esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 5 Turbulent-Flow Chromatography (TFC)
### TFC Applications  
TFC has been successfully applied to the determination of a wide range of food and beverage samples. Food samples are usually very complex matrices composed of lipids, proteins, carbohydrates, pigments, and other compounds. These compounds are present in a significant quantity and can interfere in the analysis of target analytes [80]. For these samples, clean-up and concentration steps are indispensable; thus, TFC is a suitable on-line sample preparation technique to separate analytes with low weight from high weigh interferences, without any prior handling steps.  
The analysis of beverages by TFC is advantageous once the liquid sample preparation can be kept to a minimum [81]. [PERSON] et al. [80] analyzed flavonoids and resveratrol from white, rose and red wine samples; the samples were diluted and then injected in a C18 TFC column (50 \(\times\) 1 mm, 50 \(\upmu\)m/60 A  
Figure 5: Schematic set up for automated turbulent-flow chromatography (**a**) loading position and (**b**) elution position.  
particles) at a 4 mL/min flow rate, coupled with an LC-APCI-MS. The method showed good linearity, and LOD values between 0.005-0.025 mg/L. This method proved to be fast, simple and robust, taking just 2.5 min for the extraction step.  
So far, milk is one of the matrices most frequently analyzed by TFC, although this technique has also been employed for the determination of multiclass veterinary drugs (VDs) [82; 83; 84] mycotoxins [85] and UV-filters [86] Milk is a complex sample rich in lipids and proteins, therefore the extraction and sample clean up remain a challenge when other sample preparation techniques are employed. TFC offers a perceived advantage of enabling the direct injection of milk samples into a TFC column after a simple protein precipitation step. This eliminates the time-consuming sample preparation procedure, as well as the potential analytes loses, resulting in a high throughput method suitable for multi-residue analysis.  
Regarding the analysis of VDs in milk by TFC, it is observed there was a fast development and application of this technique since the first publication by [PERSON] et al. [82] in 2010, reporting the determination of eight multi-class antibiotics. Then, in 2012, [PERSON] et al. [84] reported the identification and quantification of 36 VDs in 19 min and, more recently, in 2016, [PERSON] et al. [83] reported the simultaneous analysis of 88 VDs in 36 min. Although the VDs analyzed come from different classes, solubility, and polarity, the use of TFC coupled to MS/MS detector has demonstrated its high efficiency, practicality, and convenience in the determination of multi residual compounds in milk, and the possibility to be used for regulatory purposes.
## 7 Conclusions  
In this paper, we proposed a novel framework, hierarchical clause annotation (HCA), to segment complex sentences into clauses and capture inter-clause relations with strict definitions from the linguistic research of clause hierarchy. We aimed to explore the potentialities of integrating the HCA structural features in semantic parsing with complex sentences, avoiding the deficiencies of previous works such as RST-parsing, SRRP, TS, SSD, etc. Following the HCA framework, we built up a large HCA corpus comprising 19,376 English sentences from AMR 2.0. The annotation consisted of silver data transformed from the constituency and syntactic dependency parse trees and gold data annotated by experienced human annotators using a newly created tool, ClausAnn. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provided effective baseline models for both subtasks to generate more HCA data.  
Conceptualization, Y.F. and Z.G.; methodology, Y.F. and B.L.; software, Y.F.; annotation, all authors; writing--original draft, Y.F.; writing--review and editing, B.L., Y.S., M.G., C.S., S.C. and Z.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
(RST-DT) at [[https://catalog.ldc.upenn.edu/LDC2002T07](https://catalog.ldc.upenn.edu/LDC2002T07)]([https://catalog.ldc.upenn.edu/LDC2002T07](https://catalog.ldc.upenn.edu/LDC2002T07)) (accessed on 11 October 2022), The Georgetown University Multilayer corpus (GUM) at [[https://gucorpling.org/gum](https://gucorpling.org/gum)]([https://gucorpling.org/gum](https://gucorpling.org/gum)) (accessed on 15 April 2023), Strategic Conversation (STAC) at [[https://www.irit.fr/STAC/](https://www.irit.fr/STAC/)]([https://www.irit.fr/STAC/](https://www.irit.fr/STAC/)) (accessed on 5 April 2023).  
Our code extends two GitHub repositories [[https://github.com/gucorpling/DisCoDisCo](https://github.com/gucorpling/DisCoDisCo)]([https://github.com/gucorpling/DisCoDisCo](https://github.com/gucorpling/DisCoDisCo)) (accessed on 1 March 2023) and [[https://github.com/ntcslab-nlp/RSTParser_EMNLP22](https://github.com/ntcslab-nlp/RSTParser_EMNLP22)]([https://github.com/ntcslab-nlp/RSTParser_EMNLP22](https://github.com/ntcslab-nlp/RSTParser_EMNLP22)) (accessed on 10 March 2023), with much thanks to them.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMR & Abstract meaning representation \\ BiLSTM & Bidirectional long short-term memory \\ CPT & Constituency parse tree \\ FFN & Feed-forward neural network \\ HCA & Hierarchical clause Annotation \\ IAA & Inter-annotator agreement \\ PLM & Pretrained language model \\ POSs & Parts-of-speech \\ RST & Rhetorical structure theory \\ RST-DT & Rhetorical Structure Theory Discourse Treebank \\ SDG & Semantic dependency graph \\ SDPT & Syntactic dependency parse tree \\ SPRP & Split-and-rephrase \\ SSD & Simple sentence decomposition \\ TS & Text simplification \\ \end{tabular}
## 3 Materials and Methods
### The Smart mIoT Gateway  
The Smart mIoT Gateway provides secure and reliable multi-backhauling connectivity for ship-shore massive NB-IoT data in all navigation scenarios. At the core of the system is the Smart Switching function, which intelligently routes massive IoT data based on a configurable switching policy. This policy takes into account factors, such as connectivity availability, priority among available links, link costs, and geographic position. Depending on these parameters, the Smart Switching function dynamically selects the most appropriate backhauling link, whether it be a terrestrial or satellite channel. This ensures optimal data routing, improving network efficiency and reducing operational costs.  
Complementing this is the Local Resource Status Monitoring function, which continuously tracks the status of local equipment, network infrastructure, and computational resources. By providing real-time insights into resource usage and performance, this function helps maintain network stability and ensures that potential issues are identified and addressed promptly.  
To safeguard the onboard network, the Security Gateway function plays a critical role by providing robust network security and data protection. This function is also equipped with threat intelligence capabilities, enabling it to detect and respond to potential security threats, thereby enhancing the overall resilience of the system against cyberattacks.  
Finally, the Data-driven Event Management function allows onboard applications to effectively manage and notify of relevant events, such as alerts or alarms. This capability ensures that critical information is promptly communicated to the appropriate stakeholders, both on the ship and onshore, facilitating rapid response to emergencies or system anomalies.
# K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics
## 2 Classification Law of High-Potassium Glass and Lead-Barium Glass  
### Sample Data Acquisition and Exploratory Spatial Data Analysis  
#### 2.1.1 Data Overview  
The data were acquired from a private archaeology database, which contains the results of chemical composition detection of 58 glass cultural relics in total. Because two parts of one glass relic (for example, the inside and outside of a glass vase) may have significant differences in chemical composition, 11 glass relics underwent detection on two different points. Therefore, the data mainly consisted of 69 data points collected from 58 glass relics.  
For each glass relic, its ornamentation, color, and type (high-potassium/lead-barium) were recorded, and for each detection point, its weathering degree (unweathered, weathered, weathered) and 14 chemical compositions were recorded. The 14 chemical compositions were SiO\({}_{2}\), Na\({}_{2}\)O, K\({}_{2}\)O, CaO, MgO, Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), CuO, PbO, BaO, P\({}_{2}\)O\({}_{5}\), SrO, SnO, and SO\({}_{2}\). The first three lines of the data are shown in Table 1.  
Because the chemical detection method in archaeology may produce a slight error, which makes the sum of the 14 compositions deviate from 100%, we regard a sum between 85% and 105% as reasonable. Based on this, we dropped two data points which have the sum 79.47% and 71.89%, respectively, and 67 data points remained for further processing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Relic No.** & **Oramentation** & **Type** & **Color** & **Weathering** & **Detection Point** & **SiO\({}_{2}\)** & **CaO** & **MgO** & **Fe\({}_{2}\)O\({}_{3}\)** & **PbO** & **Sum** \\ \hline
01 & C & high-potassium & blue-green & unweathered & 01 & 69.33 & 6.32 & 0.87 & 1.74 & & 97.61 \\
02 & A & lead-barium & light green & weathered & 02 & 36.28 & 2.34 & 1.18 & 1.86 & 47.43 & 99.89 \\
03 & A & high-potassium & blue-green & unweathered & 03 position1 & 87.05 & 2.01 & 0.25 & 100 \\  & & & & & & & & & & \\ \end{tabular}
\end{table}
Table 1: The first three lines of the data.
## 3 Channel Model  
This section presents the channel models for each transmission hop, including FSO-based AWACS-to-UAV, FSO-based UAV-to-GS, RF-based AWACS-to-UAV, and RF-based UAV-to-GS. Here, the subscript \(j\in\{SR_{i},R_{i}D\}\) indicates the AWACS-to-UAV and UAV-to-GS links, respectively.
# An Introduction of NoSQL Databases Based on Their Categories and Application Industries +
## 3 The Categories of NoSQL Databases
### Document Store  
The terms related to the database model of the document store are described below [5].  
* A collection is a group of documents. The documents within a collection are usually related to the same subject, such as employees, products, and so on.
* A document is a set of ordered key-value pairs, where key is a string used to reference a particular value, and value can be either a string or a document.
* JSON (JavaScript Object Notation), BON (Binary JSON), and XML (eXtensible Markup Language) are formats commonly used to define documents.
* Embedded documents are documents within documents. An embedded document enables users to store related data in a single document to improve database performance.
* Document store databases do not require users to formally specify the structure of documents prior to adding documents to a collection. Therefore, document databases are called schemaless ones. Application programs should verify rules about the structure of a document.  
An example of a collection in a document store database is shown in Figure 2. As a JSON file format, this document stores school curriculum data. There are three courses, \"Accounting\", \"Economics\", and \"Computer Science\", in this file. Each course contains four fields, c_no, title, credits, and instructor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Row Key** & **ts** & **Column Family Products** & **Column Family Inventory** \\ \hline \multirow{6}{*}{P001} & t1 & Products: Classes = “TV” \\ \cline{2-3}  & t2 & Products: Title = “SONY 55 inch 4K OLED Smart \\ \cline{2-3}  & t2 & Networked TV” \\ \cline{2-3}  & t3 & Products: Descriptions = “TBD” \\ \cline{2-3}  & t4 & Products: Price = “24999” \\ \cline{2-3}  & t5 & Inventory: Quantity = “10” \\ \cline{2-3}  & t6 & Inventory: Place = “1A” \\ \hline \multirow{6}{*}{P002} & t7 & Products: Classes = “Laptop” \\ \cline{2-3}  & t8 & Products: Title = “ACER SF514 14-inch laptop” \\ \cline{2-3}  & t9 & Products: Descriptions = “TBD” \\ \cline{2-3}  & t10 & Products: Price = “31000” \\ \cline{2-3}  & t11 & Inventory: Quantity = “20” \\ \cline{2-3}  & t12 & Inventory: Place = “2A” \\ \hline \multirow{6}{*}{P003} & t13 & Products: Classes = “Mobile phone” \\ \cline{2-3}  & t14 & Products: Title = “ZenFone 5Z” \\ \cline{2-3}  & t15 & Products: Descriptions = “TBD” \\ \cline{2-3}  & t16 & Products: Price = “5000” \\ \cline{2-3}  & t17 & Inventory: Quantity = “8” \\ \cline{2-3}  & t18 & Inventory: Place = “2B” \\ \hline \hline \end{tabular}
\end{table}
Table 3: An example of a data table in wide column store.  
According to the statistics of the DB-Engines Ranking Website [15], the MongoDB and Couchbase Server are the more widely discussed ones of the document store databases.
# Applying Recommender Systems to Predict Personalized Film Age Ratings for Parents
## 3 Problem Formulation  
The prediction problem of personalized film age ratings for parents, viewed as a recommendation task, is formulated as follows: A set of users (\(U\)), e.g., parents, a set of items (\(I\)), such as films, and a set of age ratings (\(AR\)), such as evaluations by parents for films, is given. This dataset is divided into a training set and a test set. The training set is used to determine the system parameters. \(AR\in AC\), where \(AC\) denotes the set of age categories. Hereafter, without loss of generality, we will use the \(AC\) from the Common Sense Media dataset; however, the problem can be applied to any other dataset.  
The goal of this problem is to predict the age of a parent-film pair that is not in the training set (\(TR\)) but belongs to the test set (\(TS\)). To evaluate the systems, we use the root mean squared error (\(RMSE\)) [15; 33], which is suitable for recommender systems because it measures inaccuracies across all ratings, whether negative or positive.  
\[RMSE=\sqrt{E\{(AR-\overline{AR})^{2}\}} \tag{1}\]  
where \(AR\) denotes the set of age rating values (parents declared ratings) and \(\overline{AR}\) denotes the set of age ratings produced by the recommendation algorithm for the test set. The lower the calculated \(RMSE\), the better the prediction accuracy of the system.
## 2 Method
### Experiment Setup
#### 2.2.2 Experiment Design  
As risk-warning eHMIs lack standardization, this study adopted a triadic color scheme--red, yellow, and green (as shown in Figure 2)--to indicate crossing risk levels: red for high risk, yellow for medium risk, and green for low risk. The experiment adopted a within-subjects design, manipulating the information content conveyed by the eHMIs (as shown in Figure 2), which included the following configurations:  
1. Single-level: Low The eHMI was activated only when TTA \(\geq\) 5.1 s, displaying a green light and indicating a low-risk scenario. When TTA < 5.1 s, the eHMI was deactivated.
2. Single-level: Medium The eHMI was activated only when 3.7 s \(\leq\) TTA < 5.1 s, displaying a yellow light and indicating a medium-risk scenario. It was deactivated when TTA \(\geq\) 5.1 s or TTA < 3.7 s.
3. Single-level: High The eHMI was activated only when TTA < 3.7 s, displaying a red light and indicating a high-risk scenario. It was deactivated when TTA \(\geq\) 3.7 s.
4. Two-level: Low-medium The eHMI was activated when TTA \(\geq\) 3.7 s. It displayed a green light when TTA \(\geq\) 5.1 s, which transitioned to yellow at TTA = 5.1 s and remained yellow while 3.7 s \(\leq\) TTA < 5.1 s. The eHMI was deactivated when TTA < 3.7 s.
5. Two-level: Medium-high The eHMI was activated when TTA < 5.1 s. It displayed a yellow light when 3.7 s \(\leq\) TTA < 5.1 s, which shifted to red at TTA = 3.7 s and remained red while TTA < 3.7 s. The eHMI was deactivated when TTA \(\geq\) 5.1 s.
6. Three-level: Low-medium-high The eHMI was continuously active across the full TTA range. It displayed a green light when TTA \(\geq\) 5.1 s, transitioned to yellow at TTA = 5.1 s, and further shifted to red at TTA = 3.7 s, remaining red while TTA < 3.7 s  
Two eHMI penetration conditions were tested independently: full and partial. In the full penetration condition (as shown in Figure 3), all vehicles were AVs equipped with a risk-warning eHMI. Also, a cyan light (CIE chromaticity: x = 0.15, y = 0.38) atop each AV, per ISO/TR 23049:2018 [39], signaled its autonomously driving mode. In the partial penetration condition, 50% of the vehicles were AVs with eHMIs (Partial-AVs), while the remaining 50% were TVs without eHMI (Partial-TVs). AVs and TVs appeared in random order, with the cyan light present only atop AVs in this condition.  
Figure 3: Variations in eHMI penetration rates within vehicle platoon.  
Figure 2: Variations in risk-warning eHMI information content. The symbol “\(\times\)” denotes eHMI deactivation in specific TTA ranges for each condition.
## 3 Experimental Results
### Load-Slip Behavior  
The load-slip diagrams of the specimens are shown in Figure 4. In this figure, the applied load per channel versus relative displacement (slip) has been represented. As can be seen, at the peak of load, HSC specimens had been able to experience a relative slip in the rage of 10 to 14 mm, whereas this value is between 8 and 14 mm in the normal strength specimens. In this figure, Hh-100-50-6-8.5 and HI-75-50-5-7.5 specimens have shown the highest shear capacity, and after them, the specimen of N-100-50-6-8.5 has illustrated the highest one. Therefore, it can be concluded that although the compressive strength of concrete plays a prominent role in the shear capacity of channel connectors, the length of channel connectors seems to be the most effective parameter in the shear capacity.  
Figure 3: Specimens at the end of the test: (**a**) Channel fracture in high-strength concrete (HSC); (**b**) Channel fracture in normal strength concrete; (**c**) Crack in the concrete block; (**d**) Concrete block after channel fracture [79].  
Figure 4: Load–slip diagrams of specimen: (**a**) Hh-100-50-6-8.5; (**b**) HI-75-50-5-7.5; (**c**) HI-75-30-5-7.5; (**d**) N-100-50-6-8.5; (**e**) Hh-100-30-6-8.5; (**f**) N-75-50-5-7.5; (**g**) N-100-30-6-8.5; (**h**) N-75-30-5-7.5 [80].
# Non-Contact SpO2 Prediction System Based on a Digital Camera
## 2 Principle of SpO2 Measurement  
The variation in blood concentration due to the amount of oxygen carried by haemoglobin leads to a variation in reflected wavelength illumination captured by a photodiode or camera. This is because of the different light absorption properties of oxygenated haemoglobin (HbO\({}_{2}\)) with respect to deoxygenated haemoglobin (Hb) that absorbs light at different wavelengths. Under each illuminating wavelength, the absorption of Hb differs from that of HbO\({}_{2}\), and by measuring the absorption of the blood at both wavelengths, it is possible to estimate the SpO2 level as follows [40]:  
\[\text{SpO2 }(\%)=\frac{\text{HbO}_{2}}{\text{Hb}+\text{HbO}_{2}}\% \tag{1}\]  
According to the theory of light reflection and the Beer-Lambert law, measuring SpO2 requires two different wavelengths (\(\lambda_{1}\) and \(\lambda_{2}\)) to determine the maximum transmissive/reflective intensity of two-wavelength illuminations, which decays exponentially with the absorption coefficient and the optical path length. The absorption ratio (\(K_{a}\)) can be estimated from an RRs measurement as follows [5]:  
\[K_{a}=\frac{\mu(\lambda_{1})}{\mu(\lambda_{2})}=\frac{AC_{\lambda_{1}}/DC_{ \lambda_{1}}}{AC_{\lambda_{2}}/DC_{\lambda_{2}}} \tag{2}\]  
where \(\mu\) is the absorption coefficient, \(AC\) is pulsatile absorption, and \(DC\) is non-pulsatile absorption. To estimate the SpO2, the absorption coefficients of HbO\({}_{2}\) and Hb should differ significantly at one wavelength and be approximately equal at the other wavelength. The absorption spectrum of HbO\({}_{2}\) and Hb at different wavelength illuminations is shown in Figure 1.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Adopted Notations and Representation  
Here, \(A\) is the set of possible assignments of machines to the operations of all the jobs to be processed, \(A=\big{\{}A_{j},\ j=1,\ \ldots,\big{|}A\big{|}\big{\}}\), and \(A_{\bar{y}}\) is the projection of the \(j\)th assignment on the operations of job \(i\). \(A_{i}=\oplus_{j=1}^{|A|}A_{ij}\) is the set of all possible machine assignments for job \(i\). The overall knowledge of the allocation of machines to the different jobs is necessary to treat the case of a specific job since these jobs may use subsets of machines concurrently. The index of the machine attached to the processing of \(O_{ik}\) in the \(j\)th machine assignment is written as \(l_{\bar{i}\bar{j}}\).  
The maximum number of different assignments of machines for a job \(i\) is given by  
\[n_{i}^{A}=|A_{i}|=\prod_{k=2}^{S_{i}-1}|M_{ik}| \tag{1}\]  
and the maximum number of different machine assignments for the whole production plan is given by  
\[n^{A}=|A|=\prod_{i=1}^{n}n_{i}^{A} \tag{2}\]  
These numbers can be significantly reduced considering the layout of the plant, the existing transfer system, and the operational state of the machines. In the illustrative case of Section 2.2, we have the following: \(n_{1}^{A}=16,\ n_{2}^{A}=18\) and \(n_{3}^{A}=4\), leading to \(n^{A}=1152\), which is a rather large number. But if adopting the rule that, when two successive operations of the same job can be processed by the same machine, there is no change of machine from the first to the second operation, then the number of accepted machine assignments decreases to 96. In fact, this rule allows one to melt down these pairs of operations into a single one, reducing the size of the scheduling problem.  
Let us introduce the processing and transfer times for a given machine assignment: \(d_{\bar{i}\bar{j}}\) is the processing duration of \(O_{ik}\) by machine \(l_{\bar{i}\bar{j}}\), \(i=1\) to \(n\), \(k=1\) to \(S_{i}\), and \(j=1\) to \(n^{A}\); and \(T_{\bar{i}\bar{i}\bar{j}}\) is the nominal transfer time of product (\(i\),\(h\)) from machine \(l_{\bar{i}\bar{j}}\) to machine \(l_{\bar{i}\bar{j}}\), \(i=1\) to \(n\), \(h=1\) to \(S_{i}\), \(k\in\Gamma_{\bar{i}\bar{j}\bar{i}\bar{j}}\), and \(j=1\) to \(n^{A}\).  
When, for machine assignment \(j\), a destination machine \(l_{\bar{i}\bar{j}}\) of operation \(O_{ik}\) is busy with another operation, it is supposed that the semi-processed material of job \(i\) can wait without using energy until machine \(l_{\bar{i}\bar{j}}\) becomes available.