esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Vectorization Types  
The input/output of DNNs must be numerical. Calculated output neuron values (linked to separate classes/intents) can indicate how likely each predicted class/intent is. Input neurons linked to incoming text elements must be numerical in order to apply DNN classifiers (described in Section 4.2) on top of them. For this reason, word embeddings (also called word vectors) that project words into \(N\)-dimensional space are used. In our experiments, the highly recommended types of distributional word embeddings that are able to catch semantic similarities between words were chosen:  
* FastText embeddings [38] introduced by Facebook's AI Research Lab. In our experiments, separate English, Estonian, Latvian, Lithuanian and Russian fastText embedding models, cc.en.300.vec, cc.et.300.vec, cc.lv.300.vec, cc.lt.300.vec, and cc.ru.300.vec (the fastText embeddings were downloaded from [[https://fasttext.cc/docs/en/crawl-vectors.html](https://fasttext.cc/docs/en/crawl-vectors.html)]([https://fasttext.cc/docs/en/crawl-vectors.html](https://fasttext.cc/docs/en/crawl-vectors.html))), respectively, have been used. These models are trained on continuous bag-of-words (CBOW) architecture with position-weights, 300 dimensions, and character n-grams of a window size equal to 5 and 10 negatives [39]. Each fastText word embedding is a sum of sliding symbol n-gram embeddings composing that word (e.g., the 5-g embeddings _<chat_, _chat_, _hatbo_, _atbot_, _tbot_> compose the word _<chatbot_>). Due to this reason, fastText word embeddings can be created even for misspelled words; moreover, obtained vectors are close to their correct equivalents. It is especially beneficial for languages having a missing diacritics problem in non-normative texts. Despite the fact that the Estonian, Latvian, Lithuanian, and Russian languages have the missing diacritics problem in non-normative texts, datasets in this research contain only normative texts.
* BERT (bidirectional encoder representations from transformers) [40] introduced by Google AI. This neural-based technique (with multidirectional language modeling and attention mechanisms) demonstrates state-of-the-art performance on a wide range of NLP tasks, including chatbot technology. BERT embeddings are robust to disambiguation problems as homonyms are represented by different word vectors based on their context. In our experiments, the BERT service (available online: [[https://github.com/hanxiao/bert-as-service](https://github.com/hanxiao/bert-as-service)]([https://github.com/hanxiao/bert-as-service](https://github.com/hanxiao/bert-as-service))), with the base multilingual cased 12-layer, 768-hidden, 12-heads model for 104 languages (covering English, Estonian, Latvian, Lithuanian, and Russian), has been used.
## 3 Results and Discussion
### Prediction of Plant Yield  
The initial dataset comprises 420 rows and includes four variables: NC, T, D, and PY. Following this omission, the dataset underwent several transformations, yielding four distinct versions tailored for detailed comparative studies. These versions are denoted as Data 1 (382 cases), Data 2 (324 cases), Data 3 (324 cases), and Data 4 (299 cases). These processed datasets were each randomly divided, allocating 80% of the cases to a training set and the remaining 20% to a testing set. To ensure consistent distributions of the target variable, mitigate data leakage, identify biases, and promote model generalization, the practice of checking the distributions of training and test data in ML prediction was implemented. The histograms illustrate the PY frequency distributions across four datasets, revealing right-skewed patterns in all training sets. Notably, Data 2 is the most promising for predictive modeling due to its training and test set distributions aligning more closely (Figure S1).
## 3 Results and Discussion
### Relationship between Coffee Acceptability and Volatile Compounds
#### 3.5.2 Internal Preference Mapping  
Figure 2 is an internal preference map based on consumers' preference patterns, and it explained 36.3% of the variability for the first two principal components. This low variability seemed to be due to various factors. Most consumers liked RTD samples and were located in quadrants 1 and 4, whereas most volatile compounds were distributed mainly over quadrants 1 and 2. 3-methylphenol (V13) was positively related to consumers' liking as an exception. Colombia and Kenya samples were placed in quadrants 2 and 3, the opposite side of consumers' preference. Pyrazines were located on positive side of PC2 and two CS samples and FolgersCB were separated from others. From the PCA, pyridines isolated TwosomeplaceCS from all coffee samples. Aldehydes were grouped on PCA. As explained in 3.4. (Figure 1), Blending samples were displayed around the center of axis and CS samples, and Folgers samples were related to volatile compounds. This tendency explains that generally, lower concentrations of volatile compounds might positively affect consumers' liking. Coffee quality and its volatile compounds can be greatly influenced by coffee species, growing altitude, presence or absence of defects, postharvest processing such as washing and drying, roasting, and even storage conditions [72]. Moreover, synergistic effects among volatile compounds should also be considered when exploring volatile compound profiles. For example, maltol can be enhanced by vanillin and heliotropine [28]. Meanwhile, it appeared that the synergistic effect of volatile compounds existed according to the study of [PERSON] and [PERSON] [73]. 4-Hexanolide was added to jasmine tea samples, and even though it was under the threshold level, it had an impact on sweet and astringent odor perception in the descriptive analysis. Thus, further studies could be conducted linking volatile compounds and their synergistic impact on consumers or a descriptive panel's perception of coffee characteristics.
## 3 Results and Discussions
### Effect of Laser Scans on Channel Accuracy  
The number of laser scans along with depth per scan determine the final depth of the microchannel. This can be realized with different strategies such as a low number of scans with high depth per scan, a high number of scans in combination with low depth per scan or a medium depth per scan with medium laser scans. In order to study the effect of laser scans on microchannel accuracy, three different combinations of laser scans and depth per scan were tested for fabrication of microchannels with 200 \(\upmu\)m width and 200 \(\upmu\)m depth. Figure 10 shows the SEM images of the microchannel cross-section fabricated with 66 and 24 laser scans with 3 \(\upmu\)m and 8 \(\upmu\)m depth per scan respectively. The laser peak fluence was selected accordingly based up on the depth per scan. It can be seen that the geometry of the microchannel is more accurate with 3 \(\upmu\)m depth per scan and 66 total scans. Higher depth per scan and lower laser scans resulted in a high degree of taper.  
Figure 9: Effect of peak fluence on (**a**) TWE (**b**) BWE (**c**) D/S (**d**) MRR for the channels fabricated with 48 laser scans.  
The microchannel geometry and MRR was found to vary with laser scans. Figure 11 shows the effect of laser scans on TWE, BWE, and MRR. In general, lower scans resulted in more accurate microchannels as compared to higher scans this is because with a lower number of laser scans the depth per scan and MRR is higher, which results in geometrical inaccuracies. However, the TWE was found to be minimum for the 48 laser scans suggesting that there exists an optimal number of scans for which the TWE is minimum. At a lower number of laser scans, the high depth of cut and MRR causes high TWE and with higher laser scans, the interaction of the laser beam with the edge is higher resulting in increased TWE. It can be seen that 66 laser scans in combination with 3 \(\upmu\)m depth per scan resulted in microchannels with the least BWE and less taper. Figure 12 shows the SEM images of the microchannels fabricated using 3 \(\upmu\)m depth per scan strategy with a peak fluence of 126.45 J/cm\({}^{2}\). A total of 66 and 99 scans were carried out for the target depth of 200 \(\upmu\)m and 300 \(\upmu\)m respectively.  
Figure 11: _Cont._  
Figure 10: SEM images of microchannel cross-section fabricated with (**a**) 66 scans and (**b**) 24 scans.
# Maritime Over the Horizon Sensor Integration:
## 6 Statistical Analyses
Overall, it can be concluded that the presented algorithm eliminates approximately 23.3% of the received data, thus significantly reducing the false alarm rate.
## 2 Background  
### Equipment Validation Practices in Regulated Industries  
While all product manufacturers are subject to regulation, few are as strictly regulated as the manufacturers of medical devices and pharmaceutical products. Manufacturers in these sectors must comply with regulations from numerous regulatory organisations, depending on where the product is to be sold, including the European Medicines Agency (EMA) in the EU, Food and Drug Administration (FDA) in the USA and International Medical Device Regulators Forum at a global level [10]. To ensure compliance, manufacturers of medical device and pharmaceutical products validate their equipment and processes in line with the Good Manufacturing Practice (GMP) regulations, outlined in Chapter 5 of the Federal Food, Drug, and Cosmetic Act (FD&C Act) [11]. The FDA has defined validation as _\"the collection and evaluation of data, from the process design stage through commercial production,which establishes scientific evidence that a process is capable of consistently delivering quality product\"_[1].  
From a manufacturing equipment and control perspective, validation activities in regulated industries are defined by the Good Automated Manufacturing Practice 5 (GAMP 5) guidelines [12]. GAMP 5 is a set of guidelines which _\"provides a framework for the risk-based approach to computer system validation where a system is evaluated and assigned to a predefined category based on its intended use and complexity\"_[13]. It encompasses both hardware and software elements of the equipment and accounts for all stages of the equipment life-cycle, from the initial concept, through design and operation, and finally retirement of the equipment [14]. The design of equipment phases follows the V-Model process (see Figure 1) to provide traceability between the system requirements and the associated verification activities.  
Although it is a critical step to ensuring that products are correctly manufactured, the validation process is generally viewed in industry as a difficult, costly and resource intensive activity [15]. Validation typically accounts for 30-55% of the total project budget in terms of time and cost [16], and requires highly skilled engineers to analyse the system, author and execute the test scripts and finally document the results in accordance with regulatory requirements. The extensive documentation required to prove a system is validated has also been cited as an impediment to innovation in regulated settings [17], due to regulatory requirements to revalidate an entire system if changes are made to the initial design [12].
###### Abstract  
Social media like YouTube are increasingly used by students as a learning tool. The aim of this study was to examine the use of YouTube videos as a means of learning clinical procedures among dental students in Riyadh, Saudi Arabia. A cross-sectional observational study was conducted among dental students from six dental colleges in Riyadh, Saudi Arabia. Among the total of 331 dental students who responded to this survey, 93.9% (\(n=308\)) reported that they had used YouTube for dental learning. A total of 65.30% (\(n=201\)) of the respondents strongly agreed that they find videos of clinical procedures on YouTube helpful as a learning tool. A total of 54.40% (\(n=180\)) agreed that they always refer to YouTube videos to prepare for a clinical procedure that they have never done before. A total of 75.3% (\(n=232\)) reported that they most commonly watched clinical procedures related to restorative dentistry, 67.2% (\(n=207\)) for fixed prosthodotics procedures, 65.3% (\(n=201\)) for endodontic clinical procedures, and 62.3% (\(n=192\)) for removable prosthodotics videos. A total of 50.60% (\(n=156\)) strongly agreed that YouTube videos are helpful in relating theoretical knowledge with clinical knowledge. A total of 50.30% (\(n=155\)) reported that it is important to have faculty guidance regarding useful YouTube videos on dental procedures. A total of 91.90% (\(n=283\)) participants would like their dental school to post tutorials or videos for clinical procedures on YouTube. Even after increased availability of online videos, a majority of dental students felt that YouTube videos suggested by faculty were more valuable than videos identified through normal searching.  
YouTube; social media; digital learning; dental procedures; e-learning; clinical skills; dental education; online learning +
Footnote †: journal: Applied Physics Letters  
1 Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia 12 King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia 2
# ATLAS: Latest Advancements and First Observations
## 3 Results
### Space Object Observations  
The observations presented in this section resulted from the first campaign of observations performed with ATLAS in July 2023 and a second campaign during the last week of January 2024. The preferred candidate for observation was the ISS, as explained in Section 2.2. Figure 11 showcases one of the observations obtained. After applying the signal processing chain to the raw data, a streak of detections is visible. The object returned reflections from pulses 35 to 48 out of the 100 pulses emitted. Range migration is obvious given that the object moved to other range cells from pulse to pulse. Figure 12 depicts the range-Doppler map obtained for the observation in Figure 11.  
A total of 35 range detections and 5 velocity detections were obtained from the campaign. Table 3 shows the complete data for some of the observations. The rest of the observations were used to assess the performance of the system, as explained in Section 4.2.  
Figure 11: Image on the left shows the absolute value of the complex raw data recorded by the ADCs. The image on the right shows the detections obtained. The detections cluster into a streak, a common pattern when observing space objects due to the effect of range migration.  
Figure 10: Offsets obtained for azimuth and elevation after convergence in the cross-scanning procedure. The azimuth offset is 5.3 \(\pm\) 0.2\({}^{\circ}\), where the mainlobe can be identified roughly in the region between 4.9\({}^{\circ}\) and 5.7\({}^{\circ}\), the left sidelobe is visible between 3.5\({}^{\circ}\) and 4.5\({}^{\circ}\), and the right sidelobe is between 6.5\({}^{\circ}\) and 7\({}^{\circ}\) (incomplete). The elevation offset is 0.10 \(\pm\) 0.05\({}^{\circ}\), where the mainlobe can be identified roughly in the region between \(-\)0.27\({}^{\circ}\) and 0.47\({}^{\circ}\), and the right sidelobe is between 0.6\({}^{\circ}\) and 1\({}^{\circ}\) (incomplete).
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Extended Model A: Cold-Standby  
The extended model in Figure 6 employs Cold-Standby redundancy, in which a secondary computer is included in the design and remains mostly inactive. In the event of a primary machine failure, the secondary machine takes over to maintain system functionality. This redundancy was applied to both the Front-End and Back-End components of the evaluated structure.  
The token Front_ON signifies that the main machine is operational. If a failure occurs on the main machine, the Front_MTTF transition is activated and the token is transferred to Front_OFF, indicating that the main machine has been shut down. While the main machine is offline, the token for the secondary machine is transferred to the activation module of the secondary machine, Front2_ON, activating the secondary machine. When the primary machine's token returns to Front_ON, the token for the secondary machine will be transferred back to its default location via an immediate transition (T0) and then to Front2_Disable, indicating that the secondary machine has been turned off. The system is not available if the tokens for both machines are in Front_OFF and Front2_OFF.  
Figure 6: Extended model using cold-standby redundancy.  
The process described in the previous paragraph pertains to Cold-Standby, with the exception of modifying the component terminology and insertion values in MTTR and MTTF. Transitions include guard conditions to ensure proper system function, as outlined in Table 3. When the ON component of the main machine is equal to zero, the SWITCH_TIME transition is activated, enabling the transfer of the token. Upon the primary machine returning to its starting state, the immediate transition T is activated, allowing the secondary machine to perform the same.  
The availability was calculated using the formula below in the Cold-Standby redundancy model. Tokens provide availability in UP mode on the primary or secondary computer.  
\[\begin{split}\mathbf{A}=P\{((\#Front\_ON>0)OR(\#Front\_ON>0))\\ AND((\#Back\_ON>0)OR(\#Back\_ON>0))\}\end{split} \tag{3}\]
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 1 Introduction
To address these limitations, this study employs a laboratory-based approach to produce remolded fault gouge using a mixture of quartz, montmorillonite, and kaolinite. The choice and proportion of minerals for the remolded fault gouge are based on the XRD composition of natural fault gouge and host rocks collected in this research. This approach ensures high consistency and close resemblance to natural gouge conditions. Direct shear tests are conducted on 60 remolded samples with five water contents (10-30%), three cementation degrees (0%, 1%, 3%), and four vertical stresses (100-400 kPa). The influence of water content and cementation degree on the strength parameters of fault gouge is systematically analyzed, and SEM analyses further elucidate associated microstructural characteristics. Finally, an empirical shear strength prediction formula incorporating water content and cementation degree is proposed based on the Mohr-Coulomb criterion and experimental data. This study is expected to provide new insights and technical pathways for understanding the mechanical properties of fault gouge in complex geological environments, thereby contributing practically to fault stability assessment and engineering safety in disaster prevention applications.
## 4 Results and Discussion
### Data Analysis
#### 4.2.4 Soil Temperature  
As shown in Figure 15, the average values of soil temperature data were observed to be 27.6 \({}^{\circ}\)C, 27.9 \({}^{\circ}\)C, 27.4 \({}^{\circ}\)C, and 25.0 \({}^{\circ}\)C, respectively. Similar to the air temperature, the average soil temperature remained relatively stable at around 27 \({}^{\circ}\)C from June to August but decreased to 25.0 \({}^{\circ}\)C in September. The minimum soil temperatures were observed to be 23.5 \({}^{\circ}\)C, 22.5 \({}^{\circ}\)C, 19 \({}^{\circ}\)C, and 18.4 \({}^{\circ}\)C for the respective months, with September recording the lowest minimum soil temperature. On the other hand, the maximum soil temperatures were observed to be 33.4 \({}^{\circ}\)C, 34.5 \({}^{\circ}\)C, 35.9 \({}^{\circ}\)C, and 32.8 \({}^{\circ}\)C, respectively, with August recording the highest maximum soil temperature.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 5 Experiments
### Evaluation Metrics  
The generalization performance of the selected algorithms was evaluated using four common supervised learning metrics: accuracy, precision, recall, and false positive rate (FPR). In addition, as the alert filtering task has a highly imbalanced class distribution, we also used the F1-score, true negative rate (TNR), and balanced classification rate (BCR) to provide a comprehensive evaluation [72]. The definitions of these metrics are related to the following intermediate measures.  
True positive (TP) refers to the number of records correctly predicted as positive by the classifier, and false positive (FP) is the number of records incorrectly predicted as positive by the classifier. True negative (TN) is the number of records correctly predicted as negative by the classifier, and false negative (FN) is the number of records incorrectly predicted as negative by the classifier.  
Accuracy represents the percentage of test label data correctly identified by the classifier, calculated as:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{N}, \tag{6}\]  
where \(N\) is the size of the test set.  
Precision is the probability that the predicted positive records are classified correctly:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{7}\]  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Parameter** & **Classifiers** & **Physical Meaning** & **Grid Values** \\ \hline \(W\) & WSVM, IWSVM & Weight for positive class & \{1, 10, 100, 1000, 10,000\} \\ \hline \(\gamma\) & SVM, WSVM, IWSVM & Width parameter for SVM with RBF kernels & \{0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1\} \\ \hline \(C\) & SVM, WSVM, IWSVM & Penalty parameter for prediction error & \{10, 100,1000, 10,000\} \\ \hline \end{tabular}
\end{table}
Table 1: Experimental settings for parameter tuning.  
Recall is the probability that a record from the positive class is classified correctly:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{8}\]  
The FPR measures the probability that a negative record is predicted incorrectly as a positive record by the classifier:  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}}. \tag{9}\]  
The F1-score is the harmonic mean of precision and recall:  
\[\text{F1}=2\times\frac{\text{Recall}\times\text{Precision}}{\text{Recall}+ \text{Precision}}=\frac{2\times\text{TP}}{2\times\text{TP}+\text{FP}+\text{ FN}}. \tag{10}\]  
TNR is the number of negative samples classified correctly divided by the total number of negative samples:  
\[\text{TNR}=\frac{\text{TN}}{\text{FP}+\text{TN}}. \tag{11}\]  
BCR is the average of the recall and the positive class TNR:  
\[\text{BCR}=\frac{\text{Recall}+\text{TNR}}{2}. \tag{12}\]
# A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
###### Abstract  
In this paper a novel optical vector network analyzer (OVNA) utilizing a distributed feedback semiconductor optical amplifier (DFB-SOA) is introduced. The proposed OVNA is implemented by converting the transmission response of the optical device under test (ODUT) into the electrical domain. The main principle of the OVNA is predicated on the optical carrier restoration facilitated by the wavelength-selective amplification attribute of the DFB-SOA. The implemented OVNA effectively determined the transmission spectrum of an optical filter possessing a passband of 9-GHz bandwidth, achieving a commendable resolution of 25 MHz in the measurement process. The dynamic range of the OVNA can be broadened by adjust the driven current under the DFB-SOA. Additionally, the detection range of our system can be expanded through the utilization of broadband optoelectronic devices. Furthermore, the OVNA possesses considerable potential for integration onto a single chip.  
optical vector network analyzer; bandpass response measuring; distributed feedback semiconductor optical amplifier +
Footnote †: journal: Applied