esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Analysis of the Influence of AL on Coherent/Non-Coherent OOK Reception
### AL Research on the Non-Coherent OOK Receiver  
The experimental structure of the influence of AL on OOK non-coherent reception is shown in Figure 14.  
The central wavelength of the tunable filter was set as the optical signal wavelength, and the FWHM of the filter passband was set to 0.8 nm. The wavelength of the  
Figure 14: Experimental structure diagram of the influence of AL on OOK non-coherent reception.  
\begin{table}
\begin{tabular}{c c c} \hline
**PSD of SAL** & **Ratio of New Noise Variance to Shot Noise Variance to Shot Noise Variance (Theory)** & **Ratio of New Noise Variance to Shot Noise Variance (Experiment)** \\ \hline \(4.64\times 10^{-18}\) W/Hz (A) & 14.09 dB & 13.90 dB \\ \(3.48\times 10^{-18}\) W/Hz & 12.90 dB & 12.35 dB \\ \(2.32\times 10^{-18}\) W/Hz (B) & 11.25 dB & 10.45 dB \\ \(1.16\times 10^{-18}\) W/Hz & 8.55 dB & 8.16 dB \\ \(5.80\times 10^{-19}\) W/Hz (C) & 6.11 dB & 5.97 dB \\ \(2.32\times 10^{-19}\) W/Hz & 3.49 dB & 3.17 dB \\ \(1.86\times 10^{-19}\) W/Hz (D) & 2.98 dB & 2.76 dB \\ \(1.16\times 10^{-19}\) W/Hz & 2.09 dB & 1.90 dB \\ \(2.32\times 10^{-20}\) W/Hz & 0.51 dB & 0.31 dB \\ \hline \end{tabular}
\end{table}
Table 3: PSD of SAL, ratio of new noise variance to shot noise variance (theory and experiment).  
Figure 13: BER vs. coherent OOK optical power in different conditions.  
light source ranged from 1526.65 nm-1570.85 nm, corresponding to a frequency of \(5.53\times 10^{12}\) Hz. The SAL power was set to 1.03 \(\upmu\)W, 3.29 \(\upmu\)W, 6.58 \(\upmu\)W, 12.84 \(\upmu\)W, 25.68 \(\upmu\)W, and 51.35 \(\upmu\)W. With the EDFA, an additional 50% power loss was introduced, caused by the polarizer. The SAL power and the PSD of the SAL entering the non-coherent OOK system are shown in Table 4. In addition, we use letters that do not appear to represent the different experimental conditions, as shown in the first column of Table 4.  
We tested the sensitivity in six conditions (E, F, H, O, P, T) and non-coherently received OOK signals at a communication rate of 1 Gbit/s. The relationship curves for non-coherent reception between the BER and the optical signal power in different conditions are shown in Figure 15.  
The PSD of the sunlight coupled to the receiver SMF was \(6.84\times 10^{-20}\)\(\mathrm{W/Hz}\) at a central wavelength of 1555.74 nm. From the curves of coherent reception and non-coherent reception in Figure 16, it can be concluded that the sunlight power coupled into the receiver SMF caused 1.15 dB sensitivity deterioration to the coherent OOK receiver and 2.60 dB sensitivity deterioration to the non-coherent OOK receiver when the sun shines directly. These results are similar to those reported by [PERSON] et al. [46]. Thus, the coherent OOK receiving system has better anti-AL noise performance than the non-coherent OOK receiving system, and the coherent and non-coherent OOK receivers requiring SMF coupling can both work in direct sunlight.
# Metadata Quality for Biobanks
## 5 Metadata Quality in Biobanks
### The Structure of Metadata Quality in Biobanks  
We propose quite a simple structure for organizing metadata quality in biobanks, mainly following established standards such as ISO/IEC 25012 (Data Quality model) [35] and ISO/IEC 25024 (Measurement of data quality) [36]. It can be defined as follows:  
1. The metadata quality as a whole consists of a set of _metadata quality characteristics_. Quality characteristics help in achieving proper understanding of metadata quality, reflecting different aspects of this quality. Examples of metadata quality characteristics can be metadata completeness, consistency, reliability, or provenance. Such characteristics can be used together to obtain the integrated metadata quality.
2. Metadata quality characteristics are supplemented with _metadata quality metrics_, such metrics quantify the degree of achievement for the metadata goal related to the particular quality characteristic. Applying such a metric to measure the quality of a specific metadata element launches an instance of the metadata quality measurement process, which produces the specific metadata quality measurement value connected back to the measured metadata element. An example of such a metric can be the length of the interval of time between establishing a data item and the corresponding metadata element (a metric for metadata timeliness).
3. Metadata quality characteristics affect each other positively or negatively; some of the characteristics boost the impact of certain other characteristics, some--suppress such an impact. The direction and possible quantitative assessment of the affect is a property of the relation between a certain pair of quality characteristics.  
The metrics can be applied to metadata values defined on different levels of aggregation (e.g., the metadata quality metric calculated over collection-level metadata). Also, they themselves can be defined on different levels of aggregation (e.g., the collection-level metadata quality metric, i.e., calculated for the whole collection). This will be discussed in more detail below.
## 4 Numerical Simulation Analysis and Results
### Macroscopic Numerical Analysis
#### 4.2.2 Macroscopic Numerical Analysis in the Vehicle Braking Scenarios  
Initial state: 21 vehicles were driven at the initial speed of 12 m/s with a headway of 30 m. At time \(t=0\), the leading vehicle (Car 1) started to speed down with an initial deceleration \(-3\) m/s\({}^{2}\) that decreased in magnitude to 0 until coming to a complete stop. The remaining 20 test vehicles decelerated in response to the speed change of the leading vehicle by the extended IDM model. Finally, all vehicles came to a complete stop. Calculate and record the averages of the acceleration difference of the fleet at 0.1 s intervals, as shown in Figure 8.  
Comparing Figure 8a-d, the averages of deceleration difference of the fleet show similar trends in the four types of RV drivers. Under the most centralized arrangement of CAVs, the average of deceleration differences decreases more evenly with the increase of the penetration rate of CAVs. When the penetration rate of CAVs is below 50%, the average of deceleration differences of the fleet decreases more pronounced under the centralized arrangement of CAVs than under the decentralized arrangement of CAVs. When the penetration rate increases to 50%, the curve reaches the turning point where the positive effect of the centralized arrangement of CAVs on the fleet is most significant; the average of deceleration differences of the fleet in the four types of RV drivers is 15.08%, 15.69%, 15.86%, and 13.93% lower, respectively, than that of the fleet with decentralized CAVs. When the penetration rate of CAVs is above 70%, the averages of deceleration differences are similar under both arrangements of CAVs, which is consistent with the results in the starting scenario. Unlike the starting scenario, however, when the penetration rate of CAVs is 70%, the deceleration difference of the fleet with decentralized CAVs is slightly lower than that of the fleet with centralized CAVs, indicating that the decentralized arrangement of CAVs can better alleviate the disturbance caused to the fleet by braking.
# Query Recommendation Using Hybrid Query Relevance
## 4 Results
### Evaluation of Efficiency  
To examine the effectiveness of our approach, we compare the performance of the following algorithms:  
1. QFG [7]: This is a query flow graph model extracting queries to count the number of query co-occurrences.
2. QUBIC [12]: This is a bipartite graph model using query information and URL information in logs to build a query-URL bipartite graph.
3. \(RW_{UQ}\)[15]: This is a method calculating the bidirectional transition probability-based query-URL graph and making a strength metric of the query-URL edges.
4. CQM [6]:This is a method based on clustering processes in which groups of semantically similar queries are detected.
5. QSRSR: Our method considers query information and URL information. The relevance based on URL combines the query-URL pairs with URL ranking which can more accurately calculate the relation between query and URL. The precision of the different algorithms is shown in Figure 8.  
Figure 6: Selection of Parameter \(\beta\). (**a**) Description of Precision in the first panel. (**b**) Description of recall in the second panel. (**c**) Description of F1 in the third panel.  
Figure 7: Selection of Parameter \(\beta\). (**a**) Description of precision in the first panel. (**b**) Description of recall in the second panel. (**c**) Description of F1 in the third panel.  
From Figure 8, we can see that our method has higher precision than other query recommendation algorithms. We cannot get wide and accurate relation of queries solely using query information or URL information. It is limited to understanding query intention. Query information and user behavior information can complement each other. Using query embedding vectors to represent query semantics can better understand query; using the ranking of a URL can improve the accuracy of the strength between query and URL. We combine query information and URL information to mine more relevance between queries which can more accurately understand queries and query intention.  
Figure 9 shows the recall and F1-value on the different algorithms, respectively.  
In Figure 9a, the recall of our approach is compared with those of the other four methods. We can observe that as the number of recommendations increases, the recall rate of our method as well as other methods increases. However, our method has a higher recall than the other two methods. The results of F1-value are shown in Figure 9b. Its trend is the same as that observed in Figure 9a.  
Figure 8: Compare precision with other algorithms.  
Figure 9: Compare recall and F1 with other algorithms. (**a**) Description of recall in the first panel. (**b**) Description of F1 in the second panel.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## Preliminaries
### Algorithm Selection  
The Algorithm Selection problem, as formulated by [PERSON] [31], deals with the following question: Given a problem instance and a set of algorithms, which algorithm should be selected to solve the instance? [PERSON] identified four defining characteristics of the algorithm selection problem:  
1. the _set of algorithms_\(A\),
2. the instances of the problem, also known as the _problem space_\(P\),
3. measurable characteristics (features) of a problem instance, known as the _feature space_\(F\),
4. the _performance space_\(Y\).  
The selection procedure \(S\) is what connects these four components together and produces the final answer--given an instance \(p_{i}\), \(S\) selects from \(A\) an algorithm \(a_{i}\) based on the features \(f_{i}\) that \(p_{i}\) has, in order to maximize the performance \(y_{i}\).
# Algorithms for Calculating Generalized Trigonometric Functions
## 5 Algorithms for Calculation Space Transform Based on _Ateb_-Functions
### Orthogonal Trigonometric Transforms for Hyperbolic Ateb-Functions  
Let us construct orthogonal trigonometric Fourier transforms for _Ateb_-functions.  
These formulas are utilized to build a continuous spectrum of _Ateb_-functions. Taking into account the oddness of the hyperbolic _Ateb_-sine \(sha(n,m,\omega)\), it has the ability to be depicted as a direct sine Fourier transform \(Bs(n,m,x)\)  
\[Bs(n,m,x)=\int_{-\infty}^{\infty}sha(n,m,\omega)\sin(2\pi x\omega)d\omega. \tag{28}\]  
Then, _Ateb_-sine can be depicted by the inverse sine Fourier transform according to the expression  
\[sa(n,m,\omega)=\int_{-\infty}^{\infty}Bs(n,m,x)\sin(2\pi x\omega)dx. \tag{29}\]  
Operating the property of the hyperbolic _Ateb_-cosine, that \(cha(m,n,\omega)\) is even, we represent it in the form of the direct cosine Fourier transform \(Ac(m,n,x)\)  
\[Ac(m,n,x)=\int_{-\infty}^{\infty}cha(m,n,\omega)\cos(2\pi x\omega)d\omega. \tag{30}\]  
Then, _Ateb_-cosine is shown by the inverse cosine Fourier transform according to the expression  
\[ca(m,n,\omega)=\int_{-\infty}^{\infty}Ac(m,n,x)\cos(2\pi x\omega)dx. \tag{31}\]  
Now, we construct the Fourier transform generalization. A method of orthogonal transforms based on periodic _Ateb_-functions is developed. In the following, we will name it orthogonal _Ateb_-transform (OAT). The possibility of constructing OAT is grounded on the following statements. First, in [3], it is shown that _Ateb_-functions are a generalized case of ordinary trigonometric functions. Secondly, in [27], the orthonormality of the system of periodic _Ateb_-functions is proved. In [8], methods and algorithms for calculating _Ateb_-functions depending on the parameters are developed, which allows the proposed OAT method to be successfully used.  
We will consider transforms based on periodic _Ateb_-functions.
# Methodology for Verification of Geometrically Complex Components Through Reverse Engineering
###### Abstract  
Verification of geometrically complex components is a crucial step in ensuring the quality and functionality of industrial products. This article presents a comprehensive methodological approach to their verification using reverse engineering techniques. The study highlights the limitations of traditional measurement methods for complex geometries and proposes a framework that integrates advanced 3D scanning and digital modeling. The methodology involves obtaining accurate geometric data, with deviations during repeated measurements being well within the tolerance range. With a tolerance of \(\pm 1\) mm, the maximum deviation was \(-0.66\) mm, while the maximum deviation with the highest tolerance of \(4.5\) mm was \(1.19\) mm. These data were compared with predefined 3D models to analyze deviations and identify process errors. By applying this detailed methodology, more precise fixtures were created, resulting in measurements with deviations well below the required tolerances, compared to measurements without this methodology. A practical example of the design and verification of a welding fixture for cabin manufacturing illustrates the effectiveness of reverse engineering in detecting inconsistencies and optimizing manufacturing processes. The findings emphasize the importance of geometric accuracy in quality control and demonstrate how reverse engineering can streamline verification processes and enhance product reliability. This approach is particularly valuable for the manufacturing industry, which requires high precision in the production of geometrically complex components.  
reverse engineering; verification methodology; Kreon ACE-7-30; PolyWorks Inspector; welding; welding fixtures Article
## 1 Introduction
[27; 28; 29; 30; 31] only considers the optimization of path delay, refs. [32; 33] only considers the optimization of network load without considering the SFC constraints in the service transmission process, and some of the methods [26; 34] consider that the SFC constraints only optimize the path delay, neglecting the load optimization of the whole network. Inspired by AI-based communication algorithms [35; 36], this paper proposes a GDRL-SFCR routing decision method that integrates a GNN and DRL to systematically solve the core challenges caused by SFC constraints in LEO-MSN. For the problem of frequent path throughput due to satellite dynamic topology, a GNN is used to extract the link state and functional attributes of multi-hop neighbors, and dynamic graph embedding is used to achieve adaptive sensing of topology changes. For the multi-objective optimization with SFC constraints, low latency, and load balancing, DRL agents are used to dynamically adjust the weights of the composite reward function, and the competitiveness metrics are automatically balanced in the policy optimization. The main contributions of this paper are summarized as follows. * A more realistic LEO-MSN model: Compared to [26], we consider more constrained models. Specifically, channel model, network functions such as access network, core network, and end-to-end bidirectional traffics in satellite communication networks as specified in the latest 3 GPP NTN protocol are considered. * A novel routing method: We propose a method, GDRL-SFCR to solve the end-to-end routing problem under SFC constraints. This method uses a GNN to learn the dynamically changing topological features of the LEO-MSN network and trains using DRL to learn routing strategies to make appropriate routing decisions, which can rationally utilize network resources and improve network performance. Specifically, making routing decisions with network load balancing minimizes the end-to-end traffic transmission delay while guaranteeing the SFC constraints are met. * Performance evaluation: We constructed mega-satellite networks to validate the performance of the proposed algorithms, and to the best of our knowledge, the size of the simulation network we used is the largest among the papers available so far. The simulation results show that the proposed method outperforms other routing methods in terms of network metrics such as average end-to-end path delay, average network load, traffic access success rate, and network capacity. The remainder of this paper is organized as follows. Section 2 summarizes the relevant literature. Section 3 describes the system model and the routing decision problem under SFC constraints. Next, Section 4 describes the proposed GDRL-SFCR method in detail. Then, Section 5 presents and analyzes the simulation results. Finally, Section 6 summarizes this paper.
###### Abstract  
As the span of cable-stayed bridges continues to increase, traditional steel cables face challenges such as excessive self-weight, significant sag effects, and sensitivity to wind-induced vibrations. This study proposes two super-long-span cable-stayed bridge schemes with a main span length of 1500 m and identical girder cross-sections, employing steel cables and CFRP cables, respectively. Based on a discretized finite element model of stay cables, the global dynamic responses, cable vibration characteristics, and fatigue performance of both schemes were systematically evaluated using time-domain buffeting analysis and [PERSON]'s linear fatigue damage accumulation theory. The results demonstrate that CFRP cables, benefiting from their lightweight and high-strength properties, significantly reduce the vertical, lateral, and torsional RMS responses of the main girder under the critical 3\({}^{\circ}\) angle of attack, achieving reductions of 31.6%, 28.5%, and 20.6% at mid-span, respectively. Additionally, CFRP cables suppress cable-girder internal resonance through frequency decoupling. Fatigue analysis reveals that the annual fatigue damage of CFRP cables under the design wind speed is far lower than that of steel cables and remains well below the critical threshold, highlighting their superior fatigue resistance. This research confirms that CFRP cables can effectively enhance the aerodynamic stability and long-term durability of super-long-span cable-stayed bridges, providing theoretical support for span breakthroughs. To further ensure long-term service safety, this study recommends implementing damping measures at critical cable locations.  
superc-long span; cable-stayed bridge; carbon fiber-reinforced polymer (CFRP); time-domain buffeting analysis; cable fatigue damage Article
# Classification of Valvular Regurgitation Using Echocardiography
## 3 Methodology
### Proposed Methodology
#### 3.2.2 Image Keyframe Extraction  
This method overcomes any irregularities in an image boundary and helps reduce and eliminate any artifacts. The obtained frames are then stored in a folder, and keyframe extraction is applied. Two types of frame extraction are used in this paper. Firstly, using reference frame extraction, a static image captured during videographic echo is taken as a reference frame and compared with the frames obtained from the videographic echo. Mathematically, a reference frame \(Ref_{i}\) for each patient \(P_{i}\) is compared to frames \(F_{1},F_{2},\dots,F_{n}\) where \(n\) is the number of frames present for each patient \(i\). For this, we have used Euclidean distance to obtain a single or few frames from the videographic echo by eliminating images having a lower similarity index [39]. Similar frames will have a low value, and dissimilar frames will have higher values. The top one or three best values are taken and used for our implementation purpose. This is useful when a reference frame is available to compare but not suitable for videos with no frame as a valid reference frame. Since we have an available reference frame for each video we can compare to every frame of a particular video. Mathematically it can be represented as Equation (5):  
\[dist(i,j)=\sqrt{\sum_{j=1}^{N}\sum_{i=1}^{M}(i-j)} \tag{5}\]  
where \(i\) = Reference frame extracted by an expert, \(j\) = A single frame in a video, \(N\) = Total number of reference frames and \(M\) = Total number of frames.  
For video frames having no reference frame, uniform sampling is used where skipping every two frames has been done. This helps in removing redundancy between images. For this, for each frame \(F\) in \(F_{1},F_{2},\dots,F_{n}\) select \(F_{i}+3\) where \(i\) = 0 upto n. Mathematically for all videos (Vid) it can be written as shown in Equation (6)  
\[\sum_{i=1}^{n}Vid_{i}=\sum_{j=1}^{k}F_{j}+3 \tag{6}\]  
where \(j\) = 1 to k, k = total number of frames present in a video. After keyframe extraction, extraction of features is performed. We have extracted the features from the keyframe in two different forms: one is extraction of features with segmenting the ROI, another one is extraction of features directly from the keyframe.
## 4 Discussion  
### Discriminatory Analyses  
PCA and PLS-DA analyses are well-established tools when evaluating multidimensional data. PCA analysis serves rather as a 2D visualisation of data sets indicating group proximity. PLS-DA includes a discriminatory algorithm and may be used also to differentiate among groups. PCA analysis of the patient data collected on Day 1 against controls showed controls clustered together, whilst patients were scattered in 2D space. This suggests the great data variability in patient samples, which was more or less confirmed by PLS-DA. As PLS-DA is known to overfit the data [19], for biomarker discovery, we employed a cross-validated RF algorithm. As an output, the ROC curve was created. For the system of patients on Day 1 and controls, RF performed very well with an AUC of 0.995 for five variables with an out-of-bag error of 3/108. The variables Phe/Tyr ratio, phenylalanine, 3-hydroxybutyrate, acetate, and glucose were of the highest importance. The corresponding ROC curve is shown in Figure 2.  
Very similar performance--almost ideal discrimination--was achieved for the systems of Group A on Day 1 against controls and Group B on Day 1 against controls (details in Table 3). The five metabolites of the highest importance were identical to those before: phenylalanine, Phe/Tyr ratio, acetate, 3-hydroxybutyrate, glucose, permuted with glutamine, and proline. The possibility to discriminate between acute COVID-19 patients and healthy controls has been proven in previous studies [7; 10; 11]. These studies covered another spectrum of metabolites evaluated by different analytical tools as NMR spectroscopy. Here, we also note that metabolites that were marked as the most important in the discrimination algorithm may not be specific to COVID-19 disease, since as discussed in the next text, they are generally related to inflammation, immune response, and energy metabolism.