esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Coupling Effect of Dry Density and Moisture Content  
Figure 11 presents a novel three-dimensional graph showing the coupling effect of dry density and moisture content on the interfacial shear adhesion between kaolin and a brass mould. This graph provides a comprehensive visualisation of the relationship between these two parameters and their influence on shear adhesion. For the studied case, the light grey shaded area represents the possible shear adhesion values in relation to moisture content and dry density. The diagram of the contour lines provides valuable insights. It shows that at constant dry density, shear adhesion tends to decrease as moisture content increases. This relationship suggests that higher moisture content generally leads to a reduction in interfacial adhesion between kaolin and the brass mould.  
Figure 12 represents the adhesion change rate (\(ACR\)), which is the rate at which the adhesion value changes with variations in dry density at different water content levels. As dry density is a function of the \(ACR\), it is important to note that the \(ACR\) of shear adhesion reduction varies with dry density and can be calculated using Equation (3):  
\[ACR=\frac{\alpha_{2}-\alpha_{1}}{\rho_{d2}-\rho_{d1}} \tag{3}\]  
where \(ACR\) is the adhesion change rate; \(\alpha_{2}\) and \(\alpha_{1}\) are the final and initial adhesion values, respectively; and \(\rho_{d2}\) and \(\rho_{d1}\) are the final and initial dry density values, respectively. The information provided by these contour lines allows a more detailed understanding of the interaction between dry density, moisture content, and interfacial shear adhesion. The graph shows that changes in these parameters affect the adhesion behaviour of the kaolin. This understanding can help to optimize compaction processes and control moisture content to achieve desired shear adhesion properties.  
Figure 11: Three-dimensional contour graph of the effects of dry density and moisture content on shear adhesion of kaolin.
# Study on the Improvement of a Collision Avoidance System for Curves
## 2 Curvilinear Coordinate Conversion  
Most AEB system algorithms determine a collision risk by assuming a scenario for straight roads or a curve with a constant curvature. The relative distance is calculated by the arc length of a certain sector based on the curvature and the angle between the two vehicles [6; 7; 8]. However, if the assumptions do not hold, the error is large in existing AEB systems. Accordingly, road alignment information is necessary to accurately determine the risk.  
As the position and velocity data of the target vehicle are mostly obtained from sensors, they are expressed by a Cartesian coordinate system. However, it is difficult to calculate the relative distance reflecting geometric elements from the sensor information based on the Cartesian coordinate system. Thus, the curvilinear coordinate conversion is applied to solve this problem. Curvilinear coordinates express a transverse distance from a curved line by \(\mathrm{n}\) and a longitudinal distance along a curved line by \(\mathrm{s}\). The curvilinear coordinate system enables a geometrical complexity to be expressed in an orthogonal coordinate system with orthogonality. Thus, the problem of estimating vehicle movement can be easily solved and the relative distance between vehicles, which includes geometric information, can be derived [10; 11; 12].  
Generally, a camera sensor provides road information through a third-degree polynomial in Equation (1). Therefore, it is assumed that road information is known by the third-degree polynomial.  
\[\mathrm{y}=\mathrm{ax}^{3}+bx^{2}+cx+d \tag{1}\]  
As shown in Figure 1, the position information of the target vehicle can be identified in the orthogonal coordinate system through a sensor. The curvilinear coordinate conversion consists of three main steps: finding the closest point to an equation of a curve, calculating a vertical distance \(\mathrm{n}\), and calculating an arc length \(\mathrm{s}\).
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Comparison of the Pressure Drop in Different Zones  
Figure 15a shows that as the rotating speed is increased from 200 rpm to 1000 rpm at 200 m\({}^{3}\cdot\)h\({}^{-1}\), the ratio of pressure drop is increased slightly from 12% to 20% in the packing zone but more dramatically from 13% to 75% in the cavity zone B, but it is decreased from 48% to 22% in the outlet zone. Figure 15b shows that as the rotating speed is increased from 200 rpm to 1000 rpm at 800 m\({}^{3}\cdot\)h\({}^{-1}\), the ratio of the pressure drop is decreased from 43% to 30% in the cavity zone A and from 13% to 5% in the packing zone, but it is increased from 0.3% to 19% in the cavity zone B and more slowly from 43% to 46% in the outlet zone. Figure 15c shows that as the gas flow rate is increased from 200 m\({}^{3}\cdot\)h\({}^{-1}\) to 1000 m\({}^{3}\cdot\)h\({}^{-1}\) at 200 rpm, the ratio of the pressure drop is increased from 37% to 42% in the cavity zone A, but decreased markedly from 12% to 0.4% in the cavity zone B. Figure 15d shows that as the gas flow rate is increased from 200 m\({}^{3}\cdot\)h\({}^{-1}\) to 1000 m\({}^{3}\cdot\)h\({}^{-1}\) at 800 rpm, the ratio of pressure drop is increased from 6% to 40% in the cavity zone A, but it is kept at 44% in the outlet zone. Thus, the pressure drop mainly occurs in the cavity zone A and outlet zone. Notably, the pressure drop plays a more important role in the cavity zone B than in other zones when the gas flow rate is 200 m\({}^{3}\cdot\)h\({}^{-1}\) and the rotating speed exceeds 600 rpm.  
Figure 14: The effect of the gas flow rate on the pressure drop in the outlet zone. (**a**) Pressure drop; (**b**) static pressure drop; (**c**) dynamic pressure drop.
# Hot Carrier Photocurrent through MOS Structure
## 2 Experimental  
Boron doped p-type silicon substrate was taken as the base of the samples; impurity concentration was (1 \(\div\) 1.5) \(\times\) 10\({}^{15}\) cm\({}^{-3}\), and plane orientation was (100). The insulating 47 nm-thick SiO\({}_{2}\) layer was grown by means of dry thermal oxidation at 1050 \({}^{\circ}\)C for 20 min in the SDO-125/3 quartz tube furnace (Termotron, Bryansk, USSR). The ohmic contacts were created by thermal evaporation of Al in the vacuum equipment Vaksis PVD Vapor-5S\({}_{-}\)Th\({}^{\prime}\) (Vaksis, Ankara, Turkey) and subsequent annealing in nitrogen atmosphere for 10 min at 450 \({}^{\circ}\)C. Later, aluminum was thermally deposited to form the MOS contacts. The 0.5 mm-diameter contacts were shaped by ordinary photolithography process.  
The current-voltage (_I-V_) characteristics were measured in the dc regime using Agilent 6613C (Keysight Technologies Inc., Santa Rosa, CA, USA) power supply and Agilent 34401A multimeter, and the capacitance-voltage (_C-V_) dependencies were tested with Agilent E4980A LCR meter at 1 kHz frequency. A Q-switched CO\({}_{2}\) laser was used as a source of infrared radiation: wavelength 10.6 \(\upmu\)m, pulse duration 150 ns, repetition rate 40 Hz, and maximum intensity 1.2 MW/cm\({}^{2}\). The laser beam was directed purely onto the MOS structure from the semiconductor side thus avoiding additional photosignal formation across the p-p\({}^{+}\) junction of the ohmic contact [19]. The magnitude of photocurrent, i.e., photovoltage across the 50 \(\Omega\) load resistor, and its temporal shape was investigated using 4 GSa/s sampling rate oscilloscope Agilent DSO6102A. The schematic of the sample and measurement scheme are presented in Figure 1. Two typical types of the samples were chosen for the investigation: those having perfectly insulating oxide (MOS-1), and those slightly conducting dc current (MOS-2).  
Figure 1: Schematic of the Al-SiO\({}_{2}\)-p-Si sample and the circuit of photocurrent measurement.
## 5 Discussion
### Error Analysis  
We sampled and analyzed 300 errors, which comprise about 1% of the total errors. We categorized five main types of error: restoration, segmentation, tagging, annotation, and variant. The procedural type errors implicate errors in the subsequent procedures. For example, a restoration error usually implies both a segmentation error and tagging error. A segmentation error usually implies a tagging error. The annotation error type includes annotation mistakes and data input mistakes in the test data. Inconsistent annotations cause inconsistent learning, which will produce variable analysis results. We considered any inconsistent annotation cases that we found in the training corpus and that helped output the variant to be variant annotation errors.  
The loose definition of the corpus markup principle allows for dual standards [45; 46], which cause inconsistent annotations in the training data. A typical case is compound words that are considered to be one unit but allowed to be segmented into smaller units in the analysis, for instance, \"@@@/NNG\" (blood type) vs. \"@@@/NNG+@/XSN\", \"@@@/MAG\" (anyhow) vs. \"@@@/VA+@/EC\", and \"@@@@@@/VV+ /VT+ /ETM\" (frequent) vs. \"@@/VA+@/EC+\(\times\)]/VX+ /VTX+ /ETM\".  
In the evaluation, we measured the performance using the test data as golden labels. However, this approach is unfair in the following cases:  
1. When the golden label is incorrect and the prediction is correct (annotation errors);
2. When the golden label has dual standards and the prediction is the other standard that is not the golden label (variant errors).  
For a fair evaluation, we adjusted the numbers by reclassifying the error types of the above two unfair cases. Figure 4 shows the percentage of the measured and adjusted error types. A segmentation error is the most common type of error, comprising 44% of all errors  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**OOV Ratio (\%)**} \\ \cline{2-4}  & & **Type** & **Instance** \\ \hline Restoration & 8039 \(\pm\) 17 & 5.764 \(\pm\) 0.256 & 0.006 \(\pm\) 0.000 \\ \hline Segmentation & 12,508 \(\pm\) 18 & 6.236 \(\pm\) 0.192 & 0.010 \(\pm\) 0.000 \\ \hline Tagging & 25,079 \(\pm\) 32 & 6.293 \(\pm\) 0.174 & 0.020 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Size of the output label set and OOV ratio for the three annotation methods.  
in the measured case, and tagging errors are ranked second. After the adjustment, 40% of the adjusted errors are not errors (Ann 18% + Var 22%) and mostly originate from measured errors of segmentation errors (19% = 44% \(-\) 25%) and tagging errors (17% = 40% \(-\) 23%). This makes us believe that the evaluation result would improve if we were to adjust the test dataset for a fair evaluation.
## Nomenclature  
**Notes:** To avoid confusion of terms, in this work, the term _grid_ (or _power grid_) is used to refer to the power system's infrastructure in general or to the power and energy systems, the term _network_ is used to refer to the data networks, and the term _telco infrastructure_ is used to indicate the particular data network handled by a telecommunication operator.
## 2 Methods
### The VILIAlert System Architecture
Figure 2a portrays an overview of the tabular dataset, in its raw form, which corresponds to 5 hypothetical APA having a length of 360 min each. Each row in this tabular dataset represents one APA segregated into 24 blocks, with each block denoting one 15 min interval over which the \(TV_{15}\) values are calculated. Each block can be denoted with either 0 or 1 depending on whether it represents NDB (i.e., \(TV_{15}>8\) mL/kg) or DB (i.e., \(TV_{15}\leq 8\) mL /kg), respectively. Assuming that the time to the event occurrence in APA is defined by the position of the first block, which is marked as defective (DB), we can see that the lengths of the follow-up time for the 1 st, 2 nd, 4 th, and 5 th APA are 15, 270, 150, and 90 min, respectively (illustrated in Figure 2b). As defined earlier, the remaining 3 rd APA belongs to a genuine alert as it consists of NDB blocks only, and using survival analysis terminology, represents a censored event. Using Equations (1) and (2) the DFS and CDFS estimations at any time of interest \(t\) are a straightforward process, where \(t=i\times 15\) min, and \(1\leq i\leq 24\) is the position of the block in APA. For instance, using the example above, the probability that the APA will remain defect free until the end of the follow-up period of 360 min, given that it was previously defect free during the first 90 min, can be estimated using Equation (2) as follows:  
\[CDFS\widehat{\left(360\text{ min}\ |90\text{ min}\right)}=\frac{DFS\widehat{\left(360\text{ min}\right)}}{DFS\widehat{\left(90\text{ min}\right)}}=\frac{0.2}{0.6}=0.33,\]  
where \(DFS\left(360\text{ min}\right)\) and \(DFS\left(90\text{ min}\right)\) are estimated using Equation (1). In a similar manner, using Equation (3), the CDFS 95% CI is estimated as [0.01, 0.79]. The statistical data analytics used in this paper was performed using the R v3.5.0 programming language [32] with the survival and conditional survival models being implemented using R's _survival_ library [33; 34].
###### Abstract  
Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data.  
Keywords:partial least squares; regression analysis; filter learning; content prediction
## 3 Results
### Content of Phenolic Compounds
#### 3.3.2 Anthocyanin Content  
The highest content of anthocyanins was found in the juices obtained from mash after thermosonication. It was about 20% lower in juices pressed from mash after ultrasonic treatment and subjected to vacuum-assisted pectinolysis. In the remaining samples (from mash without treatment, with heat treatment, or pectinolysis), the content of anthocyanins was about 40% lower (Table 2). The high content of anthocyanins in the samples obtained from the mash after thermosonication, ultrasonic treatment and vacuum-assisted pectinolysis may result from better extraction of anthocyanin into the juice. Ultrasonic treatment can induce cavitation effects and accelerate plant cell disruption, which can facilitate the extraction of anthocyanins from tissue [47]. On the other hand, higher anthocyanin content may be related to the lower oxygen content in the mash. [PERSON] et al. [53] compared the content of anthocyanins in cloudy and clear juices, where higher content was noted for clear juices and was associated with a lower oxygen level and thus with a lower effect of enzymatic oxidation. Anthocyan are less susceptible to oxidation than ascorbic acid because they do not participate directly in this reaction but are degraded by o-quinones formed as a result of oxidation of non-anthocyanine phenolic compounds. [PERSON] et al. [54] indicate that anthocyanins can be degraded by o-quinones as a result of coupled oxidation. Thus, partial deaeration of the mash by thermosonication, sonication or vacuum-assisted pectinolysis may be sufficient to limit changes related to the oxidation of anthocyanins. However, this effect may be dependent on the degree of deaeration in the particular processes used for mash treatment. Lower anthocyanin content in juices obtained from mash subjected only to ultrasonic treatment may be the result of a greater share of enzymatic oxidation or sonolysis in their degradation in comparison to thermosonication. The sonochemical reaction is related to cavitation phenomena that contribute to the formation of free radicals. The generated free radicals subsequently can oxidize the bioactive compounds. Some researchers indicate that the deaeration of the mash may intensify cavitation [43]. The lower efficiency of sonication in relation to thermosonication may be the result of more effective inhibition of the activity of the enzymes responsible for oxidation reactions and more effective evacuation of oxygen from the mash as a result of using a higher temperature. In the samples from the mash after thermal or enzymatic treatment, the content of anthocyanins did not differ significantly from juice obtained from the untreated mash. Both thermal and enzymatic treatments are reported to increase the extraction of anthocyanins from the mash into the juice [55]. In the tested samples, the lack of the increase in anthocyanin content may be related to the predominance of degradation processes
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
###### Abstract  
The single-hop star-of-stars topology in low-power and wide-area networks (LPWAN) exhibits reliability and substandard coverage issues, especially in urban areas where line-of-sight (LoS) communication is difficult to achieve. Moreover, LPWAN trade-off the data rate to achieve longer coverage, preventing other end-devices from using the time resource. Locating other gateways is uneconomical as it requires infrastructure, such as the internet and a power connection. In this study, we propose a forwarding scheme with a relay to increase LPWAN coverage and reliability while not degrading the network's capacity. A relay tends to incur unnecessary forwarding that degrades the network capacity unless proper countermeasure is prepared. Our works, namely opportunistic and on-demand network coding (OODC), minimize unnecessary forwarding and make good use of multiple-receiving relays. Network coding is also applied in the relay for better transmission efficiency and reliability simultaneously. Because network coding occurs header overhead, we perform a header compression technique to counter it. According to our simulation result, our method shows better reliability than fixed path forwarding. In an adaptive data rate settings, the OODC achieves a 92% packet delivery ratio (PDR), whereas a fixed-path forwarding only achieves an 84% PDR.  
LPWAN; relay; on-demand; unnecessary forwarding; opportunistic forwarding; network coding; compressed header overhead +
Footnote †: journal: Sensors
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Biological Processes  
Microorganisms, such as microalgae and cyanobacteria, can produce hydrogen through biological processes. These processes can be carried out at low temperatures (between 30 \({}^{\circ}\)C to 60 \({}^{\circ}\)C) when compared to thermochemical processes, and at atmospheric pressure, making them less energy-consuming. Nonetheless, biological processes are more sophisticated in design and performance and present a lower hydrogen production rate [38]. Biological processes can be divided into fermentation and biophotolysis. Fermentation can be further divided into dark fermentation and photo-fermentation, and bio-photolysis can be divided into direct and indirect bio-photolysis [39].  
Figure 7: Simplified representation of biomass pyrolysis processes (adapted from [12]).  
Dark fermentation is a biological hydrogen production process where biomass is converted to hydrogen, carbon dioxide, and small amounts of methane, carbon monoxide, and hydrogen sulfide, in the absence of light [40; 41; 42]. Photo-fermentation is a three-step process, where photosynthetic bacteria, such as Rhodospirillum, Rhodobium, Rhodopseudomonas, and Rhodobacter, produce green hydrogen through the action of their nitrogenase in the presence of light and reduced compounds (organic acid) [39]. Bio-photolysis produces hydrogen through the dissociation of water molecules in biological photosynthetic microorganisms in the presence of solar radiation, and, as such, without external organic matter (biomass) [39].  
Among the processes presented, dark fermentation is considered, simultaneously, the most well-understood and most promising among the biological processes. It requires simple reactor designs, with this being one of the reasons for its attractiveness, but nevertheless presents a relatively low hydrogen yield, with maximum values of 4 mol H\({}_{2}\)/mol glucose and 6 mol H\({}_{2}\)/mol sucrose [39]. Currently, dark fermentation is an extensive research area, focusing especially on determining the best reactor configuration, and on minimizing the hydrogen non-producing microorganisms' activity and maximizing the hydrogen-producing microorganisms' activity to increase the hydrogen yield [39]. Dark fermentation is viewed as a possibility to integrate energy production with waste management.