esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Modeling of System Characteristics
### SISO System  
For the SISO system, the RF (Analog) energy consumption per bit per node (E\({}_{b\_Analog\_SISO}\)) can be calculated by replacing \(\rm{P_{PA}^{L}}\), \(\rm{P_{out}^{L}}\), \(\rm{\overline{E}_{b}^{L}}\), \(\rm{P_{c}^{L}}\) with \(\rm{P_{PA}^{SISO}}\), \(\rm{P_{out}^{SISO}}\), \(\rm{\overline{E}_{b}^{SISO}}\), \(\rm{P_{c}^{SISO}}\), respectively, and also replacing \(\rm{N_{r}}=\rm{N_{t}}=1\) as discussed in Section 5.1.1. The Base Band (Digital) energy consumption per bit per node (E\({}_{b\_Digital\_SISO}\)) can be calculated by removing \(\rm{E_{Det}}\) in Equation (16) and assigning \(\rm{N_{r}}=\rm{N_{t}}=1\).  
The total time delay (T\({}_{SISO}\)) of the SISO system can be calculated as the sum of transmission delay (\(\rm{T_{tr\_SISO}}\)), propagation delay (\(\rm{T_{pr\_SISO}}\)), and processing delay (\(\rm{T_{pc\_SISO}}\)).  
\[T_{SISO}=T_{tr\_SISO}+T_{pr\_SISO}+T_{pc\_SISO} \tag{22}\]  
\(T_{tr\_SISO}\) is given by:  
\[T_{tr\_SISO}=T_{s}\frac{M_{t}}{b_{n}} \tag{23}\]  
where \(M_{t}\) is the total number of bits transmitted. \(T_{pr\_SISO}\) is given by:  
\[T_{pr\_SISO}=\frac{d}{c} \tag{24}\]  
\(T_{pc\_SISO}\) is given by:  
\[T_{pc\_SISO}=T_{Mod}+T_{Dmod} \tag{25}\]
# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 3 Proposed Affordable Fast Early Warning System
### Hybrid Fault Model
Data distributions in this study were unbalanced between normal and abnormal subsets after DBSCAN, as shown in Table 3. Therefore, we employed SMOTE to balance the dataset, using Weka V3.6.15 [48]. The original dataset distribution was maintained by adding synthetic data close to minority data, with 50% increased abnormal class data balancing the dataset well (see Table 3), allowing machine learning to generate high classification accuracy. Figure 5: Attribute significance (attribute labels are defined in the main text above). Figure 6: (a) Optimal value for _eps_ using 5-NN (nearest neighbor) and (b) DBSCAN outlier detection. The RF algorithm is a supervised classification model formed by combining several decision tree models. Each tree within the RF is independently constructed by choosing a random subset of attributes and bootstrap samples from the dataset. Each generated tree model is accumulated using a majority voting method to brain the best final outcome [49]. RF overcomes various decision tree problems, such as low variance generation and overfitting. We applied DBSCAN to remove outliers, SMOTE to balance the resulting dataset, and RF to learn and generate the final model from the balanced dataset. The generated model was then installed on each edge device for fault prediction. Prediction output may have four possible outcomes [50]. True positive (TP) and true negative (TN) outcomes are the number of correctly classified data. False positive (FP) and false negative (FN) outcomes are the number of data incorrectly classified as normal when they are actually abnormal, and abnormal class when they actually are normal, respectively. We employed 10-fold cross-validation for all classification models, with final performance metric being the average. Table 4 shows classification performance metrics based on precision (p), recall (r), F-1 score (f), and accuracy (a).
## 2 Materials and Methods
### Questionnaires
#### 2.4.3 Presence and Reality Judgement Questionnaire (PRJQ)  
To measure participants' sense of presence and the perceived realism of the virtual environments, the Presence and Reality Judgement Questionnaire (PRJQ; [27]) was administered. This questionnaire evaluates how real and immersive participants perceived the VR environments to be, using subscales for reality judgment, emotional involvement, and perceptual congruence.
# Benchmark Sea Trials on a 6-Meter Boat Powered by Kite
## 4 Post Processing
### Equations Related to Wind Data from Onboard Sensors  
This part deals with the wind measurements carried out directly on the kiteboat. The onboard wind measurements are firstly computed in the heading reference frame, going at first from the sensor reference frame \(R_{wm}\) to the ship reference frame \(R_{s}\), and secondly to the heading reference frame \(R_{\psi}\):  
\[\underline{V}_{WM}=(\underline{M}_{\theta_{s}}\underline{M}_{\theta_{s}})( \underline{M}_{\psi_{w}}\underline{M}_{\psi_{w}}\underline{M}_{\psi_{w}})V^{ (wm)}_{WM}. \tag{4}\]  
Then, the relative wind velocity at the altitude of the measurement point \(z_{0}\) can be computed, taking into account velocity of wind sensor with respect to the point \(O_{s}\) belonging to the boat:  
\[\underline{V}_{WR,z_{0}}=\underline{V}_{WM}-\underline{V}_{W/O_{s}}= \underline{V}_{WM}+\underline{O}_{s}\underline{W}\wedge\underline{\Omega}_{ s}. \tag{5}\]  
From there, the true wind speed at the measurement point can be calculated:  
\[\underline{V}_{WT,z_{0}}=\underline{V}_{WR,z_{0}}+\underline{V}_{s}. \tag{6}\]  
The estimation of the wind at any other altitude will be discussed in another section. The relative wind on the kite can be obtain by subtracting the velocity of the kite attachment point \(\underline{V}_{A}\) to the true wind speed on the kite (tethers are considered as perfectly straight, therefore the translations motions of the kite attachment are entirely transmitted to the kite):  
\[\underline{V}_{A}=\underline{V}_{s}+\underline{AO}_{s}\wedge\underline{ \Omega}_{s} \tag{7}\]  
\[\underline{V}_{WR}=\underline{V}_{WT}-\underline{V}_{A}. \tag{8}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Domains** & **D01** & **D02** & **D03** & **D04** & **D05** \\ \hline Horizontal resolution & 8910 m & 2970 m & 990 m & 330 m & 110 m \\ \hline Vertical resolution (0–300 m altitude) & 20 m & 20 m & 20 m & 20 m & 20 m \\ \hline Modelling scale & Meso & Submeso & Submeso & LES & LES \\ \hline \hline \end{tabular}
\end{table}
Table 2: Domains, Resolutions and Scales implemented in the WRF 3.8.1 modelling applied to the France zone up to the zoom on Quiberon.
# Investigation of Mixed-Mode I/II Fracture under Impact Loading Using Split-Hopkinson Pressure Bar
## 3 Dynamic Initiation Stress Intensity Factor
### Verification of ABAQUS  
The accuracy of finite element software ABAQUS plays a significant role in calculating the dynamic initiation stress intensity factor. Before establishing a numerical model, the accuracy of ABAQUS should be validated. Therefore, a well-known benchmark model, Chen problem [38], was introduced to estimate the calculation precision of ABAQUS.  
[PERSON]'s model is a simple two-dimensional model, shown in Figure 8a. The geometry of the sample is 40 mm in length and 20 mm in width, a single crack deployed in the middle of the bar with a length of 4.8 mm. The dynamic tensile forces \(P(t)\) on both sides of the bar can be express by the Heaviside step function. Based on the model above, the numerical sample was established in the ABAQUS with the same geometry, parameters, and loading condition. The K\({}_{\text{I}}\)(t) curve of [PERSON]'s model was extracted from the figure given by [PERSON], together with the curve of calculation result by ABAQUS, the two curves were plotted in Figure 8b to make a clear comparison. It is obvious that the computation of ABAQUS agreed well with [PERSON]'s result, indicating the ABAQUS has a great capacity to calculate dynamic stress intensity factors.  
Figure 7: Crack propagation velocity curves of specimens. (**a**) specimen-0\({}^{\circ}\). (**b**) specimen-30\({}^{\circ}\).
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 2 Data and Methods
### Methods  
In predicting power outputs, traditional and statistical methods have long been employed to model relationships between variables [24; 25; 26; 27]. Traditional machine learning methods like linear regression are fundamental tools, assuming a linear relationship between predictors and the power function output [28]. Polynomial regression, an extension of linear regression, can capture more complex relationships by introducing polynomial terms. These methods, while interpretable and computationally efficient, might struggle to capture intricate nonlinear patterns present in power functions, limiting their accuracy [29]. They also ignore the temporal nature of the data. We therefore consider two different time series prediction approaches using Deep Learning (DL) methods, which have been show to be most successful in the literature [17; 30; 31; 32], for our wind turbine power output prediction as described in the following two sections.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation
#### 2.3.4 Failure Analysis and Processing  
As the previous system helps us understand and identify the primary influential factors contributing to the root cause of a failure, it becomes feasible to enhance both the diagnosis and precluding future incidents by leveraging the dynamics inherent in each variable. Consequently, one can analyze the divergence, \(\partial\mathbf{d}^{r}\), to transition the input variable \(\mathbf{d}^{r}\) from a faulty operating state to normal operating one \(\mathbf{\hat{d}}^{r}\) by considering the following expressions:  
\[\partial\mathbf{d}^{r}=(\mathbf{\hat{d}}^{r}-\mathbf{d}^{r}) \tag{11}\]  
where \(\mathbf{\hat{d}}\) and \(\mathbf{d}\) represent the relearned and faulted input vectors, respectively, and \(r\) denotes the number of remaining features that most significantly contribute to the anomaly.  
By examining these divergences, the contribution of each feature to the occurrence of faults can be explained. The proportion of the contribution of the \(i\)th input variable to all deviations is given by [18]:  
\[\partial c_{t}^{i}=\frac{\partial\mathbf{d}^{i}_{t}}{\sum_{j=1}^{r}\partial \mathbf{d}_{t}^{j}}*100 \tag{12}\]  
Finally, all these outputs of the diagnoser model are now coupled with a VR application that simulates in real time the wind farm behavior and displays automatically identified faults (detected and localized). When the diagnoser model identifies an anomaly (according to relation (10)), the diagnoser server sends it to the Unity server, which checks if this anomaly has already occurred on the associated turbine or not. If this is the case, we assess whether the time frame between the most recent occurrence of the anomaly and its current manifestation is within a certain threshold (limit of the occurrence number during the observation). The persistence indicator over time is calculated by counting the number of times \(s_{\text{T}}\) the \(j\)th relevant feature crosses the limited threshold within a sliding window of length \(k\) as follows:  
\[\mathbf{p}_{j,t}=\frac{1}{k}\times\sum_{\tau=t-(k-1)}^{t}s_{j,\tau} \tag{13}\]  
Depending on the result of this calculation, the irregularity increases and may become significant, thus becoming critical above a certain threshold. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a specific color. In this paper, \"WHITE\" defines normal behavior, \"ORANGE\" a warning, and \"RED\" a critical anomaly. The maintenance manager can use this information to identify and prevent future critical faults.
The core focus is on georeferencing underwater features by accurately determining the snorkeler's camera position without relying on GNSS. This is achieved by processing UAV-captured images into orthorectified images, which serve as a spatial reference to compute the snorkeler's camera position. Consequently, the underwater camera will be georeferenced within the same spatial frame as the terrestrial data. This innovative integration of UAV and underwater photogrammetry facilitates a seamless merging of land and underwater photogrammetric data into a unified and georeferenced 3D model. A georeferenced virtual model of the emerged and submerged areas is the result of the method, with particular attention to accurately represent underwater marine habitats in shallow water areas. The method highlights the contribution of photogrammetry to interdisciplinary research, propels the boundaries of geospatial analysis, and unlocks new possibilities for sustainable coastal management and resource evaluation.
Sensors_**2011**, _11_, 10074-10093; doi:10.3390/s111110074  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Historical Building Monitoring Using an Energy-Efficient Scalable Wireless Sensor Network Architecture**  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
ITACA Institute, Universitat Politecnica de Valencia, 46022 Valencia, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.V.C.); [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (J.J.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-963-877-007; Fax: +34-963-877-579.  
_Received: 8 September 2011; in revised form: 18 October 2011 / Accepted: 24 October 2011 / Published: 25 October 2011_  
**Abstract:** We present a set of novel low power wireless sensor nodes designed for monitoring wooden masterpieces and historical buildings, in order to perform an early detection of pests. Although our previous star-based system configuration has been in operation for more than 13 years, it does not scale well for sensorization of large buildings or when deploying hundreds of nodes. In this paper we demonstrate the feasibility of a cluster-based dynamic-tree hierarchical Wireless Sensor Network (WSN) architecture where realistic assumptions of radio frequency data transmission are applied to cluster construction, and a mix of heterogeneous nodes are used to minimize economic cost of the whole system and maximize power saving of the leaf nodes. Simulation results show that the specialization of a fraction of the nodes by providing better antennas and some energy harvesting techniques can dramatically extend the life of the entire WSN and reduce the cost of the whole system. A demonstration of the proposed architecture with a new routing protocol and applied to termite pest detection has been implemented on a set of new nodes and should last for about 10 years, but it provides better scalability, reliability and deployment properties.  
**Keywords:** wireless sensor network; low-power nodes; multi-hop networks; termites sensor; moisture sensor
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 2 Fundamentals of LoRaWAN Technology
### Regulations  
In Europe, LoRa operates in the 863-870 MHz frequency band. It can work in two sub-bands, one at 868 MHz, which offers three LoRa channels at 125 kHz, and the other at 867 MHz, which offers five LoRa channels at 125 kHz. The gateway should be able to listen to all channels simultaneously. The number of channels that can be used is \(8+2\) (1 LoRa + 1 FSK) uplink and 1 downlink, but this depends on the specific regulations adopted for the gateway. In the case of SEMTECH, LoRaWAN by default uses three channels of the 868 MHz band, but the 867 MHz band can also be configured in the uplink. There is one 869.525 MHz downlink channel with fixed \(SF\) and \(BW\) settings (12 and 125 kHz). Depending on the sub-band used, the regulations define a channel occupation time (DC) and the TP to be used. For the g1 sub-band, the DC is limited to 1% with the LBT and AFA operations for a maximum transmission power (MTP) of 14 dBm. The g3 sub-band is eligible for an occupancy rate of 10% (LBT + AFA) and a transmission power of 27 dBm. Table 1 summarises the different sub-bands used by LoRaWAN technology and the corresponding power and DC, whereas Table 2 presents the LoRaWAN default channels.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 3 System Design and Implementation
### Orchestration Information Model
The proposed IoT slice is a specialization of a network slice containing specific IoT network functions (e.g., IoT gateway). 8. Network Function is an entity with some functional behavior provided by some external interfaces. These network functions can be virtual (VNF) or physical (PNF) and are deployed at the edge, in the middle of the network or in a cloud data center. The proposed IoT gateway and server are particularizations of network functions  
As it will be seen in the implementation section, containerized applications have been selected. The open-source Kubernetes orchestration platform [20] is used to deploy the containers and Helm [60] to pack and install these applications in a Kubernetes cluster. For this reason, some of the abstractions in the IoT slice orchestrator information model (like IoT Gateway, IoT Server and Infrastructure) are extended with some additional attributes related to a Kubernetes deployment:  
* The infrastructure is specialized with some Kubernetes cluster information like Helm related data and the context information needed by the orchestrator to configure and connect to this cluster. * IoT-Gateway and IoT-Server entities are specialized with the data related to the Helm charts used to install these containerized applications in the Kubernetes cluster.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.3 Latency Constraints  
To ensure real-time performance and timely completion of tasks, latency constraints cannot be ignored. The transmission latency \(d_{j}\) for each task \(j\) must be less than or equal to the maximum acceptable latency \(D_{\text{max}}\) for the task:  
\[d_{j}\leq D_{\text{max}},\quad\forall j=1,2,\ldots,m \tag{5}\]  
where \(d_{j}\) represents the transmission latency for task \(j\), and \(D_{\text{max}}\) is the maximum acceptable latency for each task. This constraint ensures that the latency for each task does not exceed the acceptable range, maintaining the system's efficiency and real-time responsiveness. In this study, the maximum acceptable latency \(D_{\text{max}}\) is assumed to be the same for all tasks, simplifying the modeling process and ensuring system uniformity.
## 4 Results and Discussion
### Relative Length Change  
Monitoring of relative length changes also included test specimens placed in the sulfate bath (s) and specimens placed in the water bath (w). In this case, the effect of absorbency on length changes is marginal and there is no reason to study it, especially when the given water absorbency (causing a significant increase in the sample mass) does not introduce stresses that would significantly contribute to length changes. These will  
Figure 10: Dependence of the relative mass change on the immersion time of the samples in the water bath.  
only occur due to crystal pressures, and only in the case of samples in the sulfate bath. In addition, the determined values range within possible measurement errors, as indicated by data in Figures 11-13 in particular. For this reason, the importance of the observed length changes of the samples in the water bath is indeed rather insignificant.  
Figure 11: Dependence of relative length changes on the immersion time of the samples in the sulfate bath.  
Figure 12: Dependence of relative length changes on the immersion time of the samples in the sulfate (s) and water (w) bath.  
The small significance of length changes of the test specimens from the water bath can be demonstrated somewhat curiously by a comparison of the graphs in Figures 11 and 12. Figure 11 presents only the values of relative length changes acquired from the samples placed in the sulfate bath whereas Figure 12 includes also the results obtained for the samples placed in the water bath. The graph remained practically unchanged, all the added data were \"hidden\" in the lower part of the graph among the best results of mortars from the sulfate bath, i.e., among the minimum values.  
In summary, it can be stated that the worst results, i.e., the greatest elongations, are exhibited identically to the relative mass changes in the case of mortars M2 and M3. Moreover, mortar M3 with the crystallization admixture Xypex Admix C-1000 is even significantly worse than the mortar without the admixture (M2).  
For the presented project, it is again important to compare the result of the main reference mortar M1 with the newly designed MMs (concretes) containing metakaolin (M6, M7 and M8). For this assessment, it is necessary to analyze the detailed graph in Figure 13. The graph clearly shows that the MMs with metakaolin exhibit a significantly lower elongation than the reference mortar M1. Mortars M2 and M3 have been omitted completely in the selected detailed presentation since they would be outside the displayed part of the graph from the 90-day interval. Model mortars M7 and M8 have yet again essentially identical results.
# Dilated Filters for Edge-Detection Algorithms
## 3 Methodology
### Laplacian Edge Operator  
The Laplacian is a 2D isotropic measure of the second spatial derivative of an image. The Laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection. Another difference between Laplacian and other operators is that Laplacian does not take out edges in any particular direction. The formula for the Laplacian is the second-order derivative by each component of a 2D function as is presented in Equation (9).  
\[\
abla^{2}f(x,y)=\frac{\partial^{2}f(x,y)}{\partial x^{2}}+\frac{\partial^{2} f(x,y)}{\partial y^{2}} \tag{9}\]  
From the literature [22; 23; 45; 47] we can find different estimations of isotropic kernels for the Laplacian operator that we present in Figures 14 and 15 in Appendix A. For our work, we consider all of them so we can highlight changes that appear on the edge map when choosing different approximations of the Laplace function.  
We apply Equation (9) to obtain the raw edge map; however, to be able to evaluate, we need to transform the edge result from natural range of (\(-256\), \(255\)) to (\(0\), \(255\)). The obtained scaled edge map, which now has only positive values, is thinned using the Guo-Hall algorithm for the final result. We can accept that this is not the normal usage of the Laplace operator but we wanted to evaluate it separately from Laplace of Gaussian or Marr-Hildreth operators. The details are presented in Algorithm 3.  
```
Input: RGB image Output: Binary edge map Parameters: [Sigma value (\(S\)) \(-\) only LoG], Gradient Threshold (\(TG\)),
1:for edge operator do
2: Convert image to gray-scale image
3: Apply edge detection operator using \(\triangleright\) Equation (9) (for Laplace Operator) \(\triangleright\) Equation (10) (for LoG)
4: Scale the edge pixels from (\(-256\), \(255\)) to (\(0\), \(255\)) range
5: Apply global threshold algorithm
6: Apply Guo-Hall thinning algorithm [31] to remove the excess edge points
7:endfor
```  
**Algorithm 3** Laplace and LoG Operator steps.