esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Spectrophotometry  
Based on the above analysis results, we proposed a measurement method for measuring the VC concentration based on L, grayscale, and log-grayscale. In order to verify the accuracy and reliability of the method, the VC concentrations of standard solutions, fruits, and VC tablets were determined by the proposed method and compared with the measurement results by spectrophotometry. The spectrophotometry experiment used for contrast was based on the reduction of iron (III) by ascorbic acid to iron (II). Then the solution was complexed with 1,10-phenanthroline and the absorbance was measured at a 510 nm wavelength [7]. The spectrophotometer used for comparison was the 721G visible spectrophotometer (INESA Analytical Instrument Co., Ltd., China; [[https://www.instrument.com.cn](https://www.instrument.com.cn)]([https://www.instrument.com.cn](https://www.instrument.com.cn))).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Foreign Substance** & **Tolerance Ratio** & **Interference Effect** \\ \hline \(Na^{+},Cl^{-}\) & 2000 & no significant effect \\ \(NH_{4}^{+},Cl^{-}\) & 1875 & no significant effect \\ \(Cu^{2+},SO_{4}^{2-}\) & 0.025 & \(2 Cu^{2+}+4I^{-}=2 CuI\downarrow+I_{2}\) \\ \(Fe^{3+},SO_{2}^{2-}\) & 0.05 & \(2 Fe^{3+}+2I^{-}=2 Fe^{2+}+I_{2}\) \\ \(Al^{3+},SO_{4}^{2-}\) & 1777.5 & no significant effect \\ \(Ca^{2+},Cl^{-}\) & 1550 & no significant effect \\ \(Mg^{2+},Cl^{-}\) & 1600 & no significant effect \\ \(Fe^{2+},SO_{4}^{2-}\) & 1725 & no significant effect \\ Cysteine & 0.05 & reducing \(I_{2}\) \\ Methionine & 750 & no significant effect \\ Lysine & 0.075 & reducing \(I_{2}\) \\ Tryptophan & 800 & no significant effect \\ Sucrose & 3000 & no significant effect \\ Glucose & 1000 & no significant effect \\ Fructose & 2000 & no significant effect \\ \(VitaminB_{1}\) & 300 & no significant effect \\ \(VitaminB_{2}\) & 200 & no significant effect \\ Vitamin E & Insoluble in water & no significant effect \\ Oxalic acid & 0.3 & reducing \(I_{2}\) \\ Citric acid & 0.55 & reducing \(I_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of foreign substances ([VC] = 4 \(\upmu\)g / mL, n = 3).
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 3 Proposed Architecture  
### Main Description  
Our proposal creates two specialized logics for traffic routing. One is for traffic without QoS requirements, and the other is for traffic with QoS. The first one maintains a central element in the data plane called OF-GW, and all traffic has to be routed through the OF-GW to reach the destination. Key design characteristics to route traffic without the QoS requirement are faster routing, simpler routing policy, and network robustness. On the other hand, traffic with QoS requirements is routed  
Figure 3: Representation of: MPLS label, EPS-Bearer, Radio Bearer, and OF-Bearer.  
creating specific EPS-Bearers configured to comply with specific QoS traffic requirements such as ultra-low latency, ultra-reliable communication, high data rate, or other QoS requirements.  
The proposed architecture uses the two OpenFlow path setup modes [35]: the proactive mode where paths are set up in advance and the reactive mode where the Controller listens to the switches to configure routes on-demand. Both methods have considerations. When the traffic arrives, the proactive mode does not require the Controller actions; it is immediately routed using pre-configured rules, but the packets are sent using general matching criteria. On the other hand, the reactive mode is perfect for creating a specific QoS path on demand, but requires more Controller processing when the packets arrive, and it must wait for the Controller's response to be routed. Both methods are extensively used in the OpenFlow or SDN academic literature, but this work proposes another option where the traffic with QoS requirements is processed using both operation modes at the same time.  
The general idea is summarized as follows. When UE is attached in a OF-NB, it initially has a default EPS-Bearer to OF-GW, which is used to route all traffic without the QoS requirement. When UE sends a packet with the QoS requirement, the OF-NB receives the packet and sends it through two pipelines for simultaneous processing. One pipeline uses a default EPS-Bearer to route the packet immediately (pipeline processing using proactive path setup mode), and the other detects the traffic with QoS requirements and sends a copy of the packet to the new mobile elements called the Service Detector Device (SDD) responsible for extracting the specific QoS requirements. The extracted QoS information is sent to a management entity (as PCRF in 4G or PCF in 5G), and a procedure to create a specific EPS-Bearer with QoS takes place (pipeline processing using reactive path setup mode). Then, the next packets of the same QoS flows are sent using the created specific EPS-Bearer with QoS.  
In Section 4.1, an example will be shown in which the prototype initially sends the traffic to the application server through _SW1-SW3-OF-GW 1-SW4-W6_, using the default EPS-Bearer, and when the specific EPS-Bearer with QoS is created, the traffic to the application server is re-routed using the new EPS-Bearer with QoS (sent through the _SW1-SW2-SW5_ path).
## 3 Function-Oriented Networking
### Design and Processing of FON Packet  
FON Packet is a packet used in a communication between FON switches. FON is not using IP protocol so FON Packet is encapsulated directly into Ethernet Frame. FON Packet is divided into FON data packet and FON ADV packet by _ethertype_ field in Ethernet header.  
For FON Adv packet, its purpose is to synchronize and notify adjoining nodes, which is similar to LLDP. FON Adv packet uses ETH_P_FON_ADV(0 xFF03) as a _ethertype_ field. This type of packet is generated and received only through FON, not by FON Function.  
FON data packet is a packet for FON Function. FON Function exchanges each other using it. FON data packet uses ETH_P_FON(0 xFF05) as a _ethertype_ field. Figure 3 shows the design of packet. Each field is described in Table 1.
## 2 Materials and Methods
### Identification of Agricultural Drought Years
#### 2.7.2 Germany  
Germany is a temperate country in which agriculture is mainly energy-limited. However, environmental and climate conditions are changing, and positive drought trends have recently been observed in many regions of central Europe [41; 82]. Consequently, for the selection of the critical month for the yield variable and, therefore, the detection of drought years, a provincial and monthly scale analysis of agricultural drought index trends was performed. The Mann-Kendall (MK) statistical test is a nonparametric test used to identify trends in time-series data [83; 84]. It is widely used to detect whether there are statistically significant increasing or decreasing trends in hydrometeorological time series [85; 86]. The ability of this test to detect trends in hydrology studies has been demonstrated [87], and it has been applied in several agricultural drought studies [73; 88; 89]. Many studies have reported that correlations in the time series may affect the results of the MK test [90; 91]; nevertheless, in this study, the anomalies of the SM series were studied, which, according to [92], is a technique for avoiding these inconveniences. In this study, the MK test was used to detect whether there were statistically significant increasing or decreasing trends in the agricultural drought index during 2001-2020 to identify the critical month (month in which statistical significance predominates) of administrative districts. Under the null hypothesis of no trend Ho, the MK test statistic (\(S\)) was calculated as follows [83; 84]:  
\[S=\sum_{i=1}^{n-1}\sum_{j=l+1}^{n}sgn\big{(}x_{j}-x_{i}\big{)} \tag{2}\]where  
\[\text{sgn}(x)=\left\{\begin{array}{ll}1&if\ x>0\\ 0&if\ x=0\\ -1&if\ x<0\end{array}\right. \tag{3}\]  
For an upward trend, the \(S\) statistic is increased by \(+1\), while it is decreased by \(-1\) for a downward trend. The \(S\) statistics remain unchanged for a zero difference. The statistical parameter \(Z\) allows us to determine whether a trend is significant:  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{var(S)}}&if\ S>0\\ 0&if\ S=0\\ \frac{S+1}{\sqrt{var(S)}}&if\ S>0\end{array}\right. \tag{4}\]  
Throughout this study, a \(p\) value of 0.05 (confidence level of 95%) was used as the criterion for the statistical significance of a trend. Thus, for an absolute value of \(Z\) greater than 1.96, a significant trend was considered. The month with the most districts with statistically significant trends was considered the critical month for all German regions.  
Once the critical month for the yield variable in the German regions was obtained, the years with agricultural drought were identified using the same procedure explained above for the Spanish regions. Thus, for the three approaches studied (M, 2M and 3M), the 20 th percentile was used as the threshold level, below which years with agricultural drought were defined.
# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 4 Results and Discussion  
Our research explored transformer-based data augmentation approaches to improve fake news detection in Romanian. Specifically, we focused on using transformer-based models to augment the Factual_ro data set through BT, employing advanced machine translation methods such as mBART. BT via mBART is a text data augmentation technique that involves translating a sentence back to the source language using a pre-trained machine translation model. As shown in Tables 4 and 5, we performed a comparative analysis using four classifiers: Extra Trees Classifier, Random Forest Classifier, Support Vector Machine, and Logistic Regression. We investigated the impact of BT augmentation techniques on model efficacy by evaluating parameters such as accuracy, precision, recall, F1 score, AUC, and Cohen's kappa. The first experiment of the comparative analysis utilized English as the target language for the BT data augmentation process. However, in the second experiment, the target language was switched to French. The \"None\" row results in both tables show the scores for models trained on the initial data without any augmentation. The overall accuracy ranged from 0.6948 to 0.7371, \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Augmentation Type** & **Model Name** & **Acc** & **Pre** & **Rec** & **F1** & **AUC** & **Kappa** \\ \hline \multirow{4}{*}{None} & Extra Trees Classifier & 0.7277 & 0.8034 & 0.7287 & 0.7301 & **0.8211** & 0.4438 \\  & Random Forest Classifier & **0.7371** & 0.8017 & **0.7519** & **0.7389** & 0.8128 & **0.4586** \\  & Support Vector Machine & 0.6948 & **0.8137** & 0.6434 & 0.6981 & 0.7743 & 0.3950 \\  & Logistic Regression & 0.7042 & 0.7845 & 0.7054 & 0.7070 & 0.7586 & 0.3971 \\ \hline \multirow{4}{*}{Back Translation (Google Translate)} & Extra Trees Classifier & 0.7746 & 0.8293 & **0.7907** & 0.7759 & **0.8450** & 0.5340 \\  & Random Forest Classifier & **0.7793** & 0.8360 & **0.7907** & **0.7807** & 0.8404 & **0.5447** \\  & Support Vector Machine & 0.7512 & **0.8455** & 0.7209 & 0.7539 & 0.8089 & 0.4989 \\  & Logistic Regression & 0.7371 & 0.8230 & 0.7209 & 0.7398 & 0.7770 & 0.4673 \\ \hline \multirow{4}{*}{Back Translation (mBART)} & Extra Trees Classifier & 0.7700 & 0.8333 & 0.7752 & 0.7716 & 0.8309 & 0.5272 \\  & Random Forest Classifier & **0.7746** & 0.8293 & **0.7907** & **0.7759** & **0.8462** & **0.5340** \\ \cline{1-1}  & Support Vector Machine & 0.7512 & **0.8455** & 0.7209 & 0.7539 & 0.8089 & 0.4989 \\ \cline{1-1}  & Logistic Regression & 0.7418 & 0.8190 & 0.7364 & 0.7442 & 0.7720 & 0.4736 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scores for fake news detection with and without augmentation (Google Translate and mBART) using English as the target language.
# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control  
The shared control paradigm has been proposed in various forms to assist teletherapy operations. It mainly helps improve the operators' sensorimotor and spatial reasoning skills. Using this strategy, the operators control the remote robot, but they are assisted in performing the task. This approach is also referred to as cooperative or collaborative teleoperation, as it can be seen as a collaboration between either several operators (such as in the aforementioned dual-user topologies) or between a \"robot intelligence\" and the operator. In the second case, depending on the amount of assistance provided to the human operator, the cooperation is performed at different levels, ranging from low-level sub-tasks (kinematic transformations, environment motion compensation [324], force filtering, motion planning [325]) to high-level tasks (situation analysis, planning generation, and decision/proposal making [313]). There is no single definition for shared control [314]. We propose to divide the shared-control-based solutions into two types of strategies: those based on task decomposition and those based on authority blending.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 6 Carbon-Based Nanomaterials
### Carbon Dots  
Carbon dots (CDs) are zero-dimensional carbon-based nanomaterials that have recently gained attention in photocatalysis due to their non-toxicity, low cost, electron conductivity, and excellent photostability [176]. When coupled with other materials, CDs can provide highly selective adsorption sites for photocatalytic processes. Additionally, the electronic structure of CDs allows them to act as electron donors or acceptors after photoexcitation, resulting in enhanced electron transmission, photochemical stability, and tunable optical properties. The incorporation of CDs into semiconductor photocatalysts can narrow the band gap and expand their adsorption capacity in the visible light range. Research on the use of CDs for the photocatalytic reduction of carbon dioxide [177], volatile organic compounds (VOCs) [178], and other pollutants has significantly increased in recent years. To improve their properties, CDs have undergone doping, and composite formation to create heterojunctions and surface modifications with various functional groups. This class of carbon-based nanomaterials includes carbon quantum dots and graphitic carbon nitride quantum dots, each with distinct chemical properties and structures [87].  
Carbon quantum dots (CQDs) are ultra-small nanoparticles with diameters less than 10 nm, exhibiting the electronic properties of carbon nanomaterials and the optical characteristics of quantum dots [179]. The integration of CQDs into semiconductor photocatalysts can enhance their photocatalytic activity through various mechanisms, including photosensitization [180], increased adsorption of VOCs, electron mediation [181], and reduction of band gap energy. Furthermore, the surface of CQDs is rich in functional groups containing oxygen, which contributes to their water solubility [182].  
Recently (Figure 22), graphitic carbon nitride quantum dots (CNQDs) have been synthesized and combined with TiO\({}_{2}\) to improve the photocatalytic performance of this material under visible light and enhance the efficiency of surface charge carrier transfer. For example, a study on CNQDs in situ-modified TiO\({}_{2}\) inverse opal [183] demonstrated superior photocatalytic efficiency in the degradation of toluene compared to conventional inverse opal TiO\({}_{2}\), P25, and bulk TiO\({}_{2}\). The photoluminescence spectrum of the CNQDs in situ-modified TiO\({}_{2}\) inverse opal showed lower intensity than that of inverse opal TiO\({}_{2}\) and bulk TiO\({}_{2}\), indicating a reduction in the charge carrier recombination rate due to the introduction of CNQDs. Notably, the CNQDs in situ-modified TiO\({}_{2}\) inverse opal achieved a toluene removal rate of 95%, surpassing the degradation rates of inverse opal TiO\({}_{2}\) (88%), P25 (77%), and bulk TiO\({}_{2}\) (60%) [183].
## 2 Study Area and Data Processing
### Factor Selection Based on Multicollinearity Diagnosis  
The initially selected landslide factors may exhibit statistical linear correlations, potentially impacting the accuracy of the model results. A collinearity diagnosis was performed on the evaluation factors specified in Section 3.2 using IBM SPSS Statistics 27 to mitigate these errors. Factors with a tolerance more significant than 0.1 and a Variance Inflation Factor (VIF) less than 10 indicate weak collinearity between factors. The diagnosis results are presented in Table 2, revealing that the profile curvature and terrain roughness do not meet these criteria, indicating strong collinearity. Consequently, these factors were excluded from further analysis.  
Figure 3: Group of landslide evaluation factors: (**a**) elevation; (**b**) slope; (**c**) aspect; (**d**) plane curvature; (**e**) profile curvature; (**f**) TRI; (**g**) NDVI; (**h**) stream density; (**i**) TWI; (**j**) NDWI; (**k**) land cover type; (**l**) fault density; (**m**) distance to fault; (**n**) road density; (**o**) rainfall.
# Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
## 2 Materials and Methods
### Random Forests Algorithm  
Random forests, which were proposed by [PERSON] [28], is a nonparametric and tree-based ensemble technique [29; 30]. Different from traditional statistical methods, random forests contain many easy-to-interpret decision trees models instead of parametric models. By integrating the analysis results of decision trees models, a more comprehensive prediction model can be concluded. The main objective of this study is to predict the shear strength of RFM while only discussing the regression mode. The random forests regression is a non-parametric regression method that consists of a set of K trees {T\({}_{1}\)(X), T\({}_{2}\)(X),..., T\({}_{K}\)(X)}, where X = {x1, x2,..., x\(\beta\)}, is a \(\beta\)-dimension input vector that forms a forest. The ensemble produces P outputs that correspond to each tree Y\({}_{\text{p}}\) (p=1, 2,..., P). The final output is obtained by calculating the average of all tree predictions. The training procedure is adopted as follows [28; 29; 30; 31; 32]:  
1. From the available dataset, draw a bootstrap sample i.e., a randomly selected sample with replacement.
2. (Evolve a tree using the bootstrap sample with the following modifications: at each node choose the best split among a randomly selected subset of _mtry_ (the number of different predictors tested at each node) descriptors. Herein, _mtry_ acts as an essential tuning parameter in the Random Forests algorithm. The tree is grown to the maximum size and not pruned back.
3. Step (b) is repeated until the user-defined number of trees (_utree_) are grown based on a bootstrap sample of the observations.  
For regression, random forests build the number of regression trees K and average the results. The final predicted values are obtained by the aggregation of the results of all individual trees [28]. After \(k\) trees {T\({}_{k}\)(x)} are grown, the random forests regression predictor is described by the following equation:  
\[f(x)=[\sum_{k=1}^{K}T_{k}(x)]/K. \tag{3}\]  
For each random forests regression tree construction, a new training set (bootstrap samples) is established with replacement from the original training set. Therefore, each time a regression tree is constructed using a randomized drawn training sample from the original dataset. The out-of-bag (OOB) sample is utilized to examine its accuracy [28; 32].  
\[GI(t_{X(xi)})=1-\sum_{j=1}^{m}f(t_{X(xi)},j)^{2}. \tag{4}\]The inherent validation features enhance the tree robustness of the random forests when utilizing independent test data. Random forests have also been shown to be a viable method for classification and regression, and can be applied to a variety of mining and geotechnical engineering problems, such as predicting pillar stability [31] and rockburst [21] in hard rock, ground settlements induced by tunnels [30].
## 4 Upscaling to RC Beams
The localization result at different moments during the test is shown in Figure 16. The localized AE events are plotted on a picture taken when the crack measurements were performed. The measured crack widths are shown on top of each picture. Please note that the crack is exaggerated in the picture to make its position clearer. On day 5, initial events were localized mainly at the left side of the beam. However, no crack was observed by visual inspection of the beam. On day 11, a crack could be seen at the left side of the beam as predicted by the AE results. Eight events were observed close to sensors 2 and 4; however, the crack did not reach this part of the beam yet. On day 21, the crack grew towards sensors 2 and 4. As the test continued, more events could be observed. From this time lapse, it is observed that the AE technique can predict where the damage will occur before the damage can be observed by a visual inspection. The technique can therefore give an earlier warning than a visual inspection, which was also observed on scale 2. Figure 14: Top view of the unfiltered localized AE events of Beam-1 with indication of the crack that was visible on the surface. Figure 15: Top view of the localization results of Beam-1 with indication of the error ellipses and crack that was visible on the surface. The unfiltered and filtered results of Beam-2 are respectively shown in Figures 17 and 18. The unfiltered results show many reflections at the position of the sensors. Moreover, many events are localized outside the sensor array. The improved result is shown in Figure 18. The same conclusions can be made as for Beam-1. Please note that more events could be localized by 4 sensors as the horizontal distance between the sensors is less, namely 500 mm instead of 750 mm. In general, the localization error will be larger for the 2D setup of the beams than the 3D setup of scales 1 and 2. For scale 3, the sensors are placed on the same height as the rebar. Therefore, signals originating from corrosion and initial micro-cracking may be localized well. However, cracking of the concrete cover will start at the level of the rebar, but will grow towards the outside. As this is a 3D phenomenon monitored with a 2D setup, this will result in larger errors. Moreover, as the crack grows, a straight propagation path will no longer be valid as the concrete becomes more heterogeneous for wave propagation.
## 3 Results
### Assessment of Process Efficiency: Linking Physico-Chemical Indicators with Process Performance
#### 3.1.4 Comparative Total Solids Values  
Figure 5 presents the results of total solids (TS) after the ECZ and ECZ-MAG processes at different mixing speeds of 250 and 350 rpm, categorized by contact time. Results show that TS values varied depending on the experimental conditions but were significantly lower than the initial value (the initial TS value was 3.08 g/L), which can be attributed to the effective removal and binding of pollutants during the treatment. However, the data also indicate that the magnetic field contributed to a noticeable reduction in TS only in the 10 min processes at both mixing speeds, where TS values were lower compared to experiments without the magnet. This suggests that in the early stages of treatment, the magnetic field may enhance pollutant destabilization and aggregation, leading to improved removal of suspended solids.  
In contrast, for longer treatment durations (20 and 30 min), the magnetic field generally did not show a positive effect on TS reduction. This could be because, over extended processing times, other factors such as sedimentation, flocculation, and natural coagulation dominate the total solids removal and may overshadow any additional impact of the magnetic field.  
Figure 5: Comparison of initial and post-treatment total solids (TS) in ECZ and ECZ-MAG processes at mixing speeds of 250 and 350 rpm for (**a**) 10 min, (**b**) 20 min, and (**c**) 30 min. Note: standard deviation from the average value for TS is in the range \(\pm\)(0.04–0.10) g/L.