esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deeper Insight on the Stability of Water-Induced Reconstruction of Anatase (001) Surface
## 1 Introduction
Regarding the behavior of TiO\({}_{2}\) (001) surface in water, some authors have suggested that water patterned TiO\({}_{2}\) (001) surface might compete with the ADM models from the stability point of view [24; 25; 26; 27; 20]. The present authors have recently revised the properties of water patterned (001) anatase surface in a large supercell [27] and two main new findings emerged: (1) Dissociative water adsorption forms a ridge-terrace geometry similar the one of the ADM model. This surface modification is accompanied by the formation of a stabilizing network of hydrogen bonds so that the water induced reconstruction (WIR) geometry shows an apparent (2 \(\times\) 4) periodicity (see Figure 2); (2) such a water induced surface reconstruction reduces the surface stress at a larger amount than the ADM model and the surface reactivity disappears. However, the above mentioned WIR model was not studied and discussed in terms of the stability hierarchy of the possible various surface periodicities, even though previous studies have indicated the (1 \(\times\) 3) reconstructed surface as the most stable one [24]. Then, in the present article, we try to go deeper into this aspect concerning the newly found stable ridge geometry of the WIR model. Figure 1: Side view of the unreconstructed anatase (001) surface. Along the [100] direction, long and short Ti\({}_{5C}\)-O\({}_{2c}\) bonds are evidenced. Red: Oxygen; Grey: Titanium.
## 3 Data Fusion Method Based on Self-Adaptive  
### 3.1. Combined Measurement Fusion Method  
As mentioned above, the attitude angle measurement data come from visual and inertial units, which have different sampling frequencies. Due to the strong learning capability, adaptability, and flexibility of the backpropagation algorithm, it was chosen to fuse the data from visual and inertial measurement units. The fusion process is illustratedin Figure 2 below. Based on the principle of time alignment, first, the visual measurement data with a lower sampling frequency is linearly interpolated. Next, the interpolated data is fused with the inertial measurement unit data using a backpropagation algorithm.
## 4 Experiments and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Algorithm** & **Interrogation Precision** \\ \hline [PERSON] et al. [33] & PSO-SA & \(\pm\)10 pm \\ [PERSON] et al. [34] & Variable-step-size \& Crosscorrelation & \(\pm\)4 pm \\ [PERSON] et al. [36] & EDA & \(\pm\)**3 pm** \\ [PERSON] et al. [5] & Dynamic calibration algorithm & \(\pm\)**3 pm** \\ [PERSON] et al. [18] & Cascaded CNN \& BPNN & \(\pm\)5.672 pm \\ [PERSON] et al. [35] & Cascaeded BPNN \& BPNN & \(\pm\)10.39 pm \\ Ours & Cascaded GAN \& DNN & \(\pm\)**3 pm** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method with traditional algorithms (first four counting from top to bottom) and neural network-based methods (penultimate and penultimate third) regarding FBG sensor interrogation precision. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Kernel function** & **RMSE** & \(\mathbf{R^{2}}\) & **MSE** & **MAE** \\ \hline LR & / & 0.1724 & 0.9872 & 0.0297 & 0.1196 \\  & Linear & 0.1344 & 0.9934 & 0.0181 & 0.0912 \\ SVR & RBF & 0.0994 & 0.9961 & 0.0099 & 0.0806 \\  & matern & 0.1764 & 0.9841 & 0.0311 & 0.1157 \\ GPR & RBF & 0.1656 & 0.9900 & 0.0274 & 0.1134 \\ DNN & / & 0.0171 & 0.9997 & 0.0003 & 0.0145 \\ GAN + LR & / & 0.1532 & 0.9911 & 0.0235 & 0.1019 \\  & Linear & 0.1164 & 0.9968 & 0.0121 & 0.0811 \\ GAN+SVR & RBF & 0.0655 & 0.9972 & 0.0078 & 0.0672 \\  & Matern & 0.1231 & 0.9890 & 0.0255 & 0.0921 \\ GAN + GPR & RBF & 0.1341 & 0.9911 & 0.0221 & 0.0966 \\
**GAN + DNN** & / & **0.0036** & **0.9999** & **0.00001** & **0.0031** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of performance evaluation metrics (RMSE, \(R^{2}\), MSE, and MAE).
## 2 Materials and Methods
### Tissue Preparation  
The experimental animals in the L-PRP group (EG) and control group (CG) were sacrificed from groups of 4-5 mice on day 1 through week 4 following the operation. At each observation point, the experimental animals were transcardially perfused under deep anesthesia according to our previous study [27]. The maxillae were treated according to our previous study [28]. The paraffin sections were stained with hematoxylin-eosin (H&E) to evaluate morphological variations, AZAN staining to evaluate collagen-related structures in the healing pulp, and subjected to immunohistochemistry.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Operation** & **Mice Age** & **Number of Animals** & **Platelets in Whole Blood (\(\times\)10\({}^{4}\mu\)L)** & **Platelets in PRP (\(\times\)10\({}^{4}\mu\)L)** & **Times -Fold Increase** & **White Blood Cell** & **Type of Replantation** & **Target Subjects** **(n)** \\ \hline
1 & 8 weeks & 6 & 85.8 & 384.5 & 4.5\(\times\) & 138 & Immediate & 5 mice (PO1W) \\ \hline
2 & 4 weeks & 6 & 78.8 & 431.8 & 5.5\(\times\) & 52 & Immediate & 5 mice (PO5D) \\ \hline
3 & 6 weeks & 6 & 100.4 & 941.4 & 9.4\(\times\) & 172 & Immediate & 5 mice (PO1D) \\ \hline
4 & 8 weeks & 5 & 106.6 & 633.6 & 5.9\(\times\) & 110 & \
\begin{tabular}{c} Intentionally- \\ delayed (30\({}^{\prime}\)) \\ \end{tabular} &
\begin{tabular}{c} 5 mice (PO1W) \\ 5 mice (PO2W) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality of L-PRP, including platelet and white blood cell concentrations, and the type of replantation (modified from [27]). The number of experimental animals used per operation is shown in the “Target subjects (n)” column and the observation time point is indicated in parentheses.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 4 Impact of \(\Delta\)h on Vessel Waiting--Analysis in Swinoujscie
A similar approach was proposed by [PERSON] et al. [33], who developed the DWLT (Discharge-to-Water Level Transformation) method using monthly duration curves, effectively transforming global discharge model outputs into reliable water level estimates. Their study emphasizes that even in the absence of dense ground data, water level estimations from hydrological models (e.g., GEOLOWS ECMWF) can maintain high correlation with actual observations, enhancing the resilience of navigational planning in hydrologically variable regions. A Monte Carlo simulation was carried out to probabilistically assess the accessibility of the fairway under conditions of varying water levels and variable draught of vessels. For each vessel generated in the experiment, the following were drawn at random:  
* T\({}_{i}\)\(\sim\) U (11.00, 12.00)--draught of the craft (in metres);
* t\({}_{i}\)\(\sim\) U (1, \(N\))--moment of vessel entry, a randomly drawn hour from the data interval [1, N], where N is the number of hours in the study period (2008-2022). For each implementation, the condition was checked:  
\[h(t_{i})\geq h_{kr}(T_{i})=h_{0}-(D-T_{i}-\Delta h)\]  
where: \(h(t_{i})\)--water level at the time \(t_{i}\); h\({}_{\text{kr}}\)(T\({}_{i}\))--critical water level; \(h_{0}\) = 500 cm--benchmark (Amsterdam); \(D\) = 1250 cm--fairway depth; \(\Delta h\)--adopted minimum under the keel clearance; \(T_{i}\)--drawn draught of the vessel. If the condition was not met, a waiting case was recorded. The simulation was repeated for n = 1000 units to estimate the probability of encountering insufficient fairway depth conditions under given assumptions (Figure 18). The threshold value of \(\Delta\)h = 25 cm was selected as a compromise between navigational safety and the accessibility of the fairway [23; 34; 35]. It reflects a realistic minimum under-keel clearance (UKC) that aligns with hydrological variability and operational practice. The adopted under-keel clearance (UKC) safety margins are consistent with international guide  
Figure 18: Simulation of 1000 random ships—cases of waiting vs. no waiting. lines, such as PIANC WG 121 (2014), _Harbour Approach Channels--Design Guidelines_, which recommend appropriate UKC allowances based on navigational conditions and fairway characteristics [36]. Additionally, IALA Guideline No. 1089 (2012) [37] emphasizes the role of Vessel Traffic Services (VTS) in supporting safe navigation in constrained waterways, including UKC monitoring and advisory functions. The number of simulated vessels (n = 1000) was chosen to ensure statistical robustness of the Monte Carlo analysis [38]. A uniform distribution of vessel entry times was assumed to provide a neutral baseline scenario without introducing seasonal or temporal traffic bias. The analysis confirms that the value of \(\Delta\)h has a significant, albeit moderate, impact on fairway availability, and that the use of a value of 25 cm minimises the risk of downtime while maintaining the required water reserve under the keel.
### Problem Statement, Research Aim, and Objectives
The unique contributions of this paper are as follows: First, the approach of securing the data and the IoMT system by design and the process by which the IoMT system authenticates, authorises, controls access, dynamically manages the keys, and maintains data confidentiality, integrity, and availability. Second, an in-depth performance comparative study of secure integration of the interaction and engagement of the IoMT client with the user, IoMT server, and web server is performed. Third, the self-healing process during network failure and data recovery is presented. Fourth, different methods of alerting (email, display, sound, etc.) for health and well-being events are explored, as well as ways of collecting diverse sensory information (movement, temp, humidity, light, air quality, proximity, pressure, multimedia, etc.) to learn about the quality and well-being of the users and data visualisation. Lastly, exploring the best encryption and data signing processes to support real-time communication is also one of the key highlights of this paper. The proposed system of this paper ensures that Medium Access Control (MAC) spoofing will not impact the confidentiality and integrity of the data even if man-in-the-middle attacks are underway due to MAC spoofing because the sessions are encrypted. Moreover, the IoMT device client is authenticated through a unique ID (hash of user's registration data (\(\gamma\)), MAC address (\(\vartheta\)), and a 32-bit random number (\(\mu\))) to detect and identify the participating devices. MAC spoofing will not allow data tampering because of the digital signature, and non-repudiation attacks are prevented by signing the data using the sender's private key. Information disclosure is highly unlikely due to the high level of encryption and innovative dynamic key management policies. DoSand DDoS attacks are averted by monitoring the network and allowing only authorised and authenticated devices into the monitoring system. The rest of this paper is structured as follows: Section 2 presents a state-of-the-art background discussion and detailed literature study on IoT-based health monitoring, IoMT, smart hospitals, data security, data privacy, etc. Section 3 discusses the research principles and methodologies adopted in this research work. Section 4 proposes a secure-by-design IoMT framework, with technical details. Section 5 provides the results and discussion, while Section 6 concludes the paper with future directions.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 3 Transport Measurement Methods  
Knowledge of the transport properties of graphene such as sheet carrier concentration, mobility, and sheet resistance and the factors affecting them are crucial for any applications of the graphene into practical devices. These include Hall effect measurements using Hall bar devices and van der Pauw (vdP) structures, Field-effect measurements using gated devices, Raman spectroscopy (carrier concentration from G peak shift), and ARPES (discussed in Section 2.1).
## 6 Experimental Results and Evaluations
### Logic Error Assessment and Detection  
A logic error in source code generates unexpected program output. The cause of logic error is typically the incorrect application of mathematical logic in source code. Conventional compilers cannot detect or assess logic error, and thus student and professional programmers must check the entire source code line by line. This is a major problem, especially for novice programmers. A simple program with logic error is shown in Figure 8. The program takes in an array of numbers and then outputs it. In the example, four numbers are given for an array but because of incorrect logic, only three of them are output.  
Logic error assessment and detection is a challenging task for traditional compilers. The proposed attention-based language model identifies logic error candidate words in source code to reduce the time required to check for such errors. To identify logic errors, the model should be able to calculate long dependent sequences of source code. We thus designed the seq2 seq language model by combining the attention mechanism with LSTM. We compared its performance with other state-of-the-art models. Source code with logic error (an example for BS) was evaluated by the LSTM-AttM model. The results are shown in Figure 9. The source code assessment and detection results are listed in Table 5. The results reveal the effectiveness of the proposed LSTM-AttM model. The proposed model assessed and identified logic errors and their locations in source code. The estimated error probability ensures the logic error possibility on a particular line (blue outline) of source code. The model detected two logic errors on line 6 and generated the corresponding error probabilities (see Table 5). The estimated error probabilities are both more than 0.90, indicating possible logic errors on line 6 of the source code.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-4}  & **RNN** & **LSTM** & **LSTM-AttM** \\ \hline Insertion Sort & 83 & 88 & 98 \\ Greatest Common Divisor & 81 & 90 & 95 \\ Prime Numbers & 74 & 85 & 93 \\ Bubble Sort & 80 & 80 & 96 \\ Selection Sort & 69 & 78 & 92 \\
**Average** & **77.4** & **84.2** & **94.8** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assessment results of syntax error detection for erroneous source code.  
Figure 8: Example of source code with logic error and its input and output.  
To assess logic errors, we selected source code from the AOJ system that generated a runtime error (i.e., failure during execution) judge verdict. Runtime errors can be caused by invalid pointer references (segmentation fault), overflow, division by zero, memory access violations, and uninitialized memory access. In the experiment, in addition to the above-mentioned example (Figure 9) a large number of source code samples with logic errors were used. The evaluation results are listed in Table 6. As shown, the proposed language model outperformed the reference benchmark models.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 6 Experimental Verification of FOS-LW
### Early-Warning Simulation Test  
The set-up of simulation experiment is demonstrated in Figure 8. The experimental setup involves mechanically induced displacement of the sensing system. The displacement meter is used to simulate the movement of a landslide. One end of the spiral blazon sensor is fixed to the displacement meter; the other end is controlled by the drive mechanism to simulate the deformation of landslides. The power supply provides electricity to the whole system, and the alarm function is accomplished by the alarm apparatus.  
The preset measurement range of the displacement meter is 10.00 cm (corresponding to a 10.00 cm displacement of landslide). A three-level alarm system was set up. 0.00 cm (initial state) corresponds to the green level; 2.00 cm-8.00 cm corresponds to the yellow level; 8.00 cm-10.00 cm displacement corresponds the red level. The experimental procedure involved first making sure that the host was in working condition, the displacement meter was then slowly moved, representing the movement of the landslide. The dial indicator recorded the distance of the displacement meter movement. When the displacement meter reached about 2.00 cm degree, the alarm apparatus launched level one alarm response. As the displacement meter continued to about 8.00 cm, the alarm apparatus launched level  
Figure 7: The linear relationship between light loss and cylinder number.  
two alarm response. Given this very small error (0.002 cm) of the warning system, we can conclude that this system can be applied for in situ early-warning of landslides.
## 3 The Proposed Solution
### Data Collection and Geographic Information System Integration  
The stream of light data monitored from the IoT sensor are then pushed into the GIS-based informative system. The GIS stores the ID of the IoT sensors, the light measurement data, the time stamp of the measurement, and the GPS position. Potentially, more than one IoT sensor can push the data into the informative system, enabling the use of a network of IoT sensor for data mapping of the urban area. The interaction between the IoT sensor, the GIS system and the elaboration is shown in Figure 14.  
The IoT system has been equipped with a GPS sensor, allowing it to capture the location of each light intensity measure. Location data are collected in the WGS 84 (EPSG 3857) coordinate system. The accuracy of the GPS sensor (2.5 m) is enough for collecting the location of the light intensity measurement on a map. However, this accuracy could be affected by issues caused by the so-called urban canyons and issues generated by surveying areas characterized by trees with high foliage (e.g., boulevards, parks, etc.). which may determine a lower reliability in the registration of the geographic coordinates [76]. Therefore, for this research, the monitored area has been divided in a grid of 10 m \(\times\) 10 m squares (20 m\({}^{2}\) area each) and an algorithm computing the arithmetical average of the detected light intensity for each square has been developed. The details of the algorithm are described in Section 5.4. The results of this computation allow us to identify the brightest and darkest areas in the urban environment, avoiding issues related to accuracy of the GPS signal. The 20 m\({}^{2}\) grid allows us to overcome the issues related to the precision in the detection of the position of the urban elements, while correctly representing the streets and the paths in pedestrian areas.  
Figure 13: The light meter assembled from an Arduino Uno board without the mechanical envelope. The main components of the board are highlighted in the picture.
## 4 VRFormer
### Advanced Data Processing and Sensor Fusion Specifics
#### 4.6.2 Hierarchical Data Fusion Using Kalman and Particle Filters  
Within the VRDeepSafety framework, the VRFormer system uses hierarchical fusion using Kalman filters for linear motion and particle filters for non-linear motion. This dual-fusion approach enables more accurate state estimation across different dynamic conditions. Additionally, VRFormer adapts to prioritize data from LiDAR, radar, or camera based on real-time assessments of sensor reliability. This adaptive fusion strategy ensures data continuity, even when individual sensors are limited because of occlusions or environmental factors.
# Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients
## 4 Discussion
### Region of Interest (ROI) Size  
ROI size is one of the key factors that determine the power of feature identification. Previous studies have reported that the amount of texture feature information increases with ROI size; however, local characteristics of the image may be lost [29]. Because the same texture can be perceived as different in distinct scales, multi-resolution analysis is often used to enhance the discriminative power of the resulting features. Our study found that a radius (R) of \(<\)3 had a better ability to discriminate spastic muscle. Similarly, [PERSON] et al. reported that texture indicators have less variability than gray-scale indicators tested with different sized ROIs [7]. Because LBP is computationally simple and less sensitive to changes in illumination than many descriptors, it can be combined with the multiscale descriptor to develop a method for encoding fine details. Future studies could combine LPB with multiple textural features extracted from high order texture descriptors (e.g., gray level co-occurrence matrix and Haralick) to provide an ultrasound texture-based real-time computer-aided diagnosis system for determining the mechanical properties of spastic muscles and differentiating neuromuscular diseases.
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Classifier** & **Train/Test Split** & **Average MCC** & **Average F1** \\ \hline \multirow{8}{*}{Functional} & LSTM & _Documents based_ & _0.89 (SD 0.02)_ & _91\% (SD 0.02)_ \\  & & Cell based & 0.89 (SD 0.04) & 91\% (SD 0.03) \\  & **BiLSTM** & _Documents based_ & _0.90 (SD 0.05)_ & _92\% (SD 0.04)_ \\  & & **Cell based** & **0.91 (SD 0.06)** & **92\% (SD 0.05)** \\  & GRU & _Documents based_ & _0.88 (SD 0.05)_ & _90\% (SD 0.04)_ \\  & & Cell based & 0.88 (SD 0.06) & 91\% (SD 0.05) \\  & **_BiGRU_** & _Documents based_ & _0.91 (SD 0.01)_ & **93\% (SD 0.01)** \\  & & Cell based & 0.89 (SD 0.11) & 90\% (SD 0.1) \\ \hline \multirow{8}{*}{Physical} & **LSTM** & _Documents based_ & _0.77 (SD 0.01)_ & _89\% (SD 0.01)_ \\  & **Cell based** & **0.77 (SD 0.04)** & **88\% (SD 0.01)** \\  & BiLSTM & _Documents based_ & _0.76 (SD 0.01)_ & _88\% (SD 0.01)_ \\  & & Cell based & 0.75 (SD 0.04) & 87\% (SD 0.02) \\  & GRU & _Documents based_ & _0.77 (SD 0.03)_ & _89\% (SD 0.02)_ \\  & & Cell based & 0.75 (SD 0.03) & 88\% (SD 0.02) \\  & BiGRU & _Documents based_ & _0.77 (SD 0.03)_ & _89\% (SD 0.02)_ \\  & & Cell based & 0.76 (SD 0.02) & **88\% (SD 0.01)** \\ \hline \end{tabular}
\end{table}
Table 10: Classification results based on the architecture and the data training and testing splits; we compare four different classification methods based on different recurrent neural network layers.
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.3 Fuzzing and Cross-Contract Methods  
Fuzzing techniques have emerged as effective means of improving code coverage and vulnerability detection in smart contracts. Intelligent guidance and prioritization--as demonstrated by LLM4 Fuzz [88]--along with approaches such as CrossFuzz [87] have proven effective in exploring multiple execution paths to identify vulnerabilities. Evo-Fuzzer [80] employs evolutionary algorithms to generate inputs more intelligently.  
Issues in the Existing Approaches  
Although these methods enhance detection capabilities, they are often resource intensive and complex to implement. Their success depends heavily on fine-tuning, the use of multiple large language models (LLMs) for consensus, and expert configuration. Scalability and adaptability remain challenging for the widespread adoption of fuzzing techniques.