esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusion  
Cellulase hydrolysis and HPH treatment are effective means for the modification of RB-DF structure. Compared with enzymatic treatment, the combined HPH and cellulase hydrolysis showed a significant effect on the degradation of RB-DF. Hydrolysis of hemicellulose and destruction of amorphous regions of cellulose can completely break the ordered laminar structure of DF, reducing its crystallinity and forming an irregular, small and loose particle structure. This results in a decrease in thermal stability, and a significant increase in water holding, expansion, and cation exchange capacities. This study helps clarify the underlying mechanisms of a combined biological and physical treatment for the structural modification of RB-DF, and provides valuable guidance for the micronization of DF.  
[PERSON], [PERSON], [PERSON] conceived and designed the experiments; [PERSON], [PERSON], performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
This research was funded by the Natural Science Foundation of Heilongjiang Province grant number [C2018015/QC2016024], the Harbin Science and Technology Innovation Talents Research Project grant number [2017 RAQXJ097], and Harbin Food Industry Research Institute Postdoctoral Innovation Entrepreneurship Practice Base.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample.** & **WHC (g/g)** & **OHC (g/g)** & **SC (mL/g)** & **CEC (mmol/g)** \\ \hline D-RB & 3.02 \(\pm\) 0.38 \({}^{\text{a}}\) & 3.13 \(\pm\) 0.14 \({}^{\text{a}}\) & 1.00 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.21 \(\pm\) 0.02 \({}^{\text{a}}\) \\ RB-DF & 3.20 \(\pm\) 0.13 \({}^{\text{ab}}\) & 4.23 \(\pm\) 0.13 \({}^{\text{b}}\) & 1.93 \(\pm\) 0.09 \({}^{\text{b}}\) & 0.27 \(\pm\) 0.01 \({}^{\text{b}}\) \\ EH-DF & 3.60 \(\pm\) 0.27 \({}^{\text{b}}\) & 3.92 \(\pm\) 0.11 \({}^{\text{c}}\) & 3.875 \(\pm\) 0.01 \({}^{\text{c}}\) & 0.32 \(\pm\) 0.01 \({}^{\text{c}}\) \\ HPH-DF & 5.81 \(\pm\) 0.24 \({}^{\text{c}}\) & 3.43 \(\pm\) 0.17 \({}^{\text{d}}\) & 5.26 \(\pm\) 0.01 \({}^{\text{d}}\) & 0.38 \(\pm\) 0.02 \({}^{\text{d}}\) \\ \hline \end{tabular} Data were expressed by means \(\pm\) standard deviation (SD). Values in the same column with different letters are significantly different (\(p<0.05\)). CEC cation-exchange capacity, \(D\)-\(RB\) defatted rice bran, _EH-DF_ cellulase enzymatic treatment rice bran dietary fiber, _HPH-DF_ high-pressure treatment rice bran dietary fiber, _OHC_ oil holding capacity, _RB-DF_ rice bran dietary fiber, _SC_ swelling capability, _WHC_ water holding capacity.  
\end{table}
Table 1: Physiological properties of rice bran dietary fiber at different treatment stages.
## Nomenclature  
\begin{tabular}{l l} D\({}_{\text{P}}\) & Pipe diameter [mm] \\ D\({}_{\text{b}}\) & Blade diameter [mm] \\ D\({}_{\text{h}}\) & Hub diameter [mm] \\ L\({}_{\text{h}}\) & Front hub length [mm] \\ L\({}_{\text{b}}\) & Blade length [mm] \\ t\({}_{\text{b}}\) & Blade thickness [mm] \\ t\({}_{\text{c}}\) & Tip clearance [mm] \\ \(\beta_{1}\) & Hub leading edge angle [\({}^{\circ}\)] \\ \(\beta_{2}\) & Hub trailing edge angle [\({}^{\circ}\)] \\ \(\beta_{3}\) & Tip leading edge angle [\({}^{\circ}\)] \\ \(\beta_{4}\) & Tip trailing edge angle [\({}^{\circ}\)] \\ T & Torque [N-m] \\ N & Rotational speed [rad/s] \\ \(P\) & Power [W] \\ \(\Delta p\) & Pressure difference between runner inlet and outlet [Pa] \\ \(H\) & Head [m] \\ \(\rho\) & Working fluid density [kg/m\({}^{3}\)] \\ \(S\) & Gravitational acceleration [m/s\({}^{2}\)] \\ \(Q\) & Flow rate [m\({}^{3}\)/s] \\ \(\eta\) & Hydraulic efficiency [\%] \\ \(\mu\) & Dynamic viscosity [Pa-s] \\ \(u_{\tau}\) & Wall shear velocity [m/s] \\ \(\tau_{w}\) & Wall shear stress [Pa] \\ \(y\) & Wall distance [m] \\ \(y^{+}\) & Y-plus \\ \(u\) & Velocity [m/s] (in 2.5 Governing Equations) \\ \(\tau_{ij}\) & Stress tensor [Pa] \\ \(-\rho\overline{u_{i}u_{j}}\) & Reynolds stresses [Pa] \\ \(\delta_{ij}\) & Kronecker delta \\ \(\mu_{t}\) & Turbulent dynamic viscosity [Pa-s] \\ \(k\) & Turbulence kinetic energy [m\({}^{2}\)/s\({}^{2}\)] \\ r\({}_{1}\) & Blade leading and trailing edge semi-minor axis length [mm] \\ r\({}_{2}\) & Blade leading and trailing edge semi-major axis length [mm] \\ A\({}_{\text{LE}}\) & Elliptic aspect ratio at leading edge \\ A\({}_{\text{TE}}\) & Elliptic aspect ratio at trailing edge \\ \(g_{ens}\) & Prediction of the ensemble \\ \(N_{M}\) & Number of metamodels used \\ \(w_{i}\) & Weight factor for i-th metamodel \\ \(\dot{y_{i}}\) & Prediction of i-th metamodel \\ \(\mathbf{V}\) & Absolute velocity in velocity triangle [m/s] \\ \(\mathbf{V_{f}}\) & Flow velocity in velocity triangle (Vertical component of absolute velocity) [m/s] \\ \(\mathbf{V_{r}}\) & Whirl velocity in velocity triangle (Horizontal component of absolute velocity) [m/s] \\ \(\mathbf{U}\) & Blade velocity in velocity triangle [m/s] \\ \(\mathbf{W}\) & Relative velocity in velocity triangle [m/s] \\ \end{tabular}
## 2 Experiment Description
### Pre-Processing of the GNSS Data-Sets  
The recorded observations were collected in a receiver-specific binary format, which was later converted to the RINEX version 3.03 [27]. In the case of JVD receivers, the JVD proprietary JPS2 RIN software [29] was used to convert the binary data into RINEX format. In order to convert the UBX binaries, the _convbin_ tool of the RTKLib software was used [30]. It supports the conversion of the proprietary UBX multi GNSS raw observations (binary RAWX format).  
In order to pre-process the observations, the IFE-GNSS-Toolbox was utilised. It was implemented and maintained by the working group Positioning and Navigation (Prof. [PERSON]) at the IFE. In post-processing, the calculated reference trajectories were used to compute epoch-wise corrections. These correction values are subtracted from the collected raw data (code ranges, carrier phases and Doppler), which leads to the so-called Observed-Minus-Computed (OMC) values that can also be called pre-fit residuals. Unless explicitly stated otherwise, the following results always refer to the OMC values.
#### Novel Flow Imaging Techniques  
Microvascular Imaging (MVI) is a relatively novel US modality for flow imaging. In contrast to conventional Power Doppler (PD), MVI uses adaptive image analysis to achieve an increased low-velocity blood flow stability that is less dependent on motion artifacts [7]. In addition, MVI generates a high-resolution constant flow mapping of small vessels and related branches via an algorithm-driven suppression of interfering signals [8].  
In rheumatology, MVI has been investigated to detect synovitis in patients with inflammatory arthritis, such as rheumatoid arthritis and juvenile idiopathic arthritis. In these diseases, MVI was more sensitive than conventional PD US in preliminary reports [9; 10]. Potential advances of MVI may include a better follow-up of patients with minimal disease activity and the prediction or early recognition of disease flares [11]. Nevertheless, more extensive studies are lacking.  
In this single-center feasibility trial, we prospectively analyzed a cohort of SSc patients using MVI as a method to detect and quantify digital blood flow compared to HCs.
# The Perspective of Large-Scale Production of Algae Biodiesel
## 4 Results and Discussion  
The SWOT analysis finds potential and favorable strategies attractive to stakeholders and decision-makers, including sustainability and product prospects. The critical components to be discussed about algae biodiesel production refer to diverse influential factors that can be grouped in technological, environmental, economic, societal, and political factors. The analysis of threats and opportunities is pertained to the present and, even more importantly, also the future environment.  
Determining the importance of these factors by SWOT analysis gives a clearer view of the chances of success of large-scale algae biodiesel production.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Culture System** & **Raceway Ponds** & **Photobioreactors** \\ \hline Required space & High & Low \\ \hline Area/Volume ratio (m\({}^{-1}\)) & Low (5–10) & High (20–200) \\ \hline Water loss & Very high, may also cause salt & Low \\  & precipitation & \\ \hline CO\({}_{2}\) loss & High, depending on pond depth & Low \\ \hline Oxygen concentration & Usually low enough because of & The closed system requires gas \\  & continuous spontaneous outgassing & exchange devices \\ \hline Light utilization efficiency & Poor & Excellent \\ \hline Temperature & Highly variable & Cooling often required \\ \hline Cleaning & None & Required (wall-growth and dirt reduce \\  & & light intensity), but causes abrasion, \\  & & limiting photobioreactor life-time \\ \hline Contamination risk & High (limiting the number of species & \\  & that can be grown) & Low \\ \hline Biomass quality & Variable & Reproducible \\ \hline Biomass concentration (g/L) & Low, between 0.1 and 0.5 & High, generally between 0.5 and 8 \\ \hline Process control & Limited (flow speed and mixing) & Possible \\ \hline Weather dependence & High (light, temperature, rainfall) & Low \\ \hline Start-up & 6–8 weeks & 2–4 weeks \\ \hline Capital expenses & High & Very high \\ \hline Operating costs & Low & Higher \\ \hline Harvesting efficiency & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of raceway ponds and photobioreactors.
## 2 Materials and Methods
### Overlay Networks Based on Multiple Different Time Intervals
#### 2.2.1 Idea  
The proposed method assumes the ring-shaped topology similar to Chord and assigns the nodes to the key space based on one-dimensional information about time. The one-dimensional information is a bit value which represents the minimum unit such as a year, month, week, day, and time. In addition, the one-dimensional information has a length as a key space similar to Coordinated Universal Time (UTC), UNIX time, and so on. As a finger table in Chord, each node constructs shortcut links to the nodes assigned to \(2^{i}\) bits from its own key (\(i=1,2,\ldots,m-1\)). \(m\) denotes the bit length of the key space. In our proposed method, on the other hand, each node constructs shortcut links with the specific intervals which the actual users tend to request. In this case, each node constructs shortcut links to the nodes assigned to one year, one month, one week from its own key, and so on. Moreover, each node constructs shortcut links to reduce the hop number, e.g., one month, three months, six months, and 12 months (one year) from its own key. In this paper, we consider the year, month, week, day, and hour. Table 1 shows the types, keys, and number of the constructed shortcut links. The minimum unit is an hour in Table 1, and the bit length of the key space is denoted as \(m\). The minimum unit can be set to other elements such as a minute and second.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Key (Interval)** & **Number** \\ \hline Year & \(2^{0},2^{1},\ldots,2^{m-1}\) & \(m\) \\ Month & 6, 3, 1 & 3 \\ Day & 15, 7 (one week), 3, 1 & 4 \\ Time & 12, 6, 3, 1 & 4 \\ Neighbor & Successor, predecessor & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The types, keys, and number of the constructed links.  
Figure 3 shows the assumed flow to forward interval queries. Although Figure 3 is simplified without ring-shaped topology, the interval queries are recursively forwarded to the related nodes via the shortcut links. The query received nodes reply the matched data if the nodes have them.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 6 Conclusions and Future Works
The following abbreviations are used in this manuscript:  
List of Acronyms  
IoT internet of Things  
WSN Wireless Sensor Network  
MCCM Coverage Maximization and Maintaining Connectivity  
ITU International Telecommunication Union  
IERC European Research Cluster on the Internet of Things  
AoI Area of Interest  
AoT Array of Things  
\(CTC_{k}\) connected target k-coverage  
\(DCTC_{k}\) distributed connected target k-coverage  
DCHGA Dynamic Clustering of Heterogeneous WSNs using Genetic Algorithm  
CS Central Station  
SN Sink Node  
HMRC Harmony Memory Considering Rate  
AAEECC Asynchronous Algorithm for providing Energy Efficient Coverage and Connectivity  
FTCM Fault Tolerant Connectivity Maintenance  
ABSDC Angles-based Sensors Deployment Algorithm to Develop the Coverages in WSN  
GSO Glowworm Swarm Optimization  
OPDCC Optimal Deployment for k-Coverage in m-Connected Wireless Networks  
CMHN Coverage Maximization for Heterogeneous Wireless Network  
VCACWSN Voronoi coverage Algorithm based on Connectivity for Wireless Sensor Networks  
UAV Unmanned Aerial Vehicle  
**List of Symbols**  
\(D_{i}\) A device i in the network  
\(r_{i}\) Range of device \(D_{i}\)  
\(m(i,j)\)\(m-\)ratio between devices \(D_{i}\) and \(D_{j}\)  
\(d_{ij}\) Distance between devices \(D_{i}\) and \(D_{j}\), denoted \(dist\left(D_{i},D_{j}\right)\)  
\(\tau_{ij}\) Minimum required overlapping between devices \(D_{i}\) and \(D_{j}\)  
\(\Omega\) Set of devices in the network  
\(\mathcal{N}_{i}\) An island i in the network
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Anaerobic Digestion pH  
The operational pH directly affects both the digestive progress and products formed in the AD process. Literature findings show that the ideal comprehensive pH range for AD should be between 4.0 and 8.5, as per the requirement for the fermentative bacteria, although the limiting range of 6.5-7.2 is favorable for the growth of methanogens [77; 78]. The microorganism growth rate is significantly affected by the change in pH and, as such, each microbial group has a specific optimum pH. A comparative abundance of microbial species increased from 6 to 14 at pH 4.0 and 7.0, respectively [79]. Bacterial population dominance differs with changing pH, for example, at pH 6.0, _Clostridium butyricum_ is dominant, while the _Propionihacterium_ spp. thrives during anaerobic acidogenesis at pH 8 [80].  
When the pH level is controlled for the optimal growth of microorganisms, reductions in toxicity, generally from increased concentration of free ammonia (FA), are also achieved. Similarly, pH significantly affects volatile fatty acid (VFA) composition [79]. In an anaerobic reactor, instability typically leads to the accumulation of VFAs, leading to a drop in pH and, therefore, acidification. Nonetheless, this accumulation of VFAs does not always exemplify a pH drop, owing to the buffer capacity of some waste forms. There is an excess of alkalinity in manure, denoting that the VFA growth shall surpass a certain point before it can be determined as a significant change in pH [81]. When the pH in the reactor drops, the concentration of VFAs is possibly very high, and the process may previously have been affected [82]. Hydrogen sulfide and phosphate are other compounds contributing to the buffering capacity [83]. To counteract this pH imbalance, a buffer solution may be added to the bioreactor [78].
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 14 Alternative Treatment Methods  
A novel treatment for diabetic foot ulcers is the use of sono-catalytically activated C\({}_{3}\)N\({}_{4}\) sheets powered by ultrasonic waves. This method utilizes NADH present in cells to generate H\({}_{2}\), which oxidizes the cells and perforates their membranes. The resulting NADH deficiency impairs the electron transport chain, reducing cellular respiration and ATP production, ultimately leading to cellular lysis and biofilm degradation. These sheets can be applied to the wound area for up to 15 days [133].  
Photodynamic therapy using toluidine blue-chitosan coated gold-silver nanoparticles has been shown to effectively eradicate polymicrobial biofilms of _P. aeruginosa_ and _S. aureus_[134]. This treatment works primarily through the generation of reactive oxygen species [135], making it effective against multidrug-resistant organisms that form biofilms.  
Surfactin-associated antibiotic treatment is a new approach that has shown promise. It has been observed that traditional antibiotics often fail due to antimicrobial resistance, such as that seen in MRSA. However, when surfactin is used, oxacillin can effectively combat MRSA. It is hypothesized that surfactin downregulates the expression of the _icaADBC_ operon [136]. Surfactin also inhibits the expression of SortaseA, preventing the production of adhesion-promoting proteins like fibronectin proteins FnbA and FnbB, thereby disrupting both _ica_-dependent and _ica_-independent biofilm formation. Additionally, surfactin makes MRSA sensitive to beta-lactam drugs by inhibiting the expression of mecA gene sets and tetracycline destructase enzymes, allowing for effective eradication of MRSA biofilms when used with oxacillin [137].  
Nisin, a 34-amino-acid cationic antibiotic peptide, disrupts bacterial cell walls by interacting with the cell wall precursor lipid II. This interaction alters the electrostatic potential of the transmembrane domain, increasing cell wall permeability. Consequently, EDTA and other antibiotics can enter cytoplasm and induce cellular lysis. Nisin Z, a variant with an asparagine residue at position 27, has shown inhibitory effects against dual cultures of _S. aureus_ and _P. aeruginosa_ when combined with EDTA (0.4% or 4000 \(\mathrm{\SIUnitSymbolMicro mL}\)), exhibiting both bactericidal and antiboofilm effects [138]. When Nisin Z is embedded in a polyelectrolyte membrane, complete eradication of MRSA biofilms is observed [139].  
Silver sulfadiazine is another treatment option that inhibits the electron transport chain in bacteria, thereby obstructing respiration. This makes it suitable for patients with extensive bacterial biofilm-mediated infections [140]. Silver also disrupts bacterial replication and transcription by binding to DNA [141]. It has been shown to be effective against planktonic forms of _S. aureus_ and _P. aeruginosa_, with _P. aeruginosa_ being particularly sensitive. Consequently, silver sulfadiazine can be administered to patients with diabetic foot ulcers to eradicate biofilms present in the wound [142].
## 4 Applications of 3D PFFT-BEM
### Model Comparisons of the PFFT-BEM with the 3D Parabolic Equation Method
#### 4.1.4 Large Slope Effects: Gaussian Canyon  
For problems involving an ocean bottom with a very large slope (\(>10^{\circ}\)), the validity of 3D PE needs to be examined by a direct numerical method. In the next benchmark problem, we consider a waveguide problem with a Gaussian canyon. The water column has a depth of 700 m with c\({}_{1}\) = 1500 m/s and \(\rho_{1}\) = 1.0 g/cm\({}^{3}\). The sound source has frequency of 25 Hz and is fixed at (0, 0, 30 m). The bottom has the properties of \(\rho_{2}\) = 1.5 g/cm\({}^{3}\), c\({}_{2}\) = 1700 m/s and medium attenuation \(\alpha\) = 0.5 dB/\(\lambda\). The geometry of the Gaussian canyon bottom is defined as  
\[h(x)=h_{0}+h_{1}exp\left(-\frac{x_{2}}{\sigma_{2}}\right), \tag{39}\]  
where the parameters are \(h_{0}\) = 20 m, \(h_{1}\) = 180 m, and \(\sigma\) = 720 m. The largest slope in the direction across the canyon is approximately 0.21 (angle of 12.1\({}^{\circ}\)). The geometry of the canyon is plotted in Figure 16. We compare the 3D PFFT-BEM solution along the canyon with the 3D cylindrical coordinate PE results from [20]. As shown in Figure 17a, numerical convergence of PFFT-BEM is observed with increasing element numbers. This proves the accuracy of the PFFT-BEM. In Figure 17a, differences (\(\sim\)8 dB) between the 3D PE and 3D PFFT-BEM can be observed in energy-focusing regions along the canyon axis where horizontal refractions by the sidewalls of the canyon are the strongest (shown in Figure 17b). These differences are mainly due to the fact that, for 3D PE, only one of the two boundary continuity conditions can be satisfied in the marching direction. As a result, for environment geometries with large slopes in the marching directions, such as this Gaussian canyon, the 3D PE method becomes inaccurate and deviates from the direct numerical solutions by PFFT-BEM. We also show the contours of the transmission loss in Figure 17b where strong 3D scattering effects from the canyon are observed.  
Figure 16: The geometry of the Gaussian canyon used in the present study with an upper water layer with \(\rho_{1}\) = 1 g/cm\({}^{3}\), \(c_{1}\) = 1500 m/s on top of a soil bottom with \(\rho_{2}\) = 1.5 g/cm\({}^{3}\), \(c_{2}\) = 1700 m/s, and medium attenuation of \(\alpha\) = 0.5 dB/\(\lambda\).  
Figure 17: **(a)** TL along the canyon axis at \(z\) = 30 m for the Gaussian canyon: comparison between 3D direct simulations by PFFT-BEM and 3D PE by Sturm [20]; **(b)** TL obtained using PFFT-BEM on the horizontal plane at \(z\) = 30 m of the Gaussian canyon.
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 5 Experimental Results
### Detection System Phase  
#### 5.1.1 Testing Neural Network to Detect Grey Hole Attacks  
We used the fuzzified data obtained from the trace file in testing the IDS to detect grey hole attacks in external communication for self-driving vehicles. In this section, we utilised measurement of the rate of detection and the calculation of the alarms. We also made use of cross validation for SVM and FFNN to analyse the performance of the security system. In this instance, we split the dataset into twenty datasets (k = 20), of which five per cent were employed in the testing phase and ninety per cent for the training dataset. We replicated this process in the measurement of the performance for the IDS through the calculation of the rate of detection for normal and abnormal standard deviation (SD) and alarms. We show the rate of detection accuracy and the number of records that were utilised in our suggested security system in Table 4.  
Table 5 shows the rate of four alarms for grey holes.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 3 ML Procedures  
The ML problem formulation is to classify between different data rates at different detection techniques (DD and SPD) to achieve a certain key performance indicator (KPI) need. The KPIs' needs are the following spectrum. We use C-band and can use it in next-generation passive optical networks (PONs), data rates (1.25 Gbps for audio and 2.5 Gbps for video). The Q, SNR and BER are used as ML predictors. The DT and RF are both used as classifiers. The classification process is formulated in order to determine four different classes with different data rates {DD: Groups A/D, DD: Groups B/C, SPD: Groups A/D, SPD: Groups B/C}. Hence, the classifiers are used as supervised learning where the training data has an output included while training the model. The total dataset rows and columns are 1800 and 4, respectively. The four columns are the Q, SNR and BER while the fourth column is the label outcome. The number of training data points is 1200 (300 data points for each class of the four classes). Then, the number of test data points is 600 (150 data points for each of the four classes).  
The input features of the test data Q, SNR and BER are used to test the model. Then the model-predicted labels are evaluated using the output of the test data as the true label. The evaluation is done using confusion matrices and ROC curves. First, the confusion matrix shows the relation between the actual values and predicted values. Hence, the accuracy of the model can be easily extracted from it as the accuracy is the ratio between the right predictions over the whole test set. Moreover, the ROC curves are revealed as they display the relation between the true-positive rate and the false-positive rate.  
The ideal case of ROC curve occurs when the area under the curve gets to its maximum of 1. The ROC curves are binary. This means that it can be used in binary classification. As such, each unique case is shown versus the other 3 cases, e.g., DD: Groups B/C versus DD: Groups A/D, SPD: Groups B/C and SPD: Groups A/D, so that the problem formulation turns into binary classification in order to reveal each ROC curve.