esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Results
#### 4.3.3 Comparison Experiment with Detection Engines  
VirusTotal [55] can determine whether a domain name is an AGD or not; in addition, it also integrates detection results of many independent detection engines which determine whether a domain name is malicious. We obtained the recall rates of each detection engines in VirusTotal and our method on the Netlab 360 generalization testing sets. The detection effects are shown in Table 10. For the detection engines in VirusTotal, the recall rate represents the proportion of domain names predicted as malicious; and for VirusTotal itself, the recall rate represents the proportion of domain names predicted as \"DGA\". We only present engines with the recall rates over 30%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **Ours** & **VirusTotal** & **Heimdal Security** & **Antiy-AVL** & **Seclookup** \\ \hline Recall & 92.27\% & 85.75\% & 83.91\% & 43.40\% & 31.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Recall rates of different open access detection engines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Indicators** & **Wordninja** & **Wordninja + Statistics** & **Wordninja + 1, 3-Gram + Statistics** & **Wordninja + Statistics** \\ \hline \multirow{4}{*}{Dictionary-based dataset} & Accuracy & 95.00\% & 95.17\% & 96.17\% & 96.33\% & 96.19\% \\ \cline{2-6}  & Precision & 94.90\% & 95.20\% & 96.12\% & 96.34\% & 96.32\% \\ \cline{2-6}  & Recall & 95.56\% & 95.56\% & 96.55\% & 96.64\% & 96.38\% \\ \cline{2-6}  & F1 & 95.23\% & 95.38\% & 96.34\% & 96.49\% & 96.35\% \\ \hline \multirow{4}{*}{Character-based dataset} & Accuracy & 97.41\% & 97.55\% & 98.36\% & 98.64\% & 98.57\% \\ \cline{2-6}  & Precision & 97.57\% & 97.60\% & 98.27\% & 98.68\% & 98.74\% \\ \cline{2-6}  & Recall & 97.18\% & 97.44\% & 98.42\% & 98.56\% & 98.36\% \\ \cline{2-6}  & F1 & 97.37\% & 97.52\% & 98.34\% & 98.62\% & 98.55\% \\ \hline \multirow{4}{*}{Full dataset} & Accuracy & 95.46\% & 95.43\% & 96.89\% & 97.24\% & 97.10\% \\ \cline{2-6}  & Precision & 95.48\% & 96.10\% & 96.85\% & 97.22\% & 97.45\% \\ \cline{1-1} \cline{2-6}  & Recall & 95.44\% & 94.68\% & 96.92\% & 97.25\% & 96.72\% \\ \cline{1-1} \cline{2-6}  & F1 & 95.46\% & 95.39\% & 96.89\% & 97.23\% & 97.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of different feature fusion methods.  
The results in Table 10 show that, our method achieved higher recall rate than the detection engines in VirusTotal.
## 4 Results  
This section compares the performance of the RF-based image segmentation method with the performance of three other conventional segmentation methods: HSV segmentation based on color thresholding, edge detection-based image segmentation, and the convolutional neural network model called \"DeepLabv3+\" (Table 3). Moreover, the traditional RF method is also introduced to compare with the improved RF algorithm proposed herein. The HSV color space differs from the standard RGB color space, because the former separates the pixel intensity from the actual color of the image. This is useful for our dataset because illumination conditions varied between images due to outdoor conditions. Using only the hue channel from the HSV image, we applied an Otsu threshold to extract the leaf area from the other parts of the image. The edge-detection-based algorithm (EDA) completes the image segmentation by detecting the gray-level mutation part. This type of mutation generally corresponds to an extreme point of the first-order derivative, or the zero crossing of the two derivatives. In this paper, we use the Roberts operator as a differential operator for our edge detection. Furthermore, DeepLabv3+ is a convolutional neural network model designed for pixel-based semantic image segmentation. It builds upon the DeepLabv3 design and combines a spatial pyramid pooling structure with an encoder-decoder structure, in order to achieve state-of-the-art segmentation results. Note that the DeepLabv3+ is based on the open source code available at [[https://github.com/tensorflow/models/tree/master/research/deeplab](https://github.com/tensorflow/models/tree/master/research/deeplab)]([https://github.com/tensorflow/models/tree/master/research/deeplab](https://github.com/tensorflow/models/tree/master/research/deeplab)). The checkpoint was initialized by using the PASCAL VOC dataset with the following parameters. We also used the following hyper-parameters in all our experiments: The Base Learning Rate was 0.01, the Momentum was 0.9, the Dropout was 0.5, and the Iteration Time was 1000.  
\begin{table}
\begin{tabular}{c c} \hline \hline output\_stride & 16 \\ \hline crop\_size & \(513\times 513\) \\ \hline initial\_learning rate & 0.007 \\ \hline atrous rates & [6,12,18] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of DeepLabv3+.
## 4 Discussion  
The GPTMS-SiO\({}_{2}\) nanofluid has been observed to exhibit changes in wettability through contact-angle variations on limestone and dolomite under high-salinity and high-temperature conditions, as confirmed in a previous study [11]. Furthermore, viscosity, a key factor in fluid flow processes, has been analyzed under enhanced oil recovery operating conditions [21]. In this study, we focused on enhancing the oil recovery potential of this nanofluid through its reactivity with carbonate rock formations. To achieve this, flotation tests were conducted to increase the specific surface area of rock samples and maximize the fluid-rock interaction [16]. Additionally, core flooding experiments were performed to represent fluid-rock reactions in the flowing path within actual carbonate reservoirs. Throughout the experiments, we observed that this nanofluid could increase the hydrophilicity of the rock samples, altering the rock surface from oil-wet to water-wet conditions under the overall experimental conditions. Notably, the composition of the rock had an impact on the performance of the nanofluid, including nanofluid concentration and reaction time, which are operational factors affecting fluid displacement in rocks. However, note that flotation tests, as previously explained, may not quantitatively represent the reactivity of rock-fluid interactions observed in actual fluid performance. Instead, they can be qualitatively described in terms of their effectiveness in improving wettability. Furthermore, the analysis of core flow experiments indicated that the contact angle no longer changes with increasing reaction, implying the existence of a critical range. However, the permeability reduction effect continued to increase. This implies that nanoparticle adsorption on the rock surface forms not a monolayer but a bilayer or more. Considering that changes in wettability and permeability are crucial factors determining the success of EOR, selecting a critical reaction time point is deemed highly important. Beyond a critical reaction time, in our case, that is, for 24 h, no further changes in the contact angle were observed, and the permeability decreased. Therefore, determining the optimal reaction time, known as the critical time, is necessary from the perspective of oil recovery efficiency. This suggests the need for research on rock-fluid interactions to effectively operate EOR methods using nanofluids.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 2 Related Work
### Semi-Supervised Learning  
The principal advantage of semi-supervised learning (SSL) is that a substantial amount of unlabeled data can be utilized to enhance the efficacy of the model during training. The main challenge resides in designing effective supervised signals for unlabeled data. Recently, there has been a surge in research on semi-supervised medical image segmentation [19]. Two primary loss functions proposed to tackle this issue are entropy minimization [20; 21; 22] and consistency regularization [23]. The fundamental concept of entropy minimization involves assigning pseudo-labels to unlabeled data, which are then combined with manually labeled data for retraining. The core principle of consistency regularization posits that images subjected to data perturbation and geometric transformations should maintain unchanged outputs, ensuring consistent predictions.  
Among these approaches, FixMatch introduces a strong perturbation to the unlabeled image, with the training process supervised by the predictions from the weakly perturbed image. This approach incorporates the entropy minimization concept to obtain pseudo-labels while ensuring consistency across both strongly and weakly perturbed views. UniMatch demonstrated that strongly augmented data enhance model learning effectiveness, and thus, based on FixMatch, strongly augmented perturbations at the feature level were added to form a two-stream strong perturbation strategy alongside the original strongly augmented branch. Although this framework is relatively simple and efficient, the potential for improvement in pixel-level semantic segmentation of medical images remains unclear.
## 2 Materials and Methods
### Eligibility Criteria  
To be considered for inclusion in this study, articles had to meet specific eligibility criteria, as outlined by both inclusion and PICOS criteria:Inclusion Criteria:  
1. Language Requirement: Articles had to be in English.
2. Full-Text Availability: The full text of the article needed to be accessible.
3. Patient Population: The study had to involve patients with fractured jaws who were treated using miniplate fixation and had a minimum follow-up duration of 12 months.
4. Intervention and Comparison: The study should investigate rigid internal fixation with miniplates, with or without plate removal.
5. Outcome Measure: The primary outcome of interest was the percentage of miniplates that were removed.
6. Study Design: Eligible studies included clinical trials, controlled trials, retrospective studies, and case series. Exclusion Criteria:  
1. Language Exclusion: Articles published in languages other than English were excluded.
2. Non-Human Studies: Studies conducted on animals or in vitro were not considered.
3. Duplicate Publications: Duplicate articles, which could introduce bias, were excluded.
4. Insufficient Information: Articles lacking complete and detailed information about miniplate sites and numbers were excluded.
5. Publication Types: Editorial letters, case reports, and review articles were excluded.
6. Short Follow-Up: Studies with a follow-up duration of less than 12 months were also excluded.  
The screening process involved a review of article titles and abstracts, followed by a thorough examination of the full-text publications of potentially relevant studies. Two independent reviewers (NA and NI) conducted this eligibility assessment in a standardized, non-blind manner. The agreement level between the reviewers was assessed using the kappa statistic for all screening steps. The kappa value obtained was 0.83, indicating excellent agreement. Any disagreements between reviewers were resolved through consensus, and if necessary, a third reviewer (MJ) was consulted.
## 4 NDIR Sensing Module and Experimental Configuration  
As illustrated in Figure 7, the IR light source, OIR-715, was used, with a wavelength range of visible to 4.4 \(\upmu\)m, particularly for hydrocarbon detection at 3-3.5 \(\upmu\)m, CO\({}_{2}\) detection at 4154.4 \(\upmu\)m, and a cut-off wavelength of 5 \(\upmu\)m. At the opposite end of the optical tube, there is a thermopile IR detector, HTS-E21-F3.91/F4.26 with a resistance of roughly 30 k. The gas chamber is developed by a high reflectance inner layer to exclude extraneous IR from the wall.  
An ultra-low offset chopper amplifier AD8552 amplifies the sensor output signal. The amplified signal is transformed to digital code which uses a 24 bit ADC AD7799 after amplification, and an ARM-based CPU NUC120 is constructed to execute and make computation and calibration under control. The ARM controls a p-type PMOS to turn the current on and off through the heater on and off in order to increase or decrease the temperature to achieve the aim of controlling the IR light source. ARM, which is based on calibration methods and a fitting curve, measures the concentration in this experiment.  
Figure 6: Microscopic picture of IR light source.  
Figure 7: Experimental setup for NDIR modulation.
## 7 Result and Discussion
### The Accuracy Solution's Comparison  
The contribution to smart ERP is evaluated within the NSGA-II algorithm without agents and the NSGA-II algorithm using different multi-agent strategies (Ring, Master/Slave, and Hybrid). These strategies are described as follows:  
* **Master/Slave strategy:** The first strategy is focused on determining optimized global solutions from local best solutions. This strategy involves the services' local discovery and solution local evaluation using slaves, while the master agent chooses the best global solution from the selected best local solutions.
* **Ring strategy:** The second strategy explores some sub-populations in parallel to find the best solution. Each agent shares its optimal solution with its neighbor.
* **Hybrid Strategy:** To explore the advantages of both previous strategies and ensure further improvement, each slave sends its optimal solution to the master agent. Each slave shares his optimal solution with his neighbor. Finally, the master agent returns the global solution.  
All three strategies are involved in determining quality solutions in an acceptable execution time. The results of Equation (10) are crucial in determining the most efficient solution in terms of QoS and energy. The obtained results on NSGA-II with/without agents are tabulated in Tables 4 and 5. It is quite remarkable that the inclusion of the multi-agent approach performed by both QoS and energy violation degree reached about 97% accuracy, which is perfect compared to the strategy without agents.  
Figure 12 depicts the scores of different strategies. It was evaluated using Equation (10) to measure how accurately the solution was made by each strategy, with a higher score indicating better performance. It appears that in general, the hybrid strategy has the highest scores across most of the cases, indicating that is the most accurate model. However, it is important to note that the results of the hybrid strategy vary depending on the number of generations. Some solutions are better determined by different strategies.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 4 IoT Applications  
The use of Information and Communication Technologies (ICT) as an enabler of smart cities creates the concept called Urban Automation Networks (UANs), which allows a wide spectrum of applications focused in smart cities, such as garbage collection, lighting control, green zone management, environmental control, parking availability, street traffic, utility infrastructure and security. All aforementioned applications can be included within the IoT applications framework. In addition, there are many other important applications available for IoT, such as multimedia and smart/e-health applications, smart metering, smart green and integrated transport [14], home automation, consumer services, smart grids [15], smart automotive and transit, smart logistic and supply chain, smart oil, gas manufacturing and industrial applications.  
Building home automation consists on the automatic centralized control of a building in areas such as Heating, Ventilating and Air-Conditioning (HVAC), lighting, safety and security systems. Also, smart metering applications are focused on smart grids, including on demand and periodical meter reading, load management and electric service prepayments. Multimedia (audio and video devices, such as surveillance cameras or wireless speakers are not commonly considered within the IoT, but they can be used as sensors/actuators) and smart/e-health applications include phone conversations and video transmissions for emergency notification, transference of high resolution images, and smart monitoring on biomedical signals, such as electroencephalography (EEG), electrocardiography (ECG) and blood pressure (BP).
## 3 Results and Discussion
### Study Region Analysis
#### 3.2.2 Groundwater Storage and NDVI Trends  
Although groundwater is constantly replenished by precipitation that infiltrates into aquifers, change in groundwater storage, dS/dt(gw), has had a negligible response to the \"excess water\" associated with (P-Ru). Our analysis of groundwater averaged over the study region indicates a slightly decreasing  
Figure 3: Observed annual (**a**) precipitation (mm\(\cdot\)yr\({}^{-1}\)) and runoff (mm\(\cdot\)yr\({}^{-1}\)) for 1901 to 2008, (**b**) change in groundwater storage (mm\(\cdot\)yr\({}^{-1}\)) for 1955 to 2011 and (**c**) NDVI (dimensionless) for 1982 to 2009. The shaded area shows the mean \(\pm\) sample mean standard deviation (Equation (2)). Observations are averaged for the eastern United States. See text for details.  
trend. Similar to runoff, a trend detection analysis indicates that this trend in the annual dS/dt(gw) is not significant (\(p=601\)) in the statistical sense (Figure 3 and Table 2). Other work [41] suggests that areas in the northern New England and the South Atlantic-Gulf regions experienced prolonged declines in groundwater levels, but also suggests increases in most other regions, resulting in an uncertain trend for the entire region over the period extending from 1940 to 2007.  
Our analysis indicates a positive and statistically significant trend in vegetation greening (NDVI) over the study region.
## 3 Results
### Functional Categories of Genes Downregulated during Prolonged Continuous Culture  
Among downregulated genes whose function fell into one of the significantly overrepresented categories (see Figures 1 and 2 and Figure S3), there was a subset of genes involved in cell wall organization; in the biosynthesis of certain amino acids (note that some other genes for synthesis of other amino acids were upregulated); plasma membrane transporters for ammonium, amino acids (very strongly repressed) and other compounds; sizable groups of genes encoding ribosomal proteins and stress response genes and a large, heterogeneous group of genes involved in the biosynthesis of various compounds.  
Among the genes of non-overrepresented functional categories, there were genes encoding proteins localized to various cellular compartments (mitochondria, the cell periphery, endomembrane system, or nuclear pore complex); involved in various aspects of cellular functions (respiration, lipid metabolism, genome maintenance, RNA metabolism, protein modification, invasive growth, and zinc ion homeostasis). There was also an _ATG36_ gene required for pexophagy, and downregulation of this gene is compatible with the upregulation of peroxisomal metabolism genes.  
Among the most strongly downregulated were genes encoding transporters of amino acids (_BAP3_, _TAT1_, _TAT2_, _AGP1_, _MUP1_, _GNP1_), a protein involved in the regulation of mitochondrial expression of ATP synthase subunits (_NCA3_), ammonium permeases (_MEP1_, _MEP3_), desiccation stress-protecting hydrophilin (_GRE1_), sodium pumps (_ENA1_, _ENA2_), nitroreductase involved in the repression of fatty acid biosynthesis (_FRM2_), glycerol transporter (_STL1_), and several genes of unknown function. Forty-seven downregulated genes had unknown or poorly characterized functions.  
Interestingly, there were groups of genes that are functionally related yet were present either in upregulated or in downregulated sets. For instance, genes whose expression was most strongly affected encoded plasma membrane transporters for nutrients. Among those strongly induced were _OPT2_, _DAL5_, _DAL4_, and _DUR3_, whereas _GNP1_, _TAT2_, _MUP1_, _AGP1_, _TAT1_, _MEP3_, _MEP1_, and _BAP3_ were strongly downregulated. The distinction seems to be based on ligand specificity: transporters of allantoin, oligopeptides, urea, and polyamines were induced, whereas transporters of amino acids and ammonia were repressed. One could interpret these changes as resulting from a change in the medium composition; however, this was kept constant throughout the entire culture period.  
Similarly, while we observed the downregulation of stress-induced and/or stress-related genes (_PUN1_, _YJL144W_, _ECM5_, _DAK2_, _GAD1_, _YPT53_, _TPS2_, _GPP2_, _SNO4_, _HSP32_, _TPS3_, _GPP1_, _TSL1_, _SIP18_, _HSP33_, _EDC2_, _MTL1_, _HSP150_, _DOG2_, _SSA3_, _CIN5_, _PA13_, _HSP12_, _ALD3_, _CTT1_, _SED1_, _DDR2_, _GRE2_, _STL1_, _FRM2_, _GRE1_), a small number of such genes (_YGK3_, _OYE3_, _ALD2_) were upregulated.
## 1 Introduction  
In recent years, the idea of \"smart cities\" has gained a lot of attention. Smart cities can solve many of the problems that traditional cities face by combining various intelligent systems such as infrastructure, energy, transportation, and management [1].  
Intelligent transportation systems (ITS) play an important and effective role in smart cities, as they have the ability to enhance the efficiency and safety of transportation in smart cities through the use of advanced technologies and data analysis techniques [2, 3, 4]. One of these techniques is traffic sign detection and classification, which enhance traffic management, decrease congestion, and increase safety [5].  
Traffic sign detection and classification systems depend on computer vision techniques and machine learning algorithms, which give the ability to autonomous vehicles, connected cars, and intelligent transportation systems to have many advantages, one of which is increased road safety. By detecting and recognizing road signs, drivers can be alerted to speed limits, stop signs, pedestrian crossings, and other important traffic regulations. This approach can decrease the risk of accidents and make the road safer for drivers and pedestrians [6]. Also, through the analysis of the traffic signs, signal-control systems can dynamically adjust timing, leading to a decrease in travel time, reducing congestion, achieving efficient traffic flow, and improving transportation efficiency [7].  
Hence, we introduce a Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture that makes use of computer vision and machine learning techniques to identify and detect traffic signs in real-time. By minimizing the time and effort needed to detect traffic signs, our system seeks to offer users in smart cities a seamless and effective driving experience.  
The remaining contents of this paper have been arranged as follows: Section 2 provides a literature review of previous work on traffic sign detection and classification, while Section 3 covers the dataset's organization. In Section 4, the architecture of the proposed LE-CNN model is discussed. Section 5 discusses the experiment and its findings before moving on to Section 6, which provides the conclusion and recommendations for future work.