esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 2 Related Work
### Morphological Analyzer for Indo-Aryan Languages  
[PERSON] and [PERSON] proposed a morphological analyzer and generator for Hindi based on a dictionary-reliant approach [20]. In the database, all possible word forms for every root word are stored. The main focus was on words like nouns and other word classes but excludes proper nouns to some extent. This approach prefers time and accuracy to memory space, which is a drawback.  
[PERSON] et al. developed a statistical morphological analyzer trained on the Hindi tree bank (HTB) [21]. The analyzer identifies the lemma, gender, number, person (GNP), and case marker for every word in a given sentence by training separate models on the Hindi tree bank for each of them. Other grammatical features such as TAM (tense, aspect, and modality) and case are analyzed using heuristics on fine-grained POS tags of the input sentence. This analyzer achieved an accuracy of 82.03% compared with the Paradigm-Based Analyzer (PBA) for lemma, gender, number, person, case and TAM. As a a statistical model, it analyzed out-of-vocabulary words. The prediction of gender, number, and person of words in their sentential context could have been better if dependency relations were given as inputs. However, the standard natural language analysis pipeline forbids using parsing information during morphological analysis.  
[PERSON] et al. presented a morphological analyzer for Marathi using a paradigm-based approach considering only inflections [22]. The classification of postpositions and the development of morphotactic FSA was one of the important contributions, as Marathi has complex morphotactics. Though improvement was shown in shallow parsing, the morphological analyzer does not handle the derivation morphology and compound words.  
[PERSON] et al. developed a morphological analyzer using Apertium for Oriya [17]. The analyzer data conformed to Apertium's _dix_ format, and the dictionary showed correspondences between surface and lexical forms for any given word.The paper lacks a detailed analysis of the inflections for nouns and verbs. In the case of verbs, a few verbs did not fall into any of the existing verb paradigms, which can be ascribed to the lower robustness of the paradigms or to the need for separate paradigms for these verbs, which is a shortcoming. Causative verbs and verbal complexes were not handled, and these remain unanalyzed.  
From the literature survey, it is observed that the morphological analyzers developed for Indo-Aryan languages are limited to inflectional morphology and do not handle derivational morphology and compound words. Most of the methods use the dictionary-based approach and consume more memory as all the affixes and stems are stored.
## 3 Performance Evaluation
### Acquisition Performance  
#### 3.2.1 Ambiguity Function  
The ambiguity function is an important method to analyze the acquisition performance of navigation signals. If the channel delay and Doppler are \(\tau\) and \(f_{d}\), respectively, and the coherent integration time is \(T_{coh}\), the ambiguity function can be written as:  
\[A(\tau,f_{d})=\int\limits_{0}^{T_{coh}}s(t)\overline{s(t-\tau)}e^{2\pi f_{d}t} \mathrm{d}t \tag{17}\]  
Substituting (3) into (17) yields  
\[\begin{array}{ll}A(\tau,f_{d})&=\frac{1}{M}\int\limits_{0}^{T_{coh}}\sum \limits_{k=0}^{M-1}\mathbf{X_{tf}}[k]\Pi(t)e^{2\pi k\Delta f}\cdot\overline{ \mathbf{X_{tf}}[k]\Pi(t-\tau)e^{2\pi k\Delta f(t-\tau)}}e^{2\pi f_{d}t}\mathrm{ d}t\\ &=\frac{1}{M}\sum\limits_{k=0}^{M-1}e^{2\pi k\Delta f\tau}\int\limits_{0}^{T_{ coh}}\Pi(t)\overline{\Pi(t-\tau)}e^{2\pi f_{d}t}\mathrm{d}t\\ &=\frac{1}{M}\sum\limits_{k=0}^{M-1}e^{2\pi k\Delta f\tau}\bigg{(}\frac{\sin \pi f_{d}(T_{coh}-|\tau|)}{\pi f_{d}(T_{coh}-|\tau|)}\bigg{)}\bigg{(}1-\frac{| \tau|}{T}\bigg{)}\end{array} \tag{18}\]  
The ambiguity function envelope of OTFS is  
\[|A(\tau,f_{d})|=\left|\frac{1}{M}\sum\limits_{k=0}^{M-1}e^{2\pi k\Delta f\tau} \bigg{(}\frac{\sin\pi f_{d}(T_{coh}-|\tau|)}{\pi f_{d}(T_{coh}-|\tau|)}\bigg{)} \bigg{(}1-\frac{|\tau|}{T}\bigg{)}\right| \tag{19}\]  
Figure 6 shows the ambiguity function envelope of Pulse-OTFS(10,1023) with a signal period of 1 ms and Doppler bins of 10. The variation in the ambiguity function envelope along the delay axis is consistent with the absolute value of the ACF, while the variation in the correlation values along the Doppler axis is similar to the GNSS signal. However, we can see from the figure that the correlation loss of the proposed signal along the Doppler axis is much less, its 3 dB loss of the peak corresponds to a frequency offset range up to [\(-4.43\) kHz, \(4.43\) kHz]. This is because the coherent integration time \(T_{coh}\) is equal to the signal period \(NT\), but the effective coherent integration time \(T_{eff}=T\) as the Pulse-OTFS signal is not zero for only the first symbol duration \(T\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{Q}\) (bit) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) & \(\mathbf{4}\) & \(\mathbf{5}\) & \(+\infty\) \\ \hline Power ratio of the main lobe (\%) & 83.86 & 84.24 & 94.03 & 98.52 & 99.59 & 99.93 \\ Quantization loss (dB) & \(-0.76\) & \(-0.74\) & \(-0.26\) & \(-0.06\) & \(-0.01\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power ratio of the main lobe and corresponding quantization loss for the OTFS signal.
## 4 Solution Method  
Since the distribution networks generally have a large number of switches, it is impossible to enumerate all possible solution candidates (sets of switch states) and select the optimal solution from them. For example, with 100 switches, the number of possible solution candidates is \(2^{100}\) (\(\simeq\)1.27 \(\times\)\(10^{30}\)). In this paper, an enumeration-based method is extended to solve the formulated problem.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 5 Discussions and Conclusions  
In this paper, cross laser-pattern is used as a cooperative marker. A method is proposed to detect the center point and attitude of a cross-laser image based on the combination of Gaussian fitting and least square fitting. In the principal verification experiment without smoke and water mist in the light path of the cross laser-pattern, the center positioning accuracy is better than 0.2 pixels, the angle detection accuracy is better than \(\pm 10\)\", and the processing speed is better than 0.1 s.  
In the verification experiment under smoke and water mist environment, the proposed method still works, indicating that the method is robust, the center positioning accuracy is better than 0.6 pixels, the angle detection accuracy is better than \(\pm 15\)\", and the processing speed is still better than 0.1 s, which meets the requirements of real-time and precision of the project. Besides, according to the results, under a smoke and water mist environment, the proposed method still works while some other methods failed to process the digital images with poor contrast, low signal-to-noise ratio and uneven energy distribution, indicating  
Figure 27: The related linear error model.  
Figure 28: Error distribution after compensation.  
the robustness of the method. However, it affects the accuracy of the proposed method, therefore, in order to further improve the accuracy of the proposed method under smoke and water mist environment. Environment compensation study needs to be conducted in the future research.  
Investigation, Methodology, Designed the Experiments, Processed Experiment Data and Writing--original draft, H.L.; Supervision, Improved the Experiment and Writing--review and editing, Z.Q.; Developed System Software and Conducted Experiment, H.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by National Natural Science Foundation of China (NSFC) (No: 51775378).  
The authors declare no conflict of interest.
# Security Challenges in Industry 4.0 PLC Systems
## 4 Where Most Urgent Actions Are Required in Industrial Control Network Systems?
### Improvements in the Industrial Network Architecture
#### 4.2.3 Monitoring of the Data Transmission Authorization  
Monitoring the authorization of transmitted data is also one of the most important concepts in the field of industrial networks cybersecurity. The presented solution aims to limit unauthorized changes to data by introducing a system of authorizations and a cascade system of data correctness confirmation. Effective and continuous monitoring of authorization requires the creation of a central server that supports the operation of network user verification, encryption and receiving encrypted connections. Connection to the global network should be made only through the aforementioned server, which, based on the provided login data, would use the pool of available sites for each group of users.  
The main purpose of introducing a central authorization system is to secure communication between two access points that are elements of the process data exchange. This is especially important in the case of data exchange between two SCADA systems that supervise equivalent processes and require connection via the global network. In this case, it is possible to create a header containing the authorization data of each of the systems and then encrypt these data passing through the authorization server in question. Efficient encryption of data based on a key and their authorization can be performed in many ways using algorithms such as DES or SHA-3.  
The cascade data validation system assumes verification both through a central authorization system and a properly configured firewall. Such action is aimed to prevent unwanted modification of transmitted data already within the industrial network. However, it should be remembered that user authorization systems usually cannot be used in the case of older control systems and most independent measurement devices, due to the lack of support from the central data authorization mechanism.  
The advantage of introducing an authorization system is the ability to monitor network activity and encrypt connections between access points in a given network. Logs from such a system can be a good starting point when analyzing security during an audit. Additionally, the creation of a central authorization server enables blocking of unwanted connections with remote-control servers during attacks due to the lack of a unique key.
# Review of Automatic Estimation of Emotions in Speech
## 2 Emotions
### Theories of Emotion  
There is no consensus on a structure to comprehensively describe pertinent aspects of emotion, nor how to represent emotion in mathematical models. Evidence of emotion ranges on continual scales [17], but is also often described by specific named categories [18]. This has led to a wide range of theories on how to qualify and quantify emotion [19; 20], both in how it is expressed by humans and how it is perceived by others [21; 22].  
Three measures are widely accepted for continuous scales for emotion (recently termed dimensional emotion): arousal, valence, and dominance: (1) Arousal (activation and activity are similar terms) corresponds to the intensity of an emotion. (2) Valence concerns pleasantness on a positive/negative scale, e.g., joy is positive, anger negative. (3) Dominance is the degree of control by an (assumed) stimulus causing the emotion; such a third dimension is needed for emotion, as anger is dominant (+) and fear is submissive (-), while both are low valence and high arousal.  
Different theoretical models of emotion (in particular, discrete categories versus dimensional models) have not found distinctive differences in terms of SER implementation. Outputs in most SER evaluations have been based on the accuracy of specific emotional labels, as determined in datasets verified by expert listeners. SER systems usually aim to predict discrete emotion labels, although some estimate dimensional ratings.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 5 Conclusions  
This study introduces an innovative approach aimed at improving the efficiency of existing point cloud classification methods. The methods for deep learning-based point cloud processing have become increasingly intricate and often requiring long training times and high costs. We propose a new network model: a nonparametric point cloud classification network. We utilized trigonometric functions for embedding and apply Squash to smooth the features for subsequent processing. Then, we enhanced the features using the nonparametric attention mechanism ResSimAM, thereby leading to significant improvements in the purely nonparametric network for 3D point cloud analysis. Based on this, we also extended a lightweight parametric network, which allows for efficient inference with a small number of parameters. For the nonparametric model, our model achieved 83.9% accuracy on the ModelNet40 dataset without any training, which greatly saves time in training the model for the point cloud classification task. For the lightweight parametric model, we achieved 93.3% accuracy using only 0.8 M parameters, the training time was only 3.1 h, and the inference speed reached 962 samples per second, which will greatly reduce the pressure on hardware devices and keep the inference speed relatively high. Various tasks like autonomous vehicles, virtual reality, and aerospace fields demand real-time data handling, and our lightweight models could work efficiently in these tasks.  
Although our method has achieved promising results, there is still room for improvement. For nonparametric network models, the feature extraction ability of our network on diverse datasets still needs to be tested and improved. For the lightweight parametric model, although the Squash operation was used to accelerate the convergence of the network, it brings some impact on the feature extraction ability of the network. In future research, we will focus on enhancing the generality and robustness of the proposed network. Future work needs to consider the computational efficiency of the network and the feature extraction capability of the model, as well as propose more effective and concise lightweight methods. This can be achieved by designing new nonparametric modules and combining them with a small number of neural networks, as well as adopting more efficient computational methods. In future work, we will explore nonparametric models with a wider range of application scenarios.  
Conceptualization, J.G. and W.L.; methodology, J.G. and W.L.; validation, W.L.; investigation, J.G.; writing--original draft preparation, J.G.; writing--review and editing, W.L.; visualization J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hebei Province (F2019201451).  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 5 Discussion
### Future Considerations  
While recent advancements in ML have addressed many of the challenges posed by big data, significant gaps remain, particularly in the areas of sustainability, fairness, and interdisciplinarity. The environmental impact of training large-scale models continues to grow, prompting a need for energy-efficient algorithms and \"green AI\" practices. Efforts such as model pruning, quantization, and the use of renewable energy sources in data centers are promising directions but require further refinement to balance performance with resource conservation.  
Ethical considerations, including bias, fairness, and accountability, also demand sustained attention. Bias introduced during data collection and model development risks the perpetuation of systemic inequities, particularly in high-stakes domains like criminal justice and credit scoring. Techniques such as adversarial debiasing and fairness-aware ML must be integrated more comprehensively into the development life cycle of ML systems.  
Finally, the integration of emerging paradigms, such as quantum ML and multi-modal learning, offers exciting opportunities to tackle the complexities of big data. Quantum computing holds the potential to address high-dimensional problems in cryptography and optimization, while multi-modal learning enables richer insights by combining disparate data sources such as text, audio, and video. However, these fields are still nascent and require collaborative efforts across disciplines to mature into practical solutions.  
In conclusion, this discussion highlights the multifaceted challenges and opportunities at the intersection of ML and big data. By synthesizing findings, critically analyzing methodologies, and proposing a generic framework, this study provides a foundation to advance the responsible and effective use of ML in big data environments. Future work should prioritize sustainability, fairness, and interdisciplinary innovation to ensure that these technologies continue to benefit society.
## 4 Conclusions  
In this experimental research, we have reported and demonstrated a short-range bidirectional multi-wavelength VLC system based on micro-projection enabled optical terminals. To the best of our knowledge, this is the first time that a micro-projection enabled optical terminal capable of bidirectional multi-wavelength visible light communication has been presented. In order to make the proposed communication system architecture compatible with the data modulation format of future wireless communication system, several baseband modulation schemes, i.e., NRZ_OOK, M-ary PSK and M-ary QAM, which have widely employed by a multicarrier modulation scheme to raise the bandwidth efficiency, were used to investigate the system transmission performance. The measurement results show that the maximum data transmission rate of 380 Mb/s (BER = \(10^{-3}\)) can be achieved based on a pure bidirectional multi-wavelength communication scenario. The analysis of the system performance also indicated that physical bandpass optical filter can be used to improve the system crosstalk.  
Figure 11: Pure data transmission rate of Green LED with bandpass filter (BER = \(10^{-3}\)).  
Figure 12: Pure data transmission rate of Blue LED with bandpass filter (BER = \(10^{-3}\)).  
Figure 10: Pure data transmission rate of Red LED with bandpass filter (BER = \(10^{-3}\)).  
Compared with the optical terminal using CMOS optical camera as the receiver for bidirectional VLC, the proposed optical terminal provides a better approach for direct implementation of a bidirectional VLC system and offers a higher data transmission performance.  
This work was in part supported by Ministry of Science and Technology (MOST), Taiwan (MOST 106-2221-E-011-011). The authors would also like to thanks the technical support of LCoS device from Jasper Display Corp., Hsinchu, Taiwan.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and recorded the results. [PERSON] analyzed the experimental results and wrote the paper for publication.  
The authors declare no conflict of interest.
## 4 Results and Discussion
Therefore, all vehicles will park in the same location at the end of the route. This may not be the case in real-world scenarios; however, modifying the ending starting/ending locations for the vehicles can easily be changed in the proposed methodology. Another observation is that the total travel distance increases when more vehicles are used, but the overall travel time is decreased. Comparing the breakdown scenarios to the normal ones, more significant differences are observed. Specifically, comparing Scenario 3 to 5, the only difference is that Vehicle 2 breaks down, but the total distance and overall travel time increase. In Scenario 5, Vehicle 2 breaks down and Vehicles 1 and 3 split the remainder of Vehicle 3's assigned path. This effect is more drastic in cases with more vehicles, as highlighted in Scenarios 4 and 6. In Scenario 6, Vehicles 2 and 4 break down, initially leaving the remaining three vehicles to service the areas left by the broken vehicles., However, only Vehicle 3 is assigned the remainder of the path originally intended for the broken vehicles. The proposed algorithm has the flexibility to select multiple vehicles to service the remainder of the path, but assigning it only to Vehicle 3 is optimal in this case. As a result, the workload is drastically increased for Vehicle 3, resulting in a significant increase in the overall time. Since the objective of the higher-level path generation is to minimize the total distance, the overall travel time was not of interest. This results in similar total distances between the normal and breakdown scenarios but an increase in the overall time. Having more sub-areas could lead to more flexibility in the higher-level path generation algorithm, thus reducing the overall time by effectively balancing the workload. However, this was not considered for this study. The paths made by the proposed methods were made feasible using the corner cutting or Reeds-Shepp method. A sample of the scanning path can be seen in Figure 15. As seen in the figure, each method can be applied when it is appropriate, resulting in a path that consists of smooth turns and forward-backward motions. The total paths for all vehicles in all 6 scenarios can be seen in Figure 16.  
Figure 14: The starting locations for vehicles in scenario 1 (a), scenario 2 (b), and scenario 3–6 (c).
###### Abstract  
Emotion classification is a challenge in affective computing, with applications ranging from human-computer interaction to mental health monitoring. In this study, the classification of emotional states using electroencephalography (EEG) data were investigated. Specifically, the efficacy of the combination of various feature selection methods and hyperparameter tuning of machine learning algorithms for accurate and robust emotion recognition was studied. The following feature selection methods were explored: filter (SelectKBest with analysis of variance (ANOVA) \(F\)-test), embedded (least absolute shrinkage and selection operator (LASSO) tuned using Bayesian optimization (BO)), and wrapper (genetic algorithm (GA)) methods. We also executed hyperparameter tuning of machine learning algorithms using BO. The performance of each method was assessed. Two different EEG datasets, EEG Emotion and DEAP Dataset, containing 2548 and 160 features, respectively, were evaluated using random forest (RF), logistic regression, XGBoost, and support vector machine (SVM). For both datasets, the experimented three feature selection methods consistently improved the accuracy of the models. For EEG Emotion dataset, RF with LASSO achieved the best result among all the experimented methods increasing the accuracy from 98.78% to 99.39%. In the DEAP dataset experiment, XGBoost with GA showed the best result, increasing the accuracy by 1.59% and 2.84% for valence and arousal. We also show that these results are superior to those by the previous other methods in the literature.  
EEG; feature selection; SelectKBest; ANOVA \(F\)-test; lasso; genetic algorithm; random forest; logistic regression; XGBoost; SVM +
Footnote †: journal: applied sciences