifier masses in order to achieve the best performance in respect to the sensitivity.  
For this purpose different amounts of the mixture of Pd/Zr were deposited onto the graphite furnace in order to find optimal amount and still to compare with values reported by [PERSON] et al. [2] (\(m_{0}\), 5 \(\upmu\)g of Pd and 0.1 \(\upmu\)g of Zr). It turned out that the application of 1.5 times higher masses of modifiers than previously reported values results in an increase in sensitivity of about 10% (Figure 2). However, further addition of higher masses of Pd/Zr modifier led to a gradual and considerable decrease in signal intensity. The addition of 7.5 \(\upmu\)g of Pd and 0.15 \(\upmu\)g of Zr was considered as the best option and was used in all further investigations.
## 3 Results
### Preliminary Processing of the Raw Data  
Statistical significance of the data was ensured by averaging the implant deformation (ID) values over all test specimens of the same type, for each value of the force applied. The extreme outliers (defined as points located beyond 3 interquartile range) were removed from the data set. For Group 1, two outliers were identified and for Groups 2 and 3 one outlier. Thus, several 10 data points for each group were preserved. The data set considered for further analyzed consisted of 38 pairs of values (for each of the three types of implant) implant deformation (ID) versus applied force.  
Raw data from each experiment is presented graphically in Figure 12a-c for L plate, PLS plate and HEF respectively in the form of box plots showing experiments where outliers were identified ('+' symbol in Figure 12a-c) and experiments where implant device failure occurred. The mean values of implant deformation for each experiment (after removing outliers and discarding failed experiments) are presented in Figure 12d.  
Figure 12: Box plots of raw data for each implant device type (**a–c**) and processed data (**d**).  
A preliminary statistical analysis was performed by calculating Pearson and Spearman coefficients in order to confirm the existence of a correlation implant deformation--force. Although both Pearson and Spearman correlation coefficients predict a strong correlation between applied force and measured implant deformation (Table 2), these values must be interpreted cautiously.