esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Method
### Threshold Device  
The suggested approach uses e a novel 2-D figure of merit to test the network performance. A 2-D figure of merit can be seen in Figure 2. It divides the load balance (LB) fault tolerance (FT) spaces into different development conditions as follows:  
Figure 2: The proposed 2-D figure of merit.  
The FT estimation varies between three threshold intervals (FT error units are in numbers depending on the grid size) as follows (experimentally determined):  
1. From [0... 30] specifies a good system.
2. From [30... 60] specifies a medium system.
3. The bad system is above 60.  
The LB estimate ranges between three threshold periods (LB error units based on the mean response time) as follows (experimental data determined):  
1. [0... 0.1] indicates a good system.
2. [0.1... 0.3] indicates a medium system.
3. The based device over 0.3. Figure 2 can be divided into nine areas to observe the intervals discussed:  
* GG: this interval is good for both FT and LB estimation.
* GM: this interval is good for FT estimation and medium for LB estimation.
* GB: this interval is good for FT estimation and bad for LB estimation.
* MG: this interval is medium for FT estimation and good for LB estimation.
* MM: this interval is medium for both FT and LB estimation.
* MB: this interval is medium for FT estimation and bad for LB estimation.
* BG: this interval is bad for FT estimation and good for LB estimation.
* BM: this interval is bad for FT estimation and medium for LB estimation.
* BB: this interval is bad for both FT and LB estimation.  
Finally, to improve the calculation of fault tolerance, replication time and message cost must be minimized, which would raise the possibility of completed work. On the other hand, mean job response time would be reduced to boost the load balancing measurement and this will cause an improvement in the number of jobs/s. A novel two-dimension figure of merit is suggested to describe the network effects on load balance and fault tolerance estimation. The suggested model would be improved by using optimization techniques to approximate the optimum replication time value and mean job response time to achieve a GG framework in the 2-D figure of merit. Three separate optimization methods are used to achieve the optimal approach, namely: genetic algorithm, ant colony optimization, and particle swarm optimization (GA, ACO, and PSO). The aim is to compare these three optimization methods. In the first step, each user submits their computing jobs to the GCS with their hardware specifications. The GCS answers the user by sending the results after completing the job processing. This model follows the same measures as model one, but it includes extra modules called \"optimization strategies\" that take their input from the entropy estimation module (logical network) and send their output to the fault tolerance and load balancing estimation modules (outputs replication time and mean job response time, respectively).
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Software  
#### 3.3.1 DJI Ground Station (GS) Pro  
The DJI GS Pro version 2.0.17 iPad application is used for autonomous remote control of the drone during flight. The application enables multiple flight paths to be defined, with custom waypoints, speeds, altitudes, headings and end-mission actions. The fully customisable routes enable the observation of locations that are shadowed in satellite SAR data. The flight altitude impacts the spatial resolution of the SAR data, as the drone can fly immediately above or hundreds of feet above the target. The route is planned to provide the desired coverage of the target on a case-by-case basis. The estimated flight time and battery requirements for each flight path are estimated, based on the flight length and speed. This application is essential for future repeat-pass interferometry, whereby multiple, precise passes of the same flight path must be repeated over time. Furthermore, autonomous flight simplifies the procedures for the operator and reduces human error and safety issues associated with manual control. The automated flight path utilises a single onboard GPS system and the barometer for horizontal and vertical positioning, which introduces a positioning ambiguity. The aforementioned GNSS modules are used to ascertain the true drone positioning data and perform an accuracy assessment of the automated route, which may be impacted by wind, turbulence or atmospheric thermal gradients.
## 2 Materials and Methods
### Data Collection
* The Job Satisfaction items explored whether dentists felt they had sufficient time for patient care, satisfaction with their workload, and whether they felt their work was appreciated by patients. * The Time and Communication cluster assessed the clarity of treatment plan communication with patients, staff, and dental laboratories, as well as the ability to visually present treatment outcomes to patients before starting procedures. * The Patient Expectations domain included items evaluating the frequency with which patient expectations were met, the quality of dentist-patient relationships, feedback on treatment comfort, and satisfaction with aesthetic results. All closed-ended items in the second section were rated using a five-point Likert scale: Strongly Agree, Agree, Disagree, Strongly Disagree, and I Don't Know (which was not included in the statistical analysis). This format enabled the quantification of subjective experiences and attitudes, while also capturing levels of certainty or ambivalence. Respondents were additionally prompted to indicate the directionality of their perceived experience with digital tools, whether trending positively (e.g., from Strongly Disagree to Strongly Agree) or negatively (e.g., from Strongly Agree to Strongly Disagree). In the final section, responses were recorded using a simplified three-point Likert scale (Positive, Neutral, Negative), providing a succinct summary of overall attitudes toward digital integration. To ensure the instrument's methodological soundness, a validation process based on [PERSON]'s framework [15] was implemented. A multidisciplinary panel of five experts in digital dentistry, clinical AI integration, occupational health, and research methodology reviewed the preliminary version of the questionnaire and proposed refinements. Following expert validation, a pilot test was conducted with a small cohort of dental professionals to assess clarity, structure, and relevance. Feedback from this trial informed the final revision. [16]. The validated questionnaire was distributed during the 1 st Artificial Intelligence in Dentistry Congress--Intelligent Dentistry 2024, held in Faro, Portugal, on 26-27 April. Participation was limited to dental professionals attending the event. All 132 distributed questionnaires were returned, resulting in a 100% response rate. Reliability analysis of the second section, which formed the basis for much of this study's analysis, demonstrated strong internal consistency, with a Cronbach's alpha coefficient of 0.88. Data collection was conducted during and immediately following the conference, resulting in a 100% response rate, with all 132 questionnaires fully completed and returned.
## 5 Experiments and Results
### Quality Assessment Metrics  
To quantitatively evaluate the performance of the proposed MagEMNet, we utilize a set of commonly adopted metrics in machine learning and deep learning to gauge predictive accuracy. These metrics include the coefficient of determination (\(R^{2}\)), explained variance score (EV), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Each metric provides insights into different aspects of the model's predictive performance, facilitating a comprehensive quality assessment.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 3 Proposed Algorithm
### Traffic Minimization  
In linear structures, there is the possibility that several adjacent nodes detect the same event, up to five nodes that are located consecutively. Due to the value of the distance in which the nodes are separated, an event that occurred at a specific location in the linear infrastructure may cause the adjacent nodes to generate the same alarm, at the same moment in time. Therefore, they will try to notify the alarm generated to the central station, by sending a frame using the same predefined data flow, \(P_{r}\) for example. Our proposal prevents adjacent nodes from sending repeated alarms from the same event. Before sending information related to an event, the nodes will check if that information was already transmitted by an adjacent node, confirming that the frame has already been stored in the _bufferaj_ and avoid retransmitting it.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 4 Performance Comparison
### Energy Consumption  
IoT applications require careful consideration of energy consumption because many devices run on batteries and must last long before recharging to ensure long-term, sustainable operation. Low energy consumption, especially in isolated or difficult-to-reach locations, facilitates the effective deployment of large-scale IoT networks. LoRa, Sigfox, and Z-Wave are developed for portable devices with low battery capacity because of their low energy usage. They have minimal power usage as a result. In contrast, NB-IoT and LTE-M consume more energy than LoRa and Sigfox. Among these five protocols, LoRa has the highest energy efficiency of them all [61; 66; 67]. Although LTE-M and NB-IoT both have medium energy consumption rates, LTE-M performs better in favorable coverage conditions and improves as the conditions improve. In contrast, NB-IoT performs better in unfavorable coverage conditions [48]. However, for synchronous communication and QoS management, NB-IoT UE devices require more peak current, and its Orthogonal Frequency-division Multiplexing (OFDM) or Frequency Division Multiple Access (FDMA) access modes use more power [68]. RedCap's energy consumption is higher than the othertwo cellular protocols: NB-IoT and LTE-M. It focuses more on lower latency and higher data rate compared to the others at the expense of higher power consumption [22].
## 2 Materials and Methods
### Data Source and Processing  
The data used in this paper is GF-6 remote sensing satellite image data. GF-6 is an important part of China's \"High Resolution Earth Observation System.\" It was successfully launched in June 2018. Its multi-spectral camera has a 2 m panchromatic resolution and an 8 m multi-spectral resolution, and has a wide-format imaging capability (up to 90 km). GF-6 image data is mainly used in agriculture, forestry, environmental protection, and other fields, which can provide rich data support for ground object classification, vegetation coverage monitoring, land use analysis and other research. The GF-6 image data used in this study is provided by the China Resource Satellite Application Center. The data covers the research area, including panchromatic band and multi-spectral band, which can meet the research needs. For the data of local vegetation growth season (August) with a cloud  
Figure 2: Geographic location and elevation map of the study area.  
cover of 0, the time span is a multi-temporal image from 2019 to 2024. Table 1 presents the band information required for computing _kNDVI_.  
Then, the ENVI software (version 5.3) was used to preprocess the multi-spectral image and panchromatic image of the GF-6 remote sensing image, including radiometric calibration, atmospheric correction, ortho-rectification, image fusion, and image registration, to ensure the accuracy and reliability of the analysis results. Then, the mask extraction was performed on the preprocessed image to obtain the final study area.
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 1 Introduction
This category of algorithms focuses on the pattern detection of time-invariant decisions or specific decisions applicable to time series of measurements of minimal memory capabilities (real-time detection);  
**Deep learning methods:** This category includes classification or regression algorithms, capable of variable patterns detection, that can apply to either time streams or irregular time intervals of sensory data and provide the detection of erratic patterns, either real-time, close-to-real-time, or periodic. This category includes convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and neural networks of variable depth based on input. This paper focuses on this network category for detecting machinery operation abnormalities. This paper focuses on the oil refinery industry containing compressors and pumps processing flammable gases and liquids. Attributes influencing an engine's proficiency are temperature, pressure, and vibrations resulting from its operation. As a result, the examined machine's temperature for compressors and pump acceleration sensor measurements are used as data inputs. A new intelligent failure classification algorithm called the stranded-NN model is presented by the authors. This algorithm utilizes different layers of neurons based on sampling processes over the input sensory data streams. The generated model is used to detect different classes of industrial emergencies based on input time-depth of sensory measurements and can be utilized for either periodic preventive maintenance cases or real-time and close-to-real-time malfunction machinery events. The proposed stranded-NN model maintains separate neural network models trained for each input data sampling process (called data batch), received accordingly as a time series input. Then, depending on the type of data classification process (real-time, close to-real-time, periodic maintenance), different outcomes can be detected. All these neural networks are stored and retrained as a joined model entity. The paper's structure is as follows. Section 2 outlines the related work for industrial maintenance for critical events and maintenance. In Section 3, the proposed stranded-NN algorithm is presented; in Section 3.1 the stranded-NN is tested and compared to existing LSTM networks; Section 4 follows the discussion of the results and, finally, Section 5 outlines the most important findings using the stranded-NN algorithm.
# Viscosity of Clayey Soils: Experimental Studies
## 3 Results  
### Experiment to Determine the Viscosity Coefficient of Clay Soil  
In order to determine the viscosity coefficient of the fluid-plastic loam, experimental studies were carried out in a simple shear device in the kinematic loading mode (\(\dot{\gamma}=const\)) at four different shear displacement rates (\(\dot{u}=0.005\) mm/min, \(\dot{u}=0.05\) mm/min, \(\dot{u}=0.5\) mm/min, \(\dot{u}=0.5\) mm/min, \(\dot{u}=5\) mm/min) and at three different values of vertical stress acting on the soil specimen during the test (\(\sigma_{n1}=200\) kPa, \(\sigma_{n2}=400\) kPa, \(\sigma_{n3}=600\) kPa). The tests were carried out until the ultimate relative shear strain \(\varepsilon=18\%\) was reached.  
After processing the results of the laboratory tests, the graphs of the dependences of shear stresses on shear strains (\(\tau-\gamma\)) were obtained at various values of vertical stress \(\sigma_{n}\) (Figure 2).  
Taking into account that the obtained graphs of dependence of shear stresses on shear strains (\(\tau-\gamma\)) have a pronounced bilinear character, the coefficients of soil viscosity (\(\eta\)) (Table 2) were determined for two characteristic parts of the graph (Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vertical** & **Shear Displacement** & **Soil Viscosity at the First** & **Soil Viscosity at the** \\
**Stress, kPa** & **Rate, mm/min** & **Section, kPa-min** & **Second Section, kPa-min** \\ \hline \hline \(\sigma_{n}\) & \(\dot{u}\) & \(\eta_{1}\) & \(\eta_{2}\) \\ \hline \hline \multirow{4}{*}{200} & 5 & 226 & 133 \\  & 0.5 & 2310 & 1707 \\  & 0.05 & 21,296 & 21,780 \\  & 0.005 & 194,920 & 221,320 \\ \hline \hline \multirow{4}{*}{400} & 5 & 414 & 259 \\  & 0.5 & 3802 & 3023 \\ \cline{1-1}  & 0.05 & 36,740 & 40876 \\ \cline{1-1}  & 0.005 & 374,000 & 426,800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Viscosity coefficients of clayey soil.  
Figure 2: Graph of the dependence of shear stresses on shear strains (\(\tau-\gamma\)) at different values of \(\sigma_{n}\).  
The viscosity coefficients (\(\eta\)) for two characteristic sections of the graph were determined based on the simple [PERSON]'s equation:  
\[\eta=\frac{\tau}{\dot{\gamma}}, \tag{1}\]  
where \(\tau\) is the shear stress [kPa] and \(\dot{\gamma}\) is the shear rate [1/s].
## 3 Calibration Method
### The Calibration Model
Finally, the calibration model can be obtained from Equation (19):  
\[\mathbf{J}_{M}d\mathbf{X}=\mathbf{B}_{M}, \tag{11}\]0  
where \(\Delta\mathbf{q}=\left[\Delta^{e}\mathsf{A}_{1},\Delta^{e}\mathsf{A}_{2},\Delta^ {e}\mathsf{A}_{3},\cdots,\Delta^{e}\mathsf{A}_{m},\Delta^{G}\mathsf{B}_{1}, \Delta^{G}\mathsf{B}_{2},\Delta^{G}\mathsf{B}_{3},\cdots,\Delta^{G}\mathsf{B} _{m}\right]^{\mathsf{T}}\), \(\Delta\mathbf{\theta}\) is the error vector consisting of \((m-n)\) components, which are the differences of the motor rotary angles between the theoretical values and the measured counterparts. In fact, according to Equation (20), \((m-n)\) equations can be obtained from each collection pose of the CDPR in its workspace. Since there are \(r\) unknown parameters to be solved, at least \(r/(m-n)\) different poses should be collected. If \(k\) different poses are used for calibration, there are in total \(k(m-n)\) equations, which can be written as:  
\[\left[ \begin{array}{c}\mathbf{J}_{err1}\\ \mathbf{J}_{err2}\\ \vdots\\ \mathbf{J}_{errk}\end{array} \right]\cdot\Delta\mathbf{q}=\left[ \begin{array}{c}d\bm {\theta}_{1}\\ d\mathbf{\theta}_{2}\\ \vdots\\ d\mathbf{\theta}_{k}\end{array} \right] \tag{21}\]  
\[\text{Noting}\ \mathbf{J}_{clbr}=\left[ \begin{array}{c}\mathbf{J}_{err1}\\ \mathbf{J}_{err2}\\ \vdots\\ \mathbf{J}_{errk}\end{array} \right],\,d\mathbf{\theta}_{clbr}=\left[ \begin{array}{c}d \mathbf{\theta}_{1}\\ d\mathbf{\theta}_{2}\\ \vdots\\ d\mathbf{\theta}_{k}\end{array} \right],\,\text{the $k(m-n)$ equations can be written as:}\]  
\[\mathbf{J}_{clbr}\cdot\Delta\mathbf{q}=d\mathbf{\theta}_{clbr} \tag{22}\]  
Then, the error of the difference between the real structure parameter and the theory structure parameter can be obtained from the calibration model. \[\Delta\mathbf{q}=\mathbf{J}_{clbr}{}^{-1}\cdot d\mathbf{\theta}_{clbr} \tag{23}\]  
It is obvious that \(\mathbf{J}_{clbr}{}^{-1}\) is the core of calibration model. In this paper, this problem is solved by using the least squares optimization method. Then, \(\Delta\mathbf{q}\) can be obtained.
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Testing and Validation  
The testing and validation phase is critical to ensure the cybersecurity environment functions as intended and that the data generated accurately reflects real-world threat scenarios. This phase ensures that systems and data are prepared to be used effectively in cybersecurity simulations.  
Testing strategies implemented include the following:  
* Functional Tests: These are carried out to ensure that each component of the system performs the functions for which it was designed. For example, it is verified that monitoring tools correctly detect and report abnormal network traffic.
* Penetration Tests: These are carried out to evaluate the security of systems by simulating cyberattacks. This helps identify vulnerabilities and entry points that could be exploited in the real world.
* Load and Stress Tests: These are applied to determine how systems perform under heavy workloads or when under stress beyond normal operating conditions.
* Regression Testing: After each change or update to the system, a series of tests are performed to ensure that the new modifications have not introduced new errors or affected existing functionality.  
The criteria used to validate the analysis results are based on industry standards and project-specific requirements, including the following:  
* Threat Detection Precision: The _true positive (TP)_ and _false positive (FP)_ detection rate is measured to evaluate the accuracy of security tools. To quantify this criterion, the following precision formula is used: \[\text{{Presicion}}=\frac{\text{{True positives}}}{\text{{True positives}}+ \text{{False positives}}}\] (1)
* System Performance: This is evaluated using response time, CPU usage, and memory metrics during load tests. Performance expectations are based on a predefined threshold the system must meet or exceed.
* Resilience and Recovery: The system's ability to recover from errors and attacks is validated using recovery time as a critical metric.
* Regulatory Compliance: It is verified that the systems comply with the applicable cybersecurity regulations and the organization's internal policies.  
For validation, tools such as statistical analysis are used to interpret test results, and machine learning techniques are applied to identify trends and anomalies in the data. In addition, control panels and monitoring dashboards are established that allow real-time visualization of system performance and security. Combining these testing strategies and robust validation criteria ensures that our cybersecurity environment is secure, reliable, and aligned with industry best practices and standards, thus providing a powerful platform for training and assessment in cybersecurity.