esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Correlation of Packet Loss and Jitter  
As described earlier QoS parameters can exhibit correlation and this correlation can be quantified using the Pearson Correlation Coefficient (PCC) or the Spearman Rank Correlation Coefficient (SRCC). PCC measures linear correlation between quantities while SRCC evaluates non-linear correlation.  
We calculated the PCC and the SRCC from the QoS measurement data acquired from the measurements for different UK network service providers. Both the PCC and the SRCC can be any value between \(-1\) and \(1\), where \(-1\) shows that the two quantities are completely negatively correlated and \(1\) shows that they are completely positively correlated, while \(0\) means that there is no correlation between the two quantities.  
We used a million data points for each of the 4 leading UK network providers over a period of almost one month and then calculated the PCC and the SRCC between the PLR and jitter measurements. The data sets were anonymous in alignment with data privacy rules, and the measurements of jitter and PLR were used to calculate the results presented in Table 1.  
We found that all 4 major UK network providers exhibit different levels of linear and non-linear correlation. Table 1 also shows further that the jitter and PLR exhibit non-trivial (10-40%) levels of correlation. This makes it critical to take this correlation in account while investigating the propagation of the uncertainty in QoE. Therefore, we considered the correlation between PLR and jitter to evaluate propagated statistical error in QoE. Later, our results show that although there is a noticeable linear correlation between QoS parameters, this correlation has insignificant effect on propagated error in QoE of VoD applications.
###### Abstract  
Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area mapping. This study examines the performance of the Sentinel-2A Multi Spectral Instrument (MSI) bands and derived spectral indices to differentiate between unburned and burned areas. For this purpose, five pairs of pre-fire and post-fire top of atmosphere (TOA reflectance) and atmospherically corrected (surface reflectance) images were studied. The pixel values of locations that were unburned in the first image and burned in the second image, as well as the values of locations that were unburned in both images which served as a control, were compared and the discrimination of individual bands and spectral indices were evaluated using parametric (transformed divergence) and non-parametric (decision tree) approaches. Based on the results, the most suitable MSI bands to detect burned areas are the 20 m near-infrared, short wave infrared and red-edge bands, while the performance of the spectral indices varied with location. The atmospheric correction only significantly influenced the separability of the visible wavelength bands. The results provide insights that are useful for developing Sentinel-2 burned area mapping algorithms.  
Sentinel-2; fire; burned area; separability analysis +
Footnote †: journal: Remote Sensing, 2016, 8, 873; doi:10.3390/rs8100873
# Microwave-Supported Modulation of Functional Characteristics of Gluten-Free Breads
## 4 Conclusions  
1. While the specific impact of microwave hydrothermal treatment on the polyphenolic content and antioxidant activity of gluten-free flours was not particularly widely addressed in the provided references, other researchers' studies have shown that microwave radiation can modify the physical and rheological properties of rice flour, which is commonly used in gluten-free products. Additionally, gluten-free flours, including those that may have undergone microwave hydrothermal treatment, have been found to exhibit higher antioxidant activity compared to wheat flour [24; 42; 43]. Further research is needed to specifically investigate the effects of microwave hydrothermal treatment on the polyphenolic content and antioxidant activity of gluten-free flours.
2. The treated buckwheat and teff flours showed an improvement in all crucial functional parameters of bread quality as well as a modified phenolic compound profile, which impacts the resulting nutritional quality of the breads. Doughs made from microwave-treated buckwheat flour resulted in the more pleasant external presence of brown bread, and the greyish notes of both rice and buckwheat disappeared. Teff is a particular minor cereal with low bread-making potential. However, the MW treatment improves the performance of teff flour; for such a particular crop, its success still depends on the starch/protein/fiber ratio, as the better improvement was observed for white teff, which is naturally richer in protein.  
Conceptualization, T.L. and J.H.; methodology, T.L., R.O. and J.H.; validation, J.H., formal analysis, T.L. and R.O.; investigation, T.L., A.T. and R.O.; resources, J.H.; data curation, J.H.; writing--original draft preparation, T.L. and J.H.; writing--review and editing, J.H.; project administration, T.L.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
The project is financed by the National Science Centre, Poland, under the program POLS, project number 2020/37/K/ST5/03602.  
Not applicable.  
Data Availability Statement: Data are available upon request from the corresponding author. The data are not publicly available due to the project agreement under which the research was funded did not suggest publicly available data.  
[PERSON] thanks the PROM Program for International Exchange of Doctoral Students and Academic Staff founded by the Polish National Agency for Academic Exchange--contract No. PPI/PRO/2019/1/00049/U/0001.  
The authors declare no conflict of interest.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 3 Applications
### Intelligent Transport System (ITS)
Results show that depending on the headlamp location a data rate of 50 Mbps can be achieved at a separation distance of 70m between vehicles. In [45] the authors have considered VLC-based ITS for accident avoidance, particularly, when lorry fleet are moving through intersections. VLC has been used to send signals related to acceleration, deacceleration and braking to ITS infrastructure (e.g., RSUs) which can trigger appropriate signal. For example, to reduce the number of emergency brakes and lane change in a complex environment, a lorry fleet can send VLC signal to RSU which can set a green signal or express path. [PERSON] et al. have used VLC with imaging sensor-based receiver for automotive applications [46]. Two scenarios I2V and V2V are considered. The first scenario consists of a transmitter designed from LED arrays (assumed to be RSUs) while the receiver is considered to be high frame rate CMOS imaging camera. In the second scenarios, a special CMOS sensor have been developed which can receive high-speed optical signals. In the field trials, a data rate of 32 kb/s and 10 Mb/s is achieved for I2V and V2V, respectively.
## 2 Materials and Experimental Procedures
### Materials and Samples
Figure 2 shows typical micrographs indicating that base material microstructure (Figure 2a) with elongated grains in the rolling direction. The plane selected to take micrograph was normal to the loading direction to demonstrate the shot-peening effect. Around the shot peened surface (Figure 2b), an increasing of grain deformation and roughness was observed. Surface Vickers hardness tests were performed according to ASTM C1327-15 [30] using a Struers Duramin micro-hardness tester with 0.5 N load for 15 s. Micro-hardness measurements were done in the cross-section of the sample at 0.3 mm from the surface, and spaced out 0.5 mm, for both specimen types, MP and SP. The average values obtained from twenty measurements were: \(HV_{0.05}\) = 157 for MP surfaces and \(HV_{0.05}\) = 167 for SP surfaces. Therefore, shot peening surface hardness increased is more than 6%. Residual stresses were measured, in-depth and on the longitudinal surface. Residual stresses analysis was performed by X-ray diffraction using a Proto iXRD equipment. Lattice deformations of the [22] diffraction planes were measured using Cr-K\(\alpha\) X-ray radiation, with 22\({}^{\circ}\)\(\psi\) angles, in the range \(\pm\)42\({}^{\circ}\), an acquisition time of 30 s by peak and \(\pm\)2\({}^{\circ}\) oscillations in \(\psi\). For the analyzed material, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Parameter** & **Mean Value \(\pm\) Standard Deviation (\(\upmu\)m)** \\ \hline \multirow{3}{*}{MP} & Ra & 1.22 \(\pm\) 0.02 \\  & Rq & 1.50 \(\pm\) 0.02 \\  & Rz & 7.74 \(\pm\) 0.13 \\ \hline \multirow{3}{*}{SP} & Ra & 3.70 \(\pm\) 0.17 \\  & Rq & 4.60 \(\pm\) 0.21 \\ \cline{1-1}  & Rz & 23.50 \(\pm\) 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Surface roughness parameters for Mechanically Polished (MP) and Shot Pened (SP) specimens. Figure 1: (a) Shot peening machine; (b) shot peened specimen; and (c) dimensions of Compact Tension (CT) specimens in mm. and considering the radiation used, the average penetration depth of the X-rays was about 11 \(\upmu\)m. Measurements were made for all studied treatments at four points along the surface of one specimen: one point for each longitudinal surface and one point for each notch surface in the central position. The analysis of the in-depth evolution of the residual stresses in the longitudinal surface was performed by X-Ray diffraction after successive layer removal by electro polishing.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 4 Sensor Experiment and Discussion
### Baseline Noise Measurement  
Prior to sensor performance evaluation, the baseline noise of the develop sensor was tested by measuring the frequency stability of the differential oscillator, as shown in Figure 11. Excellent short-term frequency stability of \(\pm\)0.5 Hz/s and medium-term frequency stability of \(\pm\)5 Hz/h were obtained.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 3 Factors Influencing Dark Fermentation Reactions
### Effect of pH  
pH is an important parameter that influences the efficiency of H\({}_{2}\) production during dark fermentation. It affects the fermentation rate, the production of H\({}_{2}\) and organic acid, the metabolic activity and growth of bacteria, and substrate utilization during anaerobic substrate degradation [29]. Hydrogenase activity and the metabolic pathways in hydrogen-producing microbes are strongly influenced by pH but only within a reasonable range. Variations in pH can affect the conformation and activity of hydrogenase, thereby affecting the rate of H\({}_{2}\) production.  
ATP is used to maintain neutrality in the cell, and pH values that are too high or too low can decrease the number of bacteria due to lower ATP levels in the cells [32; 33]. Optimal pH enables higher H\({}_{2}\) yield, solving the issues related to solventogenesis and inhibiting methanogens. pH values between 5 and 6 are preferred for H\({}_{2}\) production, as the activity of methanogens is limited in the range between 6.5 and 7.0, although it has been observed that the optimal pH value varies depending on the type of inoculum and substrate used [34]. A study was conducted to analyze the effect of pH on the H\({}_{2}\) production ability of dark fermentation bacteria using organic wastewater with a high concentration of nitrogen-containing compounds [35]. A pH range from 4 to 11 was analyzed, with the best results achieved at pH 5, where the H\({}_{2}\) production rate was 0.053 mmol/h, the COD removal rate was 37.13 \(\pm\) 1.86%, and the nitrate reduction rate was 1.57 \(\pm\) 0.27 mg/L/h.  
According to another research comparing different types of substrates, Spirulina platensis hydrolysate, which is a protein-based substrate, has an optimal pH range for H\({}_{2}\) production between 4.5 and 5.7 [36]. However, a pH range between 5.3 and 5.6 was found to be optimal for glucose-based substrates in dark fermentation [37], proving that variations in optimal pH can be related to substrate diversity, operating conditions, and inoculum sources [38]. [PERSON] et al. [39] found that decreasing the pH to 5 negatively affected the H\({}_{2}\) yield compared to pH 8, with a decrease from 1.92 to 0.67 mol H\({}_{2}\)/mol hexose. Another study defined that the inhibition of H\({}_{2}\) production due to a pH shift from 6 to 5 is caused  
Figure 4: Factors influencing hydrogen production via dark fermentation.  
by the acidic environment, which limits the development of reducing metabolites [40]. On the contrary, the H2 yield in continuous stirred tank reactors (CSTRs) was higher at a pH of 4.0-4.5 than at a pH of 6.0-6.5 [41]. Therefore, it can be stated that pH can influence the solubility and availability of organic substrates for microbial utilization. Variations in the solubility of substrates, such as carbohydrates and organic acids, can affect their accessibility to hydrogen-producing bacteria, making pH an important parameter to optimize for high H2 yield.
## 1 Introduction  
Previously, it used to be thought that anterior cruciate ligament (ACL) injuries are rare in pediatric patients. However, as the participation of children in high-impact sports has increased over the last 20 years, it is now agreed that ACL injuries are common in the pediatric population. Recently, it was estimated that sports-playing youth athletes sustain over 400/100,000 ACL injuries each year [1; 2; 3; 4]. Furthermore, it seems that adolescents have complex knee injuries and higher rates of ACL graft re-rupture than adults [5].  
For individuals with high physical activity levels, ACL reconstruction (ACLR) is strongly recommended. In the adult population, the hamstring tendon autograft remains the most popular choice for ACLR, and it is agreed that a decent rehabilitation is crucial for restoring the pre-injury activity levels [6; 7].  
The management of an ACL injury is quite a controversial subject in current pediatric orthopedics. The controversy is due to demanding surgical techniques, as there is a risk of growth disturbance complications. Today, various techniques of ACLR have been described in the literature [8]. Currently, in the scientific literature we can see an increased interest in quadriceps tendon graft adaptation. Articles with some catchy phrases can be found, e.g., stating that it is \"the graft of the future\" and that it should be used more in modern ACLR [9; 10]. However, information about the use of quadriceps tendon in young and active patients is still lacking; therefore, more attention must be paid to it [11].  
Very few studies have been published regarding an anthropometric side-to-side comparison of the quadriceps tendon autograft using a patellar bone block with the hamstring autograft [12; 13]. Additionally, none of these studies examined the all-soft-tissue quadriceps autograft, which we promote as an excellent graft option for ACLR.  
The graft maturation process is also a key factor in relation to the rehabilitation protocol. Magnetic resonance imaging studies showed a better maturity of the quadriceps tendon autograft compared to the hamstring autograft [14]. Furthermore, the anthropometric evaluation of the anterior side-to-side tibial translation can add essential information about graft performance after ACLR [13]. This prospective study evaluated the influence of autograft selection on knee stability and possible failure in young and active patients. The examined autografts included all-soft-tissue quadriceps tendon (QT) and hamstring tendons (HT) autografts. Our main hypothesis was that the QT autograft would provide as good knee stability after ACLR as the HT autograft. We believe that our results can enhance QT usage and popularity.
## 4 Discussion
### A Promoting Effect of BF06 VOCs on Plant Growth  
_B. amyloliquefaciens_ is a PGPR well known for its ability to regulate plant growth and increase the resistance of plants to abiotic or biotic stresses [62; 63; 64]. In this study, we investigated the effects of _B. amyloliquefaciens_ BF06 VOCs on the growth of _Arabidopsis_ plants. Our data showed that the blend of VOCs produced by BF06 significantly improved plant growth, with increased biomass. The results of GC-MS analyses showed that BF06 could produce about 21 types of VOCs, including 2,3-butanediol. [PERSON] et al. [10] have reported that the release of 2,3-butanediol by GB03 is involved in promoting plant growth and activating ISR in plants. Several other PGPR strains such as _P. chlororapdis_ O6 can produce 2,3-butanediol, which induces systemic resistance and enhanced drought tolerance in plants by regulation of stomatal closure and water loss rate [10; 25]. In addition, application of 2,3-butanediol has been shown to enhance the generation of NO, and inhibition of NO synthesis impairs 2,3-butanediol-triggered stress resistance [65]. This indicated a key role for NO signaling in improving stress tolerance conferred by exposure to 2,3-butanediol.  
To clarify the mechanism of BF06 VOCs-regulated plant growth, comparative transcriptomic analysis was performed to identify the DEGs of the VOCs-exposed plants. The results of RNA-Seq indicated that the expression of some Fe acquisition-related genes such as _FIT1_, _IRT1_, and _FRO2_ was markedly upregulated in the VOCs-exposed plants. [PERSON] et al. [22] have indicated that GB03 VOCs activate Fe acquisition and enhance Fe accumulation in plants via upregulation of _FIT1_. In _fit1_ mutants, GB03 VOCs failed to induction of FIT1-mediated Fe acquisition gene expression. Therefore, the release of VOCs by BF06 induced the expression of _FIT1_ in plants, possibly due to Fe absorption. Enhanced Fe uptake has been reported to increase chlorophyll content and photosynthesis in plants [22]. As observed in this study, the VOCs-exposed plants exhibited higher chlorophyll content and photosynthetic capacity. Similar results were shown in _Arabidopsis_ plants exposed to GB03 VOCs. Intriguingly, the expression of some ABA biosynthetic and responsive genes was obviously downregulated in plants exposed to BF06 VOCs (Table S4). [PERSON] et al. [66] have reported that treatment with ABA abolishes GB03 VOCs-induced increase of photosynthesis and chlorophyll levels in plants. Moreover, GB03 VOCs exposure enhances photosynthetic efficiency in _Arabidopsis_ via the inhibition of ABA signaling pathways.
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 5 Methods
### CAMeLBERT-DA SA Model  
This CAMeLBERT-DA SA Model is implemented based on the following datasets:  
1. The Arabic Speech-Act and Sentiment Corpus of Tweets (ArSAS), where 23,327 tweets have been used [33];
2. The Arabic Sentiment Tweets Dataset (ASTD) [34];
3. SemEval-2017 task 4-A benchmark dataset [35];
4. The Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets (ArSenTD-Lev) [36].  
These datasets were combined and preprocessed in a similar way to what was done in [37; 38]. The preprocessing steps included removing diacritics, URLs, and Twitter user-names from all the tweets.  
The experimental setup adopted by [30] for CAMeLBERT-DA SA is summarized as follows:  
1. Build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic.
2. Build a fourth language model pre-trained on a mix of the three variants.
3. Build additional models pre-trained on a scaled-down set of the MSA variant to examine the importance of pre-training data size.
4. Fine-tune the models on five NLP tasks spanning 12 datasets.
5. Compare the performance of the models to each other and to eight publicly available models.
6. Analyze the results to identify the importance of variant proximity of pre-training data to fine-tuning data and the impact of pre-training data size.
7. Define an optimized system selection model for the studied tasks based on the insights gained from the experiments.
8. Make the created models and fine-tuning code publicly available.
# Topology Optimisation under Uncertainties with Neural Networks
## 2 Topology Optimisation under Uncertainties
### Stochastic Model Formulation
#### 2.2.2 Gradient Equation  
Analogous to the deterministic approach, the gradient can be defined corresponding to Equation (9) by the solution \((\varphi,\lambda,t)\in H^{1}(D)\times\mathbb{R}\times\mathbb{R}\), such that for all \((v_{\varphi},v_{\lambda},v_{t})\in H^{1}_{0}(D)\times\mathbb{R}\times\mathbb{R}\) the following equation holds,  
\[0=\frac{\varepsilon}{\tau_{\varphi}}\int_{D}(\varphi^{*}-\varphi _{n})v_{\varphi}\,\mathrm{d}\mu+\frac{\varepsilon}{\tau_{t}}\int_{D}(t-t_{n}) v_{t}\,\mathrm{d}\mu+\int_{D}\lambda v_{\varphi}\,\mathrm{d}\mu+\int_{D}( \varphi^{*}-m)v_{\lambda}\,\mathrm{d}\mu\\ +\varepsilon\gamma\int_{D}\
abla\varphi^{*}\cdot\
abla v_{ \varphi}\,\mathrm{d}\mu+\frac{\gamma}{\varepsilon}\int_{D}\frac{\partial}{ \partial\varphi}\psi_{0}(\varphi_{n})v_{\varphi}\,\mathrm{d}\mu-\int_{D}\frac {\partial}{\partial\varphi}\sigma(p,\varphi_{n})v_{\varphi}\mathcal{E}(u)\, \mathrm{d}\mu\\ +\begin{cases}\int_{D}v_{t}\,\mathrm{d}\mu,&\text{if }\int_{\Gamma_{g}}gu\, \mathrm{d}\mu-t\leq 0\\ \int_{D}(1-\frac{1}{1-\beta})v_{t}\,\mathrm{d}\mu,&\text{else}\end{cases}. \tag{11}\]  
The actual solution for one gradient step of the minimisation problem in Equation (8) with respect to (9) follows from  
\[\varphi\approx\frac{1}{S}\sum_{i=1}^{S}\varphi_{i}^{*}, \tag{12}\]  
where \(S\in\mathbb{N}\) is the number of samples of the Monte Carlo simulation. The larger \(\beta\) chosen, the larger \(t\) becomes, and thus the number of evaluations of \(u\) for which \(\int_{\Gamma_{g}}gu\,\mathrm{d}\mu-t\leq 0\) holds true increases. In order to ensure a valid simulation of Equation (12), \(N\) must be chosen sufficiently large so that an adequate number of evaluations of \(u\) in each gradient step fulfils condition \(\int_{\Gamma_{g}}gu\,\mathrm{d}\mu-t>0\). The described procedure is depicted in Algorithm A2 where the optimisation process is basically the same as in Algorithm A1. The central difference is that \(N\in\mathbb{N}\) realisations of the optimisation problem have to be computed in each iteration. In practice, these are solved in parallel for \(N\) different \(\omega\in\Omega\) and the results are then averaged. The large computational efforts caused by the slow Monte Carlo convergence are alleviated by the neural-network-based machine learning approaches presented in Section 3. In particular, gradient steps for arbitrary parameter realisations can be evaluated very efficiently and significantly fewer iterations (i.e., optimisation iterations) are required.