esa-sceva/satcom-chunk-collection huggingface dataset:
# Threatening URDU Language Detection from Tweets Using Machine Learning
## 4 Experiments and Results
### Discussion  
Threatening text detection has become an important research area owing to the wide use of social media platforms like Twitter. Often, the use of abusive, offensive, and racist language offends different individuals, groups, and communities. Timely detection of threatening text can reduce the chances of this event. As a result, research on offensive and threatening text detection is carried out in different languages, particularly English, German, Danish, Arabic, etc. Unlike these high-resource languages, low-resource languages like Urdu do not have this luxury, and the work for threatening language detection is very limited. Only a few works are available for threatening text detection in the Urdu language, such as [12; 24]. In this study, we enhance the performance of this task by utilizing a benchmark dataset compiled by [PERSON] et al. [12].  
Contrary to existing studies, which utilize the features from preprocessed text alone, this study uses verbs from the preprocessed text as features. These features are combined with raw features from preprocessed text to make a single feature set on which machine learning models are trained. First, preprocessed text was converted into a feature vector, and then, using TF-IDF and BoW, verbs were extracted from the preprocessed text and converted into a feature vector. Besides using standalone machine learning models, a stacking model is proposed that uses ET and BNB as the based learners while LR is used as the meta learner. Moreover, deep learning models CNN, LSTM, GRU, and FCN are also utilized. Experimental results suggest that the performance of machine learning models is better than deep learning models. Due to small-sized datasets, deep learning models do not obtain a good fit and thus perform poorly. The proposed stacking model outperforms using bi-gram features with TF-IDF. It performs better than the benchmark study as well.
# The Use of Ibuprofen Arginate in Pain Management Following Third Molar Surgery--A Scoping Review
###### Abstract  
Background: The management of pain and inflammation after third molar extraction is essential for patient comfort and recovery. While conventional ibuprofen is widely used for pain relief, ibuprofen arginate, a formulation aimed at faster absorption, has shown potential for faster onset and enhanced efficacy. The aim of the present scoping review was to assess current evidence on the effectiveness of ibuprofen arginate in reducing pain and inflammation following third molar extraction compared to conventional ibuprofen. Methods: A comprehensive literature search was conducted in the MEDLINE database for studies published between 2002 and 2024, focusing on ibuprofen arginate's impact on postoperative sequelae after third molar extractions. The studies included randomized controlled trials, cohort studies, and case-control studies in English. Results: Four studies, with a combined sample of 1245 patients, met the inclusion criteria. The findings suggest that ibuprofen arginate (200/400 mg) offers a faster onset of pain relief, with significant effects noticeable within 1-2 h. At six hours post administration, pain control was found to be similar between ibuprofen arginate and conventional ibuprofen. Additionally, a study found that ibuprofen arginate (600 mg) reduced postoperative swelling more effectively, although its impact on trismus was limited. Conclusions: Ibuprofen arginate seems to offer quicker pain relief and better control of swelling after third molar extractions compared to regular ibuprofen, making it a promising option for faster recovery. However, more studies are needed to fully understand its benefits and potential uses.  
ibuprofen arginate; conventional ibuprofen; third molar surgery; inflammation
## Open, Laparoscopic, and Robotic Surgeries
### MI Laparoscopic and Robotic Surgeries  
Laparoscopic surgery uses some slight cuts on the body surface, routinely of length in the range of cm. Across these little openings, dedicated surgical mechanisms and a miniature camera called the laparoscope are introduced. This technology allows, as mentioned before, different benefits related to MI incisions and enlarged laparoscope vision of the operation zone. They, respectively, help reduce pain, bleeding, healing time, and visible scars, as well as precise and manageable engagements. Laparoscopy is very commonly used in abdominal surgeries and also as a diagnostic tool [27]. Conversely, a laparoscopy procedure might be incompatible with all cases concerning patients or surgery types [28] that need, in general, dedicated tuition and instrumentation in specialized healthcare services. Actually, specific procedures that are as intricate as neurosurgery or wide-ranging might yet necessitate open technique or more sophisticated robot-assisted laparoscopic techniques [29]. Note that the difference between laparoscopy and the robotized option pertain mainly to the medical staff regarding surgical dexterity, ergonomics, and ease of execution.  
When it comes to robotic surgery, the goal is generally not to take the place of the surgeon but to increase their ability to care for the patient. The robot is therefore a surgical device, which is managed by computer, where its control is commonly mutual between the surgeon and the computer. Medical robots are therefore often referred to as surgical aides [30]. Surgical robots involve different practiced or prospective categories depending on their concept and corresponding appropriate types of surgery [31; 32]. The first brand is planned remotely by the surgeon and involves laparoscopic operation, robotic-supported suturing, and tissue handling [29]. The second is controlled through staff supervision, which reflects better abilities contrasted to the last one and can be used for surgical removals (e.g., hysterectomy, prostatectomy, or cholecystectomy) [33; 34]. The third category is the joint control of surgeon skills and robot capability and can be used in surgeries such as retinal, sinus, or orthopedic [35; 36]. The fourth involves more autonomous roboticsadministered by staff and could be used, e.g., for microsurgery, neurosurgery, tumor ablation, anastomosis [37, 38]. The last category involves an entirely autonomous surgical robot executing complex surgeries devoid of direct staff interaction (but still surveyed) and could be used, e.g., for biopsies, transplantation, intricate reconstructive surgeries, or microsurgery in sensitive bodily zones [39, 40].
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
## 2 Materials and Methods
### Parametric Study  
The value of the gap length (\(k1\) in Figure 1) in the ground plane of the patch antenna directly affects the reflection coefficient, \(S_{11}\). A parameter sweep from \(k_{1}\)\(=\) 3 mm to \(k_{1}\)\(=\) 7 mm in steps of \(\Delta k_{1}\)\(=\) 1 mm was performed to find the best value, considering the design goals. Figure 7 shows the \(|S_{11}|\) results for different gap lengths on the antenna configuration with both diodes on. At \(k_{1}\)\(=\) 5 mm, the antenna has a reflection coefficient with a WLAN and a low-UWB band with the largest bandwidth.  
Figure 6: Peak gain of the suggested patch when both diodes are turned on.  
Figure 7: Reflection coefficients of the proposed antenna for the different values of parameter \(k_{1}\).  
For values of \(k_{1}\) decreasing and increasing away from \(k_{1}=5\) mm, the position and bandwidth of the bands change. In both cases, the notch band is shifted to larger frequencies, and the bandwidth of both bands of the antenna decrease. The impedance of the antenna is sensitive to the length of the gap, and changing its value leads to a mismatch in the impedance of the antenna.  
Overall, the numerical experiment shows that the gap length in the ground plane of the antenna design can have a significant impact on its frequency response and bandwidth. Designers must carefully optimize the gap length to achieve the desired performance characteristics of the antenna.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 3 Proposed SIEM Framework
### Feature Processing Module  
The feature processing module attempts to address integrating security alert logs of varying log formats from different devices. The CEF [44] was introduced by ArcSight and is now part of HPE. The CEF has been widely adopted; however, many IDSs continue to use unique log formats. The proposed data unification component resolves this issue by parsing alert messages and converting them into standard JavaScript object notation (JSON) objects [52]. By consolidating data from different vendors into a single, centralized view, a comprehensive and precise depiction of the network's security status can be obtained. In Appendix A.1, we present a sample alert log in the CEF format and demonstrate how it is parsed and converted into a structured JSON object.  
The JSON objects generated by the data unification component contain crucial event information, e.g., event descriptions, unique identifiers, the IP addresses involved in the communication, event impacts, the URLs of risky Internet resources, and the hash values of downloaded files. These attributes are categorized into numerical, categorical, and signature types, with the most relevant attributes selected based on their descriptive and generalizable nature to represent the corresponding event. Each alert message in the log file is transformed into a numerical vector using one-hot encoding, which converts categorical features into binary attributes.  
To ensure accurate labeling of critical alerts, the data are initially examined manually by security personnel in the SOC. However, to mitigate the risk of misjudgment and alert fatigue, AI experts review the labeled data using information visualization tools to validate the accuracy of the label information. Additional details about the data used in the evaluation are provided in Section 5.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 4 Experimental Results
### Phase 1: Evaluating Gender Bias in Different CNN-Based Face Presentation Attacks Detection Methods
#### 4.1.2 Exploring Gender Bias Using VGG16
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**VGG16**} & \multicolumn{2}{c}{**DB-VAE**} \\ \cline{2-5}  & **Males** & **Females** & **Males** & **Females** \\ \hline
**Subject \#** & 20 & 20 & 20 & 20 \\
**Videos/Subject** & 3 live, 4 spoof & 3 live, 4 spoof & 3 live, 4 spoof & 3 live, 4 spoof \\
**Testing Accuracy** & 0.98 & 0.98 & 0.995 & 0.989 \\
**EER** & 0.024 & 0.022 & 0.003 & 0.011 \\
**FAR** & 0.009 & 0.026 & 0.003 & 0.016 \\
**FRR** & 0.031 & 0.011 & 0.006 & 0.010 \\
**HTER** & 0.019 & 0.018 & 0.005 & 0.012 \\
**APCER** & 0.041 & 0.013 & 0.007 & 0.008 \\
**BPCER** & 0.031 & 0.011 & 0.006 & 0.008 \\
**ACER** & 0.036 & 0.012 & 0.006 & 0.008 \\
**AUC** & 0.995 & 0.998 & 0.999 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for VGG16’s experiment and DB-VAE experiment. Figure 14: Evaluation of probabilities of spoof face detection per gender. Figure 15: Confusion matrix and classification reports for phase 2. Figure 15 shows that males have images that are more than double the number of females' FP images. It also shows that males have less FN samples than females. In addition, the bar chart seen in Figure 14 shows that the probabilities of detecting females' spoof faces using VGG16 are higher than that of males. In experiment two, the DB-VAE [9] was built, trained, and used to mitigate the bias in detecting spoof faces. As mentioned previously the probabilities of detecting spoof in females were higher than males. In experiment 1, the results of evaluation of probabilities of detecting spoof faces for males and females were 96.2 and 98.4 respectively. After applying the DB-VAE those probabilities were increased for both genders. The rate of probability distribution for males increased to 98.5 and for females to 98.6.
# Profiles of Volatile and Phenolic Compounds as Markers of Ripening Stage in Candonga Strawberries
## 2 Materials and Methods
### Analysis of Polyphenols Compounds  
6.1 Reversed Phase-High Performance Liquid Chromatographic-Diode Array Detector (RP-HPLC-DAD) Semi-Quantitative Determination of Polyphenols  
Strawberry methanolic (80%, \(v/v\)) extracts, prepared as described above (Par. 2.5), were ten-fold diluted with aqueous 0.1% (\(v/v\)) trifluoroacetic acid (TFA) and 100 uL of the diluted sample was separated using a modular HP 1100 chromatographer (Agilent Technologies, Paolo Alto, CA, USA) equipped with a 250 \(\times\) 2.0 mm i.d. C18 reversed-phase column, 4 mm particle diameter (Jupiter Phenomenex, Torrance, CA, USA) held at 37 \({}^{\circ}\)C in a thermostatic oven. HPLC runs were performed at a constant flow rate of 0.2 mL/min applying the following gradient of solvent B: isocratic elution at 5% B for 5 min, 5-60% linear gradient of B for 5-65 min and 60-100% B at 65-70 min. Fluent A and B were 0.1% TFA in HPLC-grade water and 0.1% TFA in acetonitrile, respectively. Samples were run in triplicate and monitored at wavelengths \(\lambda\) = 520, 360, 320 and 280 nm using a diode array detector (DAD), also acquiring a UV-Vis spectrum every second in the 200-700 nm range. A home-prepared multicomponent standard solution containing 15 (poly)phenols among which were gallic acid, \(p\)-coumaric acid, quercetin 3-\(O\)-glucose, rutin, EA and quercetin and kaempfer aglycones (all from Sigma-Aldrich, St. Louis, MI, USA), was used to confirm or exclude the assignment of some phenolic compounds. Flavonoids were semi-quantified by plotting the area of peaks integrated at 360 nm on an external calibration curve built with standard rutin at a known concentration in the 0.10-5.00 ug/mL range (R\({}^{2}\) = 0.99). Antocyanins and \(p\)-coumaryl-hexoside were semi-quantified based on calibration curves built with cyanidin-3-\(O\)-glucose and \(p\)-coumaric acid with absorbance at 520 and 320 nm, respectively. Data were processed using the ChemStation software(version A.10) purchased with the chromatograph. Analyses were carried out in triplicate and peak area values were averaged.
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 2 Materials and Methods
### Marker-Less Reconstruction of rpoC Mutation in BW25113  
In order to construct a marker-less _rpoC_ K370_A396 dup mutation in the wild-type BW25113 strain (F-, \(\Delta\)(_araD-araB)567_, _lacZ4787(del):rrnB-3_, \(\lambda\)-, rph-1_, \(\Delta\)(_rhoD-raB)568_, _hsdR514_), an _argE744:kan_ cassette, which resides 30,503 bp away from the _rpoC_ gene in the chromosome, from JW3929 of the Keio collection [25] was transduced via P1 transduction [26] into the evolved osmotolerant mutant G3 [24] containing the _rpoC_ K370_A396 dup mutation. The transductants with _\(\Delta\)argE744:kan_ cassette were selected on LB agar plate with 10 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\) kanamycin. The kanamycin-resistant transducant containing the mutated _rpoC_ allele was verified by colony PCR. The primers used for the colony PCR verification are: GAA ACC AAC TCC GAA ACC AA (forward) and AGT ACC GGT TCA AAT GCC TG (reverse). The transducant containing both the _\(\Delta\)argE744:kan_ cassette and the mutated _rpoC_ allele was named EJW1. The _\(\Delta\)argE744:kan_-_rpoC_ K370_A396 dup construct from EJW1 was then transduced into BW25113 selected for kanamycin resistance, and the same colony PCR screening procedure was used to isolate transducant EJW2, which contains both _\(\Delta\)argE744:kan_ and _rpoC_ K370_A396 dup. Then EJW2 was made arginine prototrophic (arg+) via P1 transduction with the wild-type _argE_ allele from BW25113 and selected on M9 minimal agar plate for arginine prototroph. The arg+ colonies that contain the _rpoC_ K370_A396 dup mutation was verified using the same colony PCR screening procedure. The resulting reconstructed mutant EJW3 therefore contains only the _rpoC_ mutation from mutant G3 without any added markers. Since the original G3 mutant contained _\(\Delta\)trpB769:kan_ deletion, EJW3 was transduced with _\(\Delta\)trpB769:kan_ cassette from JW1253 of the Keio collection and selected on an LB agar plate with 10 \(\upmu\)g mL\({}^{-1}\) kanamycincin to create a tryptophan auxotroph, EJW4, similar to the one in the original G3 mutant. The same method was used to introduce this _rpoC_ mutation in strain MG1655 (F-, \(\lambda\)-, _rpl-1_), and the resulting MG1655 _rpoC_ mutant was named EYG1. All strains used in this study are listed in Table 1.
# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 2 Related work
### OHLC Time Series Representing and Encoding for Sports Matches  
CNNs can retrieve implicit patterns from knowledge graphs and time series for classification. Therefore, we feed the matching scores and betting odds of each match into the CNN via candlestick representation with time series.  
The sports gaming market shares many similar characteristics with the financial market [48]. Odds in the betting market, which are comparable to commodity prices in the financial market, are implicit representations of various information, including past performance of players and teams, a mix of immediate and asynchronous information, and various components of sports fan psychology. The efficient market hypothesis for commodity prices in the financial market is also observed in the gaming market [49]. Therefore, we propose that the sports competition behavior is applicable to the technical analysis charts of the stock market. Historical data from the betting market, including odds and match scores, can be manipulated and used to chart candlesticks and identify matching characteristics [50; 51]. These data can also be utilized in machine learning modeling to develop predictive models [20].  
GAF is a transformation method based on a polar coordinate system that enables the visual interpretation of 1D time series by converting them into a 2D image. The original time series is represented in a polar coordinate system by encoding its values as an angular cosine, with the time stamp as the radius, while retaining temporal dependence. GAF is implemented in two forms, namely, GASF and GADF, which differ in their use of the angular perspective to consider the trigonometric sum/difference between each point and identify the temporal correlation across different time intervals. The conversion results of these GAFs do not greatly differ, and both of these approaches have been widely used in various fields to deal with time series data.  
GADF and GASF have been applied to map the measured electromagnetic interference time signals to an image for electromagnetic interference discharge source classification [52]. On the one hand, GADF has been used to transform ECG time series into images to extract sparse high-dimensional features that perform well in linear classifiers for detecting myocardial infarction [53]. On the other hand, GASF has been used to convert data from multiple wearable inertial measurement units into images to classify load-carrying modes in construction tasks [54]. For the OHLC time series of stock prices, GAF has been adopted as the image encoding method for image-based classification with CNN [55].  
We propose using a candlestick chart to represent the performance of each team in a series of sporting matches. The OHLC time series, which convey the candlesticks representation of sports matches, are encoded into images by using GAF such that the visualization features can be easily recognized for CNN to learn the implied patterns.
## 6 Calculation Method of Tunnel Face Support Pressure Based on Seepage Control  
### Determination of Target Water Pressure at Tunnel Face  
According to Equation (3), the seepage at the tunnel face is primarily controlled by the water head, tunnel diameter, water pressure at the tunnel face, and permeability coefficient. Since changing the tunnel diameter during construction is difficult, there are mainly three methods to control seepage at the tunnel face. The first method is dewatering, but this approach may significantly disturb the surrounding hydrogeology and is generally not commonly used. The second method is to control the water pressure at the tunnel face. For earth pressure balance tunnel boring machines, besides maintaining the stability of the soil, seepage occurrence can also be limited by increasing the water pressure at the tunnel face, and setting the tunnel face support pressure is relatively straightforward. The third method involves techniques such as grouting to reduce the permeability of the strata ahead of the tunnel face, but grouting processes are complex and may affect construction efficiency. This paper mainly considers controlling seepage by increasing the water pressure at the tunnel face.  
Taking \(h_{\text{nr}}=15\) m as an example, the influence of \(\eta\) on seepage at different permeability coefficients is analyzed, as detailed in Figure 15. Assuming 500 m\({}^{3}\)/d as the benchmark for controlling seepage, when the permeability coefficient is small (\(k\leq 10^{-6}\) m/s), the water pressure at the tunnel face can be set to 0. However, when the permeability coefficient is large (\(k\geq 10^{-4}\) m/s), the water pressure at the tunnel face needs to approach 1 to meet the seepage control requirements. Therefore, for different permeability coefficients of the strata, corresponding water pressure coefficients at the tunnel face need to be employed, which are expressed as follows:  
Figure 14: Failure mechanism shapes under different \(\eta\) (unit: m).  
It is important to note that the seepage flow rate \(Q\) in Equation (3) is in units of m\({}^{3}\)/h. If the computed value of \(\eta\) is less than 0, it should be taken as 0. From Equation (13), it is evident that \(\eta\) is mainly influenced by \(Q\), \(k\), and \(h_{w}\).
# Deep Learning of GNSS Acquisition
## 3 Deep Learning Method for GNSS Acquisition  
In this work, the goal is to create a neural network model that is capable of recognizing the presence/absence of satellite signals from CAF maps. To that aim, we use as inputs the CAF evaluated at the delay/Doppler grid, which can be regarded as images from the machine learning perspective. Such images (refer to Figure 1 for an exemplary situation) have certain characteristics that can be used to determine whether the signal is present or not, namely: \((i)\) in the absence of signal from a specific satellite, the image should be composed of random values (theory telling that the CAF would be exponentially distributed in that case); and \((ii)\) in the presence of a satellite, a peak should emerge from the random noise floor. This knowledge can be used to train a data-driven model (e.g., a neural network of some sort), such that a classifier can be used which learns to discriminate between \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), the hypotheses described earlier in Section 2.  
The framework presented in this work is independent of the particular NN architecture, although a Convolution Neural Network (CNN) is used without loss of generality. CNNs are very popular within the computer vision community thanks to their ability to capture complex nonlinear phenomena at the expense of larger complexity compared with multilayer perceptrons (or MLPs). The CNN model is discussed in this section after a brief overview of how the classifier is built following a probabilistic approach. The NN is used in order to provide Bayesian estimates of the hypotheses' probabilities given the observed data.