esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Research Questions' Findings
Included techniques are Language Models (LM) using Dirichlet smoothing and Jelinek-Mercer smoothing, Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and n-gram models such as unigrams, bigrams, and positional information [39, 36, 37, 38, 39, 80, 81, 82, 83];
* Text Processing: This process enables machines to examine text, understand its structure and meaning, and extract valuable information for complex tasks such as document classification and machine translation [84, 85, 86, 87, 51, 87, 84];
* Similarity Measure: This is a comparability metric used to find similar texts or documents. It encompasses methods such as approximate nearest-neighbor techniques (identifying similar items using vector representations) and Kullback-Leibler Divergence (which measures the variation in probability distributions) [54, 88];
* Tools: Various tools like TreeMagger, OpenNLP, and Stanford CoreNLP are effective for natural language processing tasks. They offer functionalities such as tokenization, part-of-speech tagging, lemmatization, named entity recognition, sentence segmentation, and more, to conduct in-depth linguistic analysis and extract relevant information from textual data for various applications [89, 90, 91]. * Semantic Analysis-Based Techniques
* Semantic Trees: This technique organizes patent documents in hierarchical structures to illustrate semantic relationships between them as well as to facilitate systematic traversal through related patent documents [71];
* Cosine Similarity: This measures and quantifies the degree of similarity between two patent documents' vector representations. Useful for document matching, grouping, and relevance ranking in patent retrieval systems, a high cosine similarity index suggests a higher level of similarity between documents in terms of content or attributes [90]. * Other techniques
* Claim Tree Structure: This represents the hierarchical relationships, including comprising, linking, prepositions, and verbs, between the various components of a patent claim in a tree-like structure to provide a thorough understanding of the claim's semantics and relationships. Each component of the claim can be a single word (unigram) or a phrase (n-gram) [90];
* Patent Ontology: This structured framework systematically arranges and represents patent-related data, such as patents, inventors, and classifications, and their relationships. It facilitates effective retrieval, analysis, and comprehension of patent data by providing a common vocabulary with shared semantics [92, 93]. Table 6 illustrates the categorization of the studies according to their various techniques. It shows that 37 studies utilized general information retrieval methods, while 56 studies deployed natural language processing-based techniques. Additionally, 15 studies made use of machine learning for patent retrieval techniques, 7 studies explored bibliometric-based  
techniques, and 3 studies applied other techniques. It should be noted that various studies employed more than one technique to enhance the outcome of their experiments.
# Phenolic and Volatile Composition of a Dry Spearmint (_Mentha spicata_ L.) Extract
## 1 Introduction  
Among the family of Lamiaceae (Labiatae), mint represents one of the most popular and cultivated official and aromatic plants [1]. The cultivation of mint is principally in temperate regions of Europe and Asia, but also in South Africa, Australia, and the United States.  
Spearmint (_Mentha spicata_ L.) is an aromatic plant that can be used fresh or as dried leaves or powder, as a seasoning and flavouring herb, or traditionally as an herbal tea. It is commonly used in traditional medicines as a remedy for gastrointestinal and respiratory problems. In addition, spearmint essential oil has economic relevance due to its use in perfumery, confectionary, and pharmaceutical preparations. Besides its flavouring properties, spearmint is also widely used as an antimicrobial agent and as a preservative in food, mainly on account of the phenolic and terpenoid content [2].  
The volatile (non-polar) profile of traditional cultivars of spearmint essential oils is mainly constituted by carvone (22%-73%) and limonene (8%-31%), with smaller quantities of 1.8-cineole (4%-7%), menthone (1%-5%), menthol, eucalypt, and other minor compounds. The profile varies based on plant variety, growth, climate conditions, and harvest time [3; 4; 5]. The antimicrobial activity of these spearmint essential oil components has been widely described in the literature. Volatilemolecules are indeed produced by the plant, serving as a defence mechanism upon predator attack (i.e., pathogens and insects) [5].  
Polar extracts of spearmint leaves are, on the contrary, characterised mainly by a high content of phenolic compounds such as rosmarinic acid, luteolin, and apigenin derivatives [6; 7]. Some of these components have been shown to have antioxidant properties; therefore, _Mentha spicata_ could also be considered an antioxidant source [7]. In fact, spearmint and spearmint extracts are often used as preservative agents to delay the oxidative degradation that occurs in food during processing or over time with storage [1]. More intriguingly, the anti-inflammatory properties of spearmint extracts rich in phenolic compounds have been demonstrated in vivo in rats [8].  
Aqueous extracts from typical commercially grown spearmint lines reportedly contain 0%-6% rosmarinic acid on a dry weight basis [9; 10]. However, based on the reported benefits of rosmarinic acid, spearmint lines were developed through selective-breeding techniques to contain higher levels of phenolic compounds such as rosmarinic acid [11]. Therefore, this study aimed to comprehensively characterise the phytochemical profile of a dried aqueous extract from these proprietary spearmint lines. The phenolic composition was fully examined by means of UHPLC-ESI-MS\({}^{n}\), while the composition of the volatile fraction was investigated using head space solid-phase microextraction (HS-SPME)/GC-MS technique.
## 5 Application of Generative AI Models for Context-Aware Vulnerability Scoring
### Methodologies of CVSS Attribute Extraction  
The following prompt was used to elaborate how each prompt evaluate the result of each attribute: \"can you explain to me how the results are generated or in other words how you think about the attributes generated\". The following is the result generated from the three LLMs:  
* ChatGPT: Rule-based NLP heuristics ChatGPT leveraged rule-based NLP heuristics to parse vulnerability descriptions. This approach involved applying predefined keyword-driven heuristics to infer CVSS attributes rather than accessing external CVE databases. The methodology aimed to mimic a human analyst's logical deductions by recognizing phrases commonly associated with specific CVSS metrics. For instance, keywords like \"remote\" indicated a network attack vector, while \"physical\" or \"local\" suggested local or physical. Phrases such as \"easily exploitable\" implied low attack complexity, whereas \"complex setup required\" suggested high. Terms like \"unauthenticated\" indicated none for Privileges Required, while \"admin\" or \"root\" denoted high. User interaction was inferred from mentions like \"user must open\" or \"social engineering\". References to broader system impact suggested a changed scope, while their absence implied unchanged. Impact metrics (confidentiality, integrity, and availability) were influenced by mentions of \"data leakage\", \"tampering\", and \"denial of service\", respectively.
* DeepSeek: Structured question-based approach DeepSeek adopted a structured question-driven methodology, explicitly querying vulnerability descriptions against predefined conditions. Each CVSS attribute was determined through specific queries designed to guide the model in analyzing relevant phrases. For the attack vector, it identified remote attacks as network and local execution as local. Attack complexity was differentiated between low-complexity attacks (\"trivial to exploit\") and high-complexity scenarios (\"race condition\"). The Privileges Required were categorized into none, low, or high based on authentication levels. User interaction involved evaluating whether explicit user action was required. Scope determined cross-system impact if stated. The impact metrics (confidentiality, integrity, and availability) assessed explicit mentions of data exposure, manipulation, or service disruption.
* Gemini: Systematic text translation using the worst-case principle, Gemini employed a systematic attribute extraction approach that relied strictly on the vulnerability description itself, avoiding assumptions beyond explicit statements. Its methodology incorporated exclusive reliance on provided text to prevent external data usage and ensure self-contained analysis. A key principle was the worst-case impact principle, where, if ambiguity existed, values were assigned based on the maximum potential risk. Attribute-by-attribute derivation iteratively evaluated each CVSS metric following strict definitional guidance.
## 3 Method
### Model Architecture
#### 3.3.5 Decoder  
The decoder stacks \(N_{de}\) decoder layers, each containing TSA, SSA, TEDA, and FFN. In the decoder layer, TSA and SSA extract spatial-temporal dependencies and correlations from \(Y^{\prime}\) to generate \(Y_{de}\). Subsequently, TEDA converts \(Y_{de}\) into the query vector \(Q_{de}\), and it converts the encoder output \(X_{en}\) into the key vector \(K_{en}\) and value vector \(V_{en}\). Next, TEDA utilizes multi-head cross-attention to capture the spatial-temporal correlations and dependencies from \(Q_{de}\), \(K_{en}\), and \(V_{en}\):  
\[MultiHead(Q_{de},K_{en},V_{en})=Concat(head_{1},head_{2},\dots,head_{N_{h}} )W^{0}, \tag{11}\]  
\[head_{i}=Softmax\Bigg{(}\frac{Q_{de}K_{en}^{\top}}{\sqrt{d_{K}}}\Bigg{)}V_{en}. \tag{12}\]  
In this way, TEDA generates the decoding sequence and utilizes an FNN to convert it into the traffic prediction at the next time step within the predictive horizon. After \(T_{P}\) round of decoding, CeT can acquire the final traffic prediction sequence \(Y^{\prime}=\{Y^{T_{H}+1}{}^{\prime},Y^{T_{H}+2}{}^{\prime},\dots,Y^{T_{H}+T_{ P_{k}}{}^{\prime}}\}\).
## 3 Results
### Sonic Test Results  
Sonic test results were divided into direct and indirect methods, totaling 336 results for the direct test and 353 for the indirect test over 79 different testing locations. The data were organized twice using both test type (direct, horizontal indirect, vertical indirect, or over the crack indirect) and approximate distance traveled by the wave to group and remove outliers. The distance category is further subdivided into categories A (20-25 cm), B (40-50 cm), C (60-75 cm), and D (80-100 cm). For the purpose of removing outliers, these grouped datasets were divided into four quartiles with equal populations, and the interquartile range between the first and third quartiles was calculated. Values beyond 1.5 times this range from the median in either direction were defined as outliers and removed, and the results were reported [28]. The results are shown in Figure 7 using boxplots.  
Reconstructing the dataset and comparing the resulting elastic moduli after filtering by each method suggests a value between 1.5 and 2.0 GPa. The former is more in line with the results from the indirect testing and the latter with those from the direct testing methods. Boxplots present the reconstructed datasets after each type of filtering; note that the results from the direct test are unchanged since the filtering criteria do not affect them, and that filtering by distance was able to remove one additional data point (Figure 8). The higher value of 2.0 GPa was used as a starting point for initial calibration of the numerical model in Section 4. The results are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Material** & **Specific Weight (kN/m3)** & **Compressive Strength (MPa)** \\ \hline Dolomite & 24 & 34 \\ Limestone & 24 & 37 \\ Marble & 26 & 40 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of results from rock testing.  
Figure 8: Results from sonic testing: (**a**) reconstructed dataset after test type filtering; (**b**) reconstructed dataset after distance filtering.  
Figure 7: Results from sonic testing: (**a**) unfiltered elastic moduli by test type; (**b**) filtered elastic moduli by test type; (**c**) unfiltered elastic moduli by test distance; (**d**) filtered elastic moduli by test distance.
## 4 Discussion
Fourth, in the GNSS horizontal direction, the N and E components exhibited a strong negative correlation (r = \(-\)0.90), and seasonal deformation was possibly controlled by changes in both hydrological loading and pore pressure. Precipitation was lower in the N component from January to March; therefore, the influence of hydrological loading gradually disappeared, and stations moved northward simultaneously as surface uplift occurred. In April, when precipitation increased, hydrological loading increased, and stations moved southward until precipitation peaked in July. Deformation also reached its extreme negative point. Subsequently, precipitation decreased, and instantaneous precipitation loading deformation weakened. Stations resumed their northward movement until October. Seasonal deformation from April to October might largely be due to an increase or decrease in an instantaneous load from precipitation. Precipitation accumulation in October peaked within a short period, and under the combined effect of other hydrological loadings, stations moved southward until December. Later, precipitation decreased sharply, and surface water was continuously lost due to surface runoff, evaporation, and other factors. The surface load gradually disappeared, and stations started moving northward again until March the following year. From October to March, the movement of the sites may be controlled by the dual effects of hydrological loading and pore pressure. The action mechanism of deformation features and influencing factors for the E component was opposite to that for the N component. Fifth, during GNSS and GRACE data processing, in addition to semi-annual and annual terms, we found longer terms, such as >1 year, which may also be related to precipitation. Many studies [6; 31; 32; 33; 34] have stated that precipitation in different areas of Sichuan and Yunnan has a periodic component from 1.0 year to 8.0 years or longer. This study used the mean precipitation of administrative areas where stations were located; however, this was not reflected well in the seasonal analysis of precipitation.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 3 Evolution, Architecture, and Implementation of the IFC Schema
### Evolution of the IFC Schema
It has been designed to comprehensively integrate the infrastructure field, accommodating a diverse range of elements for domains that include rail, roads, ports, waterways, and bridges. As such, the IFC is ever-evolving, consistently developing to adapt to the dynamic requirements of the AEC/FM industry. However, it is worth noting that there is often a considerable time lag between the development of these updates and their official release and eventual distribution. For instance, the IFC 4 Add2 TC1 update, announced in 2017, remains the most recent official version of IFC. The slow-paced updates mean that the official use of the upgrades may be delayed, but it also has its benefits: it provides time for the broad range of BIM applications to incorporate and operate with the most recent version of the IFC. More importantly, it provides stability for the extension cases, such as those investigated in this study, to explore the ideas and approaches for incorporating new products and processes. Such experiments ensure that new extensions are thoroughly tested and thought out, providing valuable insights into the future versions of the IFC.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.5 Ethyl Esters  
Among young wines, the concentrations of particular ethyl esters, such as ethyl butanoate, ethyl 2-methylbutanoate, and ethyl 3-methylbutanoate, were the highest in TM10-Y, while the total concentration of ethyl esters, predominantly driven by ethyl octanoate, was the highest in TM7-Y. In contrast, TM21-Y and TPHT-Y wines had lower concentrations of most individual ethyl esters and total ethyl esters compared to the other treatments (Table 1). Ethyl esters and volatile fatty acids share precursors and typically correlate in fermentation [49]. This study found higher ethyl ester concentrations in treatments with shorter maceration times, which was consistent with previous findings [21; 41; 51]. As suggested [21], it is possible that a longer period of fermentation with skins inhibited the formation of ethyl esters by providing either competitive substrates or inhibitors of the yeast enzymes and/or simply resulted in their adsorption onto skin phenols or other compounds and subsequent removal by precipitation. Grape solids can inhibit fatty acid synthesis in yeast cells during fermentation. This inhibition reduces the production of ethyl esters, as fatty acids are key precursors for their formation. Consequently, this leads to lower concentrations of ethyl esters in wine, as these compounds are less available for synthesis due to the decreased precursor levels in the must [47]. The decrease in ethyl esters in TM21-Y was also likely due to non-enzymatic hydrolysis [50]. It was previously noted that heating reduces ethyl ester concentrations [48], while other studies observed no significant changes [19; 31]. In this study, heating reduced the levels of ethyl butanoate, ethyl 3-methylbutanoate, and ethyl octanoate and increased the level of ethyl hexanoate (Table 1). The decrease in major medium-chain ethyl esters during aging is known to be due to a shift in the balance between esterification and hydrolysis toward the latter [32]. Despite the protection provided by sulfur dioxide, ester hydrolysis during barrel aging still occurs. Ethyl ester concentrations were shown to decrease with longer barrel aging, while the losses were lower during aging in stainless steel tanks [52; 53]. The results of this study were mostly in line with previous findings, with reduced concentrations of the majority of medium-chain ethyl esters after a 6-month aging period (Table 1). The levels of ethyl esters of short-branched-chain fatty acids, such as ethyl 2-methylbutanoate and ethyl 3-methylbutanoate, increased in TM21-A and TPHT-A wines. This increase was likely due to esterification processes reaching equilibrium concentrations, as suggested by previous studies on esterification during maturation [30].
The deployment of mobile platforms such as sUAS to integrate RFID technology will ensure the ability to collect information over large areas, encompassing numerous products, taxa, or stock-keeping units (SKU), while further reducing the amount of labor and time spent in the field. In the long run, it can improve efficiency by lowering the operational cost of obtaining nursery data, resulting in higher productivity, better inventory management, and subsequent product forecasting. The following are the objectives of this experiment:  
* To evaluate the number of flights needed to read all tags deployed in the field
* To determine the number of scans per pass, and
* To determine the optimal drone speed for reading.
## 5 Results and Analysis
### Factors Affecting the Bearing's Load Capacity  
Under different gas supply pressures, Figure 8 illustrates the trend of bearing capacity variation with the length of the rectangular orifice plate array \(a\) fixed at 40 mm and the gas film thickness maintained at 20 \(\upmu\)m across a range of throttle orifice diameters. With supply gas pressure held constant, there is an observable increase in bearing capacity as the diameter of the throttle orifices enlarges.  
Figure 9 demonstrates how bearing capacity varies with gas film thickness under varying gas supply pressures given a constant rectangular throttle hole array length of 4 mm and a supply hole diameter of 0.2 mm. When the gas supply pressure remains unchanged, a reduction in gas film thickness leads to enhancement of the bearing capacity.  
Figure 10 showcases the fluctuations in bearing capacity under divergent supply pressures and assorted distributions of throttle holes with the supply hole diameter established at 0.2 mm and the gas film thickness set at 20 \(\upmu\)m. With a constant gas supply pressure, the bearing capacity experiences an initial rise followed by a decline as the \(x\) parameter increases, peaking at \(a=24\) mm.  
Figure 6: A comparison between the finite element method, the experimental method, and reference [25].  
Figure 7: (**a**) Three-dimensional pressure distribution; (**b**) cross-sectional view of gas film pressure.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection
#### 3.4.6 Modifying the Intensity of Pixels or Voxels  
Adjusting the intensity values of pixels or voxels affects the image's overall appearance, in terms of brightness and contrast. Techniques such as gamma correction adjust the luminance, potentially highlighting hidden details in lighter or darker regions that were not initially evident. Linear contrast adjustments and histogram equalization alter the dynamic range and distribution of intensities, which can help in better differentiating between regions of interest and the background or between different tissues. These adjustments mimic the variations in image quality that might result from different imaging hardware or settings, ensuring the model is adaptable and performs well across the range of equipment found in various medical facilities. Tailoring pixel intensity also aids in training models to focus on relevant features rather than being misled by artifacts or anomalies in brightness or contrast that are not clinically relevant.