esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 2 System Model
### User Pairing Policy  
So far, we have noted that the system's GMTs are separated into \(K\) groups of two members each, but we have not specified how the GMTs are allocated to each group. Hence, in this sub section, we propose the maximum weight perfect matching (MWPM) pairing policy which takes into account both the A2G and D2D channel conditions. The primary objective is to maximize the system's total sum rate. Therefore, a matching technique must be implemented between the GMTs in order to discover those user pairs that optimize the system's overall sum rate. The MWPM method generates \(\binom{N}{2}\) pairings between the \(N\) GMTs and retains the \(K\) that maximize the system sum rate. For this purpose, it is necessary to define a binary matrix \(\mathbf{\Theta}\) that represents the pairing relationship between the GMTs as follows:  
\[\theta_{i,j}=\left\{\begin{array}{cc}1&\text{GMT}_{i}\\ 0&\text{otherwise}\end{array}\right.\quad\text{paired with}\quad\text{GMT}_{j} \tag{24}\]  
The dimension of the pairing matrix \(\mathbf{\Theta}\) that is retrieved from the MWPM method is equal to \(N\times N\). Moreover the diagonal elements of the pairing matrix \(\mathbf{\Theta}\) are all equal to zero because one GMT cannot pair itself. Also, due to the fact that the matrix components \(\theta_{i,j}\) and \(\theta_{j,j}\) both pertain the same GMT pairing, it can also be argued that \(\theta_{i,j}=\theta_{j,j}\). Therefore, the MWPM pairing policy can be expressed as the following maximization problem:  
\[\begin{split}\max_{\theta_{i,j}}&\sum_{i=1}^{N}\sum_{j =i+1}^{N}\theta_{i,j}\Big{(}R_{i}^{u}+R_{j}^{\text{COPD2D}}\Big{)},\\ \text{s.t.}&\sum_{j=1}^{i-1}\theta_{j,i}+\sum_{j=i+1}^{N }\theta_{i,j}=1,\forall i=1,2,3,\cdots,N\\ &\theta_{i,j}\in\{0,1\},\quad 1\leq i,j\leq N.\end{split} \tag{25}\]  
The maximization problem (25) can be regarded as a matching problem in a fully connected undirected graph \(G(V,E)\), where the total number of vertices is equal to the total number of GMTs \(|V|=N\). E is the set of all feasible edges \(\theta_{i,j}\), connecting all users to each other with \(i\
eq j\) and \(i,j=\{1,2,...,N\}\). In order to solve this issue optimally, we use the Blossom algorithm to obtain an optimal pairing strategy between the GMTs [31].
## 2 Background Information
### Resource Description Framework and Semantic Reasoning  
We limit our introduction of the Semantic Web to the two topics mentioned in this work: the semantic data model, i.e., the Resource Description Framework (RDF), and semantic reasoning.  
RDF is an abstract data model that has to be serialized before it can be exchanged. Three common serializations are RDF/XML (XML based), JSON-LD (JSON based), and Turtle (text based). Traditionally, merging (non-semantic) datasets from different sources is cumbersome because different (local) identifiers are used, which do not match across datasources, or due to differences in the data format (e.g., JSON versus XML).  
In RDF, instances and the relationships between them are identified using globally unique uniform resource identifiers (URIs) instead of names or numbers, allowing disambiguation between concepts that share the same name. When different data sources follow best practices and correctly reuse existing concepts to construct a dataset, these datasets can be merged without additional work. Conceptually, reuse is straightforward: properties are typically bundled in online vocabularies or ontologies and instance identifiers should be reused from an authoritarian source. RDF is a well suited means to exchange data between independent parties due to these properties [3] and has been adopted by institutions who actively share data, such as government registries [4] or libraries [5].  
By using non-ambiguous identifiers, data processing can happen in a more automated fashion. This has led to semantic reasoners, i.e., tools that derive new data by combining existing data with rules. These rules can express hierarchical properties (e.g., every ventilation machine is a machine) or expert knowledge (e.g., ventilation is required when CO\({}_{2}\) is above 600 ppm). Semantic reasoners vary in expressivity, with more expressive reasoners typically being slower [6].
# A Review on Wind Turbine Deterministic Power Curve Models +
## 4 Sigmoid Models
### Algebraic Models  
AMs are those where \(f(v,b_{0},\ b_{1})=(v/\text{b}_{0})^{-b_{1}}\), with \(b_{0}\) and \(b_{1}\) as two constants. In general, \(b_{6}=0\) in AMs, and they use between three and six parameters. Using fewer than three parameters means very low accuracy, and the accuracy actually needed is reached with the six parameter model.  
The known six parameter AM, i.e., 6 PL, is given by Equation (23). It is the result of the combination of Equation (17), where \(b_{6}=0\) and the function \(f(v,b_{0},\ b_{1})=(v/\text{b}_{0})^{-b_{1}}\).  
\[P=b_{5}+(b_{2}-b_{5})\frac{1}{\left(b_{3}+(v/\text{b}_{0})^{-b_{1}}\right)^{1 /b_{4}}} \tag{23}\]  
As it is very frequent that \(b_{5}=0\), the 5 PL is formed as shown in Equation (24).  
\[P=\frac{b_{2}}{\left(b_{3}+(v/\text{b}_{0})^{-b_{1}}\right)^{1/b_{4}}} \tag{24}\]  
As a further simplification, the 4 PL model is expressed in Equation (25) by doing \(b_{4}=1\) in the 5 PL model.  
\[P=\frac{b_{2}}{b_{3}+(v/\text{b}_{0})^{-b_{1}}} \tag{25}\]  
In this case, the simplest AM is the 3 PL, which, following the trend of the 3 PLE, consists of the 4 PL model with \(b_{3}=1\). It is shown in Equation (26).  
\[P=\frac{b_{2}}{1+(v/\text{b}_{0})^{-b_{1}}} \tag{26}\]  
However, 4 PL and 3 PL models are normally discarded due to their accuracy when applying to WTPCs. Moreover, in the case of AMs, there is no specific relationship among their parameters and the parameters of the WT.
## 3 Results and Discussion
### GC-MS Analysis of Ethyl Acetate Extract of Quercus incana  
GC-MS spectrum of ethyl acetate extract of _Quercus incana_ showed sharper and broad peaks after 18 min of retention time (Figure 7). These peaks were searched in NIST library (ADMIS 2005), but most of them did not produce matching results of good quality. Few compounds were identified by comparing the extract mass spectra with the NIST library (Table 1)  
In current study compound 4 showed retention time of 27.56 which is in agreement with literature [42] A broad and prominent peak was observed in the spectrum. The mass spectrum of compound 4 is given below (Figure 8). This was compared with the mass spectrum of dibutyl phthalate, and the maximum peaks of dibutyl phthalate matched with compound 4. Compound 7 was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S No.** & **Rt (min)** & **Compound** & **Molecular Formula** & **Molecular Weight** & **NIST ID** \\ \hline
1 & 20.39 & Cyclopentyl carboxylic acid & C\({}_{6}\)H\({}_{10}\)O\({}_{2}\) & 114 & 3217 \\
2 & 22.99 & 3-Dimethylaminacrylonitrile & C\({}_{5}\)H\({}_{8}\)N\({}_{2}\) & 96 & 53.996 \\
3 & 25.56 & 4-AminoFurazan-3-carbhydromic acid & C\({}_{3}\)H\({}_{5}\)O\({}_{2}\)N\({}_{5}\) & 143 & 262,446 \\
4 & 27.56 & Dibutyl phthalate & C\({}_{6}\)H\({}_{22}\)O\({}_{4}\) & 278 & 228.847 \\
5 & 30.34 & 3,5-Dimethyldihydropyran-2,6-dione & C\({}_{7}\)H\({}_{16}\)O\({}_{3}\) & 142 & 18,795 \\
6 & 31.39 & 7,11-Hexadecadien-1-ol, acetate, & C\({}_{8}\)H\({}_{32}\)O\({}_{2}\) & 280 & 130,865 \\
7 & 32.18 & Quinoline, decahydro cis & C\({}_{9}\)H\({}_{17}\)N & 139 & 54,094 \\
8 & 32.66 & (1-Methoxy-pentyl)-cyclopropane & C\({}_{9}\)H\({}_{18}\)O & 142 & 46,868 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compounds list by matching NIST library.  
Figure 6: TEM image of GNPs.  
Figure 7: GC–MS spectrum of ethyl acetate extract of _Quercus incana_.  
found at 32.18 min retention time (Figure 9), which was also reported for _Tecomella undulate_[43] with an amount of 17.13%, while in present study, the compound was observed to be 32.18% by area. This compound belonged to an alkaloid class of compounds and also reported for _Haliclona tuleanensis_[44]. Compounds 2 and 3 might be responsible for the reduction and stabilization of nanoparticles. It is supposed that different-shaped polyol and water-soluble heterocyclic plant constituents are mainly responsible for the reduction of gold ions.
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
###### Abstract  
A 100 Gb/s quad-lane SerDes receiver with a phase-interpolator (PI)-based quarter-rate all-digital clock and data recovery (CDR) is presented. The proposed CDR utilizes a multi-phase multiplying delay-locked loop (MDLL) to generate the eight-phase reference clocks, which achieves multi-phase frequency multiplication with a small area and less power consumption. The shared MDLL generates and distributes eight-phase clocks to each CDR. The proposed CDR uses a new initial phase tracker that uses a preamble to achieve a fast lock time of about 12 ns and to provide a constant output data sequence. The CDR utilizes quarter-rate 2x-oversampling architecture, and the PI controller is designed full custom to minimize the loop latency. To improve the dithering jitter performance of the recovered clock, the decimation factor of the CDR can be adjustable. Also, a new continuous-time linear equalizer (CTLE) receiver was adopted to reduce power consumption and achieved a data rate of 25 Gb/s/lane. The proposed SerDes receiver with a digital CDR is implemented in 40 nm CMOS technology. The 100 Gb/s four-channel SerDes receiver (4 CTLEs + 4 CDRs + MDLL) occupies an active area of only 0.351 mm\({}^{2}\) and consumes 241.8 mW, which achieves a high energy efficiency of 2.418 pJ/bit.  
SerDes; clock and data recovery; CDR; receiver; MDLL; equalizer Article  
A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR  
[PERSON] and [PERSON]
# Service for Deploying Digital Twins of QKD Networks
## 3 Functional Overview
### QKD Network Nodes  
The main components of a QKD network are the QKD nodes. To achieve the correct functioning of all QKD network features, the architecture and components of the QKD nodes must be the appropriate ones.  
As discussed in Section 2.3, there is still not a widespread layered architecture for QKD networks as in classical communications; however, there are several research lines where this is being developed [31; 32; 33]. The most common element in the proposed architectures is a Key Manager, an element that is also present in classical key distribution systems. To guide our service, we assume a basic functional architecture, built upon the QKD network site scheme from ETSI [27], thus aligning the service with current standards. The functional architecture of the QKD network digital twin sites can be seen in Figure 3. The same colour code as in Figure 1 is followed, which helps to compare the standard model with our design.  
The top layer of a site, as in the ETSI scheme, corresponds to the application layer. It can communicate with the matching layer in other network sites by classical processes, and with the QKD module of its site through the QKD network client component where the QKD API is implemented. Below this top layer is located what we call the QKD module of the site, similar to the ETSI scheme, where the QKD API is also implemented, enabling applications to communicate with the module. The QKD API has been developed following the ETSI standardized API described in [27].  
Within the QKD module, there are three layers that correspond to the QKD Key Manager Peer component in the ETSI scheme. The upper layer, the Key Management layer itself, is in charge of managing the different key streams and delivering the keys to the corresponding applications. Then, the Key Synchronization layer, ensures the keys for each key stream are exchanged in a synchronized way in all sites. Lastly, the Quantum Backend layer is responsible for communicating with the actual quantum equipment to trigger different quantum operations (e.g., creating a local qubit, creating a Bell pair shared with another node, or applying an operation to a qubit). The bottom layer of the QKD module corresponds to the quantum components. In our service, the quantum behavior and the creation of quantum keys are emulated through software.  
Components at the layers can vertically interact with components at other layers immediately above and below them, through specifically developed APIs. They can also communicate horizontally with components in the corresponding layers of other network sites. Note that although in the scheme proposed in the ETSI standard [27], shown in Figure 1, the quantum components of different sites are linked by two kinds of channel, our design implements both using a quantum emulator which is capable of emulating quantum and classical channels.
# Research on Bidirectional Reservation Method for Anti-Permeation Geomembrane Slack
## 3 Analysis and Comparison of Different Methods
### Comparison of Different Methods  
#### 3.4.1 Comparison of Bidirectional and Unidirectional Slack Reservation Methods  
In terms of cost: there is no doubt that the proposed bidirectional reservation of geomembrane slack will increase the construction cost to a certain extent.  
Effectiveness perspective: compared with the existing unidirectional reservation of geomembrane relaxation, the bidirectional reservation of geomembrane relaxation proposed in this paper is more effective and can better protect the safe operation of the geomembrane. Because of one-way reserved geomembrane relaxation, at this time the geomembrane relaxation can only cope with a certain direction of tensile stress. This is a big gamble because in practice no one knows which direction of tensile stress the geomembrane will be subjected to as a result of changes in environmental factors.  
Ease of implementation: compared to the existing unidirectional geomembrane relaxation, the bidirectional geomembrane relaxation proposed in this paper will make the construction procedure somewhat more difficult, but not by much.  
It is more reasonable to carry out bidirectional geomembrane relaxation in terms of the safety performance of the project. And the safety of the project must be the first consideration.
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation
#### 4.2.4 Slovakia: Low Values but Linear Trend--Figure 5  
In the case of Slovakia, the phenomenon of negative prices has started to manifest itself especially in the last three years, and its occurrence corresponds to the increasing share of renewable energy sources (RES) in combination with limited flexibility of consumption and transmission networks. The highest incidence of negative prices has been observed at levels of combined solar and wind generation exceeding 300 MW, corresponding to peaks of generation during sunny weekends or holidays with low demand. While negative prices were virtually unknown in Slovakia during the 2015-2020 period, they appear more frequently in 2021-2025. Slovakia's electricity mix has long been characterised by a high share of nuclear power, complemented by hydroelectric plants and a growing but still relatively small share of wind and solar resources. However, the key factor is not only the size of the RES share, but their peak production combined with limited consumer flexibility. Slovakia has a limited ability to export surplus energy in real time, which increases the likelihood of negative prices during peak periods. As in other Central European countries, the Slovak market faces challenges of insufficient storage capacity and limited development of flexible technologies. In addition, lower levels of digitalisation of consumption (e.g., smart metering) hinder effective demand-side management.  
A methodology based on a cross analysis of RES degeneration power bands and the occurrence of negative prices showed that the risk bands for Slovakia range between 13,000 and 18,000 MW. Within these ranges, the incidence of negative prices, although still relatively low, is clearly increasing. This trend suggests that the Slovak market is approaching a threshold beyond which negative prices will become more frequent. The results for Slovakia suggest the need for a proactive approach to market flexibility, such as investment in storage, digitisation of consumption, and the introduction of tariff incentives for consumption in times of surplus (e.g., cheap charging of EVs on sunny days).  
Figure 4: Share of negative price in % in Poland.
It has practical engineering significance and can serve as an auxiliary means of monitoring, providing on-site validation data for satellite remote sensing inversion. It has guiding significance for hydrological changes in the watersheds, glacier surging analysis, and disaster prevention.
## 2 Methodology and Strategy
### Support Vector Regression  
Since [PERSON] and [PERSON] proposed the support-vector network and applied it to the handwritten character recognition problem [24], this method had been used in various fields. Originally, support vector machine was only used for classification problems. Later, some scholars extended it to the field of regression and proposed support vector regression [25]. The algorithm treats all data as one class, and searches for a hyperplane in a high-dimensional space so that the total deviation of the distance from all sample points to the hyperplane is minimized to achieve regression. SVR is quite effective to solve the regression problems of high-dimensional features, and it can still maintain good results when the feature dimension is greater than the number of samples. Additionally, it is not easily affected by noisy data, and overfitting is not prone to occur. The biggest feature of the SVR is that it uses a soft boundary. If the sample point is close enough to the regression model--that is, it falls within the interval boundary of the regression model--the loss is not calculated for the sample, and the corresponding loss function is called \(\varepsilon\)-insensitive loss function. It can be defined as Equation (3):  
\[L(y)=max(0,|f(x)-y|-\varepsilon) \tag{3}\]  
where \(f(x)\) is the regression function, and \(\varepsilon\) is a hyperparameter that determines the width of the interval boundary which should be defined by the user.  
According to Equation (3), only when the outlier is more than a distance from our regression function will it be included in our function loss; otherwise, it has no effect on the regression equation. As for the regression function, it can be written as  
\[f(x)=\sum_{i=1}^{n}\omega_{i}\varphi_{i}(x)+b \tag{4}\]  
where \(\omega_{i}\) is the adjustable weight vector and \(b\) is the bias, and \(\varphi_{i}(x)\) is the kernel which can map the input space into a high-dimensional feature space. The kernel used in this paper is the radial basis function (RBF).
# Optimal Crew Scheduling in an Intensive Care Unit: A Case Study in a University Hospital
## 3 Results
### Results for Nurse/Head Nurse  
A total of 14 nurses, including 1 head nurse, were included in the study. Nurses were named from N1 to N14. Table 4 presents the scheduling results for the head nurse (N1) and nurse \"N2\" as samples. Since it is specified in the constraint equations that the head nurse should not have night shifts, the notation \"G\" indicates the days when nurse N2 is scheduled for night shifts. Table 4 depicts the results generated for one year.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
1 & N1-G & N1 & N1-G & N1-G & N1-G & N1 & N1-G & N1-G & N1-N2 & N1-G & N1-G \\ \hline
2 & N1 & N1-G & N1 & N1 & N1 & N1-G & N1 & N1 & N1 & N1-G & N1 & N1 \\ \hline
3 & N1-G & & N2-G & N1-G & N1-G & N1 & N1-G & N1-G & G & N1 & N2-G & G \\ \hline
4 & N1 & N1-G & N1 & N1 & N1 & N1 & & & N1 & N1-G & & N1 \\ \hline
5 & N1 & N1 & G & & N2 & G & G & N1 & & & G & N1 \\ \hline
6 & & N1 & & N1 & & & N1 & N1 & N1-N2 & N1 & & N1-G \\ \hline
7 & N1 & N1 & N1 & N1 & N1-G & N1 & & & N1-G & N1 & N1 & \\ \hline
8 & N2 & N1-N2 & N1 & N1 & N1 & N1-N2 & N1 & N1 & & & N2 & G \\ \hline
9 & G & N1 & N1 & & & N1 & N2 & & N1 & & \\ \hline
10 & N1 & N1 & N1 & N2 & & N1-G & N1-N2 & N1-G & & N1-G & N1 \\ \hline
11 & N1 & G & & N1-G & N1-N2 & & & & G & N1 & N1-N2 & N1 \\ \hline
12 & N2-G & N1 & & N1 & N1 & N2 & N1 & N2-G & & & N1 & N1 \\ \hline
13 & N1 & N1-N2 & G & & G & N1-N2 & N1 & N1 & N1 & N1 & & N2 \\ \hline
14 & N1 & & N1-N2 & N1 & N1 & N1 & N1-G & N1-G & N1 & N1-N2 & & N1-G \\ \hline
15 & & N1-G & & N1-N2 & N1 & N1 & & N1 & N1-N2 & & N1-N2 & N1 \\ \hline
16 & & N1 & N1 & N1 & N1 & N2 & N1 & N1 & N1-N2 & G & N1-N2 & N1 \\ \hline
17 & & & & & & N1-G & N1-G & N1 & N1-G & N1 & & \\ \hline
18 & N1 & & N2 & N2 & & N1 & & N1 & & N1-G & N1 & \\ \hline \end{tabular}
\end{table}
Table 4: Sample scheduling for ‘N1’ head nurse and ‘N2’ nurse.  
Figure 2: Total number of shifts per year for senior assistant doctors.  
Figure 3 shows the overall distribution of annual day (shift = 1) and night (shift = 2) shifts for all nurses. As the head nurse, nurse N1 does not have night shifts. When observing the distribution of all nurses except for nurse N1, it can be observed that the distribution is fair and equitable.