esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 4 Results and Discussion
### Evaluation Metrics  
The effectiveness of a proposed model was examined in this study by utilizing serval evaluation measures to evaluate how successfully the model can differentiate cyberbullying from non-cyberbullying. In this study, seven machine learning algorithms have been constructed, namely, LR, Light LGBM, SGD, RF, AdaBoost, Naive Bayes, and SVM. It is essential to review standard assessment metrics in the research community to understand the performance of conflicting models. The most widely used criteria for evaluating SM platforms (e.g., Twitter) with cyberbullying classifiers are as follows:  
Accuracy calculates the ratio of the actual detected cases to the overall cases, and it has been utilized to evaluate models of cyberbullying predictions in [60; 65; 79]. Therefore, it can be calculated as follows:  
\[\text{Accuracy}\;=\;\frac{(\text{tp}\;+\;\text{tn})}{(\text{tp}\;+\;\text{fp} \;+\;\text{tn}\;+\;\text{fn})} \tag{5}\]  
where _tp_ means true positive, _tn_ is a true negative, _fp_ denotes false positive, and _fn_ is a false negative.  
* Precision calculates the proportion of relevant tweets among true positive (_tp_) and false positive (_fp_) tweets belonging to a specific group.
* Recall calculates the ratio of retrieved relevant tweets over the total number of relevant tweets.
* F-Measure provides a way to combine precision and recall into a single measure that captures both properties.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Prediction Time** \\ \hline
1 & Logistic Regression & 90.57\% & 0.9518 & 0.9053 & 0.9280 & 0.0015 \\
2 & LGBM Classifier & 90.55\% & 0.9614 & 0.8951 & 0.9271 & 0.0515 \\
3 & SGD Classifier & 90.6\% & 0.9683 & 0.8890 & 0.9270 & 0.0016 \\
4 & Random Forest & 89.84\% & 0.9338 & 0.9134 & 0.9235 & 2.5287 \\
5 & AdaBoost Classifier & 89.30\% & 0.9616 & 0.8756 & 0.9166 & 0.1497 \\
6 & Multinomial NB & 81.39\% & 0.7952 & 0.9736 & 0.8754 & 0.0034 \\
7 & SVM & 67.13\% & 0.6713 & 1.0000 & 0.8033 & 39.9592 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Summary of Algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Parameters** & **Algorithm** & **Training/Prediction Time (s)** \\ \hline
1 & Best Training Time & Multinomial NB & 0.014 \\
2 & Worst Prediction Time & RF & 2.5287 \\
3 & Best Prediction Time & LR & 0.0015 \\
4 & Worst Prediction Time & SVM & 39.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time Complexity of Algorithms.  
The three evaluation measures listed above have been utilized to evaluate cyberbullying prediction models in [67; 79; 98; 104]. They are calculated as follows:  
\[\text{Precision}\;=\;\frac{\text{tp}}{\left(\text{tp}\;+\;\text{fp}\right)}\,, \tag{6}\]  
\[\text{Recall}\;=\;\frac{\text{tp}}{\left(\text{tp}\;+\;\text{fn}\right)}, \tag{7}\]  
\[\text{F measure}\;=\;\frac{2\;\times\;\text{precision}\;\times\;\text{recall} }{\text{precision}\;+\;\text{recall}}, \tag{8}\]
# Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry +
###### Abstract  
Improvements in radiometric calibration are needed to achieve the desired accuracy and stability of satellite-based microwave-radiometer observations intended for the production of climate data records. Linearity, stability and traceability of measurements to an SI-unit standard should be emphasized. We suggest radiometer design approaches to achieve these objectives in a microwave calibration-reference instrument. Multi-year stability would be verified by comparison to radio-occultation measurements. Data from such an instrument could be used for climate studies and also to transfer its calibration to weather-satellite instruments. With the suitable selection of an orbit, a climatology of the diurnal variation in the measured parameters could be compiled, which would reduce uncertainties in climate trends inferred from earlier microwave radiometers over past decades.  
microwave radiometer calibration; GSICS; intercalibration +
Footnote †: This paper is an extended version of our paper published in the 2019 Joint Satellite Conference, Boston, MA.
## 2 Results
### Identification of DAPs in SRPs by 2-D DIGE  
The SDS-PAGE of proteins from different SRPs showed that the gel background was clear (Figure S1). The global protein patterns on the differential in-gel electrophoresis (DIGE) gels with 1057 \(\pm\) 58 spots were stable, and 79 DAPs (termed as D1-D79), containing 49 unique proteins, were positively identified by MS (Figure 2 and Figure S2, Table S1). After treatment with ethylene for 24 h, 59 proteins were significantly up-regulated and only 12 proteins were down-regulated. Prolonging the treatment time to 48 h, less proteins were obviously changed, including 29 increased  
Figure 1: Morphological patterns of the Para rubber particles. Typical ultrastructural patterns of large rubber particles (LRPs) and small rubber particles (SRPs) were examined under TEM (**A**) and SEM (**B–D**), respectively. Under TEM, many spherical and pear-like LRPs were observed (A). Under SEM, more SRPs were detected in the total rubber particles (TRPs) collected from the fresh rubber latex (**B**). After separation, no SRPs were detected from the purified LRPs (**C**), and vice versa (**D**). The percent number (**E**) and abundances (**F**) of different rubber particles are highlighted. Bar, 1.0 \(\upmu\)m.  
and 28 decreased proteins. Although most NRB-related proteins were not significantly changed, some protein isoforms or proteeoforms were sharply induced by the addition of ethylene at 24 and/or 48 h.  
Among the identified DAPs, 23 protein isoforms were REF/SRPP family members, including 16 REF and 7 SRPP isoforms. Except for SRPP243 (spot D22), the other 22 REF/SRPP members were localized in the genome scaffold1222. All of the 23 REF/SRPP isoforms were induced after ethylene treatment for 24 h, and 9 isoforms (spots D-20, 31, 35, 50, 53, 66, 70, 73 and 74) were still induced with the elongation of treatment time, but 10 members were not significantly changed under ethylene treatment for 48 h, and even 4 isoforms (spots D-22-24 and 31) were decreased under ethylene treatment for 48 h. Three unique proteins, named REF138, REF258, and REF175, were identified from the 16 REF isoforms. Four unique proteins, named SRPP17, SRPP154, SRPP204, and SRPP243, were identified as SRPPs from seven protein isoforms (Figure 2I, Table S1).  
Figure 2: Differential in-gel electrophoresis (DIGE) analysis of SRP proteins upon ethylene stimulation. Internal standards (IS) for protein mixtures are labeled with Cy2 and shown in blue color (**A**,**E**); Cy3, green, for D24 (**B**) and D48 (**F**); Cy5, red, for E24 (**C**) and E48 (**G**). The combined images of D24, E24, and IS (**D**), and IS with D48 and E48 (**H**) are highlighted. The 79 DAPs (from D1 to D79) are numbered in different colors (red, up-regulated; green, down-regulated) and their identifications are listed in supplemental Figure S2 and Table S1. The scaffold locus and changed patterns of the rubber elongation factor (REF) and small rubber particle protein (SRPP) family members are presented (**I**).
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 6 Experimental Results  
### Training Results  
Figure 8 (bottom) shows the relations between accuracy and epochs during the training of both ENet and LNSNet models. Each line in the figure represents the average loss or accuracy of all splits during cross-validation. As the loss function decreases, the accuracy of the model increases, showing that the segmentation ability of the model is elevated. Figure 8 (top) shows variations in losses while training. The loss or cross-entropy in the figure decreases when the predictions are more accurate.  
Since there is no obvious depart between validation loss and training loss, there is no over-fitting in the training. As the first validation is conducted after the first epoch of training, the model has already improved the classification ability at that time. On the other hand, the first point of training lines shows the model accuracy right after the initialization. So, the validation curves always start from a relatively higher accuracy than the training curves. Table 2 shows accuracy, precision, and recall rates at the end of the training process for both ENet and LNSNet models.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **ENet** & **LNSNet** \\ \hline Recall & 0.9664 & 0.9442 \\ Precision & 0.9367 & 0.9290 \\ Accuracy & 0.9659 & 0.9551 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed vs ENet Accuracy in Training Data.  
Figure 7: Data augmentation: (**a**) random cropping, (**b**) random color jitter, (**c**) flipping.
## 7 Experiments and Results
### Experiment 2: Fusion of Vessel Enhancement Filters  
As described in Section 6.2.2, we have used three different segmentation pipelines that leverage different vessel enhancement filters to improve segmentation results. Specifically, the pipelines named as \(FilterAdded_{He}\), \(FilterAdded_{Ro}\), \(SegAdded_{He}\), \(SegAdded_{Ro}\), and our method (the RORPO filter with a linearly blended input image) were implemented and compared. The dice scores of different approaches performed on the test data are shown in Table 5.  
Comparison of Segmentation Pipelines  
We have compared all three segmentation pipelines and have used either 3D U-Net or 3D V-Net as the segmentation architecture. The Segmentation results are shown in Table 5. Our approach provides the best performance in this comparison. As for the specific segmentation network, the 3D V-Net leads to better results than the 3D U-Net if applied in our method.  
Figure 10: Application of individual filters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metrics** & **Equation** & **Explanation** \\ \hline Dice Score & \(\frac{2 TP}{2 TP+FP+FN}\) & The overlap of the two segmentations, divided by the total size \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+FN}\) & Correctly predicted data points out of all the data points \\ \hline Sensitivity & \(\frac{TP}{TP+FN}\) & When identifying TP and the cost of an FN is high \\ \hline Precision & \(\frac{TP}{TP+FP}\) & When identifying TP and the cost of an FP is high \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measures of segmentation quality. TP and TN stand for True Positive and True Negative (correctly segmented pixels), while FP and FN stand for False Positive and False Negative (incorrectly segmented pixels).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Communication Model  
This research considers an urban smart city environment with a vehicular ad hoc network and mRNAs installed. The network is composed of a series of mRNAs with random, non-overlapping, and overlapping neighboring coverage zones. Each RSU is powered solely by a certain quantity of energy, which is gathered and stored in a finite-capacity storage medium. We characterize each mRSU's storage device as a finite energy queue with capacity and a preserved quota for emergency messages.
# Factors Influencing the Effects of Triticale on Laying Hens' Performance: A Meta-Analysis
## 4 Discussion
### Analysis of Heterogeneity and Publication Bias  
Significant heterogeneities between studies (I\({}^{2}\) > 75%) were evident for every laying hen production parameter (EP, EW, EYC, FI, and FCR), potentially linked to the diverse triticale varieties or inclusion percentages utilized in the meta-analysis studies.  
Furthermore, the quality of studies included in this meta-analysis was assessed, revealing a publication bias in EW and EYC ([PERSON]'s test \(p<0.05\)). Publication bias occurs when research findings are distorted due to the selective publication of studies based on their characteristics or results [38]. This discrepancy in the published literature can potentially influence the outcomes of meta-analyses. To mitigate this bias, we addressed it using the trim-and-fill procedure. This approach is effective in identifying and adjusting for publication bias by removing studies contributing to asymmetrical funnel plots, thereby minimizing its impact on overall effect estimates [39]. Additionally, it involves inputting missing studies based on bias-adjusted overall estimates [40].  
The trimmed effect sizes indicated that incorporating triticale into the diet resulted in decreased egg yield and feed conversion ratio (FCR), but increased egg production, egg weight, and feed intake. However, these effects did not reach statistical significance, suggesting that dietary triticale had an impact comparable to conventional cereals on layers' production parameters. These findings align with previous research by [PERSON] et al. [20].  
Despite employing the trim-and-fill model, high heterogeneity (I\({}^{2}\) > 75%) persisted for each factor. This outcome may be attributed to the variability among layers and triticale strains, as well as environmental factors such as air movement, temperature, humidity, and the type of housing utilized across the experiments included in our meta-analysis.
## 3 Numerical Modelling and Analysis
### Lateral Force Analysis Procedure  
Eurocode 8 adopted the Lateral Force Method (LFM) for seismic design, focusing on linear static analysis of structures under orthogonal horizontal forces. The applicability depends on factors such as buildings' fundamental period and elevation regularity. LFM converts peak base shear into lateral inertia forces, aiming to balance simplicity, intuition, and seismic response prediction accuracy [4].  
Where \(m\) is the total mass of the building, \(S_{d}(T_{1})\) is the ordinate of the design spectrum, \(T_{1}\) is the structure's fundamental vibration period, and \(\lambda\) is the correction factor.  
The seismic analysis adheres to the fundamentals of [27]. Applying the values, the weight of the structure, \(m\) = 15,898.26 kN, ordinate of the design spectrum at period \(T_{1}\), \(S_{d}\) (\(T_{1}\)) = 0.92, the correction factor, \(\lambda\) = 0.85. The value of the base shear equals 12,432.44 kN which seems to be a little rough and overestimated. Therefore, we opted to apply the program-generated values from SAP2000 [28]. The results from the manual analysis can then be compared to those autogenerated by the program as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load Pat** & **Corr Fact** & **T Used** & **Coeff Used** & **Weight Used (kN)** & **Base Shear (kN)** \\ \hline EQx & 1.0 & 0.4006 & 0.6 & 15,898.26 & 9212.586 \\ EQy & 1.0 & 0.4006 & 0.6 & 15,898.26 & 9212.586 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Program-generated load patterns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Limit State** & **Definition** & **Return Period** \\ \hline \multirow{4}{*}{Damage Limitation (DL)} & Structure lightly damaged with structural & \\  & elements retaining their strength and & \\  & stiffness. Non-structural elements show & \\  & initial signs of cracking; economic losses & \\  & are minimal. No permanent drifts. & \\ \hline \multirow{4}{*}{Significant Damage (SD)} & Significant damage to the structure, & \\  & residual lateral strength, and stiffness is & \\  & evident. Damage to several & \\  & non-structural elements. Moderate & \\  & permanent drifts. & \\ \hline \multirow{4}{*}{Near Collapse (NC)} & Heavy damage to the structure, low & \\  & residual lateral strength, and stiffness. & \\  & Many non-structural elements have & \\  & collapsed. Large permanent drifts & \\  & were observed. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance requirements and compliance requirements for each limit state.  
For our study, the seismic base shear force for the principal X and Y directions were considered equal and calculated using the expression (Eurocode 1998-1 Eqn 4.5):  
\[F_{b}=S_{d}(T_{1})*m*\lambda, \tag{1}\]
## 2 Materials and Methods
### Other Analytical Methods  
For astaxanthin extraction, 1 mL of French-press-treated _H. pluvialis_ cells was vigorously mixed in a vortex mixer (Vortex 3, IKA, Germany) with 5 mL of ethyl acetate for 10 min. at room temperature. The mixture was separated by centrifugation (2000\(\times\) g, 10 min.; Combi-514R, Hanil Science Inc., Gimpo, Korea) and an astaxanthin-containing organic fraction was collected and evaporated (N-EVAPevaporator; Organomation Associates Inc., Berlin, MA, USA). After saponification reaction with 0.025 N NaOH, free (de-esterified) astaxanthin content was analyzed while using a high-performance liquid chromatography (HPLC, Agilent 1260 series, Agilent, Santa Clara, CA, USA) that was equipped with a variable wavelength detector (VWD) and 250 x 4.6 mm YMC carotenoid column (Tokyo, Japan). The detailed extraction and HPLC conditions are detailed in the literature [6; 19].  
Lipid content of the freeze-dried _H. pluvialis_ cysts was estimated by the FAME amount, according to a direct transesterification method, followed by gas chromatography (GC) [6; 19]. Briefly, for lipid extraction, 2 mL of the chloroform/methanol (2:1, v/v) was added to the algal cells (~10 mg in a 12 mL Pyrex-glass tube). For the FAME analysis, subsequently, 1 mL of chloroform containing heptadecanoic acid (C17:0) as an internal standard (500 ug/L), 1 mL of methanol, and 300 uL of H\({}_{2}\)SO\({}_{4}\) (95%) were added to the glass tube. The tube was incubated at 100 \({}^{\circ}\)C for 10 min. (HS R200 heating-block reactor, Humans, Daejeon, Korea), cooled to room temperature, and then supplemented with 1 mL of distilled water. Finally, the FAME-containing organic phase was filtered by a PVDF (polyvinyl dilluoride) syringe filter and then analyzed by GC that was equipped with a flame-ionization detector and a 0.32 mm (ID) \(\times\) 30 m HP-INNOWax capillary column (Agilent Technologies, Santa Clara, California, USA). The detailed GC conditions are available in [19].  
Carbohydrate amount was estimated according to the NREL/TP-510-42618 protocol [20]. For cell hydrolysis, approximately 300 mg of the lyophilized _H. pluvialis_ cells was mixed with 3 mL of H\({}_{2}\)SO\({}_{4}\) solution (72%) in a Pyrex-glass tube and then incubated for 60 min. at 30\({}^{\circ}\)C. Subsequently, 84 mL of purified water (Millipore Milli-Q system, Tokyo, Japan) was added and further incubated for 1 h at 121 \({}^{\circ}\)C while using an autoclave (Napco 8000-DSE, Thermo Scientific Co., Waltham, MA, USA). The carbohydrate extract was cooled down to room temperature, centrifuged at 15,000\(\times\)\(g\) for 5 min. (Combi-514R, Hanil Science Inc., Korea), and then PVDF syringe-filtered. Finally, the filtrate was analyzed using a HPLC equipped with a refractive-index (RI) detector (Agilent 1260, Agilent Co., Santa Clara, CA, USA) and Aminex HPX-87H column (300 \(\times\) 7.8 mm, Bio-Rad, Hercules, CA, USA). As an eluent, 5 mM H\({}_{2}\)SO\({}_{4}\) solution was used at a flow rate of 0.5 mL/min. The temperatures of column and RI detector were set at 60 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, respectively.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 4 Power Control Techniques
### Proposed Power Control Technique  
The proposed ANS power control technique is where the MUE will measure the interference level from the femtocell and compare it with the threshold interference level, which is the minimum required value to maintain the necessary QoS. When the interference is higher than the threshold interference, MUE will send a CQI message to the macrocell, along with the information of the femtocell. The macrocell will send back a message to the MUE and then, the MUE will forward it to the femtocell [27]. The interference threshold on \(i^{th}\) MUE can be given as:  
\[I_{i}=P_{t}\psi(R)^{-\beta} \tag{9}\]  
\[x_{k}=\ \left\{\begin{array}{ll}0,&\quad I\leq I_{threshold}\\ 1,&\quad\  
\[P_{f}=P_{macro}-PL_{MBS\to FBS}+PL_{Femto}(R_{1})+\varepsilon_{2} \tag{12}\]  
In (12), \(PL_{MBS\to FBS}\) is the macrocell-to-femtocell path loss. The line of sight (LOS) path loss is \(PL_{Femto}(R_{1})\) with the cell range \(R_{1}\). The downlink SINR for any user can be calculated using:  
\[SINR\ =\ \frac{P_{tx}^{s}PL^{s}}{\sum_{i=1}^{N}\ x_{i}I_{i}+\ \sum_{i=1}^{N}\ P_{tx}^{t}PL^{t}+P_{n}} \tag{13}\]  
where \(P_{tx}^{s}\) is the transmit power from the serving BS (either the macrocell or the femtocell) to UE; from the serving BS to the UE, the path loss is denoted as \(PL^{s}\). Similarly, \(P_{tx}^{t}\) and \(PL^{t}\) is the transmission power of the interferer BS and path loss, respectively. In (13), the total number of interferers is denoted as N and the thermal noise density is denoted as \(P_{n}\).
## 2 Bioactive Compounds Commonly Found in Green-Colored Fruits and Vegetables
### Potential Health Benefits
#### 2.1.3 Flavones: Luteolin  
General Characteristics, Structure, and Food Sources  
Luteolin, a dietary flavone, occurs naturally in the plant kingdom as pure yellow crystals and is abundant in some green-colored vegetables and fruits, such as artichoke, broccoli, cabbage, celery, green apple (skin), onion leaves, and parsley, as listed in Table 1[70]. Dietary luteolin is mostly present in its glycosylated form. Luteolin (C\({}_{15}\)H\({}_{10}\)O\({}_{6}\)) is a 3\({}^{\prime}\),5,7-tetra hydroxyflavone with a C6-C3-C6 structure and possesses two benzene rings, one oxygen containing ring, and a 2-3 carbon double bond (Table 2). The hydroxyl moieties and 2-3 double bonds are important structural features associated with its biochemical and biological activities [70; 71; 72].  
Bioavailability  
According to many studies, it is known that the oral absorption and bioavailability of flavonoids, including luteolin, is very low [73; 74]. In fact, luteolin has poor stabilization and a low absorption rate; thus, its efficacy is low because of its relatively low in vivo bioavailability. A mice study showed that the oral bioavailability of luteolin (approximately 26 \(\pm\) 6%) was higher than that of luteolin-7-O-glucoside (approximately 10 \(\pm\) 2%) [75].  
[PERSON] et al. observed that luteolin and its glycosides can be converted to glucuronides when they cross the intestinal mucosa, and that free luteolin, its conjugates and methylated conjugates can be found in rat and human plasma after administration [76]. According to the findings of [PERSON] et al., luteolin was passively absorbed in the small intestine of rats and was more efficiently absorbed in the duodenum and jejunum than in the colon and ileum, and luteolin in peanut hull extract was absorbed more efficiently than pure luteolin [77].  
Luteolin is a widely used flavone. According to preclinical studies, the ingestion of luteolin alone has been reported to have various biological and pharmacological antioxidant, anti-inflammatory, anti-diabetic, anti-cancer, and cardioprotective properties [70; 78]. Although many previous studies of luteolin have focused on cancer and inflammation, reports of its cardioprotective effects have steadily increased since the 1950s [79]. Accumulated evidence suggests that luteolin plays a pivotal role in regulating glucose production and adipogenesis, and thus may act as a modulator of insulin resistance and diabetes. A study of obese mice treated with luteolin or its glucoside (i.e., luteolin-enriched plant extracts) improved blood glucose levels and hepatic steatosis [80]. Furthermore, luteolin exhibits anti-obesity and anti-diabetic effects, in part through altering adipose tissue physiology. The mechanism of luteolin in these potential health benefits is as follows; (1) regulated adipose tissue inflammation, (2) reduced intracellular lipid accumulation, and (3) improved insulin sensitivity and glucose utilization.