esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Performance of the Guided Sampling Technique  
#### 5.2.1 Performance under Degeneracies  
To verify the efficiency of our sampling algorithm to avoid planar degeneracy, the Table dataset was used. In each instance of these data, 258 synthesized correspondences were placed on the surface of the monitor, and the number of matches from other objects of the scene was varied to get the ratio \(\lambda\). For instance, at \(\lambda=0.4\), the total number of correspondences was 645, from which 258 correspondences were located on the monitor plane. It should be noted that synthetic images were captured so that all of the objects of the scene were visible in the images, and the monitor plane only occupied a small area in each image. For any instance of the dataset at different ratios \(\lambda\), we limited the total number of hypotheses to 1000 and compared the results of our method with those of RANSAC and Multi-GS-RANSAC. That is, each algorithm was stopped when exactly 1000 sample sets were drawn. This limited number of sample sets is approximately six-times more than the theoretical number of sample sets for achieving 95% probability of drawing at least one outlier-free random sample set from the data containing 40% outliers, which is the maximum outlier ratio in the dataset instances. The performance criteria used for this experiment were (i) the percentage of outlier-free (an outlier-free sample set is a set of matches where all of the matches are inlier) sample sets among the 1000 sample sets; (ii) the percentage of non-degenerate (a degenerate sample set (due to planar degeneracy) has more than five points from the dominant plane (in this example, the monitor plane); see Section 2) and outlier-free sample sets among the 1000 sample sets that we denote as non-degenerate sample sets for simplicity and (iii) estimation accuracy (\(\mu_{d}\cdot CP\)). The medians of the results obtained after five trials are represented in Figure 4.  
Figure 4: Performance of sampling methods on the Table dataset as the ratio \(\lambda\) increases: (**a**) percentage of outlier-free sample sets, (**b**) percentage of non-degenerate sample sets and (**c**) estimation accuracy.  
The proposed algorithm drew up to 75-times more outlier-free sample sets in comparison with RANSAC within a limited budget of 1000 sample sets (Figure 4a). The percentages of outlier-free samples for our algorithm and Multi-GS-RANSAC were very close. This showed that the Multi-GS sampling strategy performed well in the absence of degeneracy. However, Multi-GS sampling failed to draw non-degenerate sample sets as the ratio \(\lambda\) increased (Figure 4b). For ratios higher than 0.7, both RANSAC and Multi-GS-RANSAC failed to estimate the model correctly (Figure 4c), while the proposed algorithm estimated the model robustly even in the presence of serious degeneracy (\(\lambda=0.9\)).
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
###### Abstract  
The precision in evaluating global ambient light profoundly impacts the performance of image-dehazing technologies. Many approaches for quantifying atmospheric light intensity suffer from inaccuracies, leading to a decrease in dehazing effectiveness. To address this challenge, we introduce an approach for estimating atmospheric light based on the polarization contrast between the sky and the scene. By employing this method, we enhance the precision of atmospheric light estimation, enabling the more accurate identification of sky regions within the image. We adapt the original dark channel dehazing algorithm using this innovative technique, resulting in the development of a polarization-based dehazing imaging system employed in practical engineering applications. Experimental results reveal a significant enhancement in the accuracy of atmospheric light estimation within the dark channel dehazing algorithm. Consequently, this method enhances the overall perceptual quality of dehazed images. The proposed approach demonstrates a 28 percent improvement in SSIM and a contrast increase of over 20 percent when compared to the previous method. Additionally, the created dehazing system exhibits real-time processing capabilities.  
dehazing; dark channel prior; polarization; atmospheric light estimation; dehazing system Article  
Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation  
1
## 3 Results  
### Volumetric Water Content (VWC), Relative Extractable Water (REW) and Rainwater Holding Capacity  
The differences of the water content in three treatments (VWC) and for the three species resulted statistically siginificant with (\(p<0.05\)). The water content (VWC) values of \(D\). _margaretae_ substrate (15.5%) was significantly higher than _F. laevis_ (12.5%) and _I. lutscens_ (7.8%) substrates. In this way, the seasonal evaluation confirms these results indeed the summer VWC values were 13.5% for \(D\). _margaretae_,10.1% for _F. laevis_ and 4.6% for _Iris lutescens_; from November to March they were 17%, 14.5% and 10.3% (Figure 2).  
Overall average values for relative extractable water (REW) highlighted in Figure 1 showed that _D. margareta_'s substrate contained more water, 38% in summer and 63% in winter. The REW value of _F. laevis_ were 26% in summer and 52% in winter. Finally, those of _I. lutescens_'s substrate varied between 10% in summer and 28% in winter.  
Our data shows (Table 2) variations in substrate volumetric water content for each species before and after rainfall events, when gravitational water (as water contained in the macropores) had drained away, 60 h after the rain had ended. These results indicate a greater degree of variation after the August and September rains than after rainfall in winter. _F. laevis_ was the species with the widest variations.
# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
## 4 Parameter Tuning of ADRC Using the QPSO Algorithm  
Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence, which simulates the cooperative behavior of biological groups, such as bird flocks and fish schools during activities like searching for food or flying. It was introduced  
Figure 4: Graph of the \(f_{aln}\) function.  
by [PERSON] and [PERSON] in 1995. The core concept of PSO is \"social information sharing\", where particles, treated as massless and volume-less individuals, collectively form a \"swarm\". These particles move through the solution space and adjust their position by changing their velocity. They search for the region occupied by the best particle found so far in the current space and use their own flight experience to determine the global best position. In each iteration, the particles select two best positions as objectives: one is the best position they have encountered up to that point, and the other is the global best position found by the entire swarm.  
Due to the advantages of the PSO algorithm, including its simple structure, efficient operation, strong robustness, and global search capability, it is widely recognized as a highly effective nature-inspired algorithm. It is extensively applied to solve both constrained and unconstrained single-objective and multi-objective global optimization problems [22]. In traditional PSO, the velocity and position of the particles are updated using the following formulas:  
\[v_{i}^{k+1}=w\cdot v_{i}^{k}+c_{1}\cdot\tau_{1}\cdot\left(p_{i}^{k}-x_{i}^{k} \right)+c_{2}\cdot r_{2}\cdot\left(g^{k}-x_{i}^{k}\right), \tag{15}\]  
\[x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1}, \tag{16}\]  
where \(v_{i}^{k+1}\) represents the velocity of particle \(i\) at the \(k+1\)-th iteration; \(w\) is the inertia weight, which governs the extent to which the particle's velocity depends on its previous velocity; \(c_{1}\) and \(c_{2}\) are the acceleration coefficients; \(r_{1}\) and \(r_{2}\) are random coefficients; \(p_{i}^{k}\) is the particle's personal best position; and \(g^{k}\) is the global best position in the search space. Figure 5 illustrates the movement of particles in the swarm.  
where \(x_{i}^{k}\) is the current search point, \(x_{i}^{k+1}\) is the adjusted search point, \(v_{i}^{k}\) is the current velocity, \(v_{i}^{k+1}\) is the adjusted velocity, \(p_{i}^{k}\) is the velocity based on the particle's own best position, and \(g^{k}\) is the velocity based on the global best position.  
Quantum-based Particle Swarm Optimization (QPSO) is an extension of the conventional Particle Swarm Optimization (PSO) algorithm. QPSO incorporates the quantum mechanical behavior model, where the state of a particle is described by a wave function. The center of the attractive potential well generated by the potential well restricts the particle's movement. Particles in a quantum-confined state can probabilistically appear at any position within the search space, allowing them to move beyond deterministic trajectories.
# A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher
## 2 Materials and Methods
### Proposed IoT System Architecture
#### 2.2.1 The Sensing Layer  
The sensing layer is made up of multiple low-power sensors that are spread on agricultural land to continuously collect soil temperature and humidity. The DHT11 sensors are used to measure the temperature value in degrees Celsius and the humidity value in percentage. The DHT-11 sensor has three pins: two of which are for power and one is for output data transmission. The three pins are used to connect the DHT11 sensor to the NodeMCU ESP8266 board. The DHT-11 library must be added to the Arduino IDE software before uploading to the NodeMCU ESP8266 board the code written to read the humidity and temperature value from the DHT11 sensor. Together, the DHT-11 sensor and the NodeMCU form the IoT node. The NodeMCU runs the MQTT client code and publishes the sensed soil parameters (the temperature and humidity readings) to the MQTT broker after encrypting the message using the Expeditious Cipher (X-cipher). The MQTT broker runs on Raspberry Pi, which is a low-power single-board computer. NodeMCU  
Figure 2: Traditional Agri-IoT ecosystem versus proposed Agri-IoT ecosystem.  
ESP8266 has a built-in WiFi module that is used as the communication protocol between NodeMCU and the Raspberry Pi. The MQTT protocol is chosen due to its short message transmission capability and low-bandwidth requirement, which makes it more convenient for machine-to-machine (M2M) communication. Moreover, the Raspberry Pi was selected as a processing layer because of its low cost and common use in IoT applications. Mosquito is an open-source lightweight MQTT broker and is selected as it is suitable for Raspberry Pi. The Mosquito broker decrypts the message and adds the information in the message to the related topic. Then, information is encrypted again before being published (broadcast) to all subscribers. The ThingSpeak cloud platform is used among subscribers that execute cloud analysis for making irrigation decisions regarding the time of irrigation and the quantity of water. Then the irrigation decision is published to the broker to be decrypted and then encrypted again before being published to the actuator. The nodeMCU at the actuator layer decrypts the message and then passes the control signal to the attached pump to activate the irrigation.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Physical Layer
#### 3.1.4 Co-Channel Interference  
NB-IoT being deployed in the existing LTE spectrum, co-channel interference may occur between NB-IoT and LTE UEs. This is due to several reasons such as sampling rate mismatch, inter-PRB interference due to power leaking between NB-IoT and LTE PRBs, etc. To mitigate the impact of co-channel interference in the NB-IoT/LTE coexistence scenario, the following works have addressed the problems and proposed potential solutions.  
In [65], the authors proposed the design guidance for channel equalization that can be used for 5G networks. The proposal set some assumptions such that the currently most used algorithms in cyclic prefix--OFDM system for pilot design, channel estimation, equalization, synchronization, and system performance analysis may no longer be applicable to NB-IoT systems. Their mathematical modeling demonstrated that channel equalization coefficients for NB-IoT UE are a set of phase-shifted CFR combination and not a simple Fourier Transforms of the channel impulse responses. This is the consequence of sampling rate mismatch between NB-IoT user and base station.  
In [66], the authors established a comprehensive system model for in-band and guard-band NB-IoT by considering sample duration. They derived the mathematical expressions of received LTE and NB-IoT signals and analyzed the close-form interference power on LTE signal from adjacent NB-IoT signal. It is observed that the sample duration of NB-IoT significantly impacts the desired signal as well as interference on LTE UE; this is due to mismatched sampling rate between NB-IoT UE and the base station. Their proposed system model and derivations match the simulations, hence can be used for coexistence analysis for NB-IoT system.  
**Summary:** This subsection has addressed the state of the art of NB-IoT PHY layer protocol. The main focus was set on different approaches to improve cell acquisition process, random access process, channel estimation, and interference mitigation. The next subsection focuses on MAC layer features by addressing the corresponding challenges and potential solutions.
## 4 Results and Discussion
### Deep Learning Pipeline  
Considering the 3D DL models, it is noticeable that the Hybrid ViT outperformed the ResNet-34. The Hybrid ViT achieved the highest values for both considered metrics, with an AUC of 0.869 and a balanced accuracy of 0.816.  
Taking into account the significant architectural differences between ResNet and Hybrid ViT, an analysis of the performances according to tumour staging was conducted (see Figure 9), where T-Stage quantifies the tumour size, N-Stage represents the involvement of lymphatic nodes and tumour spread to surrounding tissues, and M-Stage relates to the number and location of metastases [50]. Balanced accuracy was the metric selected for this evaluation since that, as the patients were grouped according to TNM staging, there were some categories that consisted of cases belonging to a single class, making the computation of AUC impossible. It was observed that the ResNet-34 consistently had a lower balanced accuracy across the categories of T-Stage, N-Stage, and M-Stage. On the other hand, the Hybrid ViT performed better across all staging categories, being able to correctly handle cases involving larger, more advanced, and potentially more heterogeneous tumours (i.e., those with higher TNM staging values), which posed greater challenges for the ResNet-34 model.  
These results demonstrate the Hybrid ViT capabilities for addressing challenging image classification tasks. As anticipated, the utilisation of a ResNet backbone for initial image processing enabled the extraction of relevant local features, that were complemented by the ViT encoder capacity to capture long-range dependencies while attending to the most significant parts of the input, providing a thorough analysis of the data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & \multicolumn{3}{c}{**Type of Features**} \\ \hline  & **First Order** & **Textural** & **Shape-Based** \\  & **Statistical** & & \\ \hline
2D Random Forest & 34.6 \% & 64.7 \% & 0.6 \% \\
3D Random Forest & 28.3 \% & 70.7 \% & 0.9 \% \\
3D XGBoost & 24.8 \% & 73.8 \% & 1.4 \% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Cumulative feature importance for each type of feature (first-order statistical, textural and shape-based features) in the (a) 2D Random Forest, (b) 3D Random Forest and (c) 3D XGBoost Classifiers.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 3 Challenges and Requirements  
In this section, we first identify the impact of long propagation delays and limited bandwidth for underwater networks. Secondly, the difficulties of replacing or recharging batteries in underwater networks mean that energy efficiency is a major concern, thus saving energy is explored. We finally describe our motivation for proposing an efficient depth-based MAC protocol.
# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment
### Mask Matrix  
This section aims to investigate the importance of features in individual travel prediction models by constructing a mask matrix based on gradient information. Our methodology is based on the observation that gradients themselves provide a direct measure of feature contributions in the model's decision-making process. The formula for calculating the gradient matrix is as follows:  
\[G=\left|\frac{\partial S_{c}}{\partial x_{i,j}}\right| \tag{19}\]  
Figure 4: Deep learning model architecture with GRU and attention mechanism for generating contribution maps.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Length** & **20** & **40** & **60** \\ \hline Training Set Accuracy & 59.04\% & 60.10\% & 59.16\% \\ \hline Test Set Accuracy & 57.82\% & 58.51\% & 57.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Predictive accuracy of the model.  
Here, \(G\) denotes the gradient matrix of the model's output with respect to the original input matrix, where each element \(g_{i,j}\) corresponds to the absolute value of the gradient of \(x_{i,j}\) in the original input matrix \(X\), indicating the gradient contribution of the \(i\)th feature of the \(j\)th sequence to the model's output for category \(c\).  
Based on the gradient matrix \(G\), we can identify the feature locations with the maximum gradient values and construct a mask matrix \(M\). This matrix has the same shape as the original matrix \(X\) but only contains 0 and 1. The formula for constructing the mask matrix is the following:  
\[M_{i,j}=\left(\begin{array}{c}1\ \text{if}\ g_{i,j}\text{=}\ \text{max}_{k}(g_{ik})\\ 0\ \text{otherwise}\end{array}\right) \tag{20}\]  
Here, \(M_{i,j}\) is the element in the \(i\)th row and \(j\)th column of the mask matrix \(M\), and \(g_{i,j}\) is the corresponding element in the gradient matrix \(G\). For each sample \(X\), we identify the feature with the maximum gradient value among all features and set that position to 0 in the mask matrix, while setting all other positions to 1. The masked matrix can then be represented as follows:  
\[\widetilde{X}=X*M \tag{21}\]  
Theoretically, this masking method essentially shields key features in the travel data, thereby reducing the content of the corresponding travel records and exploring the impact of these features on the model's predictions. The process of constructing the mask matrix is illustrated in Figure 5.
## 2 Materials and Methods
### Identification of VOCs Using HS-SPME-GC-Ms  
To test the hypothesis that the initial competitive colonization of _K. cowanii_ Cp1 on plant tissue produces a diffusible volatile organic compound with the ability to reduce infection with _P. aroidearum_ SM2, the profiles of VOCs were characterized during bacterial growth phases. First, a bacterial growth curve was constructed to determine the times of the bacterial growth phases. Second, based on previous experiments' results and growth curves, we decided to perform VOC analysis during the lag phase (2 h), log phase (4 h), and stationary phase (6 h). To do this, 40 mL of tryptic soy broth medium (Difco Laboratories; Detroit, MI, USA) was inoculated with the _K. cowanii_ Cp1 strain (10\({}^{8}\) CFU/mL) in borosilicate glass media bottles of 80 mL (Schott) and incubated for 2, 4, and 6 h at 37 \({}^{\circ}\)C and 120 rpm. The tryptic soy medium without the bacterial strain was used as a control. Once the bacterial cultures were obtained, the VOCs were analyzed using the following methodology [26]: the samples were incubated at 50 \({}^{\circ}\)C for one hour, and then the VOCs were collected on a divinylbenzene/carboxen/polydimethylsiloxane fiber (DVB/CAR/PDMS, Supelco, Sigma-Aldrich, Visalia, CA, USA). After this, manual injection was carried out in splitless mode; the injection port and transfer line temperature were set to 250 \({}^{\circ}\)C using a 7820A GC with a 5975C MSD (Agilent Technologies, Inc., Santa Clara, CA, USA) and HP-5 MS 30 m, 0.25 mm, and 0.25 \(\upmu\)m GC Column Capillaries (Agilent Technologies Inc., Santa Clara, CA, USA). The column oven was programmed at 40 \({}^{\circ}\)C, increasing to 180 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, and then at 20 \({}^{\circ}\)C/min to 260 \({}^{\circ}\)C and held at that temperature for 5 min. Helium (99.999% purity) was used as the carrier gas with a flow rate of 1.0 mL/min. Mass spectrometry analyses were conducted at an electron energy of 70 eV, and the \(m/z\) range was 33-500. Data were obtained and processed using NIST/EPA/NIH Mass Spectra Library instrumental analysis software, version 2017, Antioch, CA, USA.
# Residual Life Prediction of Gas-Engine Turbine Blades Based on Damage Surrogate-Assisted Modeling
## 1 Introduction  
Gas-turbine engines (GTE) operate in multiregime mode, and their parameters greatly depend on the operating conditions. The actual operating conditions of a GTE may significantly differ from adopted conditions in the design for a typical cycle.  
The lifetime of engine components and a host of factors that limit it depend on the scatter of the dimensions, the materials' characteristics, and the working and operating conditions; therefore, it can vary from operator to operator. A life assessment method as a part of condition-based maintenance [1; 2; 3; 4; 5] is needed to assess a component's life in order to avoid discarding components that have significant useful life left.  
Developing methods for calculating damage to GTE parts in real time is necessary for designing effective prognostic systems. Currently, there are several types of such methods:  
1. Methods based on the use of equations obtained using simplified models; for example, the simple relation between rotor speed and part stress state can be used.
2. Methods based on big-data analysis and the formation of correlation dependencies between measured parameters and failures [6].  
Methods based on nondestructive and destructive testing (service-based approach). In this method, it is necessary to find correlation between microstructural degradation and temperature exposure time, and/or service duration [7]. The methods of the first group have several disadvantages:
* The deterioration of engine parameters during operation, and the fact that individual characteristics of both parts and engines are not considered.
* Damage assessment is usually performed for only one critical zone; however, during operation, the critical zone may change.
* Numerical models are usually oversimplified and do not consider the plasticity, creep (stress redistribution), and anisotropy of material characteristics.
* As calculations are performed on the most unfavorable design point, and a substantial safety factor is used to ensure failure-free operation, this causes many components to be discarded too early.  
The purpose of this study was to enhance methods of predicting the level of stress and exhausted durability in rotor turbine blades during operation using surrogate-assisted prediction. We propose a machine-learning-based surrogate model that can be efficiently utilized in practice for the estimation of gas-turbine-engine blades' residual life, and thus for their preventive maintenance (Figure 1).