esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of F-T Cycles on the Functional Groups in S/S Soil  
According to Figure 6, a large number of Si-O-Si stretching vibrations, Si-O vibration absorption peaks, and carbonate stretching vibration can be found in the sample; this proves that substances that can enhance the stability of HMs, such as colloids and carbonate binders, are indeed present in the soil at this time [52; 53]. This is why a large number of HMs have previously undergone a transition to the steady fractions.  
Meanwhile, the types of functional groups contained in the soil are approximately the same in the six cases. The direct effect of the F-T cycle on the soil is mainly a physical reaction without the addition of new substances; therefore, the number of characteristic peaks did not increase. Meanwhile, the result of chemical speciation analysis shows that when the number of F-T cycles exceeded 14 times, the changing trend of the content of each HM forms was very slow, and gradually stabilized instead of a single decline. Therefore, there was no disappearance of the characteristic peak. Due to the above reasons, the F-T cycle did not change the types of functional groups in the system.  
In addition, carbonate stretching vibration was detected in the soil. This is consistent with the fact that there are carbonate binders in the soil, and that when the pH environment in which the HMs are located changes, the HMs react with the carbonates to varying degrees. The F-T cycles, on the other hand, increase the amount of carbon dioxide entering the soil precisely by changing the porosity of the soil, causing changes in the carbonate-bound fraction of the soil.  
Throughout the whole process of this study, it is clearly recognized that the change in the content of its stable fractions tends to slow down when the F-T cycles are performed several times. According to the experimental results, this node occurs at 30 cycles. When conducting an HM soil remediation project, the collected soil samples can be S/S in the laboratory and then subjected to a small trial test of 30 F-T cycles to test the morphological components, derive the instability limits, determine whether there is a safety hazard, and determine whether to keep the curing ratios or change the curing ratios.
# A Framework for Frugal Supervised Learning with Incremental Neural Networks
## 3 Results
### Studying Frugality of INN  
As stated earlier, frugality refers to the ability of a classifier to learn effectively from a relatively low number of observations, while achieving state-of-the-art accuracy performance. For this experiment, we use the classic iris dataset in order to guarantee reproducibility and ensure baseline comparison. The iris dataset contains measurements of four features of three different species of iris flowers: sepal length, sepal width, petal length, and petal width. The dataset is balanced and consists of 150 samples of three classes (setosa, versicolor, and virginica). To study the frugality of the INN, we split the dataset into a training set and a validation set in a stratified hold-out fashion. The size of the training set varies from 5% to 95% of the total dataset, and the remaining data are used for validation. We repeat this process for each split, and for each split, we train several classifiers on the training set and test them on the validation set. The classifiers include the INN and other common classifiers, such as nearest neighbors, linear SVM, and random forests.  
In order to study the frugality of the INN, it is interesting to determine how many training samples the classifier requires to achieve state-of-the art performance on the dataset. A a toy dataset, a baseline accuracy for iris can be set to 0.85, noting that this baseline does not take into account the split size.  
Figure 4 shows the compared plot of accuracies for the varying split sizes. From the compared accuracies computed, it appears that the INN outperforms the baseline from the split 6/94 (i.e., 6% of examples for training and the remaining 94% for validation). However, the high ratio of prototypes indicates that the classifier likely overfitted for splits before 15/75, as \(\rho\) is above 0.4. As the accuracy increases with the training set size to stabilize over 0.9, the ratio of prototypes progressively drops to less than 30%. The hyperparameters of the INN were found using a grid-search approach and were set to \(s_{inf}=1\), \(s_{conf}=0.1\)  
and \(\alpha=0.1\). The parameters of the other classifiers were taken from the literature and are detailed in the source code.
# Carbon Nanoparticles and Their Biomedical Applications
## 9 Therapy--Delivery Platform, Treatment
Furthermore, CNPs can deliver anticancer drugs to the central nervous system to treat gliomas. Their functionalization will allow them to penetrate the blood-brain barrier [89]. CNPs can not only carry anticancer drugs but also growth factors, including VEGF, to support angiogenesis in ischemic cardiac tissue and antimicrobial drugs (antibiotics, antifungal, antiparasitic, and antiviral drugs) [90; 91; 92]. Even without loading with antimicrobial drugs, CNPs can eliminate viral (especially bacterial or fungal) infections and bypass bacteria resistant to antibiotics (e.g., _Escherichia coli_, _Staphylococcus aureus_, _Streptococcus mutants_, _Perphyromonas gingivalis_, _Pseudomonas aeruginosa_, _Klebsiella pneumoniae_, _Salmonella typhimurium_). Their antimicrobial effects are enhanced by the formation of nanocomposites with Ag [93]. Importantly, CNPs can be used to treat inflammatory diseases, such as ulcerative colitis. MCNs loaded with _Musca domestica_ cerropin can accumulate in the damaged intestine and suppress inflammation, restoring the homeostasis of the intestinal microflora and improving the intestinal barrier function [94]. CNPs enable the nonviral delivery of genetic material, including plasmid DNA, miRNA, siRNA into cells, and the nucleus [74; 95]. They can regulate gene expression and induce silencing of target genes. By silencing distinct genes (selective blocking), suppression of cancer cell growth can be achieved. [PERSON] et al. prepared single-walled carbon nanotubes conjugated with siRNA. The nanocomposite was internalized by pancreatic cancer cells (PANC-1). The transfection of naked siRNA was very low, on the other hand, the transfection of the nanoparticle complex was higher than 92%. After internalization, siRNA was released and interacted with the targeted gene mutant K-Ras, which led to the downregulation of its expression. The expression of K-Ras mRNA was suppressed by 66.88 \(\pm\) 5.14% [96]. Using multidrug therapy and siRNA single-walled carbon nanotubes targeting the CCR5 gene, [PERSON] et al. inhibited inflammatory processes in nonalcoholic steatohepatitisitis and prevented hepatocellular carcinoma [97]. CNPs have enormous therapeutic potential. They can make treatments more targeted and effective, using lower doses of drugs, which will reduce the cost of treatment while reducing risks to patients.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Research Challenges
#### 8.2.2 Lower Detection Accuracy for Minor Classes  
It was also shown in the study, that most of the solutions available are inefficient in detecting minor classes even if they have a very good overall accuracy to detect abnormal behaviour. This problem comes from imbalanced datasets. This results in an accuracy that is lower for minor classes than for major classes. One way to solve the problem is by using an up-to-date dataset that contains enough instances of minor classes. However, it was shown that such a dataset still needs to be made. Another solution would be to use feature extraction methods, so that the datasets can be split into two parts, one with the major classes and one with the minor classes. Then, using a multi-level structure and different machine learning techniques the model can be trained to have good results to detect minor classes.
## 3 Model Validation and Results Analysis
### Hexagonal Prism Case  
To validate the simulation accuracy of the IIM T-Matrix method for non-rotational symmetric particles (which cannot be effectively simulated by the EBCM T-Matrix method), the results of the IIM T-Matrix method are further compared with that of the MRTD model developed by our team [27; 41].  
The MRTD scattering model is established based on the Multi-Resolution Time Domain technique. By using this model, light scattering by arbitrarily shaped particles or ice crystals can be effectively simulated. In this model, the plane wave is introduced into the computational space by the TF/SF (Total Field/Scattering Field) technique, the Convolution Perfectly Matched Layer (CPML) is applied to truncate the computational domain, and the volume integral method is employed to transform the near electric field to the far field.  
In this simulation, the light wavelength and refractive index are set as \(\lambda\) = 0.5 \(\upmu\)m and m = 1.20 - 0.0008i, and the length and bottom edge length of hexagonal prism are set as \(L\) = 2.0 \(\upmu\)m and \(a\) = 1.0 \(\upmu\)m, respectively. The results are shown in Figure 8.  
As can be seen, a high agreement is achieved between the results of the T-matrix method and those of the MRTD model, which indicates that both MRTD and IIM-T-matrix models can effectively simulate the scattering characteristics of a hexagonal prism. Similar to the conclusion drawn above, the consistency of the two models in forward scattering directions is higher than those at large scattering angles, where for the scattering phase functions F\({}_{11}\), the relative simulation errors tend to be zero in scattering angles smaller than 30\({}^{\circ}\), while in scattering directions near 170\({}^{\circ}\), the simulation accuracy is relatively lower, and the maximum simulation errors can reach 36.7%.
## 3 Materials and Methods
### Croston Method  
This technique was first developed by [22] to predict intermittent and lumpy data. This method uses the amount of demand and occurrence of demand individually to estimate the forecast value. This indicates that the likelihood of a demand occurrence does not depend on the amount of demand at a certain moment. Let \(V_{t}\) and \(Z_{t}\) be the estimated average interval for the occurrence of non-zero demand and estimated mean for non-zero demand, respectively, at time \(t\). The actual demand value at time \(t\) is represented as \(X_{t}\), and the number of consecutive periods of zero demand is indicated as \(q\). Then, \(Y_{t}\) is used to indicate an average estimate of the amount of demand. Mathematically  
\[if\ X_{t}=0\begin{cases}Z_{t+1}=\alpha\cdot X_{t}+(1-\alpha)\cdot Z_{t},\\ V_{t+1}=\alpha\cdot q+(1-\alpha)\cdot V_{t},\\ Y_{t+1}=\frac{Z_{t+1}}{V_{t+1}}\end{cases} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Series Type** & **Ref.** & **Application** & **Forecasting Method** & **MAE** & **sMAPE** & **RMSE** & **MAD** & **MASE** \\ \hline \multirow{8}{*}{2021 [39]} & \multirow{3}{*}{Vehicle industry} & SVM & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.877 \\ \cline{3-8}  & & ANN & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.930 \\ \cline{3-8}  & & RNN & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.987 \\ \cline{3-8}  & & & ARIMA & \(\times\) & 0.1616 & 3531 & \(\times\) & \(\times\) \\ \cline{3-8}  & & KNN & \(\times\) & 0.1374 & 2913 & \(\times\) & \(\times\) \\ \cline{3-8}  & & RNN & \(\times\) & 0.1333 & 2788 & \(\times\) & \(\times\) \\ \cline{3-8}  & & ANN & \(\times\) & 0.1011 & 2115 & \(\times\) & \(\times\) \\ \cline{3-8}  & & SVM & \(\times\) & 0.1101 & 1967 & \(\times\) & \(\times\) \\ \cline{3-8}  & & LSTM & \(\times\) & 0.1072 & 2267 & \(\times\) & \(\times\) \\ \cline{3-8}  & & Dilated LSTM & \(\times\) & 0.1023 & 2172 & \(\times\) & \(\times\) \\ \hline \multirow{8}{*}{2021 [43]} & \multirow{3}{*}{Electric power} & ANN & \(\times\) & 5.27 & 378 & \(\times\) & \(\times\) \\ \cline{3-8}  & & ARIMA & \(\times\) & 5.65 & 463 & \(\times\) & \(\times\) \\ \cline{3-8}  & & GRNN & \(\times\) & 5.01 & 350 & \(\times\) & \(\times\) \\ \cline{1-1} \cline{3-8}  & & LSTM & \(\times\) & 6.11 & 431 & \(\times\) & \(\times\) \\ \cline{1-1} \cline{3-8}  & & ETS+RD-LSTM & \(\times\) & 4.46 & 351 & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
else,  
\[if\ X_{t}\
eq 0\begin{cases}Z_{t+1}=Z_{t},\\ V_{t+1}=V_{t},\\ Y_{t+1}=Y_{t}\end{cases} \tag{2}\]  
However, other authors have observed that the Croston approach has a positive bias [46]. The authors of [47] first demonstrated that there is a bias and then refined the bias by adjusting the multipliers \(Z_{t}\) and \(V_{t}\) in the \((1-\alpha/2)\) form. In this section, we will compare the results obtained using the modified Croston approach.
## 3 Network Settings and Assumptions  
The network model used in the path planning model proposed in this study is assumed to have the following features:  
1. The UNN and the MA are two types of sensor nodes in the network. \(N\) number of UNNs are deployed randomly to the network with uniform distribution.
2. ROI is represented by a two-dimensional area, and a WSN with an S = 100 \(\times\) 100 m\({}^{2}\) area size has been established.
3. After the UNNs are distributed to the environment, their location information is not known at the first stage. It is assumed that all of these nodes are static, and their positions are unchanged.
4. The communication range (CR) of each sensor node in the network is fixed, and its value is CR m.
5. An MA is location-aware within the network and is ready to traverse the entire network in straight lines depending on each path pattern.
6. The MA stops at certain intervals while moving. It is preset as dx or dx\(/\sqrt{2}\) m between two successive points. The difference between intervals will be explained in Section 4.1.
7. The MA sends the location signals from its stopping points to UNNs within the CR. Both the MA and the UNN can communicate with each other only if both are in each other's CRs.
8. The distances from UNNs to MAs are estimated using the RSSI technique. When the UNN receives at least three non-collinear localization messages, it determines its location according to the APT localization method. In the WCL method, all ANs in the CR are taken into account.
9. The MA has enough energy to move and broadcast location information during the localization process. MA consumes more power than any UNN.
10. The communication model is a channel by log-normal shadowing fading. Considering the radio signal propagation loss, we assume that unknown nodes can only receive the beacon packet in a circular region within a communication radius.
11. There is no collision between the MA and the UNNs; that is, MA does not go through any UNN.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 4 Case Studies
### Microsoft IntelliCode's Enhancement of Developer Productivity and Code Quality  
Microsoft IntelliCode, an AI-driven extension for Visual Studio and Visual Studio Code, leverages models trained on thousands of open-source projects to provide intelligent code completions and suggestions [59].  
IntelliCode addresses common coding challenges such as inconsistent coding styles, manual code reviews, and the need for repetitive boilerplate code. By offering context-aware suggestions, IntelliCode accelerates the coding process while promoting best practices. Developers can further refine IntelliCode by training it on their own repositories, enhancing its project-specific recommendations.  
Empirical studies indicate a 30% increase in coding speed and a 25% reduction in bugs when teams adopt IntelliCode's recommendations. This not only expedites development cycles but also reduces technical debt, improving maintainability and easing collaborative workflows for distributed teams. Surveyed developers reported high satisfaction, citing IntelliCode's positive impact on both productivity and code quality.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
###### Abstract  
Extensive attention has been given to the use of cognitive radio technology in underwater acoustic networks since the acoustic spectrum became scarce due to the proliferation of human aquatic activities. Most of the recent studies on underwater cognitive acoustic networks (UCANs) mainly focus on spectrum management or protocol design. Few efforts have addressed the quality-of-service (QoS) of UCANs. In UCANs, secondary users (SUs) have lower priority to use acoustic spectrum than primary users (PUs) with higher priority to access spectrum. As a result, the QoS of SUs is difficult to ensure in UCANs. This paper proposes an analytical model to investigate the link connectivity and the probability of coverage of SUs in UCANs. In particular, this model takes both topological connectivity and spectrum availability into account, though spectrum availability has been ignored in most recent studies. We conduct extensive simulations to evaluate the effectiveness and the accuracy of our proposed model. Simulation results show that our proposed model is quite accurate. Besides, our results also imply that the link connectivity and the probability of coverage of SUs heavily depend on both the underwater acoustic channel conditions and the activities of PUs.  
connectivity; coverage; underwater acoustic networks; cognitive radio; spectrum availability +
Footnote †: journal: Sensors
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 6 Conclusions  
In this paper, we established a symmetric-geometric baseline analysis model to explore the effect of baseline intensity on the height-dimensional imaging performance within a fixed baseline span, and proposed a maximum baseline deviation estimation method to explore the maximum vertical baseline deviation that ensures adequate height-dimensional imaging performance. From the experimental results above, it can be concluded that the system had superior height-orientation performance when the baseline of an SMS-TomoSAR system met the allowable maximum baseline perturbation, which has practical significance for the actual system baseline design. In other words, the allowable maximum baseline perturbation can provide a reference satellite baseline control deviation for the design or spatial position control of an SMS-TomoSAR system. Finally, we will continue to investigate other issues of SMS-TomoSAR systems, in order to contribute further to the existing body of research in the future.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61501477 and 61171123.  
The authors would like to thank the College of Electronic Science and Engineering of National University of Defense Technology for providing the experiment condition.  
The authors declare no conflict of interest.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 7 Conclusions  
In this paper, two precoding techniques for a novel RIS-MU-DQSM downlink transmission system have been presented. Instead of being applied at the remote RIS, the phase shift vector is designed and applied at the BS, simplifying the system's design and implementation. Two different strategies were proposed and evaluated for the implementation of the proposed system. In the reception, the detection complexity of the system was improved 51% by incorporating a novel spatial modulation technique. Results show that the proposed RIS-MU-DQSM-I strategy has the lowest detection complexity. However, in this case, the ZF precoding imposes the condition \(N_{t}=N_{s}\). This limitation was overcomeby the RIS-MU-DQSM-II strategy, which exploits the diversity in the RIS to obtain up to 18 dB gains in BER performance when compared with the conventional MU-MIMO-SMux system based on QAM and a similar based on AF relay under the same condition and parameters. Mainly, the proposed RIS-MU-DQSM system reduces the signal processing required at the remote RIS, making it suitable for practical implementations of future wireless communication networks.  
Conceptualization, F.R.C.-S. and C.A.A.-M.; methodology, J.A.D.P.-F, F.R.C.-S. and C.A.G.; software, J.S.; validation, J.S., F.R.C.-S. and V.B.K.; formal analysis, F.R.C.-S. and V.B.K.; investigation, F.R.C.-S., C.A.A.-M., J.S., C.A.G. and J.A.D.P.-F.; resources, J.A.D.P.-F, F.R.C.-S. and C.A.G.; writing, original draft preparation, F.R.C.-S.; writing, review and editing, C.A.A.-M., V.B.K., J.S. and C.A.G.; supervision, V.B.K.; project administration, J.A.D.P.-F. and F.R.C.-S.; funding acquisition, J.A.D.P.-F. All authors read and agreed to the published version of the manuscript.  
The authors would like to thank Universidad Panamericana, for their support through the program \"Fomento a la Investigacion UP 2022\", and the project \"Diseno de sistema de comunicacion vehiculo a vehiculo MIMO-OFDM basado en modulacion por indice\" UP-CI-2022-GDL-ING, and to the Universidad Panamericana Direccion Institucional de Investigacion. The Projects FONDECYT Regular 1211132, and STIC-AmSud AMSUD220026.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion  
During our research, we adjusted the parameters of various models and selected an optimal set of parameters, thus training a decent model. Despite this, several issues remain that warrant further exploration:  
Data collection: How can we enable sensors to gather the most complete signals from numerous mechanical components? Additionally, the details concerning data transmission are worth examining. These aspects are critical to improving the data quality used in our models.  
Unknown factors: Given the diverse and complex industrial fault modes, there might still be unidentified factors affecting the accuracy of our models in real-world applications. Exploring these unknown elements is necessary to improve our diagnostic tools.  
Fault diagnosis techniques: After years of development, most fault diagnosis models have achieved high accuracy. However, we need to revisit weak fault feature extraction methods based on advanced signal processing techniques as they are crucial for intelligent fault diagnosis. One example is the noise-enhanced weak signal detection of fractional nonlinear systems and its applications in mechanical fault diagnosis. However, this area requires specialized domain knowledge to support its development.  
In summary, despite the progress we have made, many issues warrant deeper research and discussion. Through continuous effort and exploration in the future, we will gain a deeper understanding of these issues and further enhance the performance of our model and its applications.