esa-sceva/satcom-chunk-collection huggingface dataset:
# Bottlenose Dolphins' Clicks Comply with Three Laws of Efficient Communication
## 3 Results  
Overall, we identified 3618 clicks organised in 98 sequences. The clicks were produced in the broadband frequency range of \(25\div 125\) kHz, which is consistent with previous reports [26; 27; 28]. The length of sequences varied between 6 and 257 clicks (_median_ = 23, _IQR_ = 14 \(\div\) 48), and their duration varied within the range of \(178\div 9914\) msec (_median_ = 992, _IQR_ = 615 \(\div\) 1656). The duration of individual clicks varied in the range of \(0.37\div 490\) msec (_median_ = 36, _IQR_ = 27 \(\div\) 47).  
The clustering algorithm applied to the array of amplitude vector representations of individual clicks converged after 543 iterations, yielding 848 clusters or click categories. The size of the resulting clusters varied between 1 and 120 clicks. A linear regression mapping the log-rank of the signal against the log frequency of occurrence (=log size of the cluster) showed a slope of \(-0.94\), indicating that the distribution of clicks in our tested dolphins' repertoire is non-random in accord with [PERSON]'s rank-frequency law. For comparison, we also generated a matrix of pseudo-random values within the numerical range of the actual amplitudes of the clicks in our database. We ran the same categorising algorithm, and the results indicated that most categories had similar sizes (frequencies of occurrence), as expected. The results of both cases are shown in Figure 3.  
Figure 4 shows a probability density plot of sequence lengths regarding the number of pulses across the entire dataset, the fit curves, and the corresponding cumulative distribution (CDF) functions. We found that the distribution of sequence lengths indeed receives a good fit by the family of the generalised gamma distributions, particularly gamma (K-S goodness-of-fit test: \(D\) = 0.118, \(p\) = 0.1321), Weibull (K-S: \(D\) = 0.10248, \(p\) = 0.2548), and log-normal (K-S: \(D\) = 0.072666, \(p\) = 0.6788) distributions. The K-S criterion and the CDF visual inspection suggest that our click data best fit the log-normal distribution.  
Figure 4: The distribution density of sequence lengths was calculated by several pulses in the analysed corpus (**left**) and the respective cumulative distribution function (**right**), along with the fit curves.  
Figure 3: Regression of log (rank) versus log (frequency of occurrence) and lines of best fit for the click repertoires of adult dolphins.  
Finally, we found that sequence length had a statistically significant (model full vs. null: \(\chi^{2}(1)\) = 65.79, \(p\) < 0.001) and negative effect on the click duration (beta= \(-\)1.03 \(\times\) 10\({}^{-4}\), 95% CI [\(-\)1.28 \(\times\) 10\({}^{-4}\), \(-\)7.85 \(\times\) 10\({}^{-5}\)], \(t\)(3448) = \(-\)8.18, \(p\) < 0.001). Regarding the untransformed dependent variable, increasing the sequence length by one click leads to a decrease in individual click duration by about 0.4 msec.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 3 Spectrum Efficiency Analysis
### Outage Probability and Achievable Rate of the D2D Link  
**Proposition 1**.: _Given the target SINR \(\tilde{\gamma}_{d}\) for the D2D link, the outage probability of the D2D link can be obtained as_  
\[\mathbb{P}_{d}^{\mathrm{out}}=\Pr\left(\gamma_{\mathrm{d}}<\tilde{\gamma}_{ \mathrm{d}}\right)=1-\frac{\exp\left(-\lambda_{d}\tilde{\gamma}_{d}\sigma_{d}^ {2}\right)}{\left(\lambda_{d}\tilde{\gamma}_{d}b_{d}+1\right)^{a_{d}}}, \tag{13}\]  
_where_  
\[\lambda_{d}=\frac{1}{P_{\mathrm{d}}a_{d}},\,a_{d}=\frac{\left(\sum_{k=1}^{K} \alpha_{ck}\right)^{2}}{\sum_{k=1}^{K}\alpha_{ck}^{2}},\,\text{and}\,\,b_{d}= \frac{\sum_{k=1}^{K}\alpha_{ck}^{2}}{\sum_{k=1}^{K}\alpha_{ck}}.\]  
**Proposition 2**.: _The average achievable rate of the D2D link can be approximated as_  
\[\mathcal{R}_{d}=\frac{\mathcal{J}\left(\bar{u}_{d},\frac{\bar{b}_{d}}{\sigma_{d}^{ 2}}\right)}{\Gamma(\bar{a}_{d})\ln(2)}-\frac{\mathcal{J}\left(a_{d},\frac{P_{ \mathrm{c}}\bar{b}_{d}}{\sigma_{d}^{2}}\right)}{\Gamma(a_{d})\ln(2)}, \tag{14}\]  
_where_  
\[\bar{a}_{d}=\frac{\left(\sum\limits_{k=1}^{K}P_{\mathrm{c}}\alpha_{ck}+P_{ \mathrm{d}}\alpha_{d}\right)^{2}}{\sum\limits_{k=1}^{K}P_{\mathrm{c}}^{2} \alpha_{ck}^{2}+P_{\mathrm{d}}^{2}\alpha_{d}^{2}}\text{ and }\bar{b}_{d}=\frac{\sum \limits_{k=1}^{K}P_{\mathrm{c}}^{2}\alpha_{ck}^{2}+P_{\mathrm{d}}^{2}\alpha_{ d}^{2}}{\sum\limits_{k=1}^{K}P_{\mathrm{c}}\alpha_{ck}+P_{\mathrm{d}}\alpha_{ d}}.\]
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 3 Technological Background
### Traffic Signal Control Importance  
Properly located, operated, and maintained traffic lights may have multiple advantages. Suitably positioned and designed traffic light systems may provide controlled movement of traffic by assigning right-of-way to conflicting traffic flows. Thus, traffic lights can increase the effective capacity of an intersection enabling conflicting traffic flows to share the same infrastructure while controlling traffic flows reduces the possibility of  
Figure 1: Schematic overview of ITS components [14].  
accidents. Traffic signals ensure continuous movement of traffic, especially when traffic control on a particular intersection is coordinated with surrounding intersections. TSC types can be differentiated as coordinated or isolated. Isolated TSC operates independently of traffic signal controllers at surrounding intersections. Contrarily, coordinated traffic signal timing is coordinated with surrounding traffic signal controllers to achieve better throughput [17]. Considering the way the TSC systems operate, they can be classified as fixed-time, fully actuated, and semi-actuated traffic signals [17]. While fixed-time traffic signals operate according to a predefined schedule, fully actuated and semi-actuated traffic signals employ detection of the presence of vehicles or pedestrians to actuate particular phases. The difference between actuated and semi-actuated is that the latter guarantees at least one phase will be served while other phases are actuated. Fully-actuated traffic signal skips a particular phase if a vehicle or pedestrian is not detected. As one of the most efficient ways to reduce traffic congestion at intersections, TSC is a crucial instrument in managing traffic flow [18]. Further study on TSC is still required because of the recent increase in the popularity of CAV technology. When it comes to improving of TSC, adequate mobility data from CVs or CAVs will reflect the dynamics of the city's traffic more precisely and give insight into the city traffic dynamics crucial for further TSC improvement.
# Enhanced Message Replication Technique for DTN Routing Protocols
## 4 Simulation Studies  
The EMRT has been tested using the Opportunistic Network Environment (ONE) simulator, which is a Java-based open-source tool for evaluating and implementing routing protocols, particularly for DTNs [34]. The performance of the EMRT was evaluated against the original forms of Spray and Wait, EBR, and the DBRP. The experiments were conducted based on the benchmark simulation settings available in the ONE simulator, as listed in Table 2.
## 3 Results and Discussion
#### 3.3.1 Carbohydrates and organic acids content of gluten free muffins (GFM)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Maltose** & **Glucose** & **Fructose** & **Citic Acid** & **Lactic Acid** & **Acetic Acid** \\ \cline{2-7}  & **mg/g f.w.** & **mg/g f.w.** & **mg/g f.w.** & **mg/g f.w.** & **mg/g f.w.** & **mg/g f.w.** \\ \hline OR PF 0 h & \(4.43\pm 0.03\)\({}^{\text{Ade}}\) & \(20.06\pm 0.31\)\({}^{\text{Ad}}\) & \(14.72\pm 0.56\)\({}^{\text{Ade}}\) & \(1.72\pm 0.05\)\({}^{\text{Aa}}\) & n.d. & n.d. \\ SP PF 0 h & \(4.15\pm 0.23\)\({}^{\text{Aod}}\) & \(20.18\pm 0.22\)\({}^{\text{Ad}}\) & \(14.17\pm 0.45\)\({}^{\text{Acd}}\) & \(1.73\pm 0.03\)\({}^{\text{Aab}}\) & n.d. & n.d. \\ OR PF 12 h & \(4.84\pm 0.23\)\({}^{\text{Be}}\) & \(19.58\pm 0.34\)\({}^{\text{Bd}}\) & \(15.39\pm 0.39\)\({}^{\text{Be}}\) & \(1.72\pm 0.11\)\({}^{\text{Aa}}\) & \(0.44\pm 0.02\)\({}^{\text{Aa}}\) & \(0.80\pm 0.02\)\({}^{\text{Ba}}\) \\ SP PF 12 h & \(3.14\pm 0.11\)\({}^{\text{Ab}}\) & \(17.67\pm 0.67\)\({}^{\text{Ab}}\) & \(12.92\pm 0.56\)\({}^{\text{Ab}}\) & \(1.86\pm 0.43\)\({}^{\text{Aac}}\) & \(0.93\pm 0.05\)\({}^{\text{Be}}\) & \(0.62\pm 0.04\)\({}^{\text{Aa}}\) \\ OR PF 24 h & \(3.82\pm 0.03\)\({}^{\text{Be}}\) & \(18.65\pm 0.55\)\({}^{\text{Be}}\) & \(13.55\pm 0.88\)\({}^{\text{Be}}\) & \(2.27\pm 0.02\)\({}^{\text{Ad}}\) & \(0.82\pm 0.07\)\({}^{\text{Ab}}\) & \(1.69\pm 0.09\)\({}^{\text{Be}}\) \\ SP PF 24 h & \(2.58\pm 0.34\)\({}^{\text{Aa}}\) & \(16.37\pm 0.77\)\({}^{\text{Aa}}\) & \(10.57\pm 0.65\)\({}^{\text{Aa}}\) & \(2.30\pm 0.27\)\({}^{\text{Ad}}\) & \(1.52\pm 0.08\)\({}^{\text{Bd}}\) & \(1.02\pm 0.09\)\({}^{\text{Aa}}\) \\ \hline \hline \end{tabular}  
* The results are expressed in mg/100 g fresh weight (f.w.). Small letters in common indicate no significant differences between OR and SP samples withdrawn at different moments; Big different letters indicate significant differences between SP and OR samples withdrawn at the same moment; n.d.: not detected.  
\end{table} 
Table 5: Carbohydrates and organic acids of gluten free muffins manufactured with OR and SP sourdoughs at different fermentation times. of lactic and acetic acids in the final baked good, formed during sourdough fermentation, has been proved to reduce insulinemic and acute glycemic responses [26]. Citric acid possesses antimicrobial activity and could be produced during fermentation of sourdough with _Lactobacillus plantarum_[45]. Furthermore, the addition of citric acid in the manufacture of bakedleaved goods could improve their sensory characteristics, including flavor [91]. In the present study, even if \(Lp\) was able to increase the amount of citric acid, the differences were not significant (\(p<0.05\)). The same trend was noticed in the muffins made with SP 24 h, compared with OR PF 24 h, whose values were 2.20 and 2.27 mg/g f.w., respectively (Table 5).
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Results
#### 4.2.1 Results on CULane  
Table 4 presents a comparison of the proposed model (GAAC) with other mainstream lane detection methods on the CULane lane detection benchmark dataset. The proposed lane expression using grid anchors allows the direct use of convolutional results as outputs, without the need for a post-processing feature module. This results in a streamlined model architecture. The model achieves detection accuracies of 76.22% and 76.41% on the ResNet18 and ResNet34 backbone models, respectively, outperforming UFLDv2 (ResNet34) by 0.5%, with some scenarios reaching optimal results. In the normal scenario, our method achieves a first-place accuracy of 93.05%, outperforming LaneATT by 0.9% and UFLDv2 by 0.5%, demonstrating excellent lane detection accuracy in standard conditions. In the curve scenario, the model achieves a first-place accuracy of 73.27%, outperforming FOLOLane by 3.8%. This improvement is attributed to the grid anchor's flexible representation, which  
does not have the fixed shape constraints of parameter curves or line-shaped anchors, making it more suitable for accurate lane localization in curved scenarios. In the arrow scenario, the model achieves a first-place accuracy of 89.01%, demonstrating that while the model performs well in standard and curved scenarios, it still maintains discriminative capability for lanes with similar features. Additionally, in the night scenario, the model achieves a second-place accuracy of 73.27%. This performance is due to the illumination challenges in nighttime scenes that cause feature loss in the images. The use of an MLP helps recover some of the lost lane features.  
As shown in Table 4, the proposed method performs relatively well in the normal, arrow, curve, and night scenarios, while its performance in other extreme environments is relatively average. This may be attributed to the fact that the model uses convolutional outputs directly as detection results, lacking the feature post-processing layers present in other models. Additionally, the dataset contains a large number of samples in normal scenarios, while extreme environment samples are relatively scarce. This imbalance in the dataset is also a contributing factor to the model's average performance in extreme environments.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Deep Learning Methods for BC Detection and Classification  
Artificial intelligence (AI) has started to play a major role in diagnosis and decision-making procedures in the medical field. Many applications are based on machine learning (ML) or deep learning (DL) techniques for classification, identification or segmentation of BC. While mammography is the most commonly used screening method, it has limitations, such as low sensitivity, particularly for women with dense breast tissue. To address this issue, researchers have explored the use of synthetic images generation to improve breast cancer detection.  
DL techniques can handle large amounts of data and automatically extract features by analyzing high-dimensional and correlated data efficiently. DL models have been utilized and evaluated for the identification and prognosis of BC, and have shown promising results. Therefore, AI in BC screening and diagnosis involves object segmentation and tumor classification (benign or malignant); it has proved to be an impressive tool for cancer treatment. The reviewed studies are divided into three categories, based on their proposed approaches--transfer learning & data augmentation, feature extraction & multiple model-based architectures and generative adversarial networks.
## 3 Analysis Method of iNterference on DME
### Calculation for P(I)
#### 3.2.1 Calculation for \(P(J)\)  
Figure 3 shows that JTIDS and DME operate within the same band. We assume that only 3 from 51 JTIDS carrier frequencies are deemed potentially harmful to the operation of a DME interrogator, from the total population of potentially harmful JTIDS pulses, we assume in the following that a percentage 60% have sufficient strength to corrupt an overlapping DME pulse [5]. According to [10], \(P(J)\) can be calculated as follows:  
\[P\left(J\right)=1-\frac{3}{51}\cdot 60\%\cdot\frac{\tau_{J}+\tau_{m,j}}{T_{J}}=1- \frac{3}{85}\cdot\frac{\tau_{J}+\tau_{m,j}}{T_{J}} \tag{4}\]  
Figure 4: The signal flowchart of DME suffering from inter-system and intra-system interference.  
where \(\tau_{j}\) is duration of pulse symbols defined in Figure 2, \(T_{j}\) is repetition period of JTIDS slot equaling to \(1/128\) s, \(\tau_{m\_i}\) is DME interrogation pulse pair duration defined as \(\tau_{m}\) shown in Figure 2.
###### Abstract  
Sentiment analysis utilizes Natural Language Processing (NLP) techniques to extract opinions from text, which is critical for businesses looking to refine strategies and better understand customer feedback. Understanding people's sentiments about products through emotional tone analysis is paramount. However, analyzing sentiment in Arabic and its dialects poses challenges due to the language's intricate morphology, right-to-left script, and nuanced emotional expressions. To address this, this study introduces the Arb-MCNN-Bi Model, which integrates the strengths of the transformer-based AraBERT (Arabic Bidirectional Encoder Representations from Transformers) model with a Multi-channel Convolutional Neural Network (MCNN) and a Bidirectional Gated Recurrent Unit (BiGRU) for Arabic sentiment analysis. AraBERT, designed specifically for Arabic, captures rich contextual information through word embeddings. These embeddings are processed by the MCNN to enhance feature extraction and by the BiGRU to retain long-term dependencies. The final output is obtained through feedforward neural networks. The study compares the proposed model with various machine learning and deep learning methods, applying advanced NLP techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), n-gram, Word2 Vec (Skip-gram), and fastText (Skip-gram). Experiments are conducted on three Arabic datasets: the Arabic Customer Reviews Dataset (ACRD), Large-scale Arabic Book Reviews (LABR), and the Hotel Arabic Reviews dataset (HARD). The Arb-MCNN-Bi model with AraBERT achieved accuracies of 96.92%, 96.68%, and 92.93% on the ACRD, HARD, and LABR datasets, respectively. These results demonstrate the model's effectiveness in analyzing Arabic text data and outperforming traditional approaches.  
machine learning; Arabic sentiment analysis; deep learning; transformer models; natural language processing; word embedding
## 3 Results and Discussion
### Impact of Washing Procedure
Furthermore, a significant poly-dispersity in grains is observed, with diameters ranging from approximately 50 to 500 nm [44]. Complementary EDX analysis, summarized in Table 5, quantified the elemental composition of the MIL-100(Fe) material. Elemental analysis indicated that the particles were predominantly composed of carbon and oxygen, with iron as a minor component, consistent with the expected composition of MIL-100(Fe) [45; 46; 47], providing insight into the elemental stoichiometry and confirming the successful incorporation of these elements within the adsorbent's structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sorbent** & **BET Surface Area (m\({}^{2}\)/g)** & **Ref.** \\ \hline MIL-100(Fe) & 1162 & Our study \\ MIL-100(Fe) & 1000–1520 & [23; 39; 40] \\ MIL-53(Al) & 1027–1255 & [41] \\ SAPO-34 zeolite & 569–654 & [42] \\ Silica gel & 556 & [43] \\ Aerogel & 1055 & [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BET surface area of obtained MIL-100(Fe) with other porous materials. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **BET Surface** \\ **Area (m\({}^{2}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **Pore Volume** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **PSD** \\ **(nm)** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} \\ \hline MF-S1-W1—5 days & 953 & 0.295 & 1.533 & 2.5021 \\ MF-S1-W1—10 days & 905 & 0.262 & 1.509 & 2.5332 \\ MF-S1-W1—15 days & 871 & 0.257 & 1.509 & 2.5935 \\ MF-S1-W2 & 1162 & 0.377 & 1.524 & 2.6750 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Surface area and pore volume of MF-S1-W1 and MF-S1-W2 samples. The comparison of the performed analyses demonstrates that the MIL-100(Fe) sample washed by protocol No. 2 is characterized by a good compromise between crystallinity, purity, and surface area, distinguishing this batch as a suitable candidate for the subsequent gas and water vapor sorption analysis.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 2 Related Work and Background
### Deep-Learning-Based Weakness Analysis  
Traditional static analysis methods have limitations in that the weakness analysis patterns must be defined manually. To overcome these limitations, deep learning models may be used. Deep learning is a machine learning method that allows artificial neural networks to train sample data. In general, this is realized by classifying program codes with and without weaknesses into vectors and classifying multiple classes that identify types of weaknesses (2018). Typical models used for preprocessing input data for code scanning include token-based and graph-based models. For example, VulDeePecker (2018) utilized symbolic representations for program slices, and [PERSON] (2018) used graph embeddings for code attribute graphs (i.e., AST, CFG, and DFG). [PERSON] et al. (2019) have researched a data type inference method based on long short-term memory by improved features for weakness analysis in binary code. SySeVR (2019) focused on overcoming the defects in the original model and obtaining program representations that could accommodate syntax and semantic information associated with weaknesses. They detected weaknesses with a more granular slice-level segmentation than VulDeepcker [11]. [PERSON] et al. [6] researched a security scanner able to use automated learning techniques based on machine learning algorithms to recognize patterns of weaknesses in source code. [PERSON] et al. [9] proposed the use of Transformers to address the problem of weakness detection. They used a traditional BERT architecture pretrained in English text to identify flaws in computer code. Moreover, a multi-class model was constructed under the assumption that each sample belonged to a unique label (CWE).  
Although deep learning has made significant progress in automation and accuracy in the diversity of weaknesses, complexity, characteristics of the programming language used, and conventional programming methods, some problems still exist. In addition, various deep learning models have been developed; however, there are performance differences based on the architecture or algorithm used. Thus, a deep learning model that can handle the source code's syntax or semantics is required.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 3 The NOMA System-Level Simulator
### Features of the System-Level Simulator
#### 3.3.3. Nineteen-Cell Scenario with Wrap around Effect  
The wrap-around effect was used to construct a 19-cell scenario. In a 19-cell situation, border cells do not share all of their hexagonal boundaries with their neighbors. This indicates that border cells are not subjected to the same amount of interference as the inner cells. As a result, to simulate the identical interference condition as the users in the central cell, two layers of cells are considered around the boundary cells. As a result, when the link performance model calculates the BER for users in the boundary cells, the results are equivalent to those for users in the center cell. Ref. [30] explains the 19-cell situation with the wrap-around effect in detail.