esa-sceva/satcom-chunk-collection huggingface dataset:
# Fracturing Fluids and Their Application in the Republic of Croatia
## 1 Introduction
However, a few studies have researched the potential contamination of groundwater through leaky abandoned wells in shale gas development projects [32; 33]. The long-term simulation shows that only 0.02% of injected fracturing fluid reaches the aquifer, so the risk is low [32]. For assessing the potential impact of hydraulic fracturing on groundwater aquifers, a conceptual model using a generic features, events, and processes (FEP) database has been developed [34]. According to [PERSON] et al. (2020), hydraulic fracturing treatments consume at least 757 m\({}^{3}\) of water in conventional wells and up to 60,567 m\({}^{3}\) of water in unconventional wells [35]. However, 39 m\({}^{3}\) of fluid and approximately 7000 kg of proppart were used to perform one HF in Croatia, which is far less than the stated amount [3]. The use of water causes many potential damaging issues in the formation and limits the amount that can be saved for future generations. One solution is the use of waterless fracturing fluids (hydrocarbon-based, liquid CO\({}_{2}\), energized, and CO\({}_{2}\) and N\({}_{2}\) foamed fluids) which were developed to reduce or eliminate the need for water in hydraulic fracturing [35]. The aim of this research is, in a systematic way, to present fluid design and testing, different types of fracturing fluids, their advantages, disadvantages and the area of application with an emphasis on fluids for hydraulic fracturing of naturally fractured reservoirs. Along with a detailed analysis and quantification of fracturing fluids and their application for the hydraulic fracturing of naturally fractured formations, for the first time, we endeavored to comprehensively review and jointly present the available laboratory tests, fluids and additives, and their application in the practice of hydraulic fracturing. This paper can assist in fluid selection and in the design of fracturing treatments. Additionally, four field case studies of hydraulic fracturing in different types of reservoir rocks (carbonate, siltstone, and quartzite) in Croatia are presented and analyzed.
## 5 LNA Robustness  
LNA robustness refers to the ability of a Low Noise Amplifier (LNA) to maintain reliable performance under stressful conditions, particularly when subjected to high input power levels and electromagnetic interference (EMI) [23]. Verification of LNA robustness ensures that the amplifier can withstand harsh or unexpected RF environments without degrading performance or damaging the system. It is especially critical in high-reliability, high-power RF applications.  
The LNA's robustness to overdrive continuous wave (CW) and pulse wave input power can be assessed by analyzing its forward gain behavior [22]. To do this, an EM simulation is carried out to predict the forward gate current response when the LNA is subject to high CW power, Figure 20. Under input overdrive conditions, the forward gate current begins to increase, which will result in performance degradation. For safe operation, the forward gate current should not exceed 0.2 mA per finger, as recommended by the manufacturer. According to simulations in Figure 20, the maximum input power that the amplifier can tolerate before performance degradation is observed is 18 dBm at 13 GHz and 19 dBm at 15 GHz.  
Radio transceiver systems are highly susceptible to electromagnetic interference (EMI), including high-intensity radiated fields, lightning strikes, and high-power microwaves [23]. Electromagnetic pulses (EMPs) can lead to temporary malfunctions or permanent damage to internal electronics and circuitry.  
To simulate the LNA's response under exposure to high-intensity EMPs, various impulse waveforms were injected into its gate terminal. A sinusoidal signal modulated by a square pulse was used to emulate a typical EMP waveform, as illustrated in Figure 21. The pulse power was gradually increased from 5 dBm to 15 dBm, with a fixed pulse width of 50 ns and varying duty cycles of 10%, 15%, and 20%.  
When such a pulse is applied, the output becomes non-uniform and varies over time. Therefore, the calculation of the root mean square (r.m.s), as given by Equation (6) [10], is necessary to evaluate the resulting current, voltage, and power values.  
\[I_{r.m.s}=\sqrt{\frac{1}{T}\int_{0}^{T}[I(t)]^{2}\,dt}, \tag{6}\]  
Here, \(T\) represents the duration of the injected pulse, and \(I(t)\) denotes the forward gate current as a function of time. The maximum forward gate current recommended for  
Figure 20: Forward gate currents vs. different CW input power levels: (**a**) at 13 GHz, (**b**) at 15 GHz.  
safe operation using this technology is 2 mA per finger. Therefore, the LNA operates within its safe region as long as the gate currents in all stages--\(I_{S_{1}}\), \(I_{S_{2}}\), and \(I_{S_{3}}\)--do not exceed this limit.  
Table 5 summarizes the pulse values that will result in safe operation of the LNA, according to the simulation results.
## 3 Results and Discussion
### Simulated Vehicle Experiments  
The average %R, average %T, average %Res, and their corresponding standard deviation (SD) for each stabilization technology and testing surface are shown in Table 3. Four (4) replicates were conducted per stabilization technology and testing surface.
## 3 High-Energy Few-Optical Cycle Parametric Sources in the IR Spectral Region
### Available Sources Based on the OPA Design
#### 3.1.2 Sub-Two-Cycle IR Sources Based on Narrowband Parametric Amplification System
The out-coming pulses have an energy of 240 \(\upmu\)J; the pulse spectrum is very broad extending from 1.15 \(\upmu\)m to 2.15 \(\upmu\)m, but a residual chirp is present, which is compensated using a plate made up of fused silica. After compression, nearly transform-limited durations of 9.5 fs are obtained, corresponding to 1.6 optical cycles. The energy fluctuation stands around 3.5% after the OPA, around 6% after the HCF. As far as the CEP stability is concerned, a 350-mrad rms fluctuation is measured. If compared to the OPA system from [28] described in the previous subsection, the CEP fluctuations provided by the HCF-based setup are larger. This is mainly due to the fact that an additional source of jitter is introduced in [35], arising from a strong coupling effect in the HCF between pulse intensity and CEP fluctuations. As a result of this coupling effect, a slight variation in the intensity of the radiation entering the fiber can considerably affect the CEP stability. The HCF-based approach is thus very sensitive to pointing instability, as well as intensity instability after the OPA, which are anyway unavoidable due to the non-linear nature of the parametric amplification process. Feedback loop systems can however be installed for achieving better performances in terms of stability. Definitely, the sources based on HCF, as well as filamentation have the main advantage of providing more degrees of freedom to the users (type of gas, pressure, propagation distance) for achieving an optimum control on the spectral bandwidth of the output radiation, thus allowing for sub-two-cycle pulse generation. However, some limitations are set in the pulses energy, standing at the hundred \(\upmu\)J-level.
## 2 Materials and Methods
### Certainty of Evidence Assessment  
The certainty of evidence for each outcome (pain perception, treatment attitude, compliance, satisfaction, and OHRQoL) was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach [49]. Four primary domains were considered: risk of bias, inconsistency, indirectness, and imprecision. Each domain was rated as \"not serious\", \"serious\", or \"very serious\" according to pre-specified criteria: risk of bias was assessed using the JBI Critical Appraisal Checklist; inconsistency was evaluated by examining variability in study results and considering differences in study methods and populations; indirectness was evaluated by examining how closely the included studies aligned with the PICO elements defined in the eligibility criteria; imprecision was appraised based on the number of studies, sample size, and the presence or absence of pooled effect estimates and confidence intervals. Other considerations included possible residual confounding, effect size directionality, and study limitations. In addition,effect directions were summarized to indicate whether higher levels of specific personality traits were associated with positive, negative, or no significant effects on the outcomes. The overall certainty of evidence for each outcome was categorized as high, moderate, low, or very low [50].
## 1 Introduction
### Hybrid Multilevel Modulations and PDM/MDM Enabled OSC Systems  
Ultra-high speeds of several Tbps can be achieved with the deployment of PDM in multilevel modulations. An ultra-long-range 60,000 km OSC link was proposed employing PDM in CO-OFDM at 400 Gbps [37]. Further, at the same speed, a security-improved PDM-enabled 25,000 km long OSC link was reported in [38] by employing spectral amplitude codes (SAC). A 40,000 km gap between two satellites at 160 Gbps over a single channel PDM-enabled QPSK was demonstrated in [39]. A high capacity 16 \(\times\) 100 Gbps OSC system was proposed using PDM-QPSK in CO-OFDM and obtained a 15,600 km total reach [40]. Further, a futuristic and potential multiplexing technique incorporating different intensity profiles of laser light was introduced in OSC systems for high-speed and ultra-long reach links. Different 64-intensity profiles of light were generated from azimuthal (\(l\)), and radial numbers (\(m\)) of EM waves in [41] for a 40 Gbps (maximum) OSC link incorporating differential QPSK, Manchester, and DPSK modulations. Results indicated that DQPSK performed far better than DPSK and Manchester coding over 3750 km of OSC link distance. OFDM modulation has high spectral efficiency, and, therefore, it was combined with MDM using four different modes of light operating at 80 Gbps [42] and covered 10,000 km successfully. There are two main types of light modes such as LG modes and Hermite Gaussian (HG) modes. LG modes have circular profiles, and HG modes have vertical helical rotations, and both can be generated by varying \(l\) and \(m\) numbers. HG modes were used in [43], where two 50 Gbps data streams were modulated with OFDM and carried by two different HG modes over a 20,500 km OSC link. An ultra-high capacity 4 Tbps 16,000 km OSC link at 2 \(\upmu\)rad pointing errors was demonstrated using MDM in [44]. As researchers learned about the cutting-edge competence of light modes, they further investigated OAM beams in TLBC, such as in optical fiber communication (OFC) and optical wireless communication (OWC). The theory and literature of OAM in OWC systems are discussed in Section 1.3.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 3 Modeling and Parameter Identification  
The first step in the controller design of attitude stabilization system of elevation part is obtaining the mathematical model of the elevation part. The velocity model is obtained first and the angle model can be obtained by the integration of the velocity model.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 4 The Experiment and Analysis  
The proposed algorithms are implemented by Satellite Tool Kit (STK) and Matlab2016a on a laptop with Core I7-6700 HQ, 2.4 GHz CPU, 16 GB memory, and a Windows 10.1 operating system. In this study, the autonomous task planning of the small satellite within 24 h, and the dynamic changes of the task and satellite status were simulated, with a task profit range of 1-10.  
In order to verify all of the crossover and mutation operators, we design four groups of experiments in this paper, with the degree of resource adequacy and time window overlapping as two variables. To investigate the performance of the improved genetic algorithms, this paper introduces a general genetic algorithm (GA) and improved genetic algorithm (IGA) as the core of the rolling-planning framework (step 2 in Figure 3), that is, the tasks are planned using a different task planning genetic algorithm in each group of experiments  
The parameters of the rolling-planning framework are as follows:  
1. The length of the rolling window is two orbital cycles.
2. The planning unit is half an orbital period.  
The same data used in the four groups of experiments are the satellite orbit related data (as shown in Table 1) and Earth station related data (as shown in Table 2).
###### Abstract  
Intrusion detection in the Internet of Things (IoT) environments is increasingly critical due to the rapid proliferation of connected devices and the growing sophistication of cyber threats. Traditional detection methods often fall short in identifying multi-class attacks, particularly in the presence of high-dimensional and imbalanced IoT traffic. To address these challenges, this paper proposes a novel hybrid intrusion detection framework that integrates transformer networks with generative adversarial networks (GANs), aiming to enhance both detection accuracy and robustness. In the proposed architecture, the transformer component effectively models temporal and contextual dependencies within traffic sequences, while the GAN component generates synthetic data to improve feature diversity and mitigate class imbalance. Additionally, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm is employed to fine-tune the hyper-parameters of the hybrid model, further enhancing learning stability and detection performance. The model is trained and evaluated on the CIC-IoT-2023 and TON_IoT dataset, which contains a diverse range of real-world IoT traffic and attack scenarios. Experimental results show that our hybrid framework consistently outperforms baseline methods, in both binary and multi-class intrusion detection tasks. The transformer-GAN achieves a multi-class classification accuracy of 99.67%, with an F1-score of 99.61%, and an area under the curve (AUC) of 99.80% in the CIC-IoT-2023 dataset, and achieves 98.84% accuracy, 98.79% F1-score, and 99.12% AUC on the TON_IoT dataset. The superiority of the proposed model was further validated through statistically significant \(t\)-test results, lower execution time compared to baselines, and minimal standard deviation across runs, indicating both efficiency and stability. The proposed framework offers a promising approach for enhancing the security and resilience of next-generation IoT systems.  
Keywords: intrusion detection; IoT networks; transformers; generative adversarial network; non-dominated sorting biogeography-based optimization +
Footnote †: journal: Future Internet
## 2 Description of Jilt North Mining Perimeter
### Equipment and Working Technologies  
The specific technological flux existing in Jilt North open pit (throughout 2023) is presented in Figure 6, with the specification that the A02 spreader, which deposits sterile rocks in an exterior waste dump, is used only in case of emergency (if failure occurs to the conveyor system or the spreaders operating in the internal waste dump).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crt. No.** & **Characteristics** & **MU** & **Determined Value** \\ \hline
1 & Total moisture & \% & 27.27–50.77 \\
2 & Ash relative to anhydrous coal & \% & 22.36–40.20 \\
3 & Volatile matter relative to & \% & 54.70–60.70 \\  & combustible mass & \% & 2.43–2.67 \\
4 & Carbon relative to combustible & \% & 2.43–2.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative characteristics of lignite from the Jilt North open pit [42].  
Figure 5: (**a**) Calorific power and (**b**) the thickness of the lignite layers from Jilt North open pit [42].  
Figure 6: Technological flux existing in Jilt North open pit [43].  
Excavation is carried out exclusively with high-capacity BWEs, type SRs 1400--30.  
The transport of both the waste rocks and the lignite resulting from the excavations is carried out on conveyor belts. The waste rocks are transported to the internal dump (occasionally to the external one), and the lignite to the Jilt coal depot and from here to the Turceni Central Power Plant. Currently, the Jilt North open pit is equipped with the following high-capacity technological equipment [43]:  
* BWs: 6 \(\times\) SRs 1400--30/7 (E06; E07; E14; E17; E18; E19);
* BWs: 3 \(\times\) SRs 470--15/3.5 (E09; E11; E16)--removed from the technological flux (in conservation, near the premises of the administrative building and the coal depot);
* High capacity conveyor belts, types: B1400; B1600; B1800; B2000; B2250, over 30 km;
* Belt wagons;
* Spreaders: 3 \(\times\) A2 RsB 6500.90 (A01, A03, A05) and one A2 RsB 6500.60 (A02);
* Depositing machines: combined machine-type KsS 5600 \(\times\) 40 and stacking machine-type ASG 6000.  
Added to these are a variable number (being used jointly with the Jilt South open pit) of classic excavators (with wheels and tracks), dumping trucks, and bulldozers, used for related activities (profiling, access roads, leveling, etc.).  
In the Jilt North open pit, the exploitation method used is the one with the transshipment of a part and the transport of another part of the sterile rocks to internal dumps (Figure 7) and the technology of excavation, transport, and dumping in continuous flux, through the use of excavation, transport, and dumping/deposition complexes [43]. The material deposited in the waste dump is leveled with the help of different types of bulldozers.  
It is expected that the current exploitation method and technological flux will be maintained until the cessation of productive activities (2028 according to the current license, or, by its extension, 2035 at most).
## 5 Analytical Comparison of MLP, SVM, and RF in Classifying Error in Simulated CPSs
### Experiment Method
The figure presents the comparative analysis of different models based on their performance measures such as accuracy, AUC, recall, precision, F1 score, Kappa, MCC, and time taken for predictions. It helps assess and compare the models' effectiveness in the given context. The table represents the performance metrics of different models. Here's an explanation of the metrics:* Model: The name or identifier of the model. * Accuracy: The proportion of correctly classified instances by the model. * AUC: The Area Under the Receiver Operating Characteristic (ROC) curve measures the model's ability to distinguish between classes. * Recall: Also known as sensitivity or true positive rate, it represents the proportion of true positive predictions out of all actual positive instances. * Prec. : Short for precision, it indicates the proportion of true positive predictions out of all predicted positive instances. * F1: The harmonic mean of precision and recall provides a balanced model performance measure. * Kappa: [PERSON]'s kappa coefficient assesses the agreement between the model's predictions and the actual classes, considering the agreement by chance. * MCC: Matthews Correlation Coefficient, a measure of the quality of binary classifications. * TT (Sec): The time taken by the model to make predictions (in seconds). Based on the table, it can be observed that the MLP Classifier (mlp) has the highest accuracy (0.3493) and AUC (0.5215), while the Random Forest Classifier (rf) has the highest MCC (0.1950). The SVM with a linear kernel (svm) performs less across multiple metrics.
## 2 Related Work
Two low-complexity classifiers based on order statistics were presented in [50], and LSVM algorithms were investigated for classification. In [51], deep neural networks for AMR were presented, and a stock well transform for underwater acoustic channels was presented in [52]. The SVM classifier was used with the energy entropy of s-transform time-frequency spectrum signals. A high-efficiency classification system using a genetic algorithm with a backpropagation neural network for four statistical features was developed in [53]. In [54], a comparative analysis of different distance methods using KNN classification of five different modulation schemes was presented. The authors showed that the Mahalanobis distance was better in terms of classification accuracy compared to the Minkowski, Euclidean, and correlation methods. The KNN was utilized for feature selection and then detection in [55]. In [56], fourth-conjugate cumulants of the estimated symbols were used to classify the modulation types. In [57], AMR was performed using step-wise regression and hierarchical polynomial classifiers, and HOCs were used to achieve higher accuracy. The authors of [58] used variational mode decomposition (VMD) for AMR when AWGN and non-Gaussian impulsive noise were present. A three-step methodology was adopted for the recognition of QPSK, 16-PSK, 64-PSK, QAM, 16-QAM, and 64-QAM in [59]. In [60], instantaneous features, such as instantaneous amplitude, phase, and frequency parameters, were utilized for AMR. Analog and digital signal classification was reported in [61] by using statistical characterization and an artificial neural network (ANN). In [62], modulation classification based on a constellation diagram with fuzzy logic was presented for QAM signals. A comprehensive review of AMR was presented in [63] that not only compared different types of AMR methods, but also provided the different types of software packages for AMR methods, as well as practical challenges in the implementation process.
# Antibacterialativity of Endodontic Gutta-Percha--A Systematic Review
###### Abstract  
Numerous failures in root canal treatment (RCT), attributed to the persistence of adverse microbiota, prompted researchers to develop a biomaterial with effective antibacterial and antifungal properties. In our systematic review, emphasis was placed on examining the antimicrobial properties of gutta-percha, the most used material for root canal obtruation. The review aimed to determine whether gutta-percha demonstrated adequate antibacterial and antifungal features. Additionally, it sought to identify specific substances added to gutta-percha's composition that could enhance the success rate of root canal obtruation. On October 30, 2023, electronic searches were systematically performed in the PubMed, Web of Science (WoS), and Scopus databases using the specified keywords: ((antibacterial) OR (antimicrobial)) AND ((gutta-percha) OR guttapercha)). A thorough evaluation commenced, with an initial pool of 330 studies, from which 174 duplicates were methodically identified and removed. The ultimate dataset for qualitative synthesis consisted of 26 studies. The results of the compared studies did not unequivocally indicate whether the use of gutta-percha alone exhibits antibacterial or antifungal effects. Among the six studies demonstrating results supporting gutta-percha's antimicrobial activity, five showed activity against _Enterrooccus faecalis_. Conversely, six studies concluded that gutta-percha lacks any discernible antimicrobial features. One study even suggested that gutta-percha might promote the progression of bacterial development. However, eight out of nine studies demonstrated an increase in gutta-percha's antimicrobial properties after the addition of chlorechidine. Furthermore, calcium hydroxide and iodoform also improved gutta-percha's properties, showing promising results. Unfortunately, none of the materials added to gutta-percha displayed comprehensive improvement in its antimicrobial efficacy, including activity against _Enterrooccus faecalis_. The review demonstrated the benefit of enhancing gutta-percha with chlorechidine, calcium hydroxide, and iodoform. Nevertheless, achieving the inactivation of _Enterrooccus faecalis_ posed a challenge.  
antimicrobial; canal treatment; endodontic; _Enterrooccus faecalis_; RCT