esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of the Research Progress in Combat Simulation Software
## 4 Typical Combat Simulation Software
### Jtls-Go  
The JTLS-GO (Joint Theater Level Simulation--Global Operations) [13; 45] is an interactive internet-enabled simulation software that models multisided air, ground, and naval civil-military operations with logistical and intelligence support. It is frequently used as a training support model that is theater-independent and does not require knowledge of programming to operate effectively. Its primary focus is conventional joint and combined operations at the operational level of war.  
With respect to the evolution of its software architecture, JTLS changed from the C/S architecture to the web-enabled architecture in 2005. Concretely speaking, the simulation personnel can use the \"interface program WHIP\" and \"interactive recall and playback program\" based on the web host to carry out planning preparation, simulation operation, and result analysis.  
JTLS-GO provides its functions through six models, namely force control, ground operations, air operations, maritime operations, logistics support, and C3I.  
1. Force control. This includes command authority, relations between all parties, and engagement rules. The force commander can assign operations to the subordinates involved in the combat simulation. The participants need to determine the relationship between each other. Each combat unit has a specific set of engagement rules for different adversaries.
2. Ground operations. The commander can set engagement rules, establish a new ground maneuver route, and issue commands such as attack, defense, and fire support.
3. Air operations. There are three ways to implement air operations, namely automatic air mission command generator, manual input of instructions, and the combination of the two methods. The air mission command generator can create task packages composed of different types of aircrafts.
4. Marine operations. This can simulate ship-to-ship missile attacks, naval gunfire, amphibious attacks, regional patrol and anti-submarine warfare, air operations based on aircraft carriers, etc.
5. Logistics support. This includes automatic resupply, integrated supply, emergency supply, supply transportation, force transportation, etc.
6. C3I. The information obtained by any combat unit can be used by the whole force, so that the commander can master the enemy situation and make a thorough and timely plan.
# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 3 Related Works
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **OSN** & **ML/DL** & **Techniques Used** & **Accuracy (A)** & **Limitation** \\ \hline
[45] & 2023 & Instagram & Hybrid ML & Neural network & A = 93\% & Low accuracy \\ \hline
[28] & 2023 & Facebook & DL & DNN & A = 99.4\% & It focuses on a Facebook dataset \\ \hline
[29] & 2022 & X & DL & LSTM & A = 99.6\% & No combination multiple models used for more better performance \\ \hline
[30] & 2022 & Instagram & ML & XGBoost and RF & A = 96.00\% & One dataset \\ \hline
[31] & 2021 & X & Hybrid ML & LR, DE, and RF & A = 99.9\% & The datasets used did not have a relationship amongst the individual and thus made it impossible to draw a social graph \\ \hline
[32] & 2021 & X & ML & LR & A = 96.2\% & LR is vulnerable to over-fitting and can handle only small volumes of data \\ \hline
[33] & 2021 & OSNs & Hybrid ML & RF Classifier, SVM, and Optimized NB & A = 97.6\% & The research requires manual feature selection \\ \hline
[34] & 2020 & Instagram & ML & RF, ANN, LR, NB, and J48 Decision Tree & Up to A = 91.76\% & The computation time is enormous in supervised learning \\  & & & & & & Unwanted data reduces efficiency \\ \hline
[35] & 2020 & X & ML/DL & RF, SVM, and MLP & Up to A = 96.2\% & SVM performance reduces with an increase in data size \\  & & & & & & data size \\ \hline
[36] & 2020 & LinkedIn & ML & SVM, PCA, and WA & A = 87.34\% & Limited profile data \\ \hline
[37] & 2020 & OSNs & DL & Dynamic CNN & A = 94\% & Limitation of neuron number \\  & & & & & & It enlarges computing resource \\ \hline
[38] & 2019 & X & ML & EGSLA & A = 90.3\% & Iteration results are not stable \\  & & & & & & It has low accuracy \\ \hline
[39] & 2019 & X & Hybrid ML & RF, Bagging, JRip, Random tree, PART, J48, and LR & A = 99.16\% & The RF is that with an increase in tress it leads to a decrease in the algorithm performance and it has high computational cost and slow prediction generator \\ \hline
[6] & 2019 & Instagram & ML/Dl & NB, LR, ANN, and SVM & Up to & LR is prone to over-fitting \\  & & & & and SVM & F-Measure = 94\% & SVM is not suitable for large and complex datasets \\ \hline
[16] & 2019 & X & ML & LB & A = 97.7\% & difficult or complex to work with a large amount of data \\ \hline
[40] & 2018 & X & Hybrid ML & K-means integrated levy flight algorithm & A = 97.98\% & K-means algorithm cannot handle noisy data \\  & & & & & & and outliers \\ \hline
[41] & 2018 & X & DL & LSTM & A = 99\% & It focuses on an X dataset \\ \hline
[42] & 2017 & Sina Weibo & ML & Message tree & A = 95.3\% & A small variation in data can lead to a greater change in the structure of the decision less thus leading to instability \\ \hline
[5] & 2015 & X & ML & RF, Decorate, DT, AB, BN, KNN, LR, and SVM. & Up to A = 97.5\%, & Lack of parallelism, slow computation, and ineffective memory utilization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.6 Validation over a Time Series of Images  
Having assessed the classification method and parameters on a single image, its robustness over multiple images needs to be validated, as there may be different lighting conditions, water colour and turbidity, weed reflectances, etc. The untreated (U) channel was used for this purpose, as the land, weed and water areas did not change so the same validation geometries could be used for every image in the time series.  
The validation kappa for each image is shown in Table 8. The kappa is lowest in the 23 August image. This image was taken just after the channel was drained and refilled as seen in Figure 4. A visual inspection of this image reveals that some of the weed patches had been either submerged or washed away, which together with the fact that the validation geometries were kept fixed for all  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Classification Bands** & **Kappa** \\ \hline VARI & 0.48 \\ WAVI & 0.54 \\ NDAVI & 0.54 \\ NDWI & 0.71 \\ NDVI & 0.78 \\ R, G, B & 0.92 \\ NDWI, NDAVI, NDVI, VARI & 0.95 \\ NDVI, NDWI & 0.96 \\ R, G, B, RE, NIR & 0.98 \\ R, G, B, NIR & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classifier accuracy with different input image band combinations.  
Figure 6: Classification validation accuracy kappa for identifying the water, land and weed classes as a function of the image resolution.  
images in the time series is the reason for the lower kappa for this image. However, it can be seen that the accuracy is excellent, with kappa remaining above 0.9 for all images over the two-month period.
# Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry
## 2 Materials and Methods  
Existing methods for boundary detection on video are not suitable for separating video into maintenance stages. The reason is that most of them are aimed at determining the similarity between frames, but not all of them analyze the composition of the scene, which may remain the same during maintenance. In this regard, this paper proposes an algorithm based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer's hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We propose to identify a human action by analyzing hand movements. In this way, we use the movement of a human hand in the frame as the feature by which a decision about action detection is made. The approach we have chosen will allow us to process videos with any work that a person performs in any domain. However, our research is focused on the automation of maintenance and repair; thus, we pay more attention to this particular area of research. We will analyze the time series obtained as a result of the movement of the hand using spectral singular value decomposition for multivariate time series. The scheme is shown in Figure 2.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 2 Related Work
A selective approach was brought to the sections of e-mail text in the study. A cross-dataset evaluation was also performed. Feature learning was conducted on the convolutional layers. The results of the experimental study revealed that the model operated with high accuracy [41]. In another study, spam message detection was conducted by the authors using an artificial neural network and a hidden Markov model (HMM). The study aimed to weigh new words by rapidly detecting short text with high accuracy. It was revealed that the proposed hybrid model performed faster filtering compared to machine learning algorithms and attained high accuracy [42]. In another study, the Bayesian approach was used for spam e-mail detection. In this study, the Naive Bayes method was optimized with a hidden Markov model. The hidden Markov model was used to perform a sequential analysis while determining the spam e-mail messages. It was revealed that the proposed method attained a 98% success rate in spam message detection [43]. It was concluded upon the review of the studies conducted in the literature that the most significant among the existing issues for the researchers is the fact that datasets involving Turkish SMS spam contain limited data and are insufficient in number. Moreover,the data preprocessing stage complicates the identification of stop words and characters due to the general structure of the Turkish language. The selection and adjustment of hyperparameters used in deep learning algorithms, on the other hand, is an essential problem. The algorithm or methods used in the model have some parameters which must be decided by the designer while designing the machine learning models to learn from the data. There may be hyperparameter groups that are different from each other where the model attains a high performance. There is no problem in using each of these groups for designing the model. Nevertheless, the selection of the most appropriate hyperparameter group is one of the essential problems to deal with. The selection of hyperparameters generally varies in accordance with the designer's perception, experiences gained from previous issues, the reflection of applications from various domains to the designer's own problem, current trends, design dependency within the model, etc. It is concluded, upon the consideration of the above-explained studies, that a successful and useful solution has yet to be delivered for the issue of spam messages, which are an essential problem for mobile phone users, corresponding to almost the whole of humanity. In this study, a hybrid CNN + GRU model detecting both Turkish and English spam messages has been proposed. The main aim of this model is to increase the percentage of accurate classification compared to previous studies and render it a useful application in the future.
## 2 Background
### Hardware-Assisted Data Compression  
In Section 2.2, we observed that the compression tasks not only consume a lot of costly CPU resources but are also repetitive. If ZRAM uses some dedicated hardware processors to handle these resource-intensive tasks, the performance of ZRAM may be improved. Fortunately, many hardware accelerators (e.g., FPGA [25], GPU [26], ASIC [27], etc.) have emerged to assist CPUs with all kinds of computation-intensive tasks. Intel Corporation has also launched its ASIC accelerator, QAT [8], which can help the CPU in compressing or encrypting data. Many researchers have proposed efficient heterogeneous systems [28,29]. However, there are practical challenges in heterogeneous systems, one of which is how to design a high-performance hardware and software collaboration scheme.  
Direct offloading data compression to the QAT accelerator reduces CPU consumption during compression. However, the native offloading mode, as shown in Figure 3, faces some practical challenges. Although the QAT driver provides a non-blocking interface for I/O calls, synchronous I/O calls used by the compression module to submit offloading requests cannot be returned directly (i.e., block). Unfortunately, this frequent blocking may result in a waste of both CPU and QAT resources. Firstly, a large number of CPU cycles are used for waiting. After a compression request is submitted to the QAT hardware, QZRAM needs to wait for its response. Whether QZRAM adopts busy_loop or sleep for waiting, the I/O request processing will be paused for a period of time. Secondly, the parallel computing unit inside the QAT hardware has a low utilization rate. For each request process, only after the current I/O request is completed by the heterogeneous accelerator can the next compression request be submitted. In other words, only one accelerator unit can be used at the same time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Left: Read / Right: Write**} \\ \hline
**Mode** & **MiB/s** & **CPU Usage [\%]** & **Comp Ratio** \\ \hline
**LZO** & 1588 / 670 & 60.20 / 55.10 & 1.49 \\
**LZ4** & 1973 / 723 & 78.57 / 86.36 & 1.51 \\
**DEFLATE** & 588 / 65.5 & 90.57 / 96.99 & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compression algorithm performance test.  
Figure 2: ZRAM processing flow.
# Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna
## 3 Results and Discussion
This gave a radiation quality factor, \(Q\), of 1.06 (22.4) times the so-called lower bound, \(Q_{\mathrm{lb}}\), (also known as the Chu limit, see Appendix A) with \(k_{0}a\approx 0.35\) (\(k_{0}a\approx 0.46\)) for the magnetic (electric) dipole antenna. As \(k_{0}a\leq 0.5\) for both antennas, they are electrically small. For a quarter wavelength monopole, \(k_{0}a=\pi/2\); thus, water reduced the size of the antenna by a factor of 4.5 (3.2). Moreover, the magnetic dipole water-based DRA reported herein represents the electrically smallest water-based antenna to date. The cost is a reduction in radiated power (and thus the total efficiency) due to the losses in water as well as the bandwidth. Nevertheless, its fractional bandwidth is higher than many other compact antennas [9, 10]. In addition, bearing in mind that the present design does not require any expensive or rare materials, the antenna will be very cheap and easy to produce. Furthermore, water adds flexibility to the design, allowing tuning by temperature and extraction of water, which we have examined both numerically and experimentally. The results are included in Appendix B.3. Figure 3: (**a**) Photographs of the front and back of the fabricated antenna and (**b**) measured reflection coefficient with different displacement positions \(x_{\mathrm{m}}\) and monopole antenna lengths \(l_{\mathrm{m}}\), both in mm. Water temperature was 19–20 \({}^{\circ}\)C. The numerical results are shown with the optimized antenna lengths and positions at 20 \({}^{\circ}\)C.
## 4 Corrosion Protection Measures
### Anticrovise Coating
#### 4.1.3 New Coatings  
(1) polyurethane (PU) coating  
PU coating is composed of a polyol compound and an isocyanate solution. Its viscosity is regulated by low molecular weight resin. PU coating has excellent performance; its hardness is as high as HS 80-86, it has good wear resistance, strong cathodic stripping resistance, and can conduct cathodic protection even after long-term failure.  
[PERSON]'s research [94] shows that PU has good wear resistance and corrosion resistance, with especially excellent microbial corrosion resistance. It is widely used on the inner and outer surfaces of pipelines. It does not contain volatile solvents, is non-toxic and harmless, and has simple construction and good anticorrosive coating quality. It is mainly suitable for repairing damage, joints, and anticorrosive coatings and has wide application prospects.  
(2) Nano-modified anticorrosive coating  
As we all know, the surface materials involved in corrosion protection are determined by the microstructure. However, the addition of nanomaterials to such structures can improve the properties of materials. For example, nano-silicon dioxide has the characteristics of serious surface coordination deficiency, large specific surface area, and surface oxygen deficiency. Therefore, the strength, ductility, and toughness of the coating can be greatly improved by adding nanomaterials into the coating. However, the factors limiting the development of nano-modified coatings come from the nanomaterials themselves, mainly the dispersion of the nanoparticles. How to avoid the large-scale agglomeration of nanoparticles during the drying process and the content of nanoparticles added to the coating are the main difficulties limiting the application of nano-modified materials.  
[PERSON] [102] found that the anti-corrosion behavior and hydrophobicity of the nano-composite coating formed by embedding ZnO nanoparticles in the polymer are significantly improved compared with the anti-corrosion effect of the original coating. [PERSON] [103] developed a kind of self-repairing anticorrosive coating, which added nano-silica and self-healing microcapsules (POT/ETA/DEA) to the epoxy coating, significantly improving the corrosion resistance of the coating and reducing the surface corrosion rate of cold rolled steel. The schematic of corrosion protection is shown in Figure 4. The research also proves that the modified nano-coating can effectively increase the length of the diffusion path and reduce the permeability of the O\({}_{2}\) nano-composite film.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Dataset and Dataset Preprocessing  
Extensive experiments were performed to evaluate our proposed mode based on the skin lesion segmentation dataset released by the International Skin Imaging Collaboration(ISIC) in 2017 and 2018 [36; 37], and a wide range of experimental evaluations were conducted on the proposed models. The ISIC2018 dataset includes 2594 RGB skin dermoscopy images that have been labeled with real labels by dermatologists using grayscale binary image-labeling techniques. The label size is the same as the dermoscopy image size, where the values of lesion and non-lesion pixels are set to 255 and 0, respectively. The dataset is divided into training, validation, and testing sets in the ratio 7:1:2. By training the proposed models on the training set and evaluating their effectiveness and robustness on the testing set, we successfully demonstrated the validity of the approach. Additionally, we randomly selected 300 dermoscopy images from the ISIC2017 dataset as an additional test set, as shown in Table 1.  
To overcome the issue of limited sample numbers in medical image training data, data augmentation techniques were used to expand the number of samples and enhance the generalization performance of the models. In the pre-processing stage, hair removal processing was first performed on the images and the input images were uniformly resized to 256 \(\times\) 256. To optimize the model parameters, we chose the Adam optimizer for stochastic gradient descent and the batch size was 8, with a learning rate of 1 \(\times\) 10\({}^{-5}\), and conducted 60 rounds of training. In addition, to avoid overfitting, the learning rate became 0.8 times the original after every 10 epochs. At the same time, we adopted an early stopping mechanism; that is, if the loss did not decrease within 10 epochs, the training would automatically stop. Prior to training the network, various data augmentation operations were applied to the training set, such as image rotation with a maximum angle of 10\({}^{\circ}\), random horizontal and vertical offsets of 0.05, and random image magnification and reduction of 0.05. These augmentation methods enriched the dataset and effectively improved the performance of the models.
## 4 Conclusions  
The impact of baffle height on the flame characteristics of a pool fire was investigated at different crosswind speeds ranging from 0 to 5 m/s using wind tunnel experiments. The flow field near the baffle was also studied to illustrate the flame characteristics based on the simulation computation. Our major findings are listed as follows:  
When a baffle was placed at the leading edge of the pool fire, it blocked the bottom airflow. Two vortex regions formed downstream of the baffle: a recirculation zone on the surface of the pool and a much larger recirculation zone downstream of the pool.  
The recirculation zone on the surface of the pool transferred fresh air back to the vicinity of the flame. The recirculation zone downstream of the pool could retain heat and send back unignited gas to be burned rather than being blown away. In addition, increasing the crosswind speed and baffle height may improve the mixing of fuel and the oxidant. For these reasons, the HRR increased with the increase in baffle height and wind velocity.  
As the flame shape changed when the baffle height was 4-10 cm, a new definition was introduced to illustrate the flame length and flame tilt angle. Firstly, the flame length increased, then decreased with increasing crosswind speeds up to 3 m/s. Finally, as the wind speed continued to increase, the flame length remained constant. The flame height decreased as the wind velocity increased (V \(\leq\) 3 m/s), and when the wind velocity continued to increase to 5 m/s, the flame height was basically unchanged. The flame tilt angle \(\alpha\) increased quickly as the wind velocity rose to 2 m/s and then remained almost constant as the wind speed rose further to 5 m/s.  
Conceptualization, software, and writing--original draft preparation, Z.Y.; investigation, optimization model, writing--review and editing, visualization, supervision, and funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Fundamental Research Funds for the Central Universities of CAUC \"Study on fire characteristics and fire extinguishing technology of aircraft cargo compartment under variable pressure and low temperature environment\" [grant number 3122021092].  
Not applicable.  
The authors declare that they have no conflict of interest. The founding sponsors had no involvement in the study's design, in the data collection, analysis, or interpretation, in the article preparation, or in decision to publish the results.