esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### PIN Modes of Operation  
Two modes of operation are considered for the model: the PIN process on a packet-by-packet (PbP) basis and the PIN process on a block-by-block (BbB) basis. A packet is a single unit of information, and a block is defined as a group of one or more packets that could be processed as a whole.  
Both modes of operation are implemented by using IDM as the transport. The PIN is implemented by placing distributed objects into intermediate nodes. This scenario simplifies the deployment and updates the PIN phases as objects. A PIN intermediate node receives a message that transports an invocation (in one or more packets). As soon as it is complete, it can execute it at its local PIN object, generating a new invocation message that can be sent to the next node or again to the local PIN object to perform another phase.  
Importantly, the packetisation and blocking processes are performed as a part of the PIN function within the semantic sublayer of the overlay network, which allows the application to remain unchanged. The phases are described in the distributed application definition, and they depend on the specific application semantics. The PIN can be considered to be an application-oriented protocol. It uses invocation messages instead of data packet messages used by traditional network protocols.  
Figure 3: Implementation of PIN functionalities within the VQN model.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 1 Introduction
Moreover, the large noise of dynamic measurement data acquisition and the calculation time cost of Bayesian distribution steps make online real-time Bayesian network systems less accurate and precise than offline Bayesian monitoring systems [24], especially for data that are more dependent on manual observation, such as cracks, which are often scarce, and it is challenging to meet the needs of Bayesian networks for a large amount of observation data. To overcome existing limitations, this study introduces an innovative adaptive DBN model aimed at crafting a predictive module for tracking the evolution of fatigue crack damage in the digital twin systems of aerospace structures. This sophisticated model merges adaptive particle filtering with a comprehensive range of cognitive uncertainties found in crack propagation equations, spanning material characteristics, geometric configurations, model parameters, measurements, and load conditions. It adeptly monitors the progression of time-variant state variables while minimizing uncertainties in time-invariant states (diagnosis). Moreover, the model leverages real-time updates, drawing from calibrated probability distributions of parameters and forecasts, to provide probabilistic estimates of fatigue crack growth and the remaining lifespan (prognosis). Each update introduces new particles to mitigate particle impoverishment, thus ensuring more accurate predictions. The validity and applicability of this model were confirmed using data from three-point bending and single-edge crack fatigue experiments. The findings reveal that the model precisely forecasts the velocity of damage crack progression, offering a feasible tool for the real-time evaluation of structural health and lifespan assessment.
## 2 Methodology  
### Data Set  
In order to fairly compare this with other methods, the public database Ninapro DB2 was chosen. Ninapro [26] is a publicly available multi-mode database, designed for facilitating the research of artificial intelligence robots and prosthetic hands. Ninapro includes EMG, kinematics, inertia, eye tracking, visual, clinical and neurocognitive data. Ninapro's data are widely used by scientific researchers for machine learning, robotics, medicine and neurocognitive science.  
We chose 8 subjects out of the database, and those 8 subjects cover all subjects' information as much as possible. The ranges of height, weight and age were 154-187 cm, 50-90 kg and 24-35; there were 5 males and 3 females; and regarding dominant hand, for 6 subjects it was the right and for 2 the left, respectively. Since grasping movements are the most commonly used hand movements in daily lives, we selected 6 types of grasping movements, as shown in Figure 1.  
Note that we selected only 6 grasping movements, because continuous estimation tasks are more challenging than classification tasks in terms of modeling, especially when simultaneously estimating 10 joint angles as we did in this subject. To possess both good fitting capabilities and real-time performance, we could not adopt many grasping movements for modeling. Otherwise there would have been many parameters in the model, such that real time performance could not be achieved. We will design light-weight models for more movements in future studies.  
We selected the 6 movements based on the shapes and diameters of the objects grasped. The shapes included a cylinder, a ball and a flat object. The diameters included large, medium and small-diameter objects. The hand joint ranges and the coordination mechanisms of the selected movements were different such that they could be used for modeling.  
Figure 1: The 6 types of grasping movements we selected for experiments from Ninapro [26].  
Ninapro DB2 used a 22-sensor CyberGloveII data-glove to measure hand kinematics, and it adopted Delsys Trigno wireless system, including 12 wireless sEMG electrodes, to collect sEMG signals. We used a 12-channel sEMG to estimate 10 main joint angles. we chose the proximal interphalangeal point (PIP) and the metacarpophalangeal point (MCP) as estimated joints, because they are the main active joints in the grasping movement. These 10 joint angles we selected are shown in Figure 2.
## 5 Results and Discussion  
### Model Validation  
MWFs are widely used in machining workshops as they can lubricate and cool turning tools and flush metal chips produced by cutting [33]. During the machining process, a large number of oil mist particles are generated, most of which are exhausted to the outdoors through local exhaust hoods [34; 35], but a small part can still leak through the gaps of the machine and diffuse into the workshop area. Then, part of the MWFs is directly thrown onto the inner surface of the machine by the rotating tool, thereby attaching it to the workpieces. Thus, in this work, we only consider the transfer process of the liquid oil contamination on the workpiece in the workshop.  
Figure 4 shows the accumulation of oil contamination on each workbench in the SDS workshop after four hours. The oil contamination concentration on the workbench in the solid axle production unit and its corresponding assembly area is higher than that of the internal and external transfer wheels and the three-pin shaft joint. Table 2 compares the deviations between the measured and predicted values of oil contamination concentration on the surfaces of the seven selected workbenches in the workshop. The results show that the deviation of surface 6 is the largest (20.40%), whereas the smallest deviation is for surface 7 (14.26%). The average deviation of the seven surfaces is 17.58%. As the contact  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measuring Point**} & \multirow{2}{*}{**Mass of the Filter Paper/g**} & \
\begin{tabular}{c} **Mass of Oil Contamination** \\ **Carried by the Workpiece/g** \\ \end{tabular} &
\begin{tabular}{c} **Mass of Oil Deposited on the** \\ **Surface/mg/m\({}^{2}\)** \\ \end{tabular} \\ \hline
1 & 6.7500/6.7681/6.7290 & 6.7514/6.7693/6.7302 & 14.00/12.00/12.00 \\
2 & 6.7030/6.6930/6.7070 & 6.7042/6.6931/6.7083 & 12.00/12.00/13.00 \\
3 & 6.8495/6.8335/6.731 & 6.8510/6.8346/6.7326 & 15.00/11.00/15.00 \\
4 & 6.6901/6.8192/6.7564 & 6.6908/6.8215/6.7575 & 7.00/23.00/11.00 \\
5 & 6.7000/6.8235/6.847 & 6.7014/6.8255/6.8493 & 14.00/20.00/16.00 \\
6 & 6.8063/6.8187/6.7260 & 6.8076/6.8201/6.7275 & 13.00/14.00/15.00 \\
7 & 6.7410/6.6866/6.7751 & 6.7416/6.6873/6.7762 & 6.00/7.00/11.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: On-site measurement results of the three experiments.  
Figure 3: Schematic diagram of measuring points.  
behavior of workers and the transportation of workpieces from the production area to the assembly area by the automatic vehicles are both random, the deviation in the prediction results of oil contamination concentration is reasonable. Table 2 also shows that, except for surface 3, the predicted values of oil contamination concentration on the remaining surfaces are greater than the measured values. This phenomenon may be explained by the actual situation in which workers take cleaning measures during operation, which inhibits the accumulation of oil contamination to a certain extent. In general, the proposed contact transmission model can reflect the real situation of liquid oil contamination transfer in workshops.
## 5 Blasting Vibration Reduction Effect Analysis Using Electronic Detonators
### Comparison of Vibration Response between Non-Electric and Electronic Detonators
#### 5.2.2 Comparison of Pipeline Axial PPV under Two Detonation Methods  
The main focus is to observe and compare the blasting vibration response on the surface under the pipeline. The PPV values of selected elements at regular intervals are extracted for analysis.  
From Figure 14, it is seen that irrespective of whether a non-electric or electronic detonator blasting technology is used, The PPV at the pipeline section closest to the blast source remains the highest; here, the pipeline is affected the most. The increase in distance from the blast source results in a rapid decrease in PPV. The rate of decay is slow in the case of electronic and rapid in the case of non-electric detonators. The main reason for this is that when non-electric detonators are used for simultaneous multi-hole blasting, a high PPV is generated at the instant of the blast that decays continuously until the next set of the blast hole is detonated. In the case of electronic detonator hole-by-hole blasting, owing to the continuous detonation of new blast holes, the blasting energy is continuously transmitted outward, resulting in a slow attenuation of the blasting vibration velocity.  
Figure 13: PPV of the dangerous point of the pipeline using electronic detonator blasting: (**a–d**) are the PPV of \(x\)-, \(y\)-, \(z\)-direction and integrated PPV, respectively.
## 3 Proposed Methodology
### Feature Engineering
#### 3.5.1 Count Vectorizer  
In tasks involving natural language processing, the widely used text preprocessing method CountVectorizer is employed [64]. A group of text documents are transformed into a matrix that shows the frequency of each word's (term's) occurrence in each document. The matrix's columns represent distinctive terms in the corpus, while the rows represent documents. In order to convert text data into a numerical format suitable for machine learning algorithms and enable further analysis and modeling based on the term frequencies in the documents, we used the CountVectorizer function of the Scikitlearn library [65].
## 2 Materials and Methods
### Mathematical and Statistical Analysis of the Results  
Two independent experimental series (batches) of the product were manufactured under industrial conditions. The measurements were repeated several times for each batch of the product. A one-way analysis of variance (ANOVA) at \(p<0.05\) was used, first to test the treatments effect (AW, B1, Os2) and then between the storage time (0, 1, 2 months). Means and standard deviations were calculated. The Tukey's test was conducted for a mean comparison (\(p<0.05\)). The normality of distribution of all analyzed traits was checked by Shapiro-Wilk test. The Statistica 13.1 program (StatSoft Inc., Tulsa, OK, USA) was used to perform the calculations.
## 3 Proposed Method
### Semantic Segmentation Based on PointNet
The loss function for semantic segmentation is typically the cross-entropy loss, which measures the difference between predicted and true labels for each point. The network is trained to minimize this loss, ensuring the accurate classification of each point in the point cloud:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(\hat{y}_{i,c}) \tag{7}\]  
where \(N\) is the number of points, \(C\) is the number of classes, and \(\hat{y}_{i,c}\) is the predicted probability for class \(c\) for point \(i\). The common metrics for evaluating semantic segmentation performance include the mean intersection over union (mIoU), accuracy, and precision. These metrics assess the network's ability to correctly classify points across different categories. Here, \(P_{c}\) and \(G_{c}\) are the predicted and ground truth sets of points for class \(c\), respectively:  
\[mIoU=\frac{1}{C}\sum_{c=1}^{C}\frac{|P_{c}\cap G_{c}|}{|P_{c}\cup G_{c}|} \tag{8}\]  
In this paper, the number of classes is defined as two, and the types of aircraft engines to be detected are categorized. The aircraft engines are broadly classified into Boeing series engines and Airbus series engines. As shown in Figure 7, scanning data and distance data from various positions for each type of aircraft engine were collected for training. However, it was not possible to collect data for all Boeing and Airbus aircraft. Instead, the engine scanning data for the Airbus A330 and Boeing 787 models were used as representative examples. **(1)** The 3D LiDAR data of aircraft engines based on distance and coordinates (Boeing 787 model)  
**(2)** The 3D LiDAR data of aircraft engines based on distance and coordinates (Airbus 330 model)  
**Figure 7. The 3D LiDAR data of aircraft engines used for training process.
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### Sensor Multi-Agent Structure
#### 3.1.1 Management Agent  
The management agent is executed when the sensor is powered up or when it returns from the sleep mode. Its main objective is the execution of other agents and the control of the sensor sleep-awake cycle, including the execution of the applications, program in sleep mode, duration of the sleep mode interval, and return to awake mode. Its functions are the following:  
1. Turn up or turn down sensor services and devices.
2. Execute other agents.
3. Control the sensor sleep-awake cycle and the sleep mode interval.
4. Provide the sensor state (total awake time, number of executions, battery level, routing, _etc._).
5. Other maintenance functions.  
Figure 1: Multi-agent software structure in sensors.
## 2 Communication Network Infrastructure
### Planet Monitoring Nanosatellites  
The potential of a swarm of PMNs is significant. With a payload focused on science, these nanosatellites aim to explore the celestial body to which they are deployed. They can be equipped with specialized sensors such as imaging sensors, spectrometers, magnetometers, radiometers, and high-resolution cameras capable of operating in space, as illustrated in [6; 7]. The instruments and sensors on each PMN will depend on the mission's objectives and the area of the celestial body where they are operating.  
Figure 2: LSRs Arranged on a Planet.  
To capture data from different angles or locations within the deployment area, PMNs are equipped with control and maneuverability systems [7]. These systems allow them to adjust their orientation and change their orbit, which is determined based on reference points provided by the LSRs. On the other hand, due to their limited physical size, nanosatellites have constraints when it comes to energy. PMNs utilize solar panels with maximum power point tracking (MPPT) algorithms. These algorithms automatically track the maximum power point on the solar panels, maximizing the efficiency of solar energy conversion into electricity. Additionally, they work together with voltage and current sensors to always monitor energy consumption efficiency on the PMN.
## 3 The Proposed IDS Framework at the Packet Level
### Anomaly Detection Module  
The purpose of the anomaly detection module is to detect novel attacks that are blind to the training set by considering them as anomaly classes. This is important for a robust IDS because, in reality, it is impossible to include all types of attacks in the training set.  
The Q-values output generated by the agent is tested against the set manual threshold '\(\lambda\)' for anomaly detection. If the confidence score (Q-value) is smaller than \(\lambda\), the input session image will be determined as the 'anomaly' attack. Conversely, the class that belongs to the max confidence score is the expected detection result. Equation (3) shows the mathematical formulation of the anomaly detection module.  
\[y=\left\{\begin{array}{ll}\underset{a}{argmax}\ Q(s,a)&if\ max\ Q(s,a)\ >\ \lambda\\ \underset{anomaly}{argmax}&if\ max\ Q(s,a)\ <\ \lambda\end{array}\right. \tag{3}\]
## 2 Materials and Methods  
For the purposes of this study, 200 adult participants were included. An equal proportion (100 respondents) was included from Pristina, Republic of Kosovo (proportion of women, 87%) and from Skopje, North Macedonia (proportion of women, 69%). In the following text, Group 1 presents participants from Pristina while Group 2 includes those from Skopje. All subjects voluntarily participated in the body weight reduction program, with supervision, and according to the keto diet guidelines. The basics of the diet are aimed at changing macronutrient intake, and the basis of the diet is (i) reducing carbohydrate intake (<30 g/day) as well as maintaining (ii) standard protein intake (1.2-1.5 g per kg of ideal body weight) [2]. The most used versions of this diet are (i) the standard ketogenic diet (the share of macronutrients in the daily energy are as follows: 70% of fat, 20% of proteins, and the remaining 10% falls on carbohydrates); (ii) the cyclic ketogenic diet (for 5 consecutive days, the proportion of carbohydrates is minimized followed by 2 days with increased intake); and (iii) the high-protein ketogenic diet (the ratio of macronutrient intake follows fat: protein: carbohydrate = 60:35:5) [20]. All respondents (n = 200) signed the agreement such that, in compliance with the principles of the GDPR, their data can be used to monitor progress in reducing body mass and for scientific purposes. The basic anthropometric parameters are shown in Table 1 and represent the measurements of the first visit to the doctor and before inclusion in the body weight reduction program, in accordance with the recommendations [2; 21]. This study aims to analyze the significance of parameters closely related to obesity by utilizing neural networks. Obesity prediction using neural networks involves training a model on a dataset of individuals with features such as age, gender, body height, body weight, and body mass index (BMI: ratio of body weight to the square of body height (kg/m\({}^{2}\))) and health attributes as MCHC (Mean Corpuscular Hemoglobin Concentration), cholesterol, glucose, platelets, leukocytes, ALT, triglycerides, TSH (thyroid stimulating hormone), and magnesium. This approach is standard procedure in cell biology when, by the use of different simulation and or modeling tools, the trends of changes for observed parameters are identified [10; 22]. The model learns to predict the likelihood of a person being obese based on these features. MCHC, which measures the average concentration of hemoglobin in red blood cells [23; 24; 25], is typically between 32 and 36 g per deciliter (g/dL) or 320 to 360 g per liter (g/L). A normal white blood cell (leukocytes) count for adults is between 4000 and 11,000 white blood cells per uL or 4.0 and 11.0 \(\times\) 10\({}^{9}\) per L) [23; 24; 25; 26]. A normal platelet (thromocyte) count in adults ranges from 150,000 to 450,000 platelets per microliter of blood or 150 to 450 \(\times\) 10\({}^{9}\) per L. The expected normal cholesterol level in adults is less than 200 mg/dL
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 3 Antenna Design and Analysis Procedure
### Characterization of the Radiation Unit Cells
The value \(y\), which normalizes the characteristic admittance of the transmission line is given by the components of the two-port scattering matrix as follows: \(y=g+jb=-2S_{11}/(1+S_{11})\). The slot parameters are tuned to obtain the imaginary part (\(b=0\)) representing zero normalized susceptance, and the real part (\(g\)) representing the normalized conductance value of the characteristic normalized self-admittance (\(y\)) (see Figure 5). Once \(y\) is determined, an efficient design process for the resonant slotted array developed by [PERSON] et al. is applied, which greatly reduces the computation and simulation for a large array. It considers the slots as two equivalent dipoles with mutual resistance compensation, including the single waveguide slot self-admittance, reciprocity theorem, and mutual coupling between two slots. Due to random presence, we use the first part of [PERSON]'s method for estimating the initial values of the slot lengths, slot deeps, and slot offsets in the linear array.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 4 Performance Measure
### Mean Square Error  
The \(MSE\) algorithm determines the size of the normal error among the stego-image and the original image. The \(MSE\) is determined as shown below:  
\[MSE=\frac{1}{\left[R\times C\right]^{2}}\sum_{i=0}^{n}\sum_{j=1}^{m}\left(X_{ ij}-Y_{ij}\right)^{2} \tag{27}\]where \(R\) and \(C\) are the cover image's row and column counts, \(X_{ij}\) is the cover image's pixel's brightness, and \(Y_{ij}\) is the stego image's pixel's brightness.