esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Monitoring the Volatile Compound Profiles in Seaweed Fermentation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{6}{c}{**Concentration (\(\mu\)g/L)**} \\ \cline{2-7}  & **Unfermented** & **BS** & **SC** & **LA** & **LB** & **LC** \\ \hline \multicolumn{7}{l}{**Hydrocarbons**} \\ \hline Toluene & 173.85 \(\pm\) 1.65\({}^{\,\mathrm{a}}\) & 39.45 \(\pm\) 1.94\({}^{\,\mathrm{c}}\) & 62.61 \(\pm\) 2.18\({}^{\,\mathrm{b}}\) & 15.35 \(\pm\) 0.40\({}^{\,\mathrm{a}}\) & 26.97 \(\pm\) 1.89\({}^{\,\mathrm{d}}\) & nd \\ \hline
**Furans** & & & & & \\ \hline
2-Ethylfuran & 299.42 \(\pm\) 23.69\({}^{\,\mathrm{a}}\) & nd & 38.19 \(\pm\) 2.18\({}^{\,\mathrm{b}}\) & 61.68 \(\pm\) 0.39\({}^{\,\mathrm{b}}\) & 14.32 \(\pm\) 1.12\({}^{\,\mathrm{b}}\) & 16.55 \(\pm\) 0.66\({}^{\,\mathrm{b}}\) \\ \hline
**Pyrazines** & & & & & \\ \hline
2,5-Dimethylpyrazine & nd & 159.03 \(\pm\) 5.90 & nd & nd & nd & nd \\ \hline
**Sulfur-containing** & & & & & & \\ \hline Dimethyl sulfide & nd & 26.71 \(\pm\) 3.56 & nd & nd & nd & nd \\ \hline Isobutyl isothiarate & nd & 3.16 \(\pm\) 0.52 & nd & nd & nd & nd \\ \hline
**Miscellaneous** & & & & & & \\ \hline Methylethylamineelimide & 19.43 \(\pm\) 0.23\({}^{\,\mathrm{a}}\) & 6.32 \(\pm\) 0.29\({}^{\,\mathrm{a}}\) & 16.14 \(\pm\) 1.59\({}^{\,\mathrm{b}}\) & 20.84 \(\pm\) 0.46\({}^{\,\mathrm{a}}\) & 15.10 \(\pm\) 1.38\({}^{\,\mathrm{d}}\) & 12.03 \(\pm\) 1.05\({}^{\,\mathrm{d}}\) \\ \hline Dihydroactinidiolide & 56.51 \(\pm\) 3.48\({}^{\,\mathrm{a}}\) & 58.12 \(\pm\) 0.63\({}^{\,\mathrm{a}}\) & 47.21 \(\pm\) 5.19\({}^{\,\mathrm{a}}\) & 57.08 \(\pm\) 1.09\({}^{\,\mathrm{a}}\) & 43.17 \(\pm\) 1.64\({}^{\,\mathrm{b}}\) & 48.28 \(\pm\) 2.49\({}^{\,\mathrm{a}}\) \\ \hline Total Miscellaneous & 75.94 \(\pm\) 4.45\({}^{\,\mathrm{a}}\) & 64.44 \(\pm\) 0.38\({}^{\,\mathrm{b}}\) & 63.35 \(\pm\) 5.28\({}^{\,\mathrm{b}}\) & 77.92 \(\pm\) 1.81\({}^{\,\mathrm{a}}\) & 58.27\(\pm\) 1.67\({}^{\,\mathrm{b}}\) & 60.31 \(\pm\) 1.22\({}^{\,\mathrm{b}}\) \\ \hline \hline \end{tabular} All measurements were performed in triplicate and expressed as mean \(\pm\) SD (n = 3). Different letters within the same row represent significant differences; nd: non-detected.  
\end{table} 
Table 1: _Cont._
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 4 Industrial Applications of Cavitating Jets
### CAVJET Performance for Well Drilling  
The structured flow issuing from cavitating jets presents several advantages relative to the conventional nozzles used in deep-hole drilling; namely, greater ability to erode or otherwise weaken the rock, and an improved mechanism for removing chips from the hole bottom [18].  
Figure 16: (**a**) Cavitation nozzles geometry (all sizes are in mm): nozzles A and B, respectively. (**b**) Cavitation erosion pattern [68].  
Cavitating jets have been used in a number of mechanical devices for drilling, such as tunneling and mining. Therefore, continuous development was being conducted on the flow dynamics of cavitating jets in order to use the hydraulic energy more efficiently and thus, enhance the drilling performance [72]. It should be noted that pumps that generate pressures up to the 60,000 psi are used in the industry. Process of drilling can be accompanied with roller or diamond cutting surfaces and more pressures are required for these applications.  
CAVIJET is a cavitating jet that has been designed with better drilling performance for higher pressure and temperature holding systems. A typical configuration of CAVIJET nozzle design and its working apparatus is shown in Figure 17 [73]. It was found that the \"centre-body\" nozzle configuration is suitable for in-air experimentation, whereas \"Turning Vane\" or the plain nozzle designs (without vanes or center-body) can be used for deep-hole drilling on submerged surfaces [73]. Different nozzle systems are available for the induction of vortex cavitation and for flow separation while cavitation occurs. A swinging shutter plate was placed between the rock surface and the nozzle to control the exposure time of drilling in the given apparatus. Exposure time was counted to be between 5 and 60 s and it mainly depended upon the type of rock.  
The experimental results of [PERSON] and [PERSON] [73], at both atmospheric and elevated ambient pressures up to 21 MPa, have shown that the CAVIJET will perform effectively under down-hole pressure conditions. They also found that the optimum standoff distance for CAVIJET drilling decreases from about 14 nozzle diameters in the atmospheric pressure tests to about 2 nozzle diameters under a 3000 psi (21 MPa) ambient pressure. These test results have demonstrated the effectiveness of the CAVIJET method up to an equivalent borehole depth of 1770 m if using a fluid density of 1200 kg/m\({}^{3}\).
###### Abstract  
Harvest index (HI) is a complex and vital agronomic trait that is closely related to the economic benefits of rapeseed. In this study, we measured the HI and 13 HI-related agronomic traits of 104 core breeding lines of rapeseed during 3 years and sequenced the populations using the Bnapus50K array. The phenotypic analyses showed the complex connections among HI and other traits. A total of 212 significant SNPs related to the traits and 22 stable SNPs were identified. Four SNPs, A01_1783685 (PH and SYP), C06_26638717 (PH and NSS), C03_4731660 (MIL and MINS), and C09_36899682 (PH and BYP), were identified as potential pleiotropic loci. Compared to previous reports, 49 consensus loci were obtained that were related to PH, TSW, NSP, BAI, NSS, SL, BN, MINS, SYP, and BYP. Twelve stable SNPs were detected as promising novel loci related to BN (A05_19368584 and A05_19764389), SL (A06_23598999, A06_23608274, and C07_38735522), PH (C04_47349279, C04_47585236, and C09_36899680), MINS (C05_6251826), NSS (C06_22559430 and C06_22570315), and HI (C05_6554451). In addition, 39 putative genes were identified in the candidate intervals. This study provides novel insights into the genetic mechanisms of HI and HI-related traits, and lays a foundation for molecular marker development and casual gene cloning to improve the harvest index of rapeseed.  
Brassica napus L.; SNP array; GWAS; harvest index; agronomic traits; network 1
## 3 Models and Algorithms
### Feature Composition  
The algorithm for feature composition uses the features of the two adjacent RChs to the target channel to linearly interpolate the features [Y\({}^{i,ch}\)] for that channel.  
Algorithm 2 presents the pseudocode that receives as input the target channel and the set of features for the reference channels after propagation. The algorithm first finds the adjacent reference channels to the target channel (line 1 in Algorithm 2). Next, the features of the adjacent reference channels are selected from the complete set of features and used for linear fitting for each individual feature (lines 2-4). Finally, the features of the target channel are calculated by interpolation and finally returned (lines 5-6).  
```
INPUT: \(ch\), \([Y^{i,ch}]\) OUTPUT: \([Y^{i,ch}]\)
1: \(IRCh\), \(rRCh\leftarrow\)get\(AdjacentRChs(ch,[RCh])\)
2: \([Y^{i,RCh}]\leftarrow\)get\(RChFeatures(IRCh,[Y^{i,RCh}])\)
3: \([Y^{i,rRCh}]\leftarrow\)get\(RChFeatures(rRCh,[Y^{i,RCh}])\)
4: \([slope]\), \([intercept]\gets getLineSegments([Y^{i,IRCh}],[Y^{i,rRCh}])\)
5: \([Y^{i,ch}]\leftarrow\)get\(FeaturesFromSegments(ch,[slope],[intercept])\)
6: return\([Y^{i,ch}]\)
```  
**Algorithm 2** Feature Composition
# Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation
## 2 Materials and Methods
### User Evaluation  
When the system reached a fully functional prototype state, it was evaluated by 20 users who voluntarily agreed to participate in evaluation tests. Upon the completion of the VR experience, the users completed a questionnaire, consisting of 19 questions, based on the Questionnaire of Presence (QoP) [38], the most widely used presence questionnaire for virtual environments. The included questions are related to the four main categories of factors contributing to the sense of presence, Control Factors, Sensory Factors, Distraction Factors, and Realism Factors. Control Factors show at what point the user has control over the simulation environment and are affecting their immersion. Realism Factors assess the environment realism and the consistency of information and affect the user's engagement since users pay more attention to the virtual environment stimuli. Sensory and Distraction Factors measure the isolation of the user, their senses involved, and the consistency of the information the user receives.  
The questionnaire also includes three questions for evaluating the importance of haptic feedback, locomotion through the treadmill, and interacting with the elements of the scene for a more immersive experience. The last four questions aim to assess what the users think about the importance of VR experiences in general, in other fields, and if the specific experience intrigued them into exploring more about cultural heritage.  
At first, the users were given a verbal explanation of the navigation and interactions controls. In the first part of the application, the underwater scene, the users could freely explore the seabed but had to find a specific item, the main fragment from the Antikythera mechanism, to move to the next scene, on the ship. During the underwater exploration, the users came across various findings that gave more information about the Antikythera shipw and its discovery. After they reached the main fragment they were relocated on the ship carrying the Antikythera mechanism back in the first century BC. There they could move freely around and interact with various objects as described in Section 2.1. There was no time limit and they could terminate the experiment at any point they wanted. The questions asked were designed to rate their overall experience as well as more specific areas such as navigation, interactions, viewing capabilities, learnability, and memorability.
## 3 Results and Discussion
### Case Study B: Tower Footing Resistance Computation for Different Earthing Designs  
In this section, the tower footing resistance computation is presented for three different design arrangements: (i) default design with counterpoise (Design A) (ii) radial and ring electrodes (Design B) and (iii) diamond with counterpoise and electrodes (Design C). Specifically, the TFRs were computed for three conditions of soil structure, as described in Section 3.1. Figure 11 presents the TFRs for the three types of earthing design buried in a two-layer (T42), three-layer (T48) and four-layer (T50) stratified soil. Results indicated that the Tower T42 had the highest TFR value as compared with Tower T48 and Tower T50. In this case, the resistance was influenced by the first layer, which had a high soil resistivity. This particular layer had a soft topsoil, porous enough to retain ample air and water. In comparison, the middle and bottom layers were harder and more compact than the top layer. These layers did not contain any organic matter and consisted of a rock that made them very rough [45]. Changing Design A for Design C reduced the TFR of each tower by more than 50% (66% for T42, 54.7% for T48 and 63.2% for T50). Table 5 summarizes the TFR values improvements when Design B or Design C were deployed to each of the towers.  
Figure 11: Influence of design arrangements on tower footing resistance in a 2-layer, 3-layer and 4-layer soil in Towers T42, T48 and T50, respectively.  
Figure 10: Soil profile interpretation for the Tower T50.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 1 Introduction
### Background
Consequently, the study of daylight in interior spaces is essential for a myriad of reasons. However, daylighting analysis is inherently complex due to the dynamic nature of sunlight. The variability of solar irradiance--affected by factors such as time of day, season, and weather conditions--presents a challenge for designers attempting to accurately predict and optimize daylight performance [23]. Traditional static daylighting maps, which offer a snapshot of illumination at a fixed point in time, fail to capture these temporal variations, limiting their utility in comprehensive daylighting assessments. Advancements in computational capacity over the past decades have facilitated the development of new indices that allow for a more precise analysis of daylighting in interior spaces. Additionally, these new indices support the implementation of Dynamic Lighting Control Systems (DLCSs) whose effectiveness in maximizing daylight utilization and reducing energy consumption for electric lighting is well-established [24; 25; 26; 27]. Previous reviews have analyzed the impact of dynamic daylighting metrics on building energy performance, such as those by [PERSON] et al. [28], and on visual comfort, as discussed by [PERSON] et al. [29]; however, they have not explored the implications of these metrics on occupant health and well-being in-depth. Therefore, the new focus of this paper on the health and well-being impacts of dynamic daylighting metrics is both timely and pertinent.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 3 Data Preparation and Evaluation
### Evaluation Approach  
For an individual subject, we denote the available user ratings as \(\mathbf{q}\) and the according EEG values in second degree polynomial expansion as \(\mathbf{v}_{p}^{2}\). We perform a random split into a training and an evaluation data subset (80/20 split), resulting in \(\left(\mathbf{q}(\text{train}),\mathbf{v}_{p}^{2}(\text{train})\right)\) and \(\left(\mathbf{q}(\text{test}),\mathbf{v}_{p}^{2}(\text{test})\right)\). We perform logistic regression on \(\mathbf{q}(\text{test}),\mathbf{v}_{p}^{2}(\text{test})\). The regression-determined coefficients \(\mathbf{c}_{u}\) are subsequently applied to the remaining test subset, and the Mean Absolute Error (MAE) is determined.  
We repeat this individual prediction process \(n,500\leq n\) times, interrupting when a 95% confidence interval width [28] below 5 percent of the overall average MAE value is reached. This approach results in \(n\) random sub-sampling repetitions for cross-validation (Monte Carlo-type cross-validation). The determined coefficients \(\mathbf{c}_{ii}^{n}\) for each of the individual validations are averaged themselves to \(\mathbf{C}_{ii}\) and employed in the prediction of the complete dataset, i.e., without separation of training and testing data. This approach mimics the availability of a user-dependent profile that is employed to predict close to real time how users experience the media presentation (QoE). This last prediction is utilized to determine the coefficient of determination (\(R^{2}\)) score and the MAE that is described in the following Section 4 as the outcome of our evaluation process.
## 3 Numerical Analysis
### Harmonic Force with a Non-Zero Mean Value  
The second example uses a non-zero mean harmonic excitation for a SDOF system. Specifically, the system is subjected to a cosine force, \(f(t)=1-\cos\theta t\) with \(\theta/\omega=1\), \(\eta=0.1\), \(m=1\) kg, \(k=4\pi^{2}/1.28^{2}\) N/m. The calculation duration is 20.48 s and the time interval \(\Delta t\) is 0.01 s. Figure 4 shows the time history and Fourier spectrum of non-zero mean cosine force.  
The exact steady solution of this example can be expressed as:  
\[u=\frac{1}{k}-\frac{1}{(1+i\eta)k-\theta^{2}m}e^{i\theta t}, \tag{33}\]  
This example problem was solved by using four different methods including the CFDM, the DTDM, the new accurate FFDM, and the new approximate FFDM. Figure 5 compares the exact solution in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CFDM** & **DTDM** & **Accurate FFDM** & **Approximate FFDM** & **Direct TDM** \\ \hline \(u_{R}\) & 0 & 0 & 0 & 0 & 100 \\ \(u_{I}\) & 100 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relative errors of the amplitude of displacements under a zero mean value force (%).  
Figure 4: The acceleration time history and Fourier spectrum of non-zero mean harmonic force.  
Equation (33) with the numerical results from the four different approximate methods. Table 2 lists the relative errors of the amplitude of displacements of various method. The real part and imaginary part of the dynamic displacement responses obtained by the DTDM, the accurate FEDM and the approximate FEDM are also identical with the exact solution. The real part obtained by the CFDM is only slightly different from the exact solution, which caused by the small difference between \(\mathrm{Re}[1/k(1+i\eta)]\) and \(1/k\). The imaginary part of the displacement response obtained by the CFDM is completely different with the exact solution, indicating that the DTDM and the FFDM are more accurate than the CFDM.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms  
### Global Spectrum Planning  
Spectrum is the basic but scarce resource of wireless communication systems. Global spectrum planning is the basis and premise of resource utilization in MLSCS, and also an important approach of efficient utilization of spectrum resources. In the long term, the integration of spectrum from space to ground and providing differentiated services are the important direction and trend. Hence, global spectrum planning is an important function of RRSC. The spectrum usage of SBSs and beams is analyzed, predicted, and planned at the RRSC based on spectrum usage requirements, the global spectrum situation, historical spectrum usage data, user/service distribution and priority, service area planning results, etc.  
The spectrum planning system encompasses various features such as spectrum situation sensing, frequency allocation, conflict detection and elimination, interference detection and avoidance, efficiency evaluation, visualization of spectrum planning insights, a spectrum database, and other modes. The functional model of the spectrum planning system is illustrated in Figure 8 and is divided into six primary modules:
## 3 Results
### Influence of Environmental Variables on the Microbial Community  
Canonical correspondence analysis (CCA) was used to depict the relationship between the measured physicochemical factors and the relative abundances of the microbiome phyla. Based on their impacts on the dispersion of microbes between the two locations and a significant test utilizing forward selection of environmental factors, four physicochemical parameters--available phosphorus, nitrate, sulfate, and pH--were selected (Figure 7). The CCA established that the microbiome structure of the two sampling sites was influenced by soil physicochemical variables with the CCA permutation test = 0.00. Chloroflexi, Planctopocytes, and Aquifica were positively correlated with available phosphorus but negatively correlated with pH, nitrate, and sulfate. However, Chlamydiae, Ascomyca, Acidobacteria, and Verrucomicrobia were positively correlated with available phosphorus and were negatively correlated with nitrate, pH, and sulfate. Likewise, Gemmatimonadetes, Crenarchaeota, Nitrospirae, Firmicutes, Deinococcus-Thermus, Bacteroidetes, and Proteobacteria were positively correlated with nitrate and sulfate but negatively correlated with available phosphorus and pH (Figure 7). The forward selection of environmental variables was used to test the factors that best explain the variation in the microbial community. CCA showed that available phosphorus significantly (\(p\) = 0.006) contributed 86.0% of the variation, pH insignificantly (\(p\) = 0.002) contributed 78.5% variation, sulfate insignificantly (\(p\) = 0.084) contributed 35.7%, while pH insignificantly (\(p\) = 0.034) contributed 66.8% of the variation of the microbial community in the two sampling sites (Table 3).  
Figure 6: PCoA graph of the soil microbial community based on Bray–Curtis dissimilarities. Es—Eruwa rhizosphere sample, Ec—Eruwa bulk (control) sample, Ms—Mbuzini rhizosphere sample, Mc—Mbuzini bulk (control) sample.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications  
The emergence of Telco Cloud in the modern era is a marvel because of its tremendous importance in the development of various industries based on automation, and we will discuss some of the applications of Telco Cloud in the field of industry here.
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
###### Abstract  
Intrusion detection system (IDS) design for mobile adhoc networks (MANET) is a crucial component for maintaining the integrity of the network. The need for rapid deployment of IDS capability with minimal data availability for training and testing is an important requirement of such systems, especially for MANETs deployed in highly dynamic scenarios, such as battlefields. This work proposes a two-level detection scheme for detecting malicious nodes in MANETs. The first level deploys dedicated sniffers working in promiscuous mode. Each sniffer utilizes a decision-tree-based classifier that generates quantities which we refer to as correctly classified instances (CCIs) every reporting time. In the second level, the CCIs are sent to an algorithmically run supernode that calculates quantities, which we refer to as the accumulated measure of fluctuation (AMoF) of the received CCIs for each node under test (NUT). A key concept that is used in this work is that the variability of the smaller size population which represents the number of malicious nodes in the network is greater than the variance of the larger size population which represents the number of normal nodes in the network. A linear regression process is then performed in parallel with the calculation of the AMoF for fitting purposes and to set a proper threshold based on the slope of the fitted lines. As a result, the malicious nodes are efficiently and effectively separated from the normal nodes. The proposed scheme is tested for various node velocities and power levels and shows promising detection performance even at low-power levels. The results presented also apply to wireless sensor networks (WSN) and represent a novel IDS scheme for such networks.  
intrusion detection; MANET; WSN; decision trees; linear regression; accumulated measure of fluctuation (AMoF); finite sample size +
Footnote †: journal: Sensors