esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of the Solvent  
The solvents typically used for lutein extraction are methanol, dichloromethane, acetone, and ethanol [33]. In this investigation, for selection of the solvent in the extraction mixture, the previously mentioned guidelines (Section 2.7.2) and extraction protocol UAECOL (described in Section 2.7.2) were used. Methanol, ethanol, acetone, 2-propanol, and a mixture of methanol:acetone (1:1, \(v/v\)) were used. Results are shown in Table 1. The significantly higher lutein content in the hens' feed mixture was determined when using the solvent mixture methanol:acetone (1:1, \(v/v\)), and the lowest when using 2-propanol (4.572 mg/kg and 0.543 mg/kg, respectively; \(p<0.01\)). Similar amounts of lutein in the hens' feed mixture were determined when using methanol and ethanol as solvents (3.499 mg/kg and 3.201 mg/kg, respectively; \(p>0.05\)). [PERSON] et al. investigated the influence of isopropanol, acetone, ethyl acetate, and hexane on the extraction of lutein from Australian Sweet Lupin Flour [34], and [PERSON] et al. [35] determined that isopropanol is the appropiate solvent for lutein extraction. [PERSON] et al. used a mixture of n-hexane:isopropanol (3:2, \(v/v\)) to extract lutein from hens' feed mixture [36], and [PERSON] et al. concluded that hexane was the best solvent for lutein extraction under the conditions they investigated [22]. The mixture of acetone and magnesium sulfate was used by [PERSON] et al. [37], while [PERSON] et al. used butanol:acetonitrile (1:1, \(v/v\)) and hexane:chloroform (2:1, \(v/v\)) [38]. [PERSON] et al. used acetone followed by petroleum ether [39], and [PERSON] et al. used methanol:ethyl acetate:light petroleum (1:1:1, \(v/v/v\)) [40]. Meanwhile, methanol and dichloromethane (1:1, \(v/v\)) was used by [PERSON] et al. [41], acetone:methanol 8:2 (\(v/v\)) was used by [PERSON] et al. [42], [PERSON] et al. used an acetone:methanol solvent mixture (1:1, \(v/v\)), and samples were subsequently treated in an ultrasound bath [32]. A mixture of chloroform:methanol (1:1, \(v/v\)) was used by [PERSON] et al. [43].  
Figure 3: Comparison of lutein content in unground (—) and ground (—) samples obtained with the standard method (SM).  
Figure 2: Comparison of unground (**a**) and ground and sieved (**b**) hens’ feed mixture.  
For the analysis of real samples, we chose methanol:acetone (1:1, \(v/v\)) to use in the composition of the extraction mixture.
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 4 Simulation Results and Analysis
### Setup and Parameters  
The values of the relevant simulation parameters are summarized in Table 3. The LEO beam hopping satellite communication system proposed in this paper operates in the Ka-band with a carrier frequency of 20 GHz. The satellite orbit altitude is assumed to be 550 km. A single satellite is equipped with four service beams, and the satellite-covered ground area is divided into 12 positions. The total system bandwidth is 200 MHz. Each beam has five power options, and the maximum beam power is 8 dBW. The satellite transmission gain is 25 dBi, and the ground reception gain is 45 dBi. Each time slot is set to be 2 ms long, and the maximum residence time of a data packet is 70 ms. Before entering the training period, the arrival of data packets at each position follows a Poisson distribution. During the training period, the traffic demands for each beam position range from 1500 to 2200 Mbps. To reflect the time-varying characteristics of traffic demand, the traffic volume requested by each beam position in each time slot is generated based on its remaining data volume.  
Table 4 summarizes the parameters of the VDN algorithm framework. In the training phase, we set the number of training episodes to 2000 and the number of time slots in the beam hopping period for each episode to 100. The learning rate is \(10^{-4}\), and the size of the experience pool is 2000. The update frequency of the target network is 100, and the minibatch size is 64. The discount factor is set to 0.9. The initial exploration rate is 1, and the final exploration rate is 0.05.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Satellite altitude \(H\) & 550 km \\ Ka-band frequency \(f_{c}\) & 20 GHz \\ Number of LEO satellite & 1 \\ Number of beams \(K\) & 4 \\ Number of positions \(M\) & 12 \\ Total bandwidth \(B_{tot}\) & 200 MHz \\ Shadow fading variance \(\sigma_{sh}^{2}\) & 5 dB \\ Multipath fading variance \(\sigma_{m}^{2}\) & 2.5 dB \\ Total satellite power \(P_{tot}\) & 23W \\ Beam power \(P_{b}\) & 8 dBW \\
3 dB beamwidth \(\theta_{b}\) & 0.5\({}^{\circ}\) \\ Antenna transmission gain \(G_{T}\) & 25 dBi \\ Antenna reception gain \(G_{R}\) & 45 dBi \\ Noise power spectral density \(N_{0}\) & \(-173.8\) dBm/Hz \\ Noise temperature & 300 K \\ Boltzmann constant & \(1.38\times 10^{-23}\) \\ Time slot duration \(t_{s}\) & 2 ms \\ Time to survive of data packets \(T_{th}\) & \(35\times t_{s}\) \\ Traffic demand \(D\) & [1500, 2200] Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: LEO satellite simulation parameters.
## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework
#### 4.1.3 5 Growth  
5G-enabled growth in vertical industries (5 GROWTH) is a 33-month EU-funded project (approximately EUR 14 million) that started in June 2019 with the vision to design an AI-based 5G E2E solution for vertical industries to meet their respective performance targets, simultaneously. The verticals involved in the project, including Industry 4.0 and transportation, have specified use cases that have very diverse technical requirements. For instance, Industry 4.0 COMAU, a world leader in industrial automation, opts for digital twins, smart factory telemetry and monitoring apps, digital tutorials, and remote support, while in the transportation industry, EFACEC is likely to focus on safety communications [118]. On the one hand, the technical requirements of telemetry applications are high availability 99.999%, low latency 15-100 ms, low-mobility support 3-50 km/h, and high connection density \(\approx\)5000 devices/km\({}^{2}\). On the other hand, a use case in railway transportation, such as real-time, high-definition image transmission from a surveillance camera at the level crossing to the train approaching the intersection, requires sufficient macro-cellular coverage and eMBB support at high speeds \(\approx\)160 km/h [118]. To accommodate such diversity of technical requirements over the same physical infrastructure, a sophisticated management of network slices is needed, which opens new research problems, where AI/ML can play a central role.  
For an efficient use of the created network slices, the concept of slice-sharing among multiple (vertical) services can be applied. Furthermore, a single service can be decomposed into sub-services with different functionalities, such as the Services C and D in Figure 10, which are served from different slices, leveraging the idea of network slice sub-net instance (NSSI) [119]. Given the complexity of the selected use cases, it is not surprising that the consortium has contrived four innovative technology components for efficient network slicing using AI/ML [120]. These components implement dynamic and intelligent management of network slices and integrate AI/ML throughout the network for optimal orchestration and resource control, anomaly detection, QoS forecasting, and inference.  
Figure 10: Service A exclusively uses Slice 1. Leveraging the principle of NSSI, Slices 2, 3 and 4 are broken down into two subnets. Slice 2 and Slice 3 share a subnet. Services C and D are decomposed into two sub-services and one of them is served by the Subnet Slice 3a. For instance, Subnet 2a and Subnet 4a might be core network slices, while Subnet 3a might be a RAN slice. In this illustration, the Services C and D need similar functionalities at the RAN, but not at the core network; hence, they can share a RAN NSSI but use different core network NSSI. In this way, the created slices are used more efficiently as compared to the exclusive allocations of network slice instances to network services.
# Service Interface Translation. An Interoperability Approach
## 4 Performance Evaluation
### Results  
In Table 5, it can be observed that the execution time for the code generation and building of the executables lies within a feasible range and is suitable for IoT scenarios. On average, the process of generation, starting when the orchestrator calls the generator system and ending when the JAR files are successfully built, takes less than 50 ms.  
The sizes of the generated code are presented in Table 6. The values are dependant on the scenario. Nevertheless, complementary studies have shown that the size does not vary significantly.  
In the case of the use of the generated interface instance, the service consumption time for the presented scenario increases notably. The average time for consumption is approximately 100 ms, and it is possible to achieve 50 ms as a minimum value. The time employed by the interface to retrieve the information from the provider and translate the payloads is approximately 60 ms. Since the average consumption time between the interface and the provider is 33 ms, it is possible to infer that the payload translation takes place within 30 ms on average.  
To compare with a reference point, a CoAP Client was implemented to directly consume the data from the provider. The CoAP client interface is a perfect match for the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Meaning** & **Result** \\ \hline \(S_{G}\) & Size of the generated interface code & 24 KB \\ \hline \(S_{I}\) & Size of the interface JAR (Including dependencies) & 11.9 MB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sizes of the generated code.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Averaged Time [ms]** & **Standard Deviation** & **Range (Max, Min) [ms]** \\ \hline \(T_{G}\) & 25.72 & 6.33 & (64, 28.87) \\ \hline \(T_{B}\) & 17.28 & 1.13 & (24.1, 15.78) \\ \hline \(T_{I}\) & 64.64 & 15.06 & (151, 34) \\ \hline \(T_{S}\) & 101.84 & 16.46 & (193, 53) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental Results.  
Figure 7: UML Sequence diagram for service consumption using the consumer interface generator.  
provider interface. Figure 8 shows a stacked graph of the translation timing in comparison with the direct consumption. Except for a few outliners, there was little variability in the consumption of the service using the interface translation. The CoAP library used to communicate with the provider and the network seems to have been responsible for much of the variability. The direct consumption of the service is also affected by this situation, as shown in the graph.  
The times presented in this paper are dependent on the network and device characteristics; consequently, they should be considered a proof of concept to demonstrate the validity of the approach and not as strict results. There is room for improvement and optimization, which may more likely result in lower times.
## 4 Conclusions  
In our first study [21], we analysed the performances of a stirring mechanism from a real industrial reactor, that was originally employed for mixing a liquid-liquid combination, when operating with a liquid-solid combination. In that paper, we presented the significant results from the domain of CFD applied for stirring industrial reactors. An extensive presentation of the numerical setting is performed. The conclusion of the research is that the original impeller is not able to prevent the sedimentation process of the solid particles in the lower part of the reactor so that the original impeller must not be employed for mixing liquid-solid combination. The second paper [22] presents the performances of a new designed impeller against the performances of the original one. The new designed impeller has a better performance and there is no more sedimentation of the solid particles. However, the new impeller is not able to ensure the distribution of the solid particles in the entire volume of liquid. Around 20% of the volume of liquid contains no solid particles.  
Following our previous studies [21; 22], we examined in this paper a tandem configuration of the newly design three-blades impeller for stirring a liquid-solid mixture in a baffle industrial reactor. The numerical analysis, performed with the expert software Ansys(r) Fluent, Release 16 employs the Eulerian multiphase model with a sliding mesh approach. The numerical data analysed and presented are similar to the results obtained and presented in other research papers [2; 3; 4; 5; 6; 7; 8; 9; 10].  
It is shown that the tandem impeller configuration examined in this paper is definitely superior in terms of mixing capabilities compared with a single impeller. While sedimentation of the solid phase is practically mitigated, the tandem configuration reduces the fraction of liquid phase depleted from solid particles, at the minimal additional expense in terms of power consumption.  
Because the shape of the blades of the impeller is too complex and the cost of manufacturing such an impeller would be too significant, a simplified version of the impeller was manufactured. The simplified version produces the same hydrodynamic field, and this solution was implemented in the existing industrial reactor. The tests performed on the reactor fitted with the simplified tandem of impellers showed that no solid par  
Figure 10: Distribution of the velocity vectors of the liquid phase on an axial plane, instantaneous results.  
ticles were sedimented on the lower part and that the final chemical product has the required properties.  
Investigation, A.S. and A.B.; Methodology, A.S.; Supervision, R.S.-R.; Writing--review & editing, A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number 50/2017.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Technical Tips for Performing Effective Superselective cTACE
### Usefulness of Stepwise cTACE for Large HCCS  
cTACE is also widely performed for large HCCs; however, the previously reported 5-year overall survival rate was only 7-9.7% [76; 77]. In addition, the use of a large volume of iodized oil for large tumors carries the risk of catastrophic complications, such as systemic embolization of the lung or brain [78; 79] and acute tumor-lysis syndrome [80]. Therefore, we typically perform stepwise superselective cTACE for localized tumors >7 cm [81; 82]. Two to three cTACE sessions are scheduled depending on the vascular anatomy, and each session is performed in a superselective fashion using \(\leq\)10 mL of iodized oil at 3- to 10-week intervals according to the patient and tumor conditions. In our recent analysis of 25 patients with HCC \(\geq\)10 cm (mean, 130 \(\pm\) 27.6 mm [range, 101-193], single [\(n\) = 12], 2-9 [\(n\) = 6, mean, 4.2 \(\pm\) 2.6 ], and \(\geq\)10 tumors [\(n\) = 7, including 2 with \(\geq\)50 tumors], with vascular invasion [\(n\) = 5]), the 5-year survival rate was 23.1%, and that of patients with tumors \(\leq\)3 lesions was 38.9% [82]. This indicates that cTACE is also effective for localized huge HCCs. However, cTACE of extrahepatic collateral arteries was required in 84% of patients during the treatment course [82]. Therefore, the outcomes of cTACE for huge HCC may be more strongly influenced by the TACE technique compared with cTACE for small HCC. Bland embolization with gelatin sponge particles followed by cTACE is another alternative option for huge HCC to safely embolize the tumor [83].
## 4 Lead Identification and Optimization
Statistical Molecular Design (SMD) is applied to select compounds based on predictor variables—higher-order terms or cross-products of chemical features—that correlate with activity. **3**. The newly selected compounds, resulting from SMD, are represented as yellow and white stars. These compounds are subsequently synthesized and tested for their biological activity (BA). **4**. The chemical space is expanded with novel data from these compounds and their activities, represented by yellow and white stars within the corresponding ovals. This expanded chemical space is used for further QSAR model updates and development. Most of these methods are beyond the scope of the current review; therefore, they will not be discussed. Once the lead structure has been identified, the molecule must undergo further optimization to improve its affinity, selectivity, and ADMET properties. ADMET stands for Absorption, Distribution, Metabolism, Excretion, and Toxicity, which all influence how the drug behaves in the body and its pharmacological activity. It is important to recognize that lead optimization is a multi-objective process, with several factors influencing a compound's druggability. Historically, traditional lead optimization methods often relied on trial-and-error, as well as the expertise and judgment of the scientist. In most cases, lead optimization has not been a systematic process, with various properties being optimized at different stages. As a result, improvements in one area, such as absorption, may lead to deterioration in another, such as excretion. This is because it can be difficult to simultaneously analyze and incorporate all available data and information into the optimization process. Today, it is standard practice to exclude compounds from screening libraries (whether virtual or real) that do not meet [PERSON]'s Rule of Five [171], along with other filters like Ghose [172], Egan [173], Veber [174], and Muegge [175]. These filters help significantly narrow the search space. Once optimized, the drug can then proceed to pre-clinical and, eventually, clinical trials. The era of AI has brought significant advancements, transforming productivity, effectiveness, innovation, and success rates in drug discovery and development. AI has demonstrated notable successes in CADD, including target identification [176], high-throughput screening [177], structure-based drug design [178], de novo design [179], protein folding prediction from sequences [180], ADMET prediction [181], druggability prediction [182], and QSAR modeling and lead optimization [183; 184], among others [185; 186; 187].
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 2 Methods
### Sea Level Based on SNR Data  
The values of SNR in decibels (dB) for the GNSS signals can be found from some of their RINEX (Receiver Independent Exchange Format) observation files. SNR refers to the ratio of signal power to noise power, and although the reflected signals will be suppressed by the antenna gain of geodetic GPS receivers, it can still be received at low satellite elevation angles (\(<\)30\({}^{\circ}\)). Figure 2a shows the oscillations of the SNR data from the SC02 GPS station of the PRN27 satellite on 6 and 8 January 2015 with the sinusoidal values of the satellite elevation angle. The oscillations of SNR are caused by multipath effects.  
The signal power is expressed in the form of the squared amplitude of the receiving signals, and therefore the SNR is calculated as follows [27]:  
\[SNR=\frac{A^{2}}{P_{noise}}=\frac{1}{P_{noise}}(A_{d}^{2}+A_{m}^{2}+2A_{d}A_{m }\cos(\frac{4\pi h}{\lambda}\sin\theta(t))) \tag{6}\]  
Figure 2: (**a**) GPS signal-to-noise ratio (SNR)1 observation at the SC02 GPS station; (**b**) multipath pattern after detrending; (**c**) Lomb–Scargle Periodogram for the PRN27 GPS satellite on 6 and 8 January 2015.  
where \(P_{noise}\) is the noise power. In order to solve for reflector height (\(h\)), it is necessary to isolate the last item from Equation (6) by removing the first two items using a fifth-order polynomial detrending. The detrending of the \(SNR\) becomes  
\[dSNR=\frac{2A_{d}A_{m}}{P_{noise}}cos\left(\frac{4\pi h}{\lambda}sin\theta(t)\right) \tag{7}\]  
According to Equation (7), \(dSNR\) is a quasi-sinusoidal function, oscillating with the sine of the elevation angle at a frequency of \(f=2h/\lambda\). Figure 2b shows the \(dSNR\) sequence for the PRN 27 GPS satellite with the trend removed. After obtaining the dominant multipath frequency of the detrended SNR data by Lomb-Scargle Periodogram (LSP) spectral analysis (see Figure 2c), the reflector heights can be calculated as follows:  
\[h=\frac{\lambda f}{2} \tag{8}\]  
It should be noted that the multipath pattern is expressed as a function of the sine of the elevation angle, not time (\(t\)). The SNRs are evenly sampled in time, but not as a function of the sine of elevation. Thus, spectral analysis is usually performed using the LSP or wavelet analysis, because these methods can handle an unevenly spaced sampling sequence. The reflector height is negatively correlated with sea level, and the sea surface heights can be obtained from the vertical difference between the geodetic height of the receiver antenna and the estimated reflector heights.
## 3 Experimental Setup  
The phase modulation experimental system of the optical fiber Sagnac AE sensor is shown in Figure 5. It includes a super luminescent diode (SLD) light source, a delay coil, a Lyot-type depolarizer, a sensing probe, a photo-detector, a DAQ and a computer. The central wavelength of the SLD was 1295 \(\pm\) 20 nm with a power of 1.6 mW. The PD had a voltage responsivity of 50 V/W and a response time of 1.5 ns. The DAQ was NI USB-6366 with a highest sampling frequency of 2 MHz. SG1 and SG2 were the signal generators (Agilent 33500B, made by Agilent Technologies Inc., State of California, USA) used for simulating AE signals and modulating the initial phase, respectively. An aluminum alloy plate was selected as an object, which was fixed on the seismic metal test platform by four magnetic bearings, whose dimensions were 600 mm \(\times\) 600 mm \(\times\) 2 mm. The sensing probe was made of a skeleton of light hollow aluminum ring with an outer diameter of 15 mm and height of 20 mm. A short piece of single-mode fiber with the length of 3 m was wound around the skeleton. Ultraviolet glue was used to hold the fiber and prevent it from falling off the skeleton. In this experiment, the sensing probe was bonded on the plate by epoxy adhesive with the coordinates of (300 mm, 450 mm). It is difficult to ensure the production of stable and controllable signals in simulating wideband AE signals by using general methods, such as pencil-lead breaks and impact. The main purpose of this work was to investigate the changes of the sensor's sensitivity in the initial phase modulation using a constant stimulation. Therefore, a PZT attached on the plate with the coordinates of (300 mm, 300 mm) was used for simulating AE signals.  
The output voltages of the PD with eight-fold reversed amplification were transferred to the computer by the DAQ. The splitting ratio of the 2 \(\times\) 2 coupler used in this experiment was 53:47, and the insertion losses of port 3 and port 4 were 2.91 dBm and 3.58 dBm, respectively. The delay coil was wound by a single-mode fiber with the wavelength of 1310 nm. The velocity of the light was \(c^{\prime}=2.045\times 10^{8}\) m/s, so the intrinsic frequency of the sensor was \(f_{0}=1/(2\tau)\approx 100\)kHz. The Y-branch waveguide was PMD1338-M, whose half-wave voltage was 3.14 V, center wavelength was 1310 nm, splitting ratio was 50.2:49.8, and insertion loss was 2.95 dB. The phase change produced by Y-branch waveguide modulation can be expressed as  
\[\Delta\phi(t)=\frac{\pi V_{\mathrm{x}}}{V_{\pi}} \tag{11}\]  
where \(V_{\mathrm{x}}\) is the modulation voltage loaded on the Y-branch waveguide and \(V_{\pi}\) is the half-wave voltage when the change of phase modulation is \(\Delta\phi=\pi\).  
Figure 5: Experimental system of the optical fiber Sagnac AE sensor.