esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 4 Case study: Electronic Fuel Injection System
### Comparison of Automatic FMEA Tool with Legacy FMEA Tool
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & (a) Legacy FMEA table & & & & & & \\ \hline
**Item** & **Function** & **Function Info.** & **Failure Mode** & **Mission** & **Local** & **Net** & **End** & **S** & **O** & **D** & **RFN** \\ \hline TPS & Sensor & Threth position sensing & Signal error & Cruise & TPS signal error & Lack of fuel & Engine destination & 6 & 2 & 1 & 12 \\ \hline CFS & Sensor & Carushulation position & Signalerror & Cruise & CFS signal error & Lack of fuel & Engine stop & 8 & 2 & 1 & 16 \\  & sensing & sensing & Signalerror & Cruise & ECO signal error & Lack of fuel & Low gas analogue & 4 & 2 & 1 & 8 \\ \hline EGO & Sensor & Threth position & Signalerror & Cruise & MAP signal error & Lack of fuel & Engine stop & 8 & 2 & 1 & 16 \\  & sensor & pressure sensing & Signalerror & Cruise & MAP signal error & Lack of fuel & Engine stop & 8 & 2 & 1 & 16 \\ \hline  & & & & & (b) Automatic FMEA table & & & & & & \\ \hline
**Item** & **Function** & **Function Info.** & **Failure Mode** & **Mission** & **Local** & **Net** & **End** & **S** & **O** & **D** & **RFN** \\ \hline  & & & & Signal error: & & & & & & & & \\  & & Period: Permanent & Avg. error: 60\% & Avg. error: 19.9\% & Avg. error: 04.8\% & 7 & 1 & 1 & 7 \\
**TPS** & Sensor & Threth position sensing & Signal size: 40\% & Avg. error: 60\% & Max. error: 100\% & Max. error: 100\% & & & & \\ \hline  & & & Signal error: & & & & & & & & \\  & & Period: Permanent & Avg. error: 60\% & Avg. error: 100\% & & & & & & \\  & & Type edit & Signal size: 40\% & Avg. error: 60\% & Max. error: 100\% & & & & & \\ \hline  & & & Signal error: & & & & & & & \\  & & & Permanent & Avg. error: 60\% & Avg. error: 60\% & & & & & \\  & & Type edit & Signal size: 40\% & Avg. error: 60\% & Max. error: 43\% & & & & \\ \hline
**CFS** & Sensor & Carush-shaft position sensing & Signal error: & & & & & & & \\  & & Signal error: & & & & & & & \\  & & Period: Permanent & Avg. error: 50\% & Avg. error: 16\% & Avg. error: 08\% & & & & \\  & & Signal size: 30\% & Avg. error: 50\% & Max. error: 40\% & & & & & \\ \hline  & & Signal error: & & & & & & & \\  & & Period: Permanent & Avg. error: 20\% & Avg. error: 02\% & Avg. error: 03\% & & & & \\  & & Type edit & Signal size: 80\% & Avg. error: 20\% & Max. error: 60\% & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Analysis results of the failure effects of the four sensors (TPS, CPS, ECO, and MAP) in the EFI. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline  & & & & & & & & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Function Info. **} & \multirow{2}{*}{**Failure Mode**} & \multirow{2}{*}{**Mission**} & \multicolumn{4}{
# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 4 Analysis with a Spatio-Temporal Matrix  
To design an architecture for a decision-making system, a relevant methodology must be used. Among methodologies that can be used in the case of beekeeping, there is the spatio-temporal \"3 \(\times\) 3 matrix\". It consists of filling a matrix composed of two axes: the temporal horizon and the modelling level. There are several works that try to use a two-dimensional grid to design a decision-making system, for instance, [PERSON] et al. [44] proposed to couple the modelling approaches to the time horizons to design decision aid-tools for the hospital domain. [PERSON] et al. [45] propose a methodology based on studying horizon levels and system flows to evaluate the supply chain. The suggested framework in Ref. [46] has the advantages of handling decisions on two dimensions. It allows the designer to have a global view and to achieve a more complete architecture, taking into account different temporal and spatial levels. As explained in that paper, Figure 2 shows the matrix axis and its matching levels for the case of beekeeping.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Date** & **Input Data** & **Output Data** & **Hardware** & **Software** & **MM** \\ \hline \multirow{3}{*}{[PERSON] [42]} & \multirow{3}{*}{2018} & temperature, weight, sound, humidity, number of bees, acceleration, CO2 & \multirow{3}{*}{} & data collection, monitoring, graphics & \multirow{3}{*}{sensors} & web application & \multirow{3}{*}{3} \\ \hline \multirow{3}{*}{[PERSON] et al. [43]} & \multirow{3}{*}{2019} & expert rules, social network data (images, texts), beekeeper voice, GPS & \multirow{3}{*}{} & alerts, recommendations, voice detection & \multirow{3}{*}{sensors} & Chatbot, deep learning, text mining & \multirow{3}{*}{3} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: Spatio-temporal matrix.  
The authors of Ref. [46] define the spatio-temporal matrix, show its matching levels for the case of beekeeping, and illustrate its application by an example of web application for managing apiary data. This tool allows its user to take decisions at the beehive or microscopic level. These decisions are either tactic, such as planning activities for the next week or month, or operational, such as evaluating results and adapting the activities. Figure 3 shows the application of the \(3\times 3\) matrix for that use case.  
Our suggested architecture implements some levels of the matrix.
# Impact Fatigue of Viscoelastic Materials Subjected to Pounding
## 3 Results and Discussion
### Fatigue Effect on Pounding Stiffness  
In previous studies [8], it was found that the most accurate bounding force model was based on a Hertz contact element, in conjunction with a damper that is active only during the approaching period of the impact. The mathematical expression is as follows:  
\[F=\begin{cases}\beta\delta^{3/2}+c\dot{\delta}&(\text{approach period of impact})\\ \beta\delta^{3/2}&(\text{restitution period of impact})\end{cases} \tag{1}\]  
where \(\delta\) describes the deformation of the colliding bodies; \(\dot{\delta}\) denotes the relative velocity between them; \(\beta\) is the bounding stiffness; and \(c\) is the bounding damping which at any instant of time can be obtained by:  
\[c=2\xi\sqrt{\beta\delta\frac{m_{1}m_{2}}{m_{1}+m_{2}}} \tag{2}\]  
\[\xi=\frac{9\sqrt{5}}{2}\frac{1-e^{2}}{e(e(9\pi-16)+16)} \tag{3}\]  
where \(m_{1}\) and \(m_{2}\) are the masses of the two colliding bodies, and \(\xi\) is the impact damping ratio correlated with the coefficient of restitution \(e\), which is defined as the relation between the post-impact (final) relative velocity, \(\dot{\delta}_{f}\) and the prior-impact (initial) relative velocity, \(\dot{\delta}_{0}\).  
In this study, the bounding force and the displacement of the colliding bodies are recorded every 10,000 cycles. Based on this data, the bounding stiffness can be estimated by the Curve Fitting Toolbox in MATLAB/Simulink. Figure 4 shows the bounding force model established in MATLAB/Simulink. The estimation method is a nonlinear least-squares method.  
Figure 3: Surface of the sample after cyclic impacts: (**a**) after 10,000 cycles; (**b**) after 50,000 cycles; (**c**) after 100,000 cycles; (**d**) after 150,000 cycles; (**e**) after 200,000 cycles; (**f**) after 250,000 cycles; (**g**) after 360,000 cycles.  
Figure 5 shows the variation of the bounding stiffness versus the number of impacts. As shown in the figure, the impact fatigue process can be divided into two phases. The first phase is from the beginning to 180,000 cycles. In this phase, the viscoelastic material was compressed, and the thickness was reduced by the repeated impacts. The bounding stiffness was increased from 15,000 N/m\({}^{1.5}\) to 35,000 N/m\({}^{1.5}\). This phenomenon can be regarded as cyclic-hardening. After that, even though the cyclic impacts again compress the viscoelastic material and reduce its thickness, the bounding stiffness was decreased from 35,000 N/m\({}^{1.5}\) to 9000 N/m\({}^{1.5}\). This implied that the viscoelastic was softened by the continued impacts. This phenomenon was referred as cyclic-softening.
## 5 Camera Placement based on HybVOR Approach
### Deploying Camera Network
The choice of dividing Voronoi edges based on the camera range comes from the idea that each camera should be visible by the two adjacent cameras (front and rear). Hence, the distance between two successive cameras will always be less than the camera range. Therefore, if a camera fails, there will be no surveillance hole (or non-covered region) along the Voronoi edge, which makes the coverage still acceptable until repairing the broken camera. Figure 10 illustrates the coverage area due to the failure of a camera. In order to ensure an efficient surveillance, cameras must be placed at a distance that allows full observability of facades and entrances of buildings (Buildings' roofs have a lower priority in our case study according to the confirmation of the coastal guard). Therefore, it is necessary to determine the maximum allowed distance among buildings and Voronoi edges to ensure a maximum coverage of facades and entrances. Figure 11 shows the method used for calculating this distance depending on the camera range and the distance between two adjacent cameras (this distance should not exceed the camera range). Figure 11: The maximum allowed distance \(\mathrm{D_{max}}\) among buildings and the Voronoi edge. Figure 10: The coverage area after the failure of one camera. where, R is the range of cameras and D\({}_{\rm max}\) is the maximum allowed distance between a building and a Voronoi edge. Based on Figure 11, the distance D\({}_{\rm max}\) is equal to:  
\[D_{max}=(\sqrt{3}/2)\times R \tag{7}\]  
It is important to note that D\({}_{\rm max}\) in Equation (7) is an approximation according to a 2D assumption. Its calculation based on a 3D assumption will give values that are almost equal for cameras that have a height of about 10m. In the case where one of buildings is far from the Voronoi edge with a distance greater than the maximum distance mentioned earlier, a duplication of this Voronoi edge is required (Figure 12). The deployed cameras on this duplication edges have to meet the following two conditions:  
1. The distance between a duplicated Voronoi edge and the corresponding building should not be further than the distance D\({}_{\rm max}\) mentioned in Equation (7). 2. Cameras placed on a duplicated Voronoi edge should ensure a full coverage of the origin Voronoi edge. Note that if a building is far from the Voronoi edge with a given distance, the facing building will also be far from the shared Voronoi edge with almost the same distance. At this stage, the location of the camera network is obtained based on the Voronoi Diagram generated from the footprint of buildings. In Section 5.3, these cameras' positions are used to assess and calculate the level of coverage through a hybrid approach that combines raster and 3D Vector analysis of visibility. Figure 12: Duplication of the Voronoi edge and the corresponding number of cameras.
## 3 Experiments and Results
### Image Quality Metric Parameters  
In the evaluation of simulation image test results, subjective evaluation and objective evaluation are used in this paper. The subjective evaluation is the result of human observation [17]. The objective evaluation indexes include peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM). The formula for obtaining PSNR value is defined as Formula (24):  
\[PSNR=10\log_{10}\frac{255^{2}}{MSE} \tag{24}\]  
where MSE represents the mean square error. In the objective evaluation index of image processing, PSNR is widely used, which is recognized by researchers. The larger the PSNR value is, the closer the reconstructed image is to the original high-resolution image, and the better the reconstruction effect is.  
SSIM (structural similarity index) is another commonly used evaluation index, which judges image quality according to the similarity of image structure before and after reconstruction. The formula is defined as Formula (25).  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{x}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)} \tag{25}\]  
where \(\mu_{x}\) is the average of \(x\), \(\mu_{y}\) is the average of \(y\), \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variance of \(x\) and \(y\) respectively, and \(\sigma_{xy}^{2}\) is the covariance of \(x\) and \(y\). \(c_{1}=\left(k_{1}L\right)^{2}\), \(c_{2}=\left(k_{1}L\right)^{2}\). \(L\) reflects the range of pixel values. \(k_{1}=0.01,k_{2}=0.03\), SSIM value range is \((-1,1)\). The closer the SSIM value is to 1, the better the image reconstruction effect is. When the two images are identical, the value of SSIM is 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Project** & **Environment/Version** \\ \hline Operating system & ubuntu \\ CPU & i7-8700k \\ Memory & 32 GB \\ GPU & GTX1080 ti \\ Framework & pytorch \\ Python IDE & pycharm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment.  
Figure 5: (**a**) Relation diagram between maximum iteration number and PSNR values. (**b**) Relation diagram between maximum iteration number and time.
## 2 Materials and Methods
### Adsorption Studies  
The adsorption experiments were conducted by mixing 0.02 g of the adsorbent with 8 mL of antibiotic solution in tubes of 10 mL and under stirring at 20 \({}^{\circ}\)C until equilibrium was reached. The values were chosen according to the solubility results. In all the tests, the tubes were wrapped in aluminum foils to prevent light-induced decomposition. After adsorption, the solutions were separated from the adsorbent using a Sorvall RC 5C centrifuge at 8000 rpm for 20 min. The antibiotic equilibrium concentrations in the resultant supernatant were measured with a T60 UV-vis spectrophotometer at \(\lambda_{m\dot{m}x}\) corresponding to the pH value, from a previously determined calibration curve. All samples were measured in duplicate and the average value was used as isotherm data. The amount of antibiotic adsorbed on the clay mineral (\(q\)) was calculated from the initial and equilibrium concentrations, according to Equation (1),  
\[q=\frac{(C_{i}-C_{e})*V}{w} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ciprofloxacin.HCI** & **Tetracycline.HCI** \\ \hline Structure & & \\ Chemical formula & C\({}_{17}\)H\({}_{18}\)FN\({}_{3}\)O\({}_{3}\).HCl & C\({}_{22}\)H\({}_{24}\)N\({}_{2}\)O\({}_{8}\).HCl \\ Molecular weight (g.mol\({}^{-1}\)) & 366.80 & 480.90 \\ Molecular dimensions (nm) & 1.2 \(\times\) 0.3 \(\times\) 0.7 [30] & 1.2 \(\times\) 0.8 \(\times\) 1.3 [31] \\ pKa & pKa\({}_{1}\) = 5.9; pKa\({}_{2}\) = 8.9 & pKa\({}_{1}\) = 3.3; pKa\({}_{2}\) = 7.7; pKa\({}_{3}\) = 9.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics of the antibiotics used.  
where \(V\) is the antibiotic solution volume (L), \(C_{i}\) is the initial antibiotic concentration (mg.L\({}^{-1}\)), \(C_{e}\) is the equilibrium antibiotic concentration (mg.L\({}^{-1}\)), and \(w\) is the mass of clay (g).
## 3 Results  
Means and standard deviations for \(\upmu\)TBS and fractures analysis are showed in Table 2. Results of the two-way ANOVA test showed significant difference for the factor \"luting cement\" (\(p\) = 0.0002), while the factor \"time of storage\" (\(p\) = 0.0991) had no significant effect on \(\upmu\)TBS.  
Tukey post-hoc test showed only significant difference between Panavia V5 and Bifix QM, with the first achieving better results compared to the second.  
A prevalence of adhesive fractures between luting cement and Cerasmart among all the groups was observed at T0 as well as after 1 year of storage in artificial saliva. Representative SEM images are shown in Figures 2-4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Panavia V5** & **Bifix QM** & **Estelite** \\ \hline \multirow{3}{*}{T = 0} & 32.45 \(\pm\) 7.71 & 25.75 \(\pm\) 7.65 & 26.39 \(\pm\) 6.69 \\  & (42 A, 1 CC, 2 CD, 10 & (36 A, 5 CC, 4 CD, 8 & (37 A, 3 CC, 2 CD, 5 \\  & M) & M) \\ \cline{1-1}  & 35.55 \(\pm\) 6.18 & 27.75 \(\pm\) 6.46 & 26.32 \(\pm\) 6.09 \\ \cline{1-1}  & (34 A, 5 CC, 1 CD, 7 & (27 A, 6 CC, 1 CD, 8 & (29 A, 10 CC, 4 CD, 9 \\  & M) & M) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Microtensile bond strengths (mean \(\pm\) SD, in MPa) of group 1 (V5), group 2 (QM), group 3 (ES), immediately after bonding (T0) and after 1 year of ageing (T12) in artificial saliva. Number of the failures are reported in parentheses and classified as adhesive (A), cohesive in dentin (CD), cohesive in cement (CC), or mixed (M) failures.  
Figure 2: Adhesive failure observed at T0: the luting cement (BifixQM) is still bonded to the coronal dentin.  
Figure 3: A mixed failure, observed in a specimen treated with Panavia V5 and stored in artificial saliva for 1 year.
## 3 Results
### Greater Trochanter Offset  
This parameter is analyzed in two ways, discussed below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Absolute Positions of Hip Center of Rotation**} \\ \hline \hline  & \multicolumn{4}{c}{**3D-Printed Implant**} & \multicolumn{4}{c}{**Bone Graft**} \\ \hline  & \multicolumn{4}{c}{Healthy} & \multicolumn{4}{c}{Reconstructed} & \multicolumn{4}{c}{Healthy} & \multicolumn{4}{c}{Reconstructed} \\ \hline  & X & Y & Z & X & Y & Z & X & Y & Z & X & Y & Z \\ \hline Max & \(-31.2\) & \(-66.6\) & \(91.7\) & \(-23.5\) & \(-61.6\) & \(105.3\) & \(-26.4\) & \(-66.9\) & \(85.7\) & \(-12.5\) & \(-53.8\) & \(85.3\) \\ Min & \(-45.8\) & \(-88.4\) & \(-93.6\) & \(-50.3\) & \(-95.9\) & \(-91.3\) & \(-43.0\) & \(-86.7\) & \(-87.9\) & \(-47.0\) & \(-92.9\) & \(-89.9\) \\ Median & \(-36.8\) & \(-73.4\) & \(87.5\) & \(-32.1\) & \(-71.2\) & \(85.2\) & \(-33.3\) & \(-71.4\) & \(83.0\) * & \(-33.8\) & \(-71.5\) & \(80.9\) * \\ Standard Deviation & \(4.5\) & \(7.5\) & \(3.1\) * & \(9.7\) & \(11.7\) & \(8.5\) * & \(5.2\) & \(6.6\) & \(3.3\) * & \(10.0\) & \(13.2\) & \(6.4\) * \\ \hline \hline \end{tabular}  
* For both the median and the standard deviation of the \(Z\) coordinate, only the magnitude was considered, given that the hip center of rotation for both the healthy and the reconstructed pelvises varies between sides from one patient to another.  
\end{table}
Table 2: Positions of the healthy as well as the reconstructed hip centers of rotation measured based on the anatomical reference frame of the simulated healthy pelvis. All values are in millimeters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hip Center of Rotation Deviations**} \\ \hline \hline  & \multicolumn{4}{c}{**3D-Printed Implant**} & \multicolumn{4}{c}{**Bone Graft**} \\ \hline \hline  & X & Y & Z & X & Y & Z \\ \hline Max & \(8.1\) & \(9.1\) & \(16.7\) & \(15.1\) & \(18.6\) & \(3.3\) \\ Min & \(-8.8\) & \(-7.6\) & \(-4.3\) & \(-8.0\) & \(-20.3\) & \(-19.8\) \\ Median & \(5.0\) & \(-1.4\) & \(4.0\) & \(0.3\) &
## 4 Experiments and Results
### Threshold-Based Hash Storage  
In the threshold-based strategy, the blockchain module selected critical data from its local database to compute a hash only when a predefined threshold for the number of critical data entries was reached. The handling of ordinary data remained unchanged: Every 60 min, complete records of closed incidents were included in the transaction batch.  
To determine the frequency of critical events in a real-world scenario, we performed a comprehensive analysis of the Incident Dispatch Data dataset. The dataset was cleaned and filtered as follows: Girstly, records covering the span of one year, from 1 January 2023 to 31 December 2023, were extracted; subsequently, duplicates and incidents marked as cancelled were removed; and finally, entries lacking an incident conclusion timestamp were discarded. The resulting clean dataset consisted in 1,457,802 unique incident entries.  
The analysis of the clean dataset revealed consistent daily incident counts throughout the year, with an average of 3994 incidents per day and a median of 4010. For an incident, a maximum of seven critical events were counted (see Table 2), though not all entries include every critical timestamp. e.g., when an incident concludes with patient treatment at the location, timestamps for First_To_Hosp_Datetime and First_Hosp_Arrival_Datetime were not recorded and the incident was subsequently marked as closed.  
For our experiments, we selected 3 October 2023, the day with the highest recorded number of critical data entries. On this day, 4499 unique incidents were recorded, corresponding to a total of 28,119 critical timestamps. We evaluated the system's performance for writing data hashes while using four threshold values for critical data: 100, 150, 175, and 200. Figure 7 illustrates the hourly costs of writing transactions for each threshold, with the mean hourly cost of the time-based strategy included as a reference.  
Table 6 gives the total number of hash writing operations and the corresponding estimated costs. The estimates were based on a mean transaction fee of USD 0.29 (see Table 4). For comparison, the total values for the time-based strategy are also included at the bottom of the table.  
Figure 7: Analysis of cost variations based on different data threshold values on 3 October 2023, the day with the highest recorded volume of critical incident data.
# QoE Modeling on Split Features with Distributed Deep Learning
## 5 Distributed Learning Approaches on Split Feature Scenarios
### Neural Network  
The _Neural Network_ algorithm [38], is used in this study, which consists of matrix-like located neurons that are connected to each other over multiple layers. The higher the number of layers, the deeper a neural network becomes. The neurons on the same layer are not connected within each other but only to the neurons in the previous and/or in the next layer. In a forward pass, a linear transformation at every neuron occurs, where the outputs from the previous layer are computed via a weighted sum followed by addition of some bias. Next the outputs of each neuron undergo a non-linear transformation via the activation function and then passed to all or a subset of neurons of the next layer of the neural network. The error computation is made at the layer where the ground-truth actual labels are present. Depending on the magnitude of the computed error, the weights of every neuron are re-adjusted with a gradient operation (partial derivative), in other words aiming at minimizing the final computed loss. This is called backwards propagation. One can also adjust when to update the neuron weights by setting the hyper-parameter called _batch size_. If the batch size is set to \(B\), then the model weights will be updated at every time all neurons process \(B\) samples amongst the whole set. This is repeated sequentially for multiple rounds on all layers, first via forwards propagation from left-to-right, and then later via backwards propagation right-to-left, until the error does not decrease anymore. At this point, the model is considered as converged. The higher the \(B\), the more time it takes the model to update its weights, while very small \(B\) makes the model weights update very frequently. Therefore, a suitable \(B\) needs to be selected. Epoch is the number of times the whole neural network sees all training samples. In the case of \(N\) training samples, and if all batches are different and complementary, it then mathematically takes \(N/B\) rounds (i.e., all sample set size divided by the batch sample set size) for the model to perform one epoch of training. We first set the batch size to the training set size. In the last section, where we further perform hyper-parameter tuning to improve energy-efficiency of the \(\mathit{vFL}\) training, we change the batch size and hence the number of data samples to perform forward-pass and backward-propagation in every round.