esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Analysis of the Effects of Passenger Ship Abandonment Training Using VR
## 3 Materials and Methods  
In this study, we aimed to quantitatively determine the impact of VR-based passenger ship abandonment training by means of a comparison with video-based training methods.  
For video and VR content development, the scope of content was determined based on the IMO Model Course 1.41. \"Assist passengers en route to assembly and embarkation stations\" was selected from the contents of the Model Course 1.41 and developed as video and VR training content. Thereafter, the participants were divided into two experimental groups: 30 participants in Group 1 who were trained using video clips, and 30 participants in Group 2 who were trained using VR. Next, a survey was conducted in three areas: an area related to education and training satisfaction, an area related to the understanding and importance of education and training, and an area to check the participants' understanding of the training procedure.  
Education satisfaction was investigated by assessing the overall satisfaction of the two groups with the training methods using a 5-point Likert scale, and [PERSON]'s needs assessment [40] was used to analyze the importance and performance of the training; the latter was expressed by the locus for focus models [41], and these models have been used in the field of education to identify changes in the education system and to conduct education demand analysis [12; 42; 43].  
Finally, to establish whether participants understood the training procedure, they were examined on the related procedures. The examination was conducted immediately after the video and VR training--in accordance with the requirements of the 1978 SOLASConvention that ship abandonment training should be performed once a month--and participants were examined on the same content a month later. The results between Group 1 and Group 2 were then compared. To analyze the results, data preprocessing and analysis were performed using Python, statistical analysis was performed using SPSS, and data were visualized using OriginPro. The experiment participants were ship officers who visited the Korea Institute of Maritime and Fisheries Technology (KIMFT) to undergo training. Figure 1 outlines the method and procedure of this study.
## 2 Materials and Methods
### Microcontroller  
The ATMega4808 was selected to support compact hardware design and low-power consumption features. This microcontroller is a MegaAVR family unit with a low-power architecture [43] with an 8-bit core size, multiple operating frequency up to 20 MHz, a wide range of Flash size up to 48 KB, SRAM size up to 6 KB, and EEPROM size 256 bytes. The module has a wide operating voltage from 1.8 to 5.5 V and provides 3 sleep modes: idle with immediate wake-up functionality, standby, and power-down with limited wake-up functionality. This microcontroller also has a wide range of current consumption that depends on operating frequency; active mode is from 1.2 \(\mu\)A to 11.4 mA, and idle mode is from 1.8 \(\mu\)A to 2.8 mA. In standby mode, it consumes a typical current of 0.7 \(\mu\)A and a maximum current of 16 \(\mu\)A. While in power-down mode, it consumes a typical current of 0.1 \(\mu\)A and a maximum current of 15 \(\mu\)A [44].  
The ATMega4808 is easy to purchase at a low cost, which is another criterion for choosing this microcontroller. Several air-quality sensors with common interfaces can also be coupled with this microcontroller using interfaces such as I\({}_{2}\)C, SPI, and UART/USART.  
C.CO\({}_{2}\) sensor module  
The SCD30 sensor module from Sensorion was chosen to sense CO\({}_{2}\) concentration. It offers accurate and stable CO\({}_{2}\) sensing results and is fully calibrated and linearized. The sensor-based NDIR (Non-Dispersive InfraRed) technology has more advantages regarding long-term stability, accuracy, and power consumption rate during CO\({}_{2}\) measurement [45]. The sensor module embedded with SHT31 can measure CO\({}_{2}\), humidity, and temperature. The sensor module has a small package of 35 \(\times\) 23 \(\times\) 7 mm\({}^{3}\), as shown in Figure 4. Therefore, it is suitable for the CO\({}_{2}\) sensing front end of the developed sensor node.  
Figure 3: PCB design of CO\({}_{2}\) sensor node: (**a**) top layer; (**b**) bottom layer.  
The SCD30 sensor module is a calibrated sensor with CO\({}_{2}\) accuracy of \(\pm\) 30 ppm, a wide detection range of 400-10,000 ppm, and a response time of 20 s. It works in supply voltage ranges from 3.3 to 5.5 V and offers low current consumptions with an average supply current of 19,000 \(\mu\)A and a maximum supply current of 75,000 \(\mu\)A [46]. The measurement output is delivered using I\({}_{2}\)C, Modbus, or PWM interfaces
## 4 Results and Discussion
### Dependence on the Reference Strain Level, \(\xi\)  
Up to this point, the presented results used the foreknown strain value, \(\xi\), as the maximum available strain from the experiments, i.e., \(\xi=\varepsilon_{max}\approx 150\mu\varepsilon\). The proposed technique can, however, use any foreknown strain level. In this section, the effect of taking a different reference strain is evaluated by using a lower value: \(\xi=\varepsilon_{min}\approx 20\mu\varepsilon\).  
Figure 14 exemplifies the \(M_{\phi\xi}(\tau,f)\) function for the _STFT_-based filter with \(\xi=\varepsilon_{min}\) and \(\xi=\varepsilon_{max}\), where the phase threshold in all cases is \(\phi=10^{\circ}\). As can be seen, many fewer components were kept with \(\tilde{\varepsilon}=\varepsilon_{min}\). This happens because the \(\phi\)-sensitive components with lower foreknown strain are actually more sensitive than with a higher reference strain since they present the same amount of phase shifting but relative to a lower strain value. As a consequence, the focused signals \(y_{\phi}(\varepsilon,t)\) obtained with \(\tilde{\varepsilon}=\varepsilon_{min}\) are expected to be more affected by strain, i.e., more sensitive, than the ones obtained with greater reference strain state. However, as more components are nullified, TREF should decrease more. This behavior can be verified in Figure 15 where the peak sensitivity and TREF as a function of \(\phi\) for the _STFT_-based filter with \(\xi=\varepsilon_{min}\) and \(\xi=\varepsilon_{max}\) are compared. Clearly, the peak  
Figure 13: _STFT_–filtered focused signals at: (**a**) no filter; (**b**) \(\phi=15^{\circ}\); and (**c**) \(\phi=30^{\circ}\). Amplitude is normalized related to \(y_{\phi}(0,t)\)  
sensitivity increases more rapidly with \(\tilde{z}=\varepsilon_{min}\), while TREF becomes virtually zero from about \(\phi=20^{\circ}\), which only happens with \(\tilde{\varepsilon}=\varepsilon_{max}\) from about \(\phi=70^{\circ}\).
## 2 Related Works
### Simulators of Autonomous Vessels  
Autonomous vessels are currently used for applications such as search-and-rescue operations, inspecting bridge structures, maintaining security, and monitoring the environment. Most autonomous AI testing is conducted through simulators focused on modeling physical conditions, such as waves, buoyancy, water currents, and wind currents, and they do not account for realistic visual images obtained from RGB sensors. However, visual images play an important role in autonomous vessel control. For example, object detection models can be used to identify other boats or objects in images; furthermore, there is potential for the utilization of segmentation models in detecting shorelines or separating water from other structures. As listed in Table 1, autonomous-vessel simulators [4; 5; 6; 7] can simulate vessel control using waves, buoyancy, water currents, and wind-current conditions, but the visual representativeness of these simulators in terms of reality is low, and they do not incorporate diversity. Therefore, in this study, we propose a virtual-to-realistic marine image style transfer method to provide diverse and realistic marine RGB images by converting virtual images to real images, which overcomes the limitations of the lack of visual quality and diversity exhibited by existing autonomous vessel simulators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulator** & **Waves** & **Buoyancy** & \begin{tabular}{c} **Water** \\ **Currents** \\ \end{tabular} & \
\begin{tabular}{c} **Wind** \\ **Currents** \\ \end{tabular} &
\begin{tabular}{c} **Camera** \\ **(RGB)** \\ \end{tabular} \\ \hline Freefloating Gazebo [4] & O & O & O & X & O \\ \hline VREP [5] & O & O & X & X & O \\ \hline RobotX Simulator [6] & O & O & X & O & O \\ \hline USVSim [7] & O & O & O & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing simulators for autonomous vessels.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Experimental Procedure
#### 4.1.2 Fog Generation Network Training  
The Fog Generator Model, described in Section 3, was trained using the O-HAZY training subset. Clear images were input to the network, which synthesized foggy outputs to match the real hazy images in the dataset. The training utilized a composite loss function combining L1, perceptual, and dark channel losses, as defined in Equation (3). We employed the Adam optimizer with a learning rate of \(1\times 10^{-4}\) and trained for 100 epochs on an NVIDIA RTX 3090 GPU with CUDA support. The batch size was set to 1 due to memory constraints. This process ensured that the network learned to generate realistic fog distributions aligned with real-world conditions observed in O-HAZY.
## 3 Data Acquisition and Processing
### Data Processing and Analysis
#### 3.2.2 Test of Differences in Influencing Factors  
To verify whether there are statistically significant differences between general hospitals and specialized hospitals, ANOVA was conducted to examine the differences in data across four dimensions for these two types of hospitals. Results were considered statistically significant when \(p<0.05\). Table 2 indicates that the factors influencing visitation volume differ significantly between the two types of hospitals. Based on these verification results, this study grouped the data according to hospital type and separately analyzed the measurement model and structural model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Latent Variables**} & \multirow{2}{*}{**Observed Variable**} & \multicolumn{4}{c}{**General Hospital**} & \multicolumn{4}{c}{**Specialized Hospital**} \\ \cline{3-8}  & & \multirow{2}{*}{**Mean**} & **Standard** & **Statistical** & \multirow{2}{*}{**Mean**} & **Standard** & **Statistical** \\  & & & **Deviation** & **Distribution** & & **Deviation** & **Distribution** \\ \hline \multirow{4}{*}{hospital size} & construction area (m\({}^{2}\)) & 161,640.75 & 120,243.58 & 12,000–610,000 & 87,291.08 & 59,819.43 & 11,295–260,000 \\  & number of beds (beds) & 1646.97 & 768.93 & 241–4000 & 999.09 & 616.73 & 50–3200 \\  & number of medical departments (count) & 49.72 & 12.02 & 24–73 & 30.76 & 14.20 & 12–69 \\  & number of staff (persons) & 2569.34 & 1422.51 & 600–7000 & 1457.40 & 839.68 & 200–3813 \\ \hline \multirow{4}{*}{medical level} & number of positive comments after diagnosis (count) & 2649.69 & 3200.11 & 1100–13,478 & 2135.96 & 2467.42 & 100–9806 \\  & number of national key specialties (count) & 6.14 & 7.63 & 2–31 & 4.31 & 3.86 & 2–14 \\ \hline \multirow{4}{*}{traffic accessibility} & number of bus routes (routes) & 13.56 & 7.31 & 4–37 & 10.41 & 3.53 & 3–20 \\  & number of parking spaces (spaces) & 698.14 & 391.84 & 130–2236 & 376.82 & 266.04 & 120–1500 \\  & number of subway routes (routes) & 1.22 & 1.266 & 0–5 & 1.30 & 1.01 & 0–6 \\ \hline \multirow{4}{*}{traffic attraction} & average daily outpatient volume (persons) & 5272.11 & 2947.27 & 1370–13,500 & 4496.79 & 2640.45 & 1959–11,600 \\  & average daily inpatient volume (persons) & 198.75 & 111.25 & 49–1022 & 146.12 & 100.17 & 36–497 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of sample data.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 5 Opportunities for Further Research
### Feature Selection Methodology  
Too few features may affect the generalization ability of the path loss predictor. In the above analysis, only system-dependent parameters like antenna-separation distance and frequency are selected as features. It has been shown that these machine-learning-based models agree well with measured data. With limited generalization property, they may be only suitable for similar urban scenarios. Moreover, the usage of more features may not mean better performance. Too many features not only increase the computational requirement, but also probably cause the curse of dimensionality and degrade the prediction performance. Therefore, methodologies need to be developed to guide the feature selection for the path loss predictors based on machine learning.
## 2 Architectures of 5G and 6G Networks
### ML Algorithms for Detection  
The effectiveness of ML in intrusion detection is largely attributed to its diverse algorithms, each suited to specific detection scenarios:  
Figure 3: Proposed three-layered security architecture highlighting key mechanisms across the physical, connection, and service layers.  
* **Supervised Learning:** Algorithms such as Random Forest and Gradient Boosting are employed to classify network traffic as benign or malicious based on labeled datasets. These models are trained on historical data to recognize patterns indicative of known attacks. For instance, a study on wireless sensor networks demonstrated the application of Logistic Regression, Decision Trees, and Gradient Boosting for detecting Denial of Service (DoS) attacks, achieving notable accuracy rates [24].
* **Unsupervised Learning:** Techniques such as Isolation Forest and Autoencoders are in use for the detection of zero-day threats, as the lack of labeled data requires anomaly detection without established attack patterns. These methods are very good at detecting new attacks by learning what the normal behavior of the network is and alerting on anything that deviates from that norm.
* **Deep Learning Architectures:** Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are used to find complex temporal and spatial patterns in network traffic. These architectures prove to be remarkably effective in analyzing sequences and spatial hierarchies in data, which enables the detection of sophisticated attack strategies. A recent study proposed a Bagging-Deep Reinforcement Learning-based intrusion detection model that combines Multi-Layer Perceptron (MLP), CNN, and Optimized Recurrent Neural Networks (O-RNN) to enhance detection accuracy in Internet of Things (IoT) networks [25].  
Layer-oriented threat telemetry. Taking the layer perspective further, we map ML sensors to concrete data sources: (i) CSI snapshots and spectrum waterfalls feed physical-layer anomaly detectors; (ii) SDN-controller flow logs and slice-orchestration messages populate connection-layer models; (iii) API traces, HTTP/3-QUIC headers, and container events drive service-layer detectors. This mapping ensures that supervised or unsupervised models learn features that are both attack-relevant and layer-specific, an approach highlighted as a research gap in [22].  
Table 2 depicts a comparative overview of machine learning techniques used for intrusion detection and prevention in 5G/6G networks, outlining their purposes, advantages, and associated challenges.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods  
### Method Overview  
Our proposed HMMATrack for ball detection and tracking includes 3 sequentially executed modules, as shown in Figure 1: Heuristic Compound Sampling Strategy, MNet-based detection, Tracking of Multilevel Cooperative Matching, and automatic trajectory correction.  
Figure 1: The framework of the proposed HMMATrack model. The left part is the Heuristic Compound Sampling Strategy, which includes three mechanisms: tiling sampling, composite (patch level and image level) sampling, and hard sample resampling. The upper right part is the MNet-based  
detection module. The lower right part is the tracking module including multilevel collaborative trajectory matching and automatic trajectory correction, where trajectory matching includes Kalman filter matching, linear motion matching, and appearance feature matching.
# Indole Degradation in a Model System and in Poultry Manure by _Acinetobacter_ spp.
## 3 Results
### Indole Removal from Poultry Manure  
This experiment was designed to test the practical application and potential of the strains NTA1-2A and TAT1-6A to remove malodorous indole compounds from poultry manure. The bacterial strains were used to ferment the manure for 8 days. The pretreatment indole content of the manure was determined and used as reference (which was \(6.29\pm 0.44\) mg/kg of three replicates) to analyze the removal efficiency of the strains. The NTA1-2A, TAT1-6A, and the mix of the strains removal efficacy (Table 2) of indole from the manure was 78.67%, 83.28%, and 83.70%, respectively in 8 days. The strains showed significant removal efficiency (\(p<0.05\)) compared with the control group but no significant difference between the two strains and the mix (\(p>0.05\)).
## 5 Computational Simulation Results and Discussion
### The Synchronous Response and the Stability Assessment
The saddle-node bifurcation was identified as the real part of the leading eigenvalue crossing the unit circle in the location of the positive real axis. The variation of the maximum module of the eigenvalues on the nondimensional speed is depicted in Figure 5a,b. It is  
Figure 5: Dependence of the module of eigenvalues of the transition matrix on the different values of the nondimensional SFD constant and unbalance. evident that the unstable response occurred when the largest eigenvalue modulus exceeds unity. Figure 5b illustrates the situation when the isolated solution almost connects to the resonance peak. The actual connection occurs for the nondimensional unbalance value of 0.417315. The qualitative changes in the response curves as a result of varying the nondimensional unbalance are depicted in Figure 6a. The computations were carried out for SFD with the nondimensional constant of 0.25. As the unbalance increases, the isolated solution gets closer to the resonant peak, and at a value of 0.417315, it connects to the resonant peak. In the vicinity of this point, the nonlinear forces have the strongest influence on the response. In case of large unbalance, the journal can exhibit synchronous response with a circular orbit with an eccentricity close to the damper clearance. In Figure 6b, the bent of the resonance peak towards the higher rotational speeds is shown for the higher value of the dimensionless unbalance of 0.7. Additionally, the shift of the isolated solution to much higher rotational speeds is demonstrated for a small unbalance value of 0.2.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to the Users' Distinction, Interference and Wideband Sensing
However, comprehensive research in this area should be performed in order to assess the inclusion of the ED method in such a collaborative sensing approach. Sensing inception and sensing duration must be coordinated among the SUs in order to minimise the time duration needed for identifying spectral opportunities and to maximise transmission time in collaborative sensing systems. However, opportunistic and/or dynamic spectrum access with ED as one of the sensing methods is still rudimentary. Different economic, regulatory and technical challenges must be solved before the advantages of the ED method can be fully exploited in collaborative sensing systems.
## 2 Material and Methods
### Patient Variables  
Patient data was assessed by [PERSON], to evaluate the residual liquid (ml) in the patient's returned bottles from the subjects. Patients' perception of the product and possible adverse effects were registered in a questionnaire the subjects were asked to complete on the last visit by placing a mark on a 10 cm visual analog scale (VAS) (Table 1). All study subjects were evaluated in a dental office by a principal investigator ([PERSON]). G.I. and P.I. Indexes were registered at days 0 and 7 of the pre-treatment phase), day 17 (end of the first rinse period), day 27 (end of the first resting phase), day 37 (end of the second rinse period), day 47 (end second resting phase), and day 57 (end of the third rinse period) and day 67 (end of the fourth rinse period). After the experiment was concluded, the participants received the last week of plaque removal through prophylaxis (according to instructions in oral hygiene.  
Figure 2: The mouth rinses used in the study: (**a**) Bottle A (Sea4), (**b**) Bottle B (PerioKin CHX 0.20%), and (**c**) Bottle C (saline).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Questionaire** & **Positive Control** & \multicolumn{2}{c}{**CIK**} & \multicolumn{2}{c}{**Sea ENCIAS**} & \multicolumn{2}{c}{**Intergroup Statistics**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Anova\_p Value** \\ \hline Do you have statins in your teeth and tongue after rinsing? & 0.66 & 0.11 & 0.89 & 0.28 & 0.29 & 0.22 & 0.0131 \({}^{*}\) \\ Not, yes, very much & & & & & & & \\ \hline Moouthwash flavour: Quality & & & & & & & \\ How was the taste of the Product? & 6.12 & 0.72 & 6.29 & 0.41 & 7.45 & 0.51 & 0.0351 \({}^{*}\) \\ (very good—very bad) & & & & & & & \\ \hline Moouthwash flavour: Duration & & & & & & & \\ How long did the taste remain in the mouth: (short period of time—long & 5.45 & 0.23 & 6.81 & 0.15 & 7.78 & 0.43 & 0.451 \\ period of time) & & & & & & & \\ \hline How was your taste of food and drink after mouths nine? & 5.42 & 0.37 & 8.56 & 1.94 & 5.54 & 0.76 & 0.0248 \({}^{*}\) \\ \hline Was the use of the mouthsrine convenient? & 5.56 & 0.76 & 7.91 & 0.92 & 8.34 & 0.74 & 0.3729 \\ \hline Do you feel oral numbers or a burning sensation on your mucosa due to & & & & & & & \\ \hline the mouthsrine sweet? & 1.92 & 0.23 & 5.51 & 0.66 & 3.81 & 0.56 & 0.0231 \({}^{*}\) \\ absolutely not—very much & & & & & & & \\ \hline What was your perception of the plaque reduction? & 1.87 & 0.61 & 7.89 & 0.66 & 8.51 & 0.12 & 0.1689 \\ \hline What is your overall opinion of the Moouth fine used in this study? & 2.1 & 0.7 & 7.65 & 0.32 & 9.76 & 0.23 & 0.0141 \({}^{*}\) \\ very bad—very good & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Patients’ perception of the product—VAS. n.s., no statistically significant difference between each treatment, \(p>0.0125\). \({}^{*}\) Using Friedman test, \(p<0.05\). \({}^{*}\) Using Wilcoxon signed-rank test with Bonferroni adjustment for multiple comparison, \(p<0.0125\).