esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nmr  
The compound CP13 was characterized based on the analysis of 1H NMR and 13C NMR spectra. In the \({}^{1}\)H NMR spectrum (Figure 4), methyl signals at \(\delta\) 1.13 (s, H-17), 1.14 (s, H-18), and 3.87 (s, H-13) were observed on three carbon atoms. Additionally, four olefinic signals were detected at \(\delta\) 6.24 (t, H-2), 7.95 (t, H-3), 7.53 (t, H-7), and 7.03 (t, H-8). Analysis of the \({}^{13}\)C NMR spectrum (Figure 5) revealed a total of 15 carbon atoms in the compound, including three signals of CH\({}_{3}\), one signal of CH\({}_{2}\), five signals of CH, and six signals of C. The \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra displayed characteristic signals for a coumarin nucleus substituted on seven by a methoxyl group. The molecular formula of CP13 was determined as C\({}_{15}\)H\({}_{18}\)O\({}_{5}\) based on the results obtained from MS, in conjunction with the analysis of the \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra. The compound was identified as a meranzin hydrate through structural elucidation. Meranzin hydrate was previously identified as a major constituent of the traditional Chinese medicine Chaihu-Shugan-San. In the reported studies, mice on a high-fat diet were subjected to gastric injections of high and low doses of Chaihu-Shugan-San and meranzin hydrate. Depressive-like behavior was evaluated through a sucrose preference test, open field test, light-dark test, and tail suspension test. The anti-atherosclerotic and anti-depressive properties of the compound meranzin hydrate were demonstrated by measuring arterial atherosclerotic plaques, blood lipid levels, and inflammatory cytokine levels [17]. In the four samples analyzed, this compound exhibited the highest proportion, indicating its significant research potential.
## 3 Results
### Web Application  
WASP was developed as a backend, implemented in Java using the Spring Boot framework, and as a frontend, using Angular and TypeScript. The two components are deployed together using the Gradle build tool and communicate over HTTP requests with each other. The code repository is available at [[https://gitlab.com/tessa00/wasp-supporting-snomed-ct-postcoordination](https://gitlab.com/tessa00/wasp-supporting-snomed-ct-postcoordination)]([https://gitlab.com/tessa00/wasp-supporting-snomed-ct-postcoordination](https://gitlab.com/tessa00/wasp-supporting-snomed-ct-postcoordination)) (accessed on 20 April 2023). WASP offers three related main functions, which are summarized in Figure 4 and explained in detail in the following subsections. Firstly, the application offers an interface to create a postcoordinated expression based on the general constraints of SNOMED CT's Compositional Grammar and Concept Model. Secondly, a PCE can be created according to an Expression Template to enforce further use-case-specific constraints. Thirdly, a new Expression Template can be created interactively if no appropriate Template is available for usage within the second function.  
Throughout all three functions, the same paradigms are applied to both simplify the user's task as well as prevent any erroneous input. Based on the respectively applicable constraints, the user interface is generated dynamically, which then resembles an automatically prefilled form. Like this, required elements are already present, and only relevant items are shown. For the remaining variable data fields, an autocomplete function provides appropriate SNOMED CT concepts according to the user's input and the respectively applicable subset. Furthermore, several features for user guidance were added: Additional information on several elements, e.g., explaining an Attribute's meaning, is provided within the user interface, including references to the SNOMED browser [37] or relevant SNOMED CT documents. To notify the user of any mandatory elements not yet filled out,error messages and highlighting are employed. Created PCEs are displayed according to the SNOMED CT Diagramming Guideline [38], providing a concise summary of the yielded input.  
While the application was developed in conjunction with a local instance of Ontoserver, the user can choose to connect WASP to a different terminology server (TS) fulfilling the requirements stated in Section 2.4, such as the public Ontoserver hosted by the developers in Australia [39]. Based on the availability on the TS, different versions and editions of SNOMED CT can be chosen as well.
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 2 Background and Related Work
### Fog and Mist Computing for IoT  
Fog computing is a new approach for dealing with the enormous amount of data generated by IoT smart applications [37]. It addresses three key challenges: (a) decreasing latency for real-time application; (b) reducing data traffic between devices and cloud, and; (c) providing a load balancing alternative to soften the processing burden of the cloud. The fog metaphor is similar to the cloud one, but the former is closer to the ground and the people than the latter [38]. The underlying fog infrastructure is a virtualized platform that provides computing, storage, and communication services between the users and the cloud datacenter [39]. In other words, fog is the distributed computing infrastructure for IoT. Here we consider fog computing and edge computing as interchangeable terms, even though minor differences might exist [40].  
While the fog aims at reducing bandwidth usage, latency, and data communication with the datacenter, mist computing extends this concept further in IoT-based systems, closer to the sensors and actuators [12]. Although there is no consensus about the definition of mist, some common understanding can be found, such as being located closer to the devices [21; 41]. Mist can be considered the lowest level of a hierarchical fog computing system.  
Usually, mist nodes host communication facilities, such as a LoRaWAN gateway or a Wi-Fi access point. Additionally, they can also host light processing and temporary storage, typically using ARM processors, such as Raspberry Pi and similar. According to the Internet Engineering Task Force (IETF), these devices belong to a category of general-purpose constrained nodes, making the difference between sensing devices based on microcontrollers [42]. The mist infrastructure may be composed of different nodes working together to provide services to devices within the same geographical area, which not necessarily are packed into the same box.
## 2 Main Result
### Robustness Lyapunov Analysis for Error System
#### 2.5.6 Determining the Bounds for \(\Delta L\)  
In order to determine a radius of convergence for \(\Delta L\), recall \(V_{L}=\frac{1}{2}\operatorname{tr}(\Delta L\gamma_{y}^{-1}\Delta L^{\dagger})\) from Equation (16). Using the cyclic property of the trace, \(V_{L}\) can be alternatively expressed as follows:  
\[V_{L}=\frac{1}{2}\operatorname{tr}(\Delta L^{\dagger}\Delta L\gamma_{y}^{-1}). \tag{58}\]  
Applying the Cauchy-Schwarz inequality on Equation (58),  
\[\begin{split}\frac{1}{2}\operatorname{tr}(\Delta L^{\dagger} \Delta L\gamma_{y}^{-1})&\leq\frac{1}{2}\operatorname{tr}( \Delta L^{\dagger}\Delta L\Delta L^{\dagger}\Delta L)^{\frac{1}{2}} \operatorname{tr}(\gamma_{y}^{-1}\gamma_{y}^{-1})^{\frac{1}{2}}\\ &\leq\frac{1}{2}\underbrace{\operatorname{tr}(\Delta L^{\dagger }\Delta L)}_{=\|\Delta L\|_{F}^{2}}\operatorname{tr}(\gamma_{y}^{-1}),\end{split} \tag{59}\]  
where \(\|\Delta L\|_{F}\) is the Frobenius norm of \(\Delta L\). Given that \(\|\Delta L\|_{F}\) can be lower-bounded by additional norms, the norm of \(\Delta L\) must be constricted to \(\|\Delta L\|_{F}\). Using the previous assumption from Equation (43) on Equation (59) results in the following:  
\[V_{L}\leq\frac{1}{2}\|\Delta L\|_{F}^{2}\underbrace{\left(\frac{M_{v}}{aM_{k} }\right)^{2}}_{=\operatorname{tr}(\gamma_{y}^{-1})}. \tag{60}\]  
Therefore, \(V_{L}^{\frac{1}{2}}\) can be expressed as follows:  
\[V_{L}^{\frac{1}{2}}\leq\frac{1}{\sqrt{2}}\|\Delta L\|_{F}\bigg{(}\frac{M_{v}}{ aM_{k}}\bigg{)}. \tag{61}\]  
Meaning, Equation (51) can alternatively be lower bounded by \(V_{L}^{\frac{1}{2}}\) :  
\[V_{L}^{\frac{1}{2}}\leq\frac{1}{\sqrt{2}}\|\Delta L\|_{F}\bigg{(}\frac{M_{v}}{ aM_{k}}\bigg{)}\leq V_{eL}(\tau)^{\frac{1}{2}}\leq e^{-\pi\tau}V_{eL}(0)^{\frac{1}{2}} +\frac{\sqrt{2}(\lambda_{\max}(P_{x})^{\frac{1}{2}}+1)M_{v}}{2a}(1-e^{-a\tau}). \tag{62}\]  
Solving for \(\|\Delta L\|_{F}\) in Equation (62):  
\[\|\Delta L\|_{F}\leq\bigg{(}\frac{\sqrt{2}aM_{k}}{M_{v}}\bigg{)}e^{-a\tau}V_{ eL}(0)^{\frac{1}{2}}+\Big{(}\lambda_{\max}(P_{x})^{\frac{1}{2}}+1\Big{)}M_{k}(1-e^{-a \tau}). \tag{63}\]  
Taking the _supremum_ of \(\|\Delta L\|_{F}\) as time approaches infinity results in the following:  
\[\limsup_{t\to\infty}\|\Delta L\|_{F}\leq\bigg{(}\sqrt{\lambda_{\max}(P_{x})}+1 \bigg{)}M_{k}\equiv R_{**}. \tag{64}\]Therefore, upper-bounding the correction variance norm to a radius of convergence of \(R_{**}\). Alternatively, if the control law in Equation (56) is considered, the _supremum_ of \(\|\Delta L\|\) as time approaches infinity results in the following:  
\[\limsup_{t\to\infty}\|\Delta L\|_{F}\leq\sqrt{\lambda_{\text{max}}(P_{x})}M_{k }\equiv R_{**}. \tag{65}\]
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.2 Generalization of NDR-Net on Velocity  
In this part, in order to explore the generalization performance of the proposed algorithm NDR-Net on the moving speed under the same channel model, SNR is 15 dB, TDL-A channel is 3 Km/h and 30 Km/h respectively, and TDL-B channel is 30 Km/h and 120 Km/h respectively. The TDL-C channel is trained at 120 Km/h and 350 Km/h respectively. The Doppler shifts corresponding to 3 Km/h, 30 Km/h, 120 Km/h and 350 Km/h are 9.72 Hz, 97.22 Hz, 388.89 Hz and 1134.26 Hz respectively. Figures 6 and 7 show the simulation results for the test channels TDL-A-30 km/h and TDL-B-120 Km/h, respectively. It can be seen that in the same channel, the algorithm is not sensitive to the moving speed. When the test SNR is also 15 dB, the model also has good adaptability when the moving speed is not matched. Even under low or high SNR, the proposed algorithm can accurately estimate the doppler shift and determine the correct moving speed. In other words, even if SNR and moving speed do not match, the DL algorithm can achieve better estimation effect than the traditional algorithm and has good robustness.  
Figure 5: Generalization performance of the trained channel estimator with different SNR for the test channel TDL-B-120 Km/h.  
The maximum mobile speed supported by 5G communications has reached 500 Km/h. Figure 8 represents the generalization effect under the TDL-C channel model trained at 350 Km/h and 500 Km/h moving speed respectively. It can be seen that the SNR loss of the model trained at 500 Km/h is no more than 0.1 dB compared with that of the model trained at 350 Km/h, indicating that the proposed algorithm is not sensitive to the moving speed. Thus the proposed algorithm has an excellent generalization effect even when the velocity is very large.  
Figure 6: Generalization performance of TDL-A channels at different training velocities when testing velocity = 30 Km/h.  
Figure 7: Generalization performance of TDL-B channels at different training velocities when testing velocity = 120 Km/h.
## 4 ADRC Adaptive Law
### Stability Analysis of ADRC Adaptive Law  
From Equation (8), the transfer function between the feedback \(y\) and the controller output \(u\) can be written:  
\[u(s)=\bigg{[}-\bigg{(}\frac{{b_{0}}^{2}{\beta_{1}}+{b_{0}}{\beta_{1}}^{2}+{ \beta_{1}}{\beta_{2}}}{{b_{0}}^{2}+{b_{0}}{\beta_{1}}}\bigg{)}\frac{1}{s+{b_{ 0}}+{\beta_{1}}}+\bigg{(}-\frac{{\beta_{2}}}{{b_{0}}+{\beta_{1}}}\bigg{)}\frac {1}{s}\bigg{]}y(s) \tag{11}\]  
Next, the inverse Laplace transform of Equation (11) can be applied:  
\[u(t)=-\bigg{(}\frac{{b_{0}}^{2}{\beta_{1}}+{b_{0}}{\beta_{1}}^{2}+{\beta_{1}} {\beta_{2}}}{{b_{0}}^{2}+{b_{0}}{\beta_{1}}}\bigg{)}e^{-({b_{0}}+{\beta_{1}})t} y(t)+\bigg{(}-\frac{{\beta_{2}}}{{b_{0}}+{\beta_{1}}}\bigg{)}\int_{0}^{t_{1}}y(t)dt \tag{12}\]  
Substituting Equation (12) into Equation (10), then:  
\[\left\{\begin{array}{l}\int_{0}^{t_{1}}\left(\frac{{b_{0}}^{2}{\beta_{1}}+{b _{0}}{\beta_{1}}^{2}+{\beta_{1}}{\beta_{2}}}{{b_{0}}^{2}+{b_{0}}{\beta_{1}}} \right)e^{-({b_{0}}+{\beta_{1}})t}y^{2}(t)dt\geq-{r_{11}}^{2}\\ \int_{0}^{t_{1}}[\frac{{\beta_{2}}}{{b_{0}}+{\beta_{1}}}\
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 8 Conclusions  
Recent academic efforts have been dedicated to standardizing Dynamic Link Prediction (DLP) as a machine learning task. The goal is to equip the task with its own evaluation pipelines, baseline methods, and benchmark datasets. However, as a consequence of the high dimensionality of the data and their non-independent, non-identically distributed nature, deriving a single consistent model validation has resulted in numerous challenges.  
In this paper, we have explored several key aspects of these challenges. On the one hand, we have investigated the effect of time-based train-test splitting on the set of nodes and edges through novel Birth-Death diagrams, and discussed examples of these visualizations on datasets from diverse domains. Moreover, we have shown how to rely on the proposed Birth-Death diagrams to derive more challenging negative samples, based on the hypothesis that the error depends on the NS strategy. To illustrate the effect of these negative samples, an empirical assessment of the impact of different negative samples on performance was conducted. The relative performance of methods across datasets was compared over time by plotting the prediction ranks as a time series, revealing interesting insights into the failure modes of the different methods.  
This work raised several open questions that can be explored in future work. First, our evaluation tools could be used to conduct a more exhaustive comparison of existing heuristics such as the ones introduced in [26], first with simple learning-based approaches such as the ones discussed in Section 2, and then with more recent representation learning methods, with an emphasis on fair comparison. In terms of visualization, the proposed Birth-Death diagrams could be leveraged to visualize higher-order structures such as cliques or triangles, or any repeatedly occurring subgraph that can be uniquely identified.  
Conceptualization, R.R., M.B., T.D.B. and J.L.; Methodology, R.R., M.B. and J.L.; Software, R.R.; Validation, R.R.; Formal analysis, R.R.; Writing--original draft, R.R.; Writing--review & editing, M.B., T.D.B. and J.L. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the Special Research Fund (BOF) of Ghent University (BOF20/IBF/117), from the Flemish Government under the \"Onderzoeks-progromma Artificiele Intelligentie (AI) Vlaanderen\" programme, and from the FWO (project no. G0P9816N, 3G042220, G073924N).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 4 Discussion
Nevertheless, this process generates large amounts of water, which may constitute 20% to 70% of the total facility wastewater volume discharged into the sewage contributing to water (and energy) use inefficiencies and environmental contamination [24]. Therefore, it is crucial to promote the sustainable management of backwashed water. The preliminary results showed that this water could not be directly reused for irrigation because of its high concentration of TSS, free residual chlorine, and turbidity. However, the sedimentation process seemed to effectively reduce these parameters to permissible values, allowing the reuse of this water for irrigation purposes or for flushing cisterns, according to the parameters proposed in the available regulation [31; 32; 33]. Similar trends were found by [21; 22; 23; 24]. Furthermore, these authors also verified that the sedimentation process was effective in removing nitrogen and phosphorus. Therefore, considering these results, it is plausible to assume that the management of filter washings is possible in this facility after the installation of a relatively simple system for their treatment, such as a settler or a settling tank. Furthermore, considering that the annual amount of water necessary for irrigating the lawn of the local football stadium, located in the surroundings of the swimming pool complex, is around 7200 m\({}^{3}\) (data provided by municipality), the backwashed water could be fully reused for this purpose. Nevertheless, the treated filter backwashing could also be used in the flushing cisterns or, after a broader treatment, reintroduced back into the swimming pool [41]. However, due to the lack of specific legislation for backwashing monitoring and quality control, other indicators such as nutrients, micropollutants, and other chlorine forms [41; 42] should be determined in future approaches.
## 2 Materials and Methods
### Characterization of Packaging Materials  
All used packaging materials were analyzed for their thickness, transparency, and water vapor and gas permeability properties (oxygen and carbon dioxide). Considering that PA/PE is a two-layer film, for some analysis, both sides, the smooth (s) and the ribbed (r), were analyzed separately.  
The material thickness (\(l\)) was measured with a digital micrometer with a precision of 0.001 mm ([PERSON], HP, Helios Preisser, Gammertingen, Germany) and expressed as the average value of 10 measurements taken from random positions on each film surface.  
The transparency of the films was measured using a UV-VIS spectrophotometer (Lambda 25, PerkinElmer, Waltham, MA, USA) by recording the absorbance in the 200-800 nm spectrum. The measurements were performed in three replicates. The value at 600 nm was used to calculate the transparency of the material (\(T_{600}\)) according to Equation (1):  
\[T_{600}=A_{600}/x \tag{1}\]  
where \(A_{600}\) is the absorbance measured at 600 nm; and \(x\) is the material thickness (mm).  
The transmittance, i.e., percentage of the light that passes (transmitted) through the sample, was calculated following Equation (2):  
\[T\left(\%\right)=10^{-A}\cdot 100 \tag{2}\]  
The water vapor permeability of polymer materials was determined using a gravimetric method according to the modified ASTM E96-80 standard (ASTM, 1980) [28]. The glass cells were filled with 20 mL of distilled water, covered with samples of the tested packaging films, and secured with Teflon rings. Four repetitions of each sample were stored in a ventilated climatic chamber (Mermert HPP110, Memmert, Germany) at 23 \({}^{\circ}\)C and a relative humidity of 30% (so that the gradient RH was \(\Delta RH=70\%\)).  
The water vapor permeability (\(WVP\), g/m s Pa) was calculated according to Equation (3):  
\[WVP=\left(\Delta m/\Delta t\cdot A\cdot\Delta p\right)\cdot l \tag{3}\]  
and the water vapor transfer rate through the sample (\(WVTR\), g/s m\({}^{2}\)) according to Equation (4):  
\[WVTR=\Delta m/\Delta t\cdot A \tag{4}\]  
where \(\Delta m/\Delta t\) is the weight loss of water vapor per unit of time (g/s); A (9.08 \(\cdot 10^{-4}\) m\({}^{2}\)) is the film surface area exposed to moisture transfer; \(l\) is the film thickness (m); \(\Delta p\) is the difference in water vapor pressure between the two sides of the film (Pa).  
Gas permeability was measured by the manometric method ([PERSON], GDP-C, Brugger Feinmechanik GmbH, Munich, Germany) [29] and expressed as oxygen (\(PO_{2}\)) and carbon dioxide (\(PCO_{2}\)) permeability coefficients calculated from the permeance and film thickness. The measurements were performed at 24 \({}^{\circ}\)C and 0% RAH and carried out in three repetitions.
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 5 Application
### Simplified Model of the Bridge  
To analyze the cable-stayed bridge using the simplified VBI model proposed in this paper, the bridge needed to be simplified, and the elastic foundation beam model could be used to simplify the cable-stayed bridge by making the following assumptions:  
1. The stiffened beam was simplified to a uniform Euler beam with the same boundary conditions as the real bridge.  
Figure 11: Identification curves of bridge damping coefficients.  
Figure 12: The case bridge.  
2. The tension cables were simplified to vertical springs distributed continuously along the length of the stiffened beam.
3. The axial force of the stiffened beam was uniformly distributed in the beam. The governing differential equation for the simplified bridge can be written as: \[EI\frac{\partial^{4}u}{\partial x^{4}}+m\frac{\partial^{2}u}{\partial t^{2}}+P \frac{\partial^{2}u}{\partial x^{2}}+K_{w}u=0\] (25) where \(P\) is the axial force of the stiffener beam and \(K_{w}\) is the equivalent spring stiffness.  
The case bridge was modeled with the parameters \(EI=3.75\ \times 10^{11}\) Nm\({}^{2}\), \(m=2.25\ \times 10^{4}\) kg/m, \(K_{w}=3\ \times 10^{5}\) N/m, and \(P=3\ \times 10^{7}\) N. The obtained vertical self-oscillation frequencies of the first three orders were basically consistent with the measured results, as shown in Table 2.  
Using Equation (25), the double-beam system could be constructed according to the method in Section 2 to obtain the damping ratio calculation equation for the simplified axle coupling system. The expression of the formula is in the same form as Equation (17), and the specific derivation process can be found in [35]. After obtaining the simplified damping ratio formula for the axle system, the damping ratio of the bridge could be extracted by combining the measured data according to the method in Section 4.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 4 Proposed Algorithm  
### Virtual Network Request Classification  
In order to flexibly respond to virtual network requests with differentiated resource requirements and QoS requirements, we propose a virtual network embedding algorithm that distinguishes network types. The algorithm flow chart is shown in Figure 1. When a virtual network request arrives, the virtual network type is first classified. According towhether the virtual network has delay constraints, virtual network requests are divided into ordinary requests and delay-sensitive requests, and the sub-algorithms mentioned in Sections 4.2 and 4.3 are used to solve them, respectively. Before embedding the virtual network, it is necessary to judge whether the substrate network resources satisfy the demands and constraints of the virtual network. After the embedding is successful, update the substrate network, wait for the arrival of next virtual network request, and start timing the virtual network lifetime. When the virtual network request leaves, release the occupied substrate network resources and update the substrate network.