esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 2 Related Work
### Adversarial Training in Cybersecurity  
Adversarial training has emerged as a critical technique to enhance the robustness of machine learning models against adversarial attacks in cybersecurity. Traditional machine learning models, including deep neural networks (DNNs) and GNNs, are vulnerable to adversarial perturbations that manipulate input data to deceive detection systems.  
[PERSON] et al. [15] propose the vulnerability of GNNs under structural perturbations and use a certified robustness framework based on randomized smoothing. This method mathematically proves the prediction stability of GNNs under structural attacks such as node deletion or addition. Its core innovation lies in extending the randomized smoothing technology to the field of graph data. Specifically, by adding random noise to the input graph and smoothing it, the model can theoretically guarantee robustness to structural perturbations within a certain range. [PERSON] et al. [28] focuses on the problem of robustness degradation during model updating and proposes an adversarial training strategy based on non-regression constraints. This method effectively reduces the phenomenon of \"negative flips\" by forcing the model to retain robustness to historical adversarial samples when updating the model. The updated model misjudges adversarial samples that were previously correctly classified. Specifically, by introducing the robustness-congruent adversarial training loss function, the model must simultaneously meet the prediction consistency of historical adversarial samples when optimizing new data, so that the model has good results for both post-adversarial samples and historical samples.  
In order to obtain a highly robust graph representation learning network for intrusion detection methods, we need to delete/add nodes and edges or perturb features of the graph data of the graph neural network model to achieve adversarial training results. At the same time, the balance between adversarial samples and historical samples should be fully considered in the training objectives, and negative flips should not occur. It is necessary to effectively design the adversarial method and loss function.  
In summary, the limitation of existing research is mainly reflected in the vulnerability of GNNs. As adversarial attacks are a practical strategy to improve the robustness of the model, the AH-EAT proposed in this study uses PGD-based adversarial training to inject edge feature perturbations and graph structure noise.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 3 Methodology
### Model Incorporation  
Incorporation of the optimized MOD_17 and Biome-BGC model was used to make full use of space-continuity of the remote sensing-based model and the time-continuity characteristics of the process-based model and calibrate the multi-parameters of the process-based model.  
The extremely heterogeneous forest landscape (e.g., distribution, age, height) and environmental conditions (e.g., location, weather, terrain) of the study site made it necessary to globally calibrate the parameters (e.g., ecophysiological parameters) of the Biome-BGC model [10]. Site parameters, such as vegetation type, soil properties, and terrain information, were derived from vegetation and soil type maps, and digital elevation models (DEMs). The ecophysiological parameters for the dominant tree species and the surrounding atmospheric parameters of the study area were difficult to determine due to a lack of prior knowledge.  
For this study, the incorporation of the optimized MOD_17 and Biome-BGC model was performed by calibrating sensitive parameters of the Biome-BGC model. Eight-day GPP estimates over five years (2003-2007) during the growing seasons were computed for the 10 selected plots by running the optimized MOD_17. The estimates were then used as reference data for calibrating the Biome-BGC model configurations. As a result, a total of 1150 (10 plots \(\times\) 23 layers/plot/year \(\times\) 5 years) GPPs from the Biome-BGC model were plotted against the reference values obtained from the optimized MOD_17 model. When a best fit was found, the optimal configuration for the Biome-BGC model was established.
## 2 Isolated Single Tree Radio Channel Characterization
### Simulation Results  
In order to perform wireless channel characterization considering blockage, the implemented simulation scenario considers the location of a static transmitter and a mobile terminal with a thick tree in the propagation path, as depicted in Figure 2. The transmitter source (TX), operating at 2.4 GHz is located at a height of 2 m and is highlighted by a red circle. Material properties and simulation parameters are detailed in Tables 1 and 2, respectively. The simulation scenario has been implemented with boundary conditions defined by air in order to avoid unwanted reflection components. Note that the parameters regarding the radio signal communications (transmitted power level, antenna type and operation frequency) have been chosen in order to fit the parameters used by the real devices. It is important to mention that the 3D-RL simulation tool has the option to include the effect of diffraction phenomenon. For the current analysis, the diffraction has been activated since it is a relevant phenomenon in the proposed case under study. Simulation results have been obtained for the complete scenario volume. For the sake of clarity, as a specific example, propagation losses for the linear TX-RX radials defined by the yellow dashed lines depicted in Figure 2b,d have been considered for height cut planes of 1, 2 and 3 m.
## 3 Results
### In Situ vs. Video Presentation
Participants had shorter fixation durations for the video-based stimuli presentation (video-based: 659.57 \(\pm\) 178.06; in situ: 728.11 \(\pm\) 129.27; \(F\)[8,1] = 5.24, \(p\) = 0.02, \(\eta^{2}\) = 0.05). No differences could be reported for the number of fixations (video-based: 5.15 \(\pm\) 1.38; in situ: 5.35 \(\pm\) 0.91; \(F\)[8,1] = 0.82, \(p\) = 0.37, \(\eta^{2}\) = 0.01) and locations (video-based: 4.98 \(\pm\) 1.09; in situ: 5.30 \(\pm\) 0.86; \(F\)[8,1] = 2.77, \(p\) = 0.10, \(\eta^{2}\) = 0.03). The study results show evidence for the hypothesis that eye movements depend on the type of stimulus presentation (Q1). According to this, the statement applies only to fixation duration but not to the number of fixations and locations. The last study in this field of research was conducted by [PERSON] et al. [29]. Thirteen adults (eight females) with no sports context were examined. Participants had to ride a high-quality bicycle path and a low-quality bicycle path (\(\pm\)700 m) by bike. The number of fixations and saccades between Areas of Interest was measured in the in situ and video conditions. Overall, a significant Pearson correlation coefficient of r = 0.507 (\(p\) < 0.001) could be found for dwell time (in %) between the laboratory and field conditions. A comparison of the two road conditions shows a significant Pearson correlation coefficient for the low-quality cycle path (\(r\) = 0.663; \(p\) < 0.001). However, this effect could not be confirmed for the high-quality cycle path (\(r\) = 0.030; \(p\) = 0.821). Under real conditions (field conditions), the participants showed an increased visual search along the sight's vertical line. The authors conclude that the cognitive and motor requirements when cycling were more demanding in the field condition than in the laboratory because steering no longer played a role. The study again shows differences in gaze behavior between field and laboratory and a dependency on external task constraints (Q1 and Q2).
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## 3 Results
### Expression and Activity of DgoKerA  
In order to obtain purified DgoKerA, a recombinant plasmid carrying keratinase gene without signal peptide was constructed for the _E. coli_ expression system. DgoKerA was expressed in _E. coli_ BL21 (DE3) strains, purified on a Ni-column, and dialyzed against 40 mM Tris\(-\)HCl (pH 8.0). The protein bands detected in the SDS-PAGE after purification migrated at approximately 28 kDa, which corresponds to the molecular mass of the mature keratinases (Figure 3). The MALDI-TOF MS results showed that the purified protein did not contain the N-propeptide sequence. This indicates that the DgoKer N-propeptide is cleaved normally in _E. coli_ and that the purified product contains only the mature region keratinase. In this study, 1% soluble keratin and feather powder were used as substrates to measure the keratinase activity. The keratinase DgoKerA specific enzyme activity was 51,147 \(\pm\) 442.80 U/mg with 1% soluble keratin as the substrate, as shown in Table 2. In addition, the DgoKerA activity was 3277.88 \(\pm\) 172.62 U/mg with feather powder as the substrate.
# Theoretical Study on Geometries of Electrodes in Laboratory Electrical Resistivity Measurement
## 1 Introduction  
The electrical resistivity method is one of the most promising geophysical methods for characterizing subsurface conditions, including soil profiling, anomaly detection, and groundwater chemistry [1; 2; 3; 4]. Cylindrical electrodes are usually employed to increase the contact area with the medium, and thus minimize grounding resistance [5; 6]. Note that the high ground resistance, induced by imperfect contact, hinders the flow of current into the medium and produces unstable electrical resistance of the medium [7]. In addition, electrical resistivity tests have been widely conducted at diverse scales, from a few centimeters for laboratory-scale tests to kilometers for field tests [8; 9; 10; 11; 12]. Large distances among electrodes in field tests force well-known theoretical equations (e.g., Wenner or Schlumberger method) to ignore the geometries of an electrode (radius and penetrated depth) and to only reflect electrode spacing [1; 2]. With relatively short electrode spacing (e.g., laboratory tests), the penetrated depth of the electrodes has a significant effect on the electrical resistivity measurements [13; 14]. In addition, the distortion of current flow occurs near the container and produces inaccurate electrical resistance [15]. However, previous studies failed to adequately take into account all of the geometries (radius and penetrated depth) of the electrodes, electrode spacing, and container size.  
In this study, theoretical electrical resistances are formulated for cylindrical electrode geometries (radius and penetrated depth) and spacing in a container. The method of image charges is adoptedto replace the container geometry with imaginary electrodes and facilitate analyzing the electrical resistivity variation near the container. Then, experimental tests are performed to validate the theoretical equations. The concept of the spherical equivalent electrode is revisited with the proposed formulations. Emphasis is placed on considering the electrode geometries in laboratory-scale measurements.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Absorption Spectrometry (AAS) Techniques
#### 2.2.2 Graphite Furnace Atomic Absorption Spectrometry (GFAAS)
compressa_ collected from 10 different stations in the lower Gangetic delta complex over three seasons was evaluated with AAS fitted with a graphite furnace atomizer [66]. A quantity of 1 g of the dried sample was digested using a mixture of H\({}_{2}\)O\({}_{2}\) and HNO\({}_{3}\), followed by HCl. The order of accumulated heavy metals was Zn > Cu > Pb, and the seasonal variation pattern was monsoon > postmonsoon > premonsoon, which was similar to the trend of dissolved heavy metals. The authors concluded that traditional rope culture of _E. compressa_ in the vicinity ofthe industrial discharge points might be an eco-friendly approach to cost-effectively purify contaminated water.
## 2 Experimental Setup
### Test Loading Device and Loading Process  
The wall specimen is placed in the reaction frame. The horizontal load of the specimen is applied by the SCHENCK electrohydraulic servo loader, and the vertical load is applied by the jacks at top of the wall. In order to make the vertical load as uniform as possible, a load beam is designed at the top,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **f\({}_{\text{cu}}\) (MPa)** & **f\({}_{\text{c}}\) (MPa)** & **E\({}_{\text{c}}\) (M/mm\({}^{2}\))** \\ \hline PCFI-A \& SWA & 27.7 & 19.7 & 2.90 \(\times\) 10\({}^{4}\) \\ PCFI-C \& SWC & 30 & 19.5 & 2.98 \(\times\) 10\({}^{4}\) \\ PCFII-C & 35.3 & 31 & 3.14 \(\times\) 10\({}^{4}\) \\ \hline \hline \end{tabular} Note: \(\ell_{\text{cu}}\) is the standard value of the concrete cube compressive strength, \(\ell_{\text{c}}\) is the standard value of the concrete axial compressive strength, and E\({}_{\text{c}}\) is the elastic modulus.  
\end{table}
Table 2: Material parameters of concrete.  
Figure 3: Fabrication of the laminated reinforced concrete wall: (a) fabrication of the prefabricated part; (b) fabrication of the cast-in-place part; and (c) the connector between the bottom beam and the specimen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reinforcement** & **Measured Diameter** & **Type** & \
\begin{tabular}{c} **f\({}_{\text{y}}\)** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **f\({}_{\text{b}}\)** \\ **(MPa)** \\ \end{tabular} & **E\({}_{\text{s}}\) (MPa)** & \(\varepsilon_{y}(\mu\varepsilon)\) \\ \hline Column longitudinal & 14 mm & I & 382.5 & 655 & 2913 \\ reinforcement HRB335 & 14 mm & II & 372.8 & 546.93 & & 1864 \\ \hline Strengthening & 10 mm & I & 357.5 & 480 & 2.0 \(\times\) 10\({}^{5}\) & 1788 \\ reinforcement HRB400 & 10 mm & II & 380.24 & 604.5 & & 1901 \\ \hline Distributing & 8 mm & I & 370 & 647.5 & & 2850 \\ reinforcement HRB400 & 8 mm & II & 385.03 & 617.04 & & 1925 \\ \hline \hline \end{tabular} Note: \(\ell_{\text{y}}\) is the measured yield strength of the reinforcement, \(\ell_{\text{b}}\) is the measured ultimate strength of the reinforcement, E\({}_{\text{s}}\) is the elastic modulus, and \(\varepsilon_{y}\) is the yield strain.  
\end{table}
Table 3: Material parameters of reinforcement.  
and the jack is connected to the reaction frame by some rollers to reduce the friction so that the top of the specimen can move freely in the horizontal direction. The test setup is shown in Figure 4a.  
A displacement control loading method is used in the test, and the load process is shown in Figure 4b. Before the test, a vertical load is applied to the design value of 1200 kN at the top of the wall and is kept constant throughout the loading process. The loading process is divided into two phases: displacement is increased by 1 mm for each stage in the elastic phase, and each cycle is reloaded for one cycle. After entering the yield stage, displacement is increased by 2 mm for each stage and cycled three times. The test is stopped when the load drops to 85% of the maximum load or when the specimen is destroyed.
# Investigating the Secondary Thermal Neutron Intensity of Neutron Capture-Enhanced Proton Therapy
###### Abstract  
This study aimed to investigate the distribution of thermal neutron fluence generated during proton-beam therapy (PBT) scanning, focusing on neutrons produced within the body using Monte Carlo simulations (MCSs). MCSs used the Particle and Heavy Ion Treatment Code System to define a 35 \(\times\) 35 \(\times\) 35 cm\({}^{3}\) water phantom, and proton-beam energies ranging from 70.2 to 228.7 MeV were investigated. The MCS results were compared with neutron fluence measurements obtained from gold activation analysis, showing good agreement with a difference of 3.54%. The internal thermal neutron distribution generated by PBT was isotropic around the proton-beam axis, with the Bragg peak depth varying between 3.45 and 31.9 cm, while the thermal neutron peak depth ranged from 5.41 to 15.9 cm. Thermal neutron generation depended on proton-beam energy, irradiated particle count, and depth. Particularly, the peak of the thermal neutron fluence did not occur within the treatment target volume but in a location outside the target, closer to the source. This discrepancy between the Bragg peak and the thermal neutron fluence peak is a key finding of this study. These data are crucial for optimizing beam angles to maximize dose enhancement within the target during clinical applications of neutron capture-enhanced particle therapy.  
protons: neutron capture-enhanced particle therapy; Monte Carlo simulation 2020 IEEE/RSJ International Conference on, 2015, pp. 15-22, 2015
## 2 Related Works
The established start-stop rules contributed to improving result interpretation and accelerating the assessment process. In article [18], MRMI was compared with the MAS scale as a tool for evaluating mobility in stroke patients. In the area of prognostic models for predicting rehabilitation outcomes, significantly fewer studies have been conducted. In article [19], a predictive model for ADL (activities of daily living) scores upon discharge for patients with spinal cord injury was proposed, based on machine learning algorithms. The HHO-RF (Harris Hawks optimizer with Random Forest) algorithm was found to be more effective in predicting outcomes compared to traditional RF, suggesting the applicability of this model for developing more effective rehabilitation programs. A systematic review [20] investigated prognostic factors affecting upper limb recovery after stroke. Results suggest that task-oriented interventions have the greatest effect, especially in the subacute and chronic phases. In article [21], a retrospective study on mobile rehabilitation (mRehab) was conducted, analyzing data from stroke patients during a six-week home-based rehabilitation program. It was shown that combining clinical and demographic data with movement phenotype data significantly improved the accuracy of rehabilitation outcome predictions, which could be useful in developing personalized rehabilitation strategies. Study [22] aimed to identify the most effective milestones for predicting the complexity level of patient mobility at an early stage of recovery. The results showed that \"maintaining balance while sitting\" and \"sitting unsupported\" were the best predictors of future patient mobility, and these assessments can be performed in everyday clinical practice. Article [23] evaluated whether progress in balance, measured using the Berg Balance Scale, and hospitalization duration could serve as independent predictors of outcomes after inpatient rehabilitation. The results indicated that balance condition at the beginning of rehabilitation and progress made during rehabilitation were strong indicators of discharge balance outcomes, which could help tailor rehabilitation programs for patients with severe motor impairments. The literature review suggests that various approaches to stroke patient rehabilitation, both using advanced machine learning tools and more traditional methods, are effective in different contexts. Predictive models such as HHO-RF can be valuable in clinical evaluation of rehabilitation programs, allowing for a more personalized approach to patient treatment. Moreover, simple milestones like \"maintaining balance while sitting\" are easily measurable predictors of rehabilitation outcomes, making them valuable in daily clinical practice.
## 3 IPTMS Architecture Design
### IPTMS with Advanced EPS and TMS
Additionally, the trade-off studies can assess the capability of an IPTMS under different operating conditions. When the power required for cooling constitutes a small proportion of the total power output, it is possible to reduce additional weight by using advanced TMS architectures, or to decrease TMS requirements by employing advanced EPS architectures. If the cooling power constitutes a big proportion of the total power output and affects the battery heat load significantly, it is necessary to manage the power supply strategies of the battery. Therefore, power management optimisation is needed.
## 5 Conclusions  
In this study, alterations in the surface roughness and chemical composition of SLA titanium implants were investigated after irradiation with diode lasers of various wavelengths. The temperature of the implant surface during laser irradiation was significantly different according to the type of dental diode laser. The mean surface roughness of the titanium disc irradiated at 940 nm was slightly higher than that at 980 nm. Moreover, the mean surface roughness of the titanium disc was higher at 3.0 W compared with those at 1.0 and 2.0 W. However, the type of laser and power did not significantly change the RA, so the irradiation of a diode laser with 1.0, 2.0, and 3.0 W for 15 s can decontaminate the SLA titanium surface without damaging titanium. Nevertheless, these findings were conducted in vitro, and clinical trials will be needed to verify the results of this study.  
Conceptualization, H.-K.K.; Methodology, S.-Y.P. and K.S.; Validation, K.-B.L.; Formal Analysis, Y.-G.K.; Investigation, W.-J.Y.; Data Curation, J.-M.L.; Writing--Original Draft, H.-K.K. and S.-Y.P.; Visualization, H.-K.K.; Supervision, J.-M.L.; Project Administration, K.-B.L.; Writing--Review and Editing, K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute for Advancement of Technology (KIAT) through the National Innovation Cluster R&D program (P0006691).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### ABC Transporter  
The ATP-binding cassette (ABC) transporters are a kind of protein that couple two processes, ATP hydrolysis and substance transport, and significantly influence bacterial physiology [35]. [PERSON] et al. revealed that ABC transporter genes _Rv1217c_ and _Rv1218c_ had the most significant upregulation in biofilm cells compared with planktonic cells, as high as 9.2-fold and 10.6-fold, respectively. These two genes were verified to be associated with biofilm formation, and their expression could be suppressed by efflux pump inhibitors (EPIs), piperine, and 1-(1-naphthylmethyl)-piperazine (NMP) [25]. One of the mechanisms of ABC transporters that are involved in biofilm formation is the LpqY-Sug ABC transporter, which can transport mycolic acids outside of the bacteria [26; 35; 36]. Furthermore, other ABC transporters, including Rv1273 [37] and Rv3270 [38], act as multidrug efflux pumps and also contribute to mycobacterial biofilm formation.