esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 2 Literature Review
CAMeLBERT, designed specifically for Arabic NLP, is capable of handling linguistic variations, including Classical Arabic (CA), Dialectal Arabic (DA), and Modern Standard Arabic (MSA). It has demonstrated great performance in MSA tasks, particularly in emotion analysis and named entity recognition. Studies indicate that when paired with DL techniques, fine-tuned CAMeLBERT models outperform other models in sentiment analysis [50], achieving high accuracy, recall, and F1-scores in named entity recognition [51; 52]. Its capacity to manage morphological and linguistic diversity also makes it suitable for dialect detection, translation, and the processing of the CA literature--an essential step in applying NLP techniques to historical texts [53]. Although BERT has gained prominence in NLP, studies [54; 55; 56] suggest that its application to Arabic text classification remains limited. However, transformer models specifically trained on extensive Arabic corpora--such as CAMeLBERT, AraBERT, and MARBERT--have shown superior performance. This growing body of research not only reveals promising avenues for addressing current challenges but also highlights the potential for expanding the efficacy of Arabic language applications through the use of specialized transformer architectures.
# Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
## 4 Analysis and Discussion
### Analysis of Aftershock and Bath's Law  
Unlike the distribution of energy and waiting time, some distributions are supposed to be stable and is not sensitive with material structure. In earthquake catalog analysis, distribution of aftershock and Bath's law holds from worldwide to local scales, and for quite different tectonic environments [32]. The number of aftershocks (AS) is described [33] as often employed for the analysis of earthquakes. This states that the number of AS decays as the power law after each mainshock (MS). We define MS as AE signals with energies E\({}_{\text{MS}}\) between 10\({}^{\text{k}}\) to 10\({}^{k+1}\) aJ, with k = 1, 2, and 3. The sequence of AS is then continued until another MS is found, which terminus the AS sequence. The results of distribution of aftershock are shown in Figure 6 with different main shock energy levels. The exponent of aftershock distribution was about \(-1\) for both tectonic and non-tectonic coal. The Bath's law states that the average ratio of the energy magnitudes of one MS and the largest aftershock, AS*, is around 1.2 [34; 35]. The ratio or'relative magnitude' is stable, and can be defined as \(<\)\(\Delta\)M\(>=\)log(E\({}_{\text{MS}}\)/E\({}_{\text{AS}^{*}}\)). Figure 7 shows the relative magnitude as a function of mainshock energy E\({}_{\text{MS}}\), and the result agrees well with [PERSON]'s law, with no significant differences between non-tectonic and tectonic coal.  
Figure 5: Probability density distribution of waiting time for (**a**) non-tectonic and (**b**) tectonic coal. The waiting time distribution of non-tectonic coal follows the typical brittle fracture’s double power law behavior, while the waiting time distribution of tectonic coal shows the single power law.  
Figure 6: Rate of aftershocks per unit time, r, as function of the time lapse to the main shock for different main shock energy levels. The black dashed line indicates the Omori’s behavior with slope \(=-1\), (**a**) for non-tectonic and (**b**) tectonic coal.
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
###### Abstract  
In the context of increasing urbanisation, the question arises as to whether urban environments can provide honeybee colonies with floral resources comparable to those available in rural areas. The present study sought to evaluate the impact of land cover on nectar collection by bees in urban and rural apiaries. To this end, changes in the mass of 10 hives located in five urban-rural site pairs were monitored over two years (2021-2022) to assess nectar yield, weight loss, and the number of foraging days. The 3 km surroundings of each apiary were analysed using Sentinel-2 satellite imagery from the S2 GLC-PL (National Satellite Information System 2025). The analysis identified eight distinct land cover classes: anthropogenic, agricultural, broad-leaved forest, coniferous forest, grassland, shrubs, wetlands, and water bodies. The findings revealed no statistically significant variation in the total nectar collected between urban and rural colonies (72.9 kg vs. 64.5 kg; \(p>0.6\)). However, urban colonies exhibited a significantly higher number of foraging days (67 vs. 56). No significant correlations were identified between specific land cover types and nectar yield. Principal component analysis (PCA) and clustering revealed distinct landscape gradients, yet these did not influence nectar collection. The findings of this study indicate that diverse urban environments have the capacity to support beekeeping to a similar extent as rural areas and may even have superior conditions, provided that the continuity and diversity of nectar plants are maintained.  
urban beekeeping; rural beekeeping; environmental impact; nectar gathering; land cover; beehive weight gain Article  
1  
2025  
1 Apiculture Division, The National Institute of Horticultural Research, 24-100 Pulawy, Poland 1  
2 Department of Animal Environment Biology, Warsaw University of Life Sciences, 02-78 Warsaw, Poland; [EMAIL_ADDRESS]  
2025
# FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making
## 6 Results
### Critical Analysis: Benefits and Constraints of FairRAG
#### 6.5.2 Limitations of FairRAG  
Despite these advantages, it is crucial to acknowledge the constraints of the current FairRAG implementation to guide future work.
* Generalizability and Scalability Constraints: Our evaluation was conducted on two structured tabular datasets within the churn domain. The framework's effectiveness has not yet been validated on more complex multi-modal datasets (e.g., including unstructured text) or in different domains, such as healthcare. Moreover, while the in-memory vector search is sufficient for our datasets, scaling the knowledge base to millions of enterprise-level records would necessitate more sophisticated vector database solutions (e.g., FAISS and Milvus) to maintain efficient retrieval performance.
* Incomplete Privacy-Utility Analysis: Although we have successfully implemented differential privacy, this work does not present a comprehensive sweep across a range of privacy budgets \(\varepsilon\) to fully map the privacy-utility trade-off curve. The optimal balance is application-dependent and requires further study.
* Gaps in Formal Fairness Evaluation: A significant limitation is the absence of a rigorous fairness audit. The name \"FairRAG\" reflects an aspiration, but the current work does not formally measure or mitigate algorithmic bias. We have not evaluated the model's performance equity across demographic subgroups using established fairness metrics (e.g., Demographic Parity, Equalized Odds). This remains the most critical next step before the system could be considered for deployment in fairness-critical applications.
## 4 Results
### Estimates of Parameters Obtained by the Markov Chain Monte Carlo Method  
For the analysis of the results in this study, it is recommended that the initial states of the chain are discarded as if they formed a heating sample. With increasing iterations, the chain tends to converge towards the distribution of interest, which is the posterior probability distribution of the parameters. Parameter estimates' results were obtained considering a 99% credible interval (CI). To exemplify the convergence of the MCMC, Figure 2 corresponds to the states of the Markov Chains for the parameter q estimated in models 1, 2, and 3, respectively.  
After the Markov Chain heating states, the posterior probability distribution was obtained, the result of which can be seen in Table 4 in terms of means and 99% credible intervals. Although the adjusted parameters are restricted to this study, the model equation is general and can be used in other bioleaching procedures. For example, _Acidithiobacillus ferrooxidants_ is widely used in the bioleaching of a variety of metals from ores, including copper, nickel, cobalt, gold, and rare earth elements, in addition to uranium [45; 46].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**P** & **Model 1** & **Model 2** & **Model 3** & **Units** \\ \hline \(k_{1}\) & 108.86 (106.82; 111.34) & 110.08 (109.84; 110.75) & 105.83 (104.12; 107.66) & g/m3h \\ \(k_{2}\) (10\({}^{3}\)) & 6.51 (6.43; 6.58) & 6.61 (6.57;6.63) & 6.31 (6.20; 6.38) & g/m3 \\ \(k_{3}\) & 0.009 (0.004; 0.004) & 0.004 (0.004; 0.004) & 0.004 (0.004; 0.004) & g1\({}^{-}\)P/(m3)1\({}^{-}\)Ph \\ \(k_{4}\) & 0.402 (0.398; 405) & 0.412 (0.410; 0.414) & 0.403 (0.397; 0.406) & Dimensionless \\ \(k_{5}\) (10\({}^{3}\)) & 3.00 (2.96; 3.06) & 2.94 (2.92; 2.95) & 2.82 (2.79; 2.86) & g/m3 \\ \(n\) & 0.382 (0.380; 0.388) & 0.371 (0.369; 0.373) & 0.478 (0.467; 0.485) & Dimensionless \\ \(m\) & 2.89 (2.85; 2.92) & 2.89 (2.89; 2.90) & 2.87 (2.84; 2.91) & Dimensionless \\ \(q\) & 1.38 (1.37; 1.39) & 1.38 (1.38; 1.38) & 1.37 (1.36; 1.38) & Dimensionless \\ \(p\times(-1)\) & 0.402 (0.398; 405) & 0.412 (0.410; 0.414) & 0.403 (0.397; 0.406) & Dimensionless \\ \(Y_{cell}\)/Fe\({}^{2+}\) (10\({}^{9}\)) & 2.28 (2.25; 2.31) & 2.33 (2.32; 2.34) & 2.28 (2.24; 2.32) & cell/(gFe2\({}^{+}\)) \\ \(\mu^{\text{max}}\) & 0.097 (0.096; 0.098) & 0.096 (0.096; 0.096) & 0.095 (0.093; 0.096) & h\({}^{-1}\) \\ \(k_{p}\) (10\({}^{5}\)) & - & 2.73 (2.72; 2.75) & - & g/L\({}^{-1}\) \\ \(k_{si}\) (10\({}^{5}\)) & - & - & 2.75 (2.71; 2.79) & g/L\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 4: Results of parameter estimates in terms of mean and 99% credible intervals.  
Figure 2: Evolution of Markov Chains for the constant q, case 2, (**a**) model 1, (**b**) model 2, and (**c**) model 3.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 5 Experimental Results and Analysis
### Experimental Results and Analysis  
To ensure reliable results, all experiments were repeated 20 times. The average of these experiments provided a robust performance evaluation.  
Taking the ClaMP dataset as an example, when evaluating the BO-SVM model, a series of data preprocessing tasks was first performed on the dataset with the file format of.csv (as shown in Section 5.2.1). Then, 80% of the data were randomly divided into the training set T1 and the remaining 20% into the test set S1. After that, the top 20 features were selected using the PCA algorithm to obtain the training set T2 and test set S2. The BO-SVM model was then trained continuously using the training set T2 until its accuracy reached a predetermined value. After that, the trained BO-SVM model was tested using test set S2, and if it met the requirements, the model was used for detection; if it did not meet the requirements, the model was retrained. When evaluating the LSTM-BO-SVM model in the ClaMP dataset, in order for the LSTM to capture the long-term and short-term dependencies between the software more comprehensively, the LSTM model was trained and evaluated using the training set T1 and the test set S1, because some software features may be lost due to dimensionality reduction. The predictions of the LSTM model for training set T1 were then added as new feature columns to the training set T2 (20 features) to form a new training set (21 features). The BO-SVM model was then trained using the new training set. The subsequent evaluation steps were the same as for the BO-SVM model.  
The evaluation process on the CICMalDroid-2020 dataset is consistent with the ClaMP dataset evaluation process.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 2 Related Works
### Localization Methods Based on Deep Learning  
With the rapid development of deep learning, the performances of target positioning can be further improved by employing iterating calculations to reduce the NLOS errors [29; 30; 31; 32]. In [29], hierarchical voting, known as the policy-based algorithm, was conducted before the measured signals enter the conventional filters. The authors of [30] processed the database of AOA signals with deep convolutional neural (DCN) networks. The performance of location prediction was improved by optimizing the coefficient weights in DCN. The algorithm based on the deep neural network in [31] was capable of reaching convergence fast under noisy and varying environments. A gradient-related operation named proximal policy optimization (PPO) was executed on AOA measurement to correct the NLOS variances in the positioning results [32].  
The author of this paper proposed a genetic algorithm (GA)-based method to estimate the MS location [33]. GA was utilized to determine the MS location in this positioning optimization problem. First, the coordinates were transformed to the binary chromosome by an encoding scheme. A solution to a problem was represented as a chromosome in the population. Then, the fitness of each chromosome was calculated in the reproduction scheme. We chose the chromosomes with a better value of the objective function, to generate the next generation. Crossover and mutation schemes could promote the performance of an individual's multiplicity by modifying the chromosome sequence. The chromosomes approached the optimal location gradually during the iterations. The estimated MS location was obtained from decoding the best chromosome until the convergence conditions meet.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Performance Metrics  
The number, type, and location of keypoints in both datasets differed; thus, the evaluation metrics were also different. The evaluation metric for the MPII dataset is the percentage of correct keypoints (PCK). PCK considers a keypoint prediction correct if the distance between the predicted keypoint and ground truth coordinates is less than a specified threshold. We used [EMAIL_ADDRESS], with a threshold of 0.5 based on the person's head size (Equations (1) and (2)). Furthermore, \(gx_{i}\) and \(gy_{i}\) denote the coordinates of the \(i\) ground truth keypoint, and \(px_{i}\) and \(py_{i}\) denote the \(i\) predicted keypoint. Additionally, \(d_{i}\) represents the distance between the predicted and ground truth coordinates of joint \(i\), and \(h\) represents the length between the keypoints composing the head and neck. \(K\) denotes the number of keypoints, and \(\delta\) is an indicator function that is 1 if the condition is true and 0 if the condition is false.  
\[d_{i}=\sqrt{\big{|}\big{(}gx_{i}-px_{i}\big{)}^{2}+\big{(}gy_{i}-py_{i}\big{)} ^{2}\big{|}}, \tag{1}\]  
\[[EMAIL_ADDRESS]=\frac{1}{K}\sum_{i=0}^{K}\delta(d_{i}\leq 0.5h). \tag{2}\]  
The COCO dataset uses object keypoint similarity (OKS) (Equation (3)) to evaluate the predicted pose and assess model performance at various thresholds, using average precision and recall.  
\[OKS=\frac{\sum_{i}\exp\big{(}-d_{i}^{2}/2s^{2}k_{i}^{2}\big{)}\delta(v_{i}>0 )}{\Sigma_{i}\delta(v_{i}>0)}. \tag{3}\]Let \(i\) be the joint index, where \(d_{i}\) represents the distance between the predicted and ground truth coordinates of joint \(i\). The variable \(s\) denotes the person's size, \(k_{i}\) is a constant for each joint type, and \(v_{i}\) indicates whether the joint is annotated. The function \(\delta\) serves as an indicator for PCKh. The calculated OKS ranges from 0 to 1 and evaluates the model performance by assessing the Euclidean distance and vector similarity between keypoints.  
Subsequently, the average precision (AP) and average recall (AR) were measured. AP is the precision measured at OKS thresholds starting from 0.5 up to 0.95 (AP50 and AP75 represent the precision at OKS thresholds of 0.5 and 0.75, respectively). AR is the recall measured at OKS thresholds of 0.5-0.95.  
APM and APL represent the average precisions for medium (32 \(\times\) 32-96 \(\times\) 96) and large (\(\geq\)96 \(\times\) 96) objects, respectively, at OKS thresholds of 0.5-0.95. These metrics were used to evaluate pose estimation model performance and compare the precision and recall at various OKS values.  
We took measurements 10 times for each model and used the average values to compare the performances and minimize the randomness in deep learning models.
###### Abstract  
In this paper, we report a high-performance balun with characteristics suitable for future broadband sub-THz differential circuits. The idea of the balun is based on three asymmetric coupled lines, which enhance the odd mode capacitances to equalize the even/odd mode phase velocities. The inner line of the three asymmetric coupled lines is configured to form the open stub (\(\lambda/2\)), while the outer lines form short stubs (\(\lambda/4\)). To further reduce the phase imbalance, the short stubs in one of the arms of the balun are connected with vias and a lower metal layer. The balun is developed using the standard 130-nm SiGe BiCMOSback-end process and EM simulated with ADS momentum and Sonnet. The \(-\)10-dB reflection coefficient (S\({}_{11}\)) bandwidth of the balun is 136 GHz (88-224 GHz). It shows insertion loss (including RF pads) \(<\)1.5 dB, phase imbalance \(<\)7 degrees, and amplitude imbalance \(<\)1 dB at 94-177 GHz. Furthermore, a scaled-down version of the balun operates on the WR-6, WR-5, and WR-4 frequency bands without significant degradation in its performance. Such characteristics of the balun make it an ideal candidate for various broadband differential circuits.  
broadband balun; SiGe BiCMOS; THz balun; D-band; G-band; differential circuits +
Footnote †: journal: Applied Soft 2019
# Modal Processor Effects Inspired by [PERSON] Tonewheel Organs
## 4 Hammondizer Modal Processor Implementation  
The Hammondizer effect system architecture is shown in Figure 5. It turns out that this structure closely resembles that of the modal reverberator (Figure 3), which forms a room response as the parallel combination of room vibrational mode responses. Both have inputs designated by \(x(t)\), a column of narrow-band outputs designated by \(\mathbf{y}(t)\), summed to form the system output \(y(t)\).  
In the Hammondizer, the input signal \(x(t)\) is heterodyned to baseband by a column of modulating sinusoids \(\mathbf{\varphi}(t)\):  
\[\mathbf{n}(t)=\mathbf{\varphi}(t)x(t) \tag{20}\]  
These baseband signals are smoothed by a column of pre-smoothing filters \(\mathbf{g}_{\text{pre}}(t)\)  
\[\mathbf{n}^{\prime}(t)=\mathbf{g}_{\text{pre}}(t)*\mathbf{n}(t) \tag{21}\]  
A column of tonewheel amplitudes \(\mathbf{a}(t)\) is formed by the drawbar routing matrix \(\mathbf{\Gamma}(\mathbf{r}(t))\),  
\[\mathbf{a}(t)=\mathbf{\Gamma}(\mathbf{r}(t))\mathbf{n}^{\prime}(t) \tag{22}\]  
and further smoothed by a column of post-smoothing filters \(\mathbf{g}_{\text{post}}(t)\):  
\[\mathbf{a}^{\prime}(t)=\mathbf{g}_{\text{post}}(t)*\mathbf{a}(t) \tag{23}\]  
A set of mode outputs \(\mathbf{y}(t)\) is formed by the tonewheel processing stages \(\mathbf{\psi}(t)\), which include a column of pickup models \(\mathbf{p}()\) and modulating signals \(\mathbf{x}(t)\)  
\[\mathbf{y}(t)=\mathbf{p}\left(\mathbf{x}(t)\odot\mathbf{a}^{\prime}(t)\right) \tag{24}\]  
An individual tonewheel processing stage is shown in Figure 6. Notice the slight change in architecture from the analogous Figure 2. In Figure 6, the pickup distortion has been moved to  
Figure 5: Block diagram of the Hammondizer effect.  
operate on the output rather than the raw tonewheel signal. The reason for this change is artistic--it disambiguates the effects of the memoryless pickup nonlinearities and the distortion of the tonewheel basis functions.  
Finally, the output \(y(t)\) is formed by summing all of the mode outputs:  
\[y(t)=\mathbf{1}^{\top}\boldsymbol{y}(t) \tag{25}\]  
In the rest of this section, we describe in detail how aspects of the modal processor are tuned and adapted to create the Hammondizer. Pitch processing adaptations include tuning the modes to the particular frequencies and frequency range of the [PERSON] organ Section 4.1), introducing drawbar-style controls to pitch processing (Section 4.2), adding vibrato to mode frequencies (Section 4.3), and adding crosstalk between nearby modes to simulate crosstalk between nearby tonewheels (Section 4.4). Distortion processing adaptations include adapting saturating nonlinearities for each mode to mimic the pickup distortion of each tonewheel (Section 4.5) and replacing modulation sinusoids with sums of sinusoids to mimic non-sinusoidal tonewheel shapes (Section 4.6).
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
###### Abstract  
Titanium alloy has extensive applications in numerous fields, such as in aerospace, shipbuilding, petrochemical, and bio-medical. However, under the condition of high temperature (above 700 \({}^{\circ}\)C), it is difficult to meet the requirements of the performance of the turbine blades, piston rings, valves, etc. In this paper, the alloy powder is composed of Ni60A, TiN, Al, and Si in accordance with the proportion of 1:3:42 (mass ratio), and the composite coating, such as TiN, TiB, Ti\({}_{5}\)Si\({}_{3}\), and Al\({}_{3}\)Ti were synthesized on Ti-6 Al-4V alloy by Yttrium Aluminium Garnet(YAG) laser. The friction and wear experiments were carried out on Ti-6 Al-4V alloy and alloyed coatings at different loads (3 N, 6 N, 9 N), and different temperatures (25 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 700 \({}^{\circ}\)C). The impact of load and temperature on wear performance were analyzed by analyzing the friction traces. The results showed that the abrasion loss, wear width, and depth, wear volume, and wear ratio of the alloyed coatings were less than Ti-6 Al-4V alloy, and the surface of furrow was shallow and the wear was less. However, under high temperature conditions, the alloy coating wears worse than Ti-6 Al-4V alloy, and the high temperature wear resistance is not ideal.  
Ti-6 Al-4V alloy; laser alloying; wear resistance
## 5 Results and Analysis
### Parametric Study  
#### 5.3.1 Axial Force  
According to the different burial depths of the tunnels in the Shanghai metro, the axial force is divided into three conditions: 600 kN, 1000 kN, and 1400 kN. These correspond to three burial-depth types, shallow burial, medium burial, and deep burial, respectively (see Figure 12). With the axial force increasing, the stiffness in the initial stage was higher due to the closer contact of the external concrete. Nevertheless, the slope of the curves was generally the same in the ultimate stage. The underlying cause is that the stiffness was entirely provided by SCCP, since the concrete had almost completely cracked at this point. In other words, the strength of the SSCP was sufficiently high to ensure that the SSCP could sustain its reinforcement effect until the concrete failed under different axial forces, which indicates the extensive applicability of SSCP reinforcement in tunnels with different burial depths.