esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This paper gives the theoretical background and describes the multi-objective optimization procedure for regional deep hyperthermia planning using a multi-element dipole antenna array based on the finite-difference time-domain (FDTD) simulation. The described problem investigates the use of the linearity and superposition of dipole antenna signals. The selection of optimal dipole sources properties seems to be a non-trivial issue. The current paper omits the technical aspects of supplying individual dipole antennas, focusing only on the optimization of their basic parameters, in terms of the phase and input power, in order to achieve optimal treatment of breast tumors through the thermal effects of MW radiation. The multi-objective optimization algorithm, also used in [17, 35], adjusts thephase and powers of a dipole antenna array for a specific clinical case using cancer-targeted EM power deposition in the terms of the SAR parameter. This professional tool that uses the computational capabilities of the commercially available software Sim4 Life [36], is discussed in an example of female breast cancer. Finally, we investigate how tumor temperature, derived from optimal antenna array settings, depends on the size of the tumor. Only detailed numerical tests on female breast tumor models of various sizes will give the readers a real insight into how the optimization of crucial parameters in the dipoles can influence heating of the targeted tumor. Such a practice verifies the effectiveness of the applied optimization method, regardless of the various stages of breast tumor development. In this paper, the authors' contribution to the current state of knowledge about MW hyperthermia is the application of the aforementioned optimization procedure using an anatomically correct female breast model, which was combined with the Pennes' bioheat equation and non-linear perfusion in the tumor together with its various sizes. Such an investigation has, to the best of the authors' knowledge, never been published before.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
###### Abstract  
The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vulnerable to DDoS attacks and propagating malware. A DDoS attack detection model is crucial for attacks in various industries, ensuring the availability and reliability of their networks and systems. The model distinguishes between legitimate and malicious traffic by analyzing network traffic patterns and identifying anomalies. This safeguards critical infrastructure, preserves business continuity, and protects the user experience, minimizing the impact of DDoS attacks. Numerous scholars have studied the notion that protecting lightweight IoT networks essentially requires improving intrusion detection systems. This research is valuable, as it follows a tailored pre-processing methodology specific to IoT network challenges, addressing a pressing need in cybersecurity by focusing on a growing concern related to IoT devices and DDoS attacks, enhancing the security of essential network systems in various industries by effectively detecting DDoS attacks, and developing a lightweight intrusion detection system that aligns with the limited resources of IoT devices. This manuscript proposes a compact and lightweight intrusion detection system that blends machine learning classifiers with a fresh approach to data pre-processing. The handling of missing values, data standardization using Standard Scalar, feature selection using ExtraTreeClassifier wherein only the 15 best features are extracted, and anomaly detection using a classifier are performed. The network dataset of TON-IOT and BOT-IOT datasets is used for experiments, specifically binary classifications and multiple-class classification for the experiment with DDoS and all attacks, respectively. There is an imbalance between the TON-IOT and BOT-IOT attack classes. In trials using the TON-IOT and BOT-IOT datasets, the classes were balanced using several iterations of the SMOTE approach. This research provides a number of classifier types, namely logistic regression, random forest, naive bayes, artificial neural network, and k nearest neighbor algorithms, which are used to build a lightweight intrusion detection system that is ideally suited for protecting against DDoS attacks in IoT networks. The time taken to train and predict the DDoS attacks is also implemented. Random forest performed well under TON-IOT and naive bayes performed well under BOT-IOT under binary and multiple-class classification, achieving an accuracy of 100% with less training and prediction time.  
machine learning; BOT-IOT; TON-IOT; DDoS; SMOTE; IoT, logistic regression; KNN; ANN; random forest; naive bayes +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Assessment of Single-Leg Vertical Hop Test with Sensor-Based Digital Medical Device  
A single-leg vertical hop test (SLVHT) was performed three times in a row under standardised testing conditions using an insertional digital sensor (Orthelligent(r), OPED GmbH, Valley, Germany) attached by a rubber strap to the proximal tibia of the respective leg (Figure 1A). The best attempt was recorded. This digital sensor, also technically called an IMU, contains a three-axis acceleration sensor and a three-axis gyroscope sensor capable of detecting and measuring different movement patterns on the basis of pre-installed algorithms [26]. The calculations consist of detecting two time points: the take-off moment and the landing. For these detections, combined values of position (angle) and acceleration are considered and calculated, and the exceedance of threshold values is derived from this. The reliability of hop tests was previously shown [28], and assessments by the digital sensor used herein were examined previously [26; 27].  
The SLVHT was trainer-administered and performed barefoot on a standardised surface (industrially manufactured carpet, OPED Inc.) with both hands attached to the hips (Figure 1B), since countermovements and arm swings have been shown to influence jump height significantly [29]. Jump height was determined in both the non-injured and injured limb by means of the time interval (foot off the ground) measured by the insertional sensor. Measurement values were transmitted via Bluetooth to a software application on a digital tablet (Figure 1C). The software (mobile application Orthelligent, OPED GmbH, Valley,  
Figure 1: (**A**) Wearable sensor attached to the proximal tibia, containing an accelerometer transferring data via Bluetooth to a digital platform (tablet) (Orthelligent, OPED Inc., Valley, Germany). (**B**) Single-leg vertical hop test: trainer-administered, standardised test performance, barefoot on test carpet, both hands fixed to the hip, wearable sensor fixed to the proximal tibia via rubber strap. (**C**) Wearable sensor and mobile software application.  
Germany; used in the most current version; [[https://www.o-dhs.com/de/produkte-und-loesungen/orthelligent-pro](https://www.o-dhs.com/de/produkte-und-loesungen/orthelligent-pro)]([https://www.o-dhs.com/de/produkte-und-loesungen/orthelligent-pro](https://www.o-dhs.com/de/produkte-und-loesungen/orthelligent-pro))) calculated the jump height and the limb symmetry index (LSI), which was used for intraindividual comparison of the injured with the non-injured leg (LSI = (jump height injured leg/jump height non-injured leg) \(\times\) 100). The LSI is a common parameter used in ACL rehabilitation. As a side-to-side difference, the individual deficit can be expressed in a single term at different time points during the rehabilitation phase.
## 5 Conclusions  
This study utilized remote sensing data to drive the BEPS model to simulate the carbon fluxes from bamboo forests in China during 2001-2018, and analyzed the spatiotemporal evolution pattern of carbon fluxes and the response of climatic factors to these changes. Our study showed that the simulated values had a good correlation with the observed values, and the R\({}^{2}\), RMSE, and aBIAS were 0.58, 1.43 g C m\({}^{-2}\) day\({}^{-1}\), and 1.21 g C m\({}^{-2}\) day\({}^{-1}\), respectively. It provided a feasible way for the study of bamboo forest carbon cycles on a large spatial scale. In addition, our study also suggested that climate change was a driver that affected the spatiotemporal dynamic evolution of carbon fluxes in bamboo forests, and its driving effect exhibited evident spatial variations. This provided a theoretical basis of bamboo forests to cope with climate change.  
However, this study still has some limitations. For example, (1) the low resolution of the data limited the simulation of bamboo forest carbon flux; (2) fewer flux observation sites may lead to certain deficiencies in verifying the model simulation results; and (3) we only considered the impact of climatic factors (temperature and precipitation) on the carbon fluxes of bamboo forests. In the future, these limits can be further improved to better simulate the carbon fluxes of bamboo forests in China.  
Conceptualization, H.D.; Data Curation, F.K., X.L. and F.M.; Formal Analysis, F.K., H.D., X.L., F.M., Y.X. and Z.H.; Funding Acquisition, G.Z., H.D. and F.M.; Investigation, Y.X., Z.H., J.J., J.W. and F.K.; Methodology, F.K. and X.L.; Supervision, G.Z.; Project Administration, H.D.  
and G.Z.; Validation, X.L. and F.K.; Writing--Original Draft Preparation, F.K.; Writing--Review and Editing, H.D. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Natural Science Foundation of China (No. 32171785, U1809208, 31901310), the State Key Laboratory of Subtropical Silviculture (No. ZY20180201), Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-Efficiency Utilization (No. S2017011), and the Key Research and Development Program of Zhejiang Province (2021C02005).  
Not applicable.  
Models data comes from NASA ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov))), accessed on 11 December 2020.  
The authors gratefully acknowledge the support of various foundations. The authors are grateful to the editor and anonymous reviewers, whose comments have contributed to improving the quality of this study.  
The authors declare that they have no competing interests.
## 3 Research Process  
### Research Framework  
This study uses scientific papers and patent research in the field of cybersecurity to represent two types of research work: scientific research and technological innovation [29]. Specifically, this study selects scientific papers from the Web of Science Core Collection database and patent research from the Derwent Innovation Index database as the basic data sources. The research period is set from 2005 to 2024. Since the start of 21 st century, mobile devices have been widely prevalent, technology has advanced rapidly, network attacks have become increasingly stealthy and complex, and cybersecurity has faced new challenges and threats. To cover the majority of this period, 20 years is selected. It can fully capture the long-term trends in the cybersecurity field, from the accumulation of basic technologies to innovative breakthroughs. Moreover, this time series will not result in incomplete data and the mixing of different technological generations due to an excessive length of time. The specific steps are as follows.  
Firstly, in this paper, we obtain relevant scientific papers from the Web of Science Core Collection database and relevant patent research from the Derwent Innovation Index database. Subsequently, we conduct preprocessing operations on the acquired scientific papers and patent research. Second, in this paper, we apply the LDA model for topic modeling and use perplexity as the measurement standard to determine the optimal number of topics, thereby extracting scientific and technological topics. Third, we multiply the LDA probability values corresponding to the topic words at each stage by the word vectors obtained from Word2 vec training to calculate the weighted sum values, thereby obtaining the vectorized representation of the topics. Subsequently, science-technology topics are formed. Meanwhile, their similarities are calculated based on the weighted vectors, and a similarity threshold is set to distinguish different types of evolutionary paths of the topic pairs. Finally, in this paper, we select the strongly correlated science-technology topics for visual analysis to reveal the association between them. The overall research framework is shown in Figure 1.  
Figure 1: Overall research framework.
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 4 Experiments
### Ablation Studies  
We examine each component's contribution and the impact of balance factor through ablation studies and provide a depth analysis in this section.  
Effect of each module. We depict how each core component of our method--containing focus-based sampling, cube query, and the number of SA layers--affects detection performance. The corresponding results are given in Table 1. The ball query, i.e., SASA in this work, is employed as a baseline. The baseline, ball query, obtains APs of 82.94%, 57.53%, and 72.02% at the moderate level. Compared with normal objects, regarding the small objects, the baseline has a lower AP. With FocS, model A provides +0.57% and +0.48% improvements in AP for the pedestrian and cyclist classes at the moderate level. Replacing the ball query with the cube query, in model B, the APs of pedestrians and cyclists reach 59.09%, 58.92%, and 52.26% and 92.27%, 73.13%, and 67.20%, respectively. The integration of FocS with pillar query in model C yields notable performance improvements in pedestrian detection, while exhibiting marginal enhancements in other categories. To further validate our design, we constructed model D with only two SA layers. This configuration achieves an AP of 59.90% and 71.74% for pedestrians and cyclists. This degradation demonstrates the necessity of incorporating three SA layers, as the additional layer enhances the representational capacity. The combined implementation of these dual modules demonstrates competitive detection accuracy, with the evaluation metrics reaching 82.81% AP for cars, 62.47% AP for pedestrians, and 73.24% AP for cyclists in the moderate-difficulty category. These results show that our presented block, i.e., OMSA, can improve performance significantly, indicating that these two components help reduce the information loss and strengthen the corresponding feature learning.  
Effect of balance factor \(\lambda\). We validate the impact of \(\lambda\) with a range from 0 to 4 on the performance, as shown in Figure 4. When \(\lambda\) is set to 0, our method eliminates the module of focus-based sampling. Our experimental results demonstrate that the AP for small objects surpasses the baseline (\(\lambda=0\)) within the lambda range of 0.5 to 2.5. Consequently, we adopt lambda values within this interval. Furthermore, this experiment empirically validates the effectiveness of the module.
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 2 Materials and Methods
### Meteorological and Soil Data  
This study employs common model inputs from the TerraClimate dataset as a source of meteorological and soil information for regional wheat carbon flux estimation. The TerraClimate dataset provides monthly terrestrial meteorological and climatic water balance data globally, including key climate variables such as maximum temperature (Tmax), minimum temperature (Tmin), precipitation (Pr), vapor pressure (Vap), and vapor pressure deficit (Vpd). Additionally, it includes derived variables such as potential evapotranspiration (Pet), soil moisture (SM), and Palmer Drought Severity Index (PDSI). For key climatic variables like temperature, vapor pressure, and precipitation, the TerraClimate dataset offers supplemental information, including monthly surface soil moisture data. These data are calculated using a water balance model that integrates precipitation, evapotranspiration, and interpolated soil water holding capacity.  
Meteorological data from TerraClimate were spatially interpolated to match the finer resolution of the MODIS remote sensing indices (500 m) using the Kriging interpolation method. Kriging is a geostatistical interpolation technique that leverages the spatial correlation among data points, estimating values at unsampled locations based on surrounding measurements and their spatial covariance structure. Specifically, we first computed empirical semivariograms for each meteorological variable (e.g., temperature, precipitation, vapor pressure) to characterize spatial dependencies and determine optimal variogram parameters. Subsequently, these parameters guided the ordinary Kriging interpolation, producing continuous meteorological surfaces at 500 m resolution. This method effectively captures local variations while ensuring consistency with observed regional climate patterns, thereby minimizing interpolation errors and enhancing compatibility between meteorological and remote sensing datasets.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 2 Materials and Methods
### Scoops3D Model  
Scoops3D was developed by the USGS for evaluating slope stability throughout a digital landscape represented by a DEM [33]. Scoops3D applies the limit equilibrium method to analyse slope stability. This method predefines the potential failure surface and investigates the equilibrium state (the equilibrium of forces or moments) of the predefined failure surface. Assumptions of the limit equilibrium method are as follows: (1) the failure volume consists of rigid materials and slides along a single failure plane; (2) failure occurs simultaneously along the potential slip surface without progressive movement. The factor of safety (_FoS_) is uniform everywhere along the predefined slip surfaces and one predefined slip surface has one single _FoS_; and (3) the deformation and strain of the potential failure volume, as well as dynamic loading are ignored [69]. The equilibrium state is expressed by a ratio, _FoS_, and can be calculated based on Equation (2):  
\[FoS=\frac{R}{T} \tag{2}\]  
where _FoS_ is the factor of safety for the predefined failure surface, \(R\) is the upslope stabilising force, and \(T\) is the downslope destabilising force.  
Two major outputs of Scoops3D were LSI (or minimum _FoS_) and VOL. The LSI maps were then classified into five susceptibility groups, namely very unstable (_FoS_\(\leq\) 0.5), unstable (_FoS_: 0.5-1.0), moderate (_FoS_: 1.0-1.5), stable (_FoS_: 1.5-2.0), and very stable (_FoS_\(\geq\) 2.0). The VOL maps were also classified into five groups using the natural break method in ArcGIS: very low (1.0 \(\times\) 10\({}^{3}\)-2.0 \(\times\) 10\({}^{8}\) m\({}^{3}\)), low (2.0 \(\times\) 10\({}^{8}\)-4.0 \(\times\) 10\({}^{8}\) m\({}^{3}\)), moderate (4.0 \(\times\) 10\({}^{8}\)-6.0 \(\times\) 10\({}^{8}\) m\({}^{3}\)), high (6.0 \(\times\) 10\({}^{8}\)-8.0 \(\times\) 10\({}^{8}\) m\({}^{3}\)), and very high (8.0 \(\times\) 10\({}^{8}\)-1.0 \(\times\) 10\({}^{9}\) m\({}^{3}\)
## 4 Challenges of VANETs  
In Intelligent Transport System (ITS), which varies from traffic protection applications to infotainment applications, different applications are used. Such a set of applications presents different specifications for protocols for vehicular communication [19]. Such requirements lead to new challenges:  
* Bandwidth limitations: VANETs endure channel overcrowding, particularly in a high-density zone, because of the absence of a central controller that handles the use of restricted bandwidth and comfortable activity [20].
* Delay constraints: Frequent topology changes in VANET have rigorous time rules. Hence, it is important to consider a fair time delay in designing effective vehicle transmission protocols.
* Privacy rights: Vehicular contact must resolve the tradeoff between privacy and accountability. Each car has to believe the source of the data it receives.
* Cross-layering protocols: Real-time applications have rigorous limitations in terms of time and place. The routes are often altered due to the complex topology. Thus, delivering reliable links via the transport layer is effective in such a situation.
* Security threats: Because of the open environment of VANETs, vast amounts of attacks can be targeted. Therefore, it is a challenging problem to discover new incidents related to vehicular interaction and protect the clustering protocols compared to such attacks [21].
* High dynamic and disconnected topology: In order to deal with such conditions, a new research model is therefore implemented called Vehicular Delay Tolerant Networks [22].
## 3 Results and Discussion  
### Surface Modification Method  
The surface of the Mg disks is modified by immersion of the Mg metal disks in carbonated water. The aqueous carbonate solution contains Ca\({}^{2+}\) and HCO\({}_{3}\)\({}^{-}\) ions in concentrations of 311 and 1892 mg/L, respectively, and CO\({}_{2}\) content of 2500 mg/L. The concentrations of both calcium and dissolved inorganic carbon (DIC) are key parameters for the surface modification method to favor the nucleation of calcium carbonate on the surface while avoiding its nucleation in solution. Precipitation of calcium carbonate from the solution and not directly on the surface can interfere with the lateral growth of a continuous, homogeneous carbonate coating layer on the magnesium surface. The solution is allowed to reach equilibrium spontaneously under normal laboratory conditions (~25 \({}^{\circ}\)C, 1 atm) without compositional or pH control while modification of the metal surface occurs.  
Figure 1 shows digital photographs of the coating process at different magnifications, taken from the top of the glass beaker with the Mg disks immersed in the carbonated water solution. The images in Figure 1a,b exhibit gas bubble columns arising from the disks, and Figure 1b is magnified by optical zoom to show the intense evolution of gas bubbles from the Mg surfaces during the coating. Continuous gas release from Mg disks is observed for more than one hour, gradually losing intensity, with fewer bubbles emanating from the disk. After 1 h immersion, individual bubbles are observed instead of a bursting column of them. Figure 1c shows gas bubbles standing on the surface of an Mg disk after 1 h immersion. No significant gas release is observable after over 24 h immersion.  
Mg disks are left immersed for different times, from 15 min up to 1 week (144 h), in order to study the evolution of the surface by carbonate solution coating. The evolution of Mg disks is illustrated in the photographs in Figure 2, taken after immersion at increasing immersion times. The initial disks have a metallic shine (Figure 2a). The first effect after immersion is the loss of the metallic shine, observed almost immediately, and the darkening of the surface in various shades of gray while degasification of the carbonated water takes place (Figure 2b). Visual inspection of the disk reveals the gradual homogenization of the gray surface of the Mg disks with increasing immersion time during the first hour: after 1 h (Figure 2c), the original cutting lines on the surface of the metal are hardly noticeable. After more than one hour of immersion, surface modification becomes slower, as well as the evolution of gas bubbles. Leaving the disks in solution for longer times, from 1 day to 1 week, causes the development of irregular depositions with a lighter color, clearly observable over most of the disk surface after 1 week of immersion (Figure 2d).