esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Extraction of Valuable Compounds from Rubber Seed Trees: A Path to Sustainability
## 8 Challenges and Future Directions
### Future Direction for Rubber Seeds Oil by Green Extraction for Biodiesel  
The future direction for rubber seed oil extraction through green methods for biodiesel production promises a transformative journey towards sustainable energy solutions. This trajectory is poised to be guided by several key themes and innovations. Firstly, researchers will continue to explore and develop cutting-edge extraction techniques to maximize the yield of rubber seed oil. Innovations in ultrasound-assisted extraction, pulsed electric field extraction, and other emerging technologies will be at the forefront, boosting efficiency and reducing energy consumption. Secondly, customization will play a pivotal role as extraction protocols become tailored to specific bioactive compounds within rubber seeds. This deeper understanding of compound properties and interactions will optimize conditions for higher oil yield and improved biodiesel quality. Thirdly, comprehensive compound characterization will move beyond identification and quantification to unravel the biological activities and mechanisms of action, guiding their application in biodiesel production. Fourthly, real-time process monitoring and control technologies will ensure consistency and high-quality biodiesel production. Fifthly, green extraction methods will align with the principles of the circular economy, exploring waste valorization strategies to repurpose residues. Sixthly, market expansion and commercialization will be driven by industries and consumers seeking sustainable energy solutions. Seventhly, regulatory compliance and quality assurance will remain a priority to ensure the safety and compliance of biodieselproduced from rubber seed oil. Finally, global collaboration between researchers, industries, and governments will foster innovation and accelerate the development and adoption of green extraction methods for rubber seed oil-based biodiesel. In summary, this future direction holds the potential to contribute significantly to sustainable energy solutions and the reduction in greenhouse gas emissions.
## 2 Methodology  
We have used the well-known TDLAS approach to precisely quantify the pressure-broadening coefficient of the NO transition line in the mid infrared (Mid-IR) band. For the TDLAS, the transmitted intensity through a gaseous sample can be estimated using the renowned Beer-Lambert's law [23; 24]:  
\[I(\upsilon)=E(t)+I_{0}(\upsilon)\cdot T(t)\cdot exp[-S(T)\cdot g(\upsilon- \upsilon_{0})\cdot n\cdot L] \tag{1}\]  
with \(E(t)\) being the background emission at time \(t\), \(I_{0}(\upsilon)\) being the initial laser intensity, and \(T(t)\) representing the losses due to broadband transmission, which is simultaneously derived from the individual raw signals and absorption profiles, respectively. The absorption line strength at gas temperature \(T\) is represented by the term in the exponential term, which is \(S(T)\cdot g(\upsilon-\upsilon_{0})\) is the normalized (integrated area = 1) line shape function with a centered wavenumber of \(\upsilon_{0}\), \(n\) is the absorber number density, and \(L\) is the optical path length. Finally, one can compute the absorbance spectrum from the following equation as stated:  
\[\alpha=-\ln\biggl{(}\frac{I(\upsilon)-E(t)}{I_{0}(\upsilon)\cdot T(t)}\biggr{)} =S(T)\cdot g(\upsilon-\upsilon_{0})\cdot n\cdot L \tag{2}\]  
To model the absorbance spectrum's lineshape in Equation (2), the Voigt function was taken, as it considers the combined effects on the spectral line caused due to Doppler and collisional broadening. Its effect is distinguished by the Doppler broadening full width at half maximum (FWHM), \(\Delta\upsilon_{D}\) and the collisional-broadening FWHM, \(\Delta\upsilon_{L}\), given by  
\[\Delta\upsilon_{D}=\upsilon_{0}\sqrt{\frac{8k_{\rm B}T\ln 2}{Mc^{2}}} \tag{3}\]  
\[\Delta\upsilon_{L}=2\cdot p\cdot\gamma_{\rm self}\cdot x+2\cdot p\cdot\gamma_{ \rm f}\cdot(1-x) \tag{4}\]in which \(x\) represents the NO amount fraction, \(k_{\text{B}}\) is the Boltzmann constant, \(M\) is the molecular mass of the absorbing species, \(c\) is the speed of light, \(p\) is the total pressure, and the coefficients \(\gamma_{\text{self}}\) and \(\gamma_{t}\) are the self and foreign broadening coefficients that depend on the gas temperature \(T\), respectively. Since the gas temperature is measured in the experiment, the Doppler broadening coefficient can be determined using Equation (3). It is also important to keep in mind that the broadening coefficients (\(\gamma_{\text{self}}\), \(\gamma_{t}\)) determined by applying Equation (4) are the corresponding values at an experimental temperature, \(T\), and have not been modified to the reference temperature \(T_{0}=296\) K, which is utilized in HITRAN [25]. The broadening coefficients must be converted to a specific temperature using the self- and foreign-temperature dependency coefficients (\(n_{\text{self}}\) and \(n_{\text{f}}\)). Typically, \(n_{\text{self}}\) and \(n_{\text{f}}\) often have values between 0.4 and 0.9 [25].
# Application of Thermochemical Method to Determine the Crystallinity Degree of Cellulose Materials
###### Abstract
In the early investigations, it was found that the presence of residual moisture in cellulose significantly reduced the exothermic effect of wetting [19]. It was also discovered that the increase in water temperature from 0 to 40 \({}^{\circ}\)C reduced the relative value of wetting enthalpy by 14% for the same cellulose sample [20]. More recent studies have shown that the wetting enthalpy of dry and pure samples depends on the structural state of cellulose, in particular, on the ratio between accessible amorphous and inaccessible crystalline domains. For example, it was found that the enthalpy of wetting for cotton fibers is almost twice lower than for viscose fibers, which was explained by the lesser crystallinity of the latter [21]. Estimating the content of accessible amorphous domains in cellulose by the amount of water sorbed in them, a linear correlation between moisture content in cellulose and wetting enthalpy was obtained [22; 23]. In addition, a directly proportional relationship was found between the index of non-crystallinity estimated by the WAXS method and the wetting enthalpy for various cellulose samples, both natural and regenerated fibers [23]. These publications show that there is a real opportunity to develop a thermochemical method for determining the degree of amorphicity or its inverse value, the degree of crystallinity of cellulose, which is the main purpose of this study.
# Some Plant Food Products Present on the Polish Market Are a Source of Vitamin B12
###### Abstract  
Cyanocobalamin is the most widespread form of vitamin B12, which is sufficient for humans. Vitamin B12 is mainly found in animal products. However, supplementation does not have to be necessary because certain amounts of vitamin B12 are present in plant products. Previous studies showed significant contents of cyanocobalamin in sea buckthorn and in sauerkraut. In this study, selected products such as sea-buckthorn jam and fermented plant products (obtained by lactic acid fermentation) were tested in a search for vitamin B12. Bacteria involved in this type of fermentation have the potential to produce cyanocobalamin. Popular fermented plant products on the Polish market were selected, namely sauerkraut and pickled cucumbers, as well as parsley juice, beetroot juice and white borscht. The analysis was carried out using HPLC-UV. Most of the analyzed products did not contain significant levels of vitamin B12. Only sea-buckthorn jam and pickled parsley juice can provide the amount of vitamin B12 needed to prevent deficiency.  
v vitamin B12; cyanocobalamin; cobalamin; fermented plant products; HPLC-UV; vegan diet
## 5 Numerical Simulation of Fiber-Reinforced Bamboo Wallboard
### Analysis of Lateral Force Resistance of Fiber-Reinforced Bamboo Wallboard  
To investigate the lateral force resistance of the fiber-reinforced bamboo wallboard, ABAQUS was employed to simulate the wallboard under continuous loading. The dimensions of wall panels, the modeling parameters, and constraints applied are consistent with those described in Section 5.1. A continuously increasing concentrated load was applied to the side of the wall to simulate the lateral force resistance of the wall panels. The lateral load was gradually increased in two stages: the first stage involved an increase of 4 kN per stage for eight iterations, while the second stage involved an increase of 1 kN per stage for seven iterations. The load-displacement curve of the wall obtained through analysis is shown in Figure 18, revealing that the covered wall panel serves as the main lateral force-resistant component in the composite wall.  
As the lateral horizontal load increases, the effectiveness of the panel gradually increases until the specimen reaches its ultimate bearing capacity. The results in this study indicate that with increasing load, displacement also increases, and that the relationship between load and displacement is linear at the initial stage. When the lateral concentrated force reached 29 kN, the structural stiffness decreased. Considering the material characteristics of the fiber-reinforced bamboo substrate, the yield displacement was defined as the maximum displacement at the end of the elastic stage, with a value of 0.6773 mm. It could thus be inferred that the ultimate lateral resistance of the fiber-reinforced bamboo wallboard was 29 kN, indicating the wall has significant lateral force resistance.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study
### Threats to Validity  
The results of our within-subject study need also be considered in the light of certain limitations. First, in a random participant group, it is always possible that there are users that are more experienced than others. Consequently, we faced the risk of an unexpected heterogeneity of the participants assigned to G1 and G2. Therefore, the final results could be influenced by the varying BPMN & IoT knowledge of the participants as well as by their domain knowledge. Although all the participants were randomly divided into two groups (i.e., G1 and G2), we attempted to minimize this risk by pre-training all participants to create a similar background as well as knowledge of the technologies required to perform the task. In this context, we provided a short tutorial on both the BPMN 2.0 basics and the BPMNE4 IoT framework. In this tutorial, the participants learned how to apply BPMNE4 IoT and BPMN 2.0, respectively. We provided the study participants with a more detailed explanation of the artifacts and events we had introduced for modeling IoT-driven business processes. Finally, we gave a introduction to both the BPMN.io and the BPMNE4 IoT modeling tool.  
Second, the two groups were built out of fourteen students, three doctoral students, and thirteen employees from the IT field (i.e., industry). Note that study participants with prior knowledge in modeling IoT-driven BPs with BPMN 2.0 still reported a lower task completion time when using BPMNE4 IoT compared to the use of BPMN 2.0, even though they were using BPMNE4 IoT for the first time. However, we acknowledge that further studies are needed with participants who are both process and IoT experts to generalize the results.  
Third, the participants had to model an IoT-driven process based on a given process description. In future studies, participants should model multiple processes from different domains to verify whether the given process description or domain has an impact on the study outcome. However, we expect that BPMNE4 IoT performs better than BPMN, as the involvement of IoT can be explicitly and unambiguously modeled using the introduced artifacts (cf. Section 5). Finally, process modeling is more straightforward when using BPMNE4 IoT.  
Finally, the study was conducted with a relatively limited sample size of 30 participants. Further studies with a larger number of participants are needed to generalize findings. Nevertheless, the obtained process models, combined with the NASA-TLX questionnaire and the required modeling time, provided promising initial results and insights into the workload and efficiency of modeling IoT-driven BPs with BPMNE4 IoT.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Challenges and Enabling Antenna Technologies  
The next question that naturally arises is what technologies are needed for such an endeavor. While various novelties and innovations are needed in the radio protocols and architecture, we target here the hardware rf front end part of the communicating system. Indeed, the base station (BS) (Access or Relaying Point, [RIS/Satellite]), Antenna Evolution, and Mobile Phone or IoT transceiver Evolution are our focus. Furthermore, we report the needed innovations with respect to relative challenges.
## 2 Methods
### Wireless bandwidth allocation  
Bandwidth estimation is necessary to transmit the data, because nodes within the transmission and interference range of each other will consume the bandwidth of each other. Suppose the LTE-A system is used in this inter-vehicle network architecture, the available wireless bandwidth between any two adjacent mobile nodes (\(i\) and \(j\)) may be figured out using following formula.  
\[BW_{i,j}= B_{i}\cdot\log_{2}(1+\frac{p_{i}G_{i,j}}{N_{0}+p_{c}G_{c,j}}), \tag{5}\]  
where \(p_{i}\) denotes the transmitting power of sender \(i\), \(p_{c}\) denotes the transmitting power of the cellular user c, which will allocate a portion of channel bandwidth to the data transmission between \(i\) and \(j\). \(B_{i}\) denotes channel bandwidth of c. \(G_{i,j}\) denotes the link gain between sender \(i\) and receiver \(j\). \(G_{c,j}\) denotes the link gain between c and \(j\). \(N_{0}\) is the noise power.  
Obviously, \(BW_{i,j}\) must reach the level of corresponding application business, otherwise, the route building will not succeed.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 4 Comparison and Analysis of Experiential Results  
He [8] calculated the first 0.1% of the pixel points from the dark channel image of the hazy image according to the brightness and then calculated the value of atmospheric light \(A\) by looking for the pixel-point of the corresponding position in the original image. However, if the picture has a third-party direct light source such as sunlight, headlights, and reflected strong light. The value of the atmospheric light obtained by He [8] is obviously too large, which will affect the final dehazing effect.  
To overcome these limitations, this paper refers to the quad-tree subdivision method [30] to estimate atmospheric light A. First, the input image is divided into four matrix regions, and the average pixel value of each block is calculated. Then, the block with the largest average is selected and further divided into four smaller matrix regions. This process is repeated until the size of the selected area is less than a prespecified threshold (typically the threshold is set to 5%*\(w\)*\(h\), where \(w\) and \(h\) represent the width and height of the input image). The defogging algorithm in this paper is the result of the quad-tree subdivision method and the adaptive tolerance correction transmittance. The transmittance map and the restored image obtained by processing the haze image of Figure 4 by the improved algorithm of the paper are as shown in Figure 6.  
To verify the effectiveness of the proposed algorithm, we test various hazy images and compare them with Tarel [7], Cai [13], He [8], and the algorithm of introducing fixed tolerance values. The comparison includes a qualitative, subjective evaluation and quantitative, objective evaluation.  
Figure 5: The restored image corresponding to the different judgment values.  
Figure 6: (**a**) hazy image. (**b**) Transmission map. (**c**) Restored image with (**b**). (**d**) Magnification of rectangle (**c**).
###### Abstract  
Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.  
indoor positioning; multivariable fingerprints; convolutional neural network; path-loss model; 5G +
Footnote †: journal: Sensors
## 3 Results and Discussion  
To validate the developed models for real-time BDS-3 clock and IFCB estimation, RINEX3 (Receiver INdependent Exchange format 3) observation data from about 80 globally distributed IGS network stations (Figure 2) spanning 1 April to 8 April 2021 are used for the simulated real-time numerical experiments. All of the stations are able to track BDS-3 quad-frequency signals. As mentioned in the previous section, the satellite antenna information for BDS-3 B1I/B3I/B1C signals is available in the jigs14.atx file, the corresponding values for the receiver antenna are still missing. However, within the jigsR3.atx file for the IGS 3 rd reprocessing campaign ([[http://acc.igs.org/repro3/repro3.html](http://acc.igs.org/repro3/repro3.html)]([http://acc.igs.org/repro3/repro3.html](http://acc.igs.org/repro3/repro3.html)) accessed on 29 January 2021), the antenna calibrations of about 40 stations, which are equipped with SEPT POLARX5 or SEPT POLARX5 TR receivers, are made available for BDS-3 B1I/B3I/B1C/B2a signals. Taking account of the limited receivers with BDS-3 quad-frequency antenna information, the jigs14.atx file is used when conducting clock estimation with the UC model, and the jigsR3.atx file is used for IFCB determination with the GFIF model due to the smaller number of unknowns. Furthermore, to be consistent with the IGS ACs' clock definition, the code observations are corrected with the code bias product in OSB (Observation Specific Bias) format from CAS (Chinese Academy of Sciences). The detailed processing strategy and filter settings are summarized in Tables 1 and 2, respectively. The post-processed solutions from IGS ACs are used for clock accuracy assessment with the commonly used DD (Double-Difference) method to remove the clock datum differences. Given the DBD (Day Boundary Discontinuities) of post-processed clocks, the clock STD will be calculated on a daily basis. In all, 24 BDS-3 MEO satellites, well-distributed on three orbital planes, will be the focus of this study. The UC models with B1I/B3I dual-frequency signals, B1C/B2a dual-frequency signals, B1I/B3I/B1C triple-frequency signals, B1I/B3I/B2a triple-frequency signals and B1I/B3I/B1C/B2a quad-frequency signals are conducted for real-time BDS-3 clock and IFCB estimation. For comparison, the IF model with B1I/B3I dual-frequency signals is performed in addition to the five UC models.  
Figure 2: Stations for real-time BDS-3 clock and IFCB estimation, the blue and red icons denote receivers without and with antenna calibrations for BDS-3 multi-frequency signals, respectively.
# Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
## 2 Materials and Methods
### Dataset Description
Such modifications include the addition of spandrel beams, or wall sections that do not cover the full height of the story [8]. * Benchmark year: Various Seismic Codes define the so-called \"benchmark years\" where an improved version of the Code took effect. * Soil type: Generally, Seismic Codes classify soil types ranging from rock and semi-rock formations to loose fine-grained silt [19]. The quality of the underlying soil is a significant factor of the overall seismic behavior of the building. * Lack of Seismic Code design: This attribute pertains to structures that were designed without adhering to the provisions of a dedicated Seismic Code. * Wall filling regularity: When the infill walls are of sufficient thickness and with few openings, they can support the surrounding frames during an earthquake, leading to improved overall performance. * Previous damages: This attribute pertains to structures whose previous damages have not been adequately repaired, leading to a deterioration of their overall seismic capacity. As it is evident from the above, the existing Seismic Codes consider distinct feature vectors in their respective RVS procedures. The selection of these features is based on experts' knowledge, which varies from country to country and from institution to institution. Even when the same features are used, different weights might be assigned to them and the calculation of the final vulnerability score might be carried out using different procedures. This calls for the need for the use of Machine Learning data-driven models. These models can learn from the spectrum of the available data vectors, and they can lead to ranking improvements, which is the aim of this study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Damage Label** & **Number of Structures** \\ \hline Black & 93 \\ Red & 201 \\ Yellow & 69 \\ Green & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of structures across the damage spectrum. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attribute** & \multicolumn{5}{c}{**Seismic Code**} \\ \cline{2-6}  & **FEMA 1988** & **FEMA 2002** & **OASP (2000–2004)** & **India** & **Canada** & **New Zealand** \\ \hline Structural type & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Significant height & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ \\ Poor condition & ✓ & ✗ & ✓ & ✗ & ✓ & ✗ \\ Vertical irregularity & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Horizontal irregularity & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Soft story & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Torsion & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ Pounding & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ Heavy non-structural elements & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ Short columns & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ Benchmark year & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Soil type & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Moderate height & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ \\ Lack of Seismic Code design & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ \\ Wall filling regularity & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ \\ Previous damages & ✗ & ✗ & ✓ & ✗ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attributes employed by each Seismic Code.