esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Results  
This study applied two analytical approaches, namely variance-based structural equation modeling (PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that factors identified significant in influencing decision-makers' intention towards adopting CC. Second, the normalized importance among those significant predictors was ranked utilizing the ANN. This section explains the data analysis and results in detail.
## 3 Industrial Consumption, BESS, and Grid Modelling
### BESS Model
#### 3.2.2 Battery Model
In order to parametrize both models, a least-square method has been applied, so the error between the model output and the real battery data is minimized. The data for parametrizing the models need to be obtained from experimental tests. The complete parametrization process is detailed in [33]. A minimum set of 4 tests at different SoC and temperature are needed for the calendar ageing, and a minimum of 5 tests with different combinations of C rate, DoD and temperature are needed for the cycling model. Cycling ageing is related to the capacity fade when the battery is subject to charging and discharging cycles, i.e., when the current is flowing through the battery. It depends on several factors, among which \(C_{rate}\), temperature, and DoD are the most relevant [37]. The cycling ageing model published in [38] for lithium-ion batteries has been implemented:  
Figure 5:4  
where \(Q_{loss-cyc}\) is the cycling loss capacity (Ah), \(Ah\) the Ah that have flown through the battery (in or out), and \(a\), \(b\), \(c\), \(d\), and \(e\) are constants depending on the battery chemistry. On the other hand, calendar ageing is related to the capacity fade in the battery because of the time passed from the moment it was manufactured. It is dependent on the \(SoC\) of the battery, the temperature, and the time [37]. The calendar ageing model published in [39] has been implemented:  
Figure 5:5  
where \(a\), \(b\), and \(c\) are constants that depend on the battery chemistry, and \(z\) can take values between 0.5 and 1 and depends on the chemistry as well. Apart from the ageing factors previously mentioned, additional ageing factors such as DoD or mean SoC along a cycle are indirectly considered by the model. The Ah dependency included in the cycling ageing is indirectly considering the DoD, since the higher the DoDthe higher the Ah that flow through the battery. Moreover, the mean SoC along a cycle is considered in the calendar ageing equation, since the latter is constantly computing the battery SoC contribution.
## 2 Basic Theory
### Second Stage on Structural Sealant Damage Localization  
After the damaged edge is identified in the first stage, the exact damage location of structural sealant can be further identified in the second stage. According to the changes in BMC caused by damage, the difference in the BMC \(D_{j,i}^{r}\) can be defined as follows:  
\[D_{j,i}^{r}=\left|\frac{\left(C_{j,i}^{r}\right)_{u}-\left(C_{j,i}^{r}\right) _{d}}{\left(C_{j,i}^{r}\right)_{u}}\right| \tag{4}\]  
Since the structural sealant is an elastic boundary and affected by spring support, when local damage occurs to the structural sealant, the closer to the damage location, the bigger the changes in the BMC. Due to the influence of noise, there are also certain differences in the BMC obtained from the actual testing. The differences in the BMC caused by noise can be considered to follow a normal distribution. Therefore, the characteristics of the normal distribution can be used to identify the damage location. For the damaged  
Figure 1: Boundary mode of glass panel.  
edge, the average and standard variance of the difference in the BMC for each measured point can be expressed as  
\[\overline{E}_{i}^{r}=\frac{1}{n_{r}}\sum_{j=1}^{n_{r}}D_{j,i}^{r} \tag{5}\]  
\[\sigma_{i}^{r}=\sqrt{\frac{1}{n_{r}}\sum_{j=1}^{n_{r}}\left(D_{j,i}^{r}- \overline{E}_{i}^{r}\right)^{2}} \tag{6}\]  
where \(\overline{E}_{i}^{r}\) and \(\sigma_{i}^{r}\) are the average and the standard variance for mode \(i\) and edge \(r\), respectively. At a certain confidence level \(\alpha\), the data judged as anomalous can be identified using [30], as follows:  
\[D_{j,i}^{r}\leq\overline{E}_{i}^{r}-z\frac{\sigma_{i}^{r}}{\sqrt{n_{r}}}\text { or }D_{j,i}^{r}\geq\overline{E}_{i}^{r}+z\frac{\sigma_{i}^{r}}{\sqrt{n_{r}}} \tag{7}\]  
where \(z\) is quantile, usually 1.645. When \(D_{j,i}^{r}\) satisfies Equation (7), the point-\(j\) of the edge-\(r\) is identified as the damage location.  
To clearly describe the two-stage damage location identification method, Figure 2 illustrates the basic steps of the two-stage identification method. Firstly, the damaged edges are identified in the first stage; then, the specific damage location are identified based on the difference in ACBMC in the second stage.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 5 Results Validation  
The developed sensor placement optimization methodology and algorithms were validated numerically by comparing the accuracy of structural load predictions using simulated sensor measurements, where the sensors are placed in their optimal positions and orientations, against analogous predictions, with sensors in random positions and orientations.  
In previous research [35], different ML models were compared for the considered application (operational load monitoring), taking into account their accuracy and computational time. Four different types of ML models (of optimized hyperparameters) were considered: ANNs, adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVMs), and Gaussian processes for machine learning (GPML). The accuracy of predictions was measured and was of the same order of magnitude for each model. GPML and ANFIS turned out to be less accurate and more computationally demanding than the SVM model. The ANN was about 3.4 times faster than the SVM. For this reason, the feedforward artificial neural network presented in Figure 26 is considered the prediction model here, in the validation.  
Figure 26: Architecture of the ANN considered as prediction model.  
In the validation process the optimal solutions concerning three sensors are considered. Therefore, the network has three inputs--the measurements of the sensors (temperature or strain). There is one hidden layer with 10 neurons of sigmoid activation function and one neuron in the output layer with linear activation function. The single output of the ANN is considered the monitored load value. In the case of the temperature loads and sensors, the output is the maximal temperature in the entire structure. In the case of force loads and strain measurements, two cases are considered, where the output is either the force value applied to the structure or maximal equivalent von Mises stress in the entire structure. The whole point is that the single prediction model should operate for all load cases without the information of the current load location.  
In the validation procedure, the accuracy of the predictions of the ANN is statistically compared for two cases: for 500 training trials, where the sensor locations and orientations are random each time (considering the same constraints as in the optimization process), and for 500 training trials, where the positions and orientations of the three sensors are fixed to the obtained optimal values. There are seven load cases, and for each load case, 100 reference input-output data in the considered load range were prepared using a Latin hypercube sampling plan. This resulted in 700 reference data points. For each training trial a random 70% of the reference data was considered as training, 15% as validation, and 15% as testing data. The Levenberg-Marquardt algorithm was utilized for training.
## 3 Failure Mode  
### Control Beam A0  
At the initial loading stage, there was no obvious crack on the surface of the specimen, and the specimen remained in an elastic state. When the load reached 112.7 kN, the first bending crack emerged in the beam, with a width of 0.02 mm. At this moment, the stiffness of the beam decreased for the first time. When the load reached 144.0 kN, the first oblique crack appeared in the shear span near the loading point, with a width of 0.02 mm. The stiffness of the beam underwent a second decline, and the maximum width of the bending crack expanded to 0.03 mm. Upon continuous loading, the number of bending cracks no longer increases, and the existing cracks cease to extend further. The oblique cracks gradually multiply and broaden continuously. Before the beam fails, the maximum width of the oblique crack is 1.41 mm. It can be observed from the strain curve of the stirrups in Figure 7 that, prior to the emergence of the oblique cracks, the stirrups do not withstand shear force, and the shear force at this point is mainly resisted by the concrete. When the first oblique crack emerged, and the strain of the stirrups began to increase, the stirrups began to undertake the shear force. Due to the configuration of the stirrups being relatively rare, the stirrups promptly reached the yield state. Subsequently, the RC beam yielded (the equivalent energy method is employed in this paper to determine the yield point). When approaching the ultimate load, the stiffness further decreased, eventually causing failure, with the ultimate load being 292.1 kN.  
Figure 7: Load–deflection/stirrup strain curves.
## 2 Materials and Experiment
### Anti-Icing Asphalt Mixture
#### Determination of Anti-Icing Filler Distribution  
The asphalt mixture specimens containing 5% of V-260 were made according to the gradation in Section 2.2.1. The cross-sectional image of anti-icing asphalt mixture specimens were obtained by CT scanning technology and then processed by digital image processing technique [29; 30]. The anti-icing filler was separated from the asphalt mixture. Taking the Marshall specimen as an example, the images were firstly extracted along both longitudinal and transverse directions, and then equally divided into 12 pieces with equal areas; the number of anti-icing filler points in each piece was counted. The variance of the number was calculated. The uniformity of anti-icing filler distribution in the image can be characterized by Equations (1) and (2):  
\[U=-\log(S) \tag{1}\]  
\[S=\frac{\sum_{i=1}^{12}(x_{i}-\overline{x})^{2}}{12} \tag{2}\]  
where \(U\) is distribution uniformity value, \(S\) is variance, \(x_{i}\) is number of anti-icing filler points, and \(\overline{x}\) is average of the number of anti-icing filler points.  
The calculated distribution uniformity value (\(U\)) in Equation (1) represents the uniformity of anti-icing filler distributed in the entire image. A greater value of \(U\) reflects more favorable uniformity of the distribution of characteristic points. If \(U\) is greater than or equal to \(-2\), the uniformity is good; otherwise, the uniformity is poor [31].
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 3 Prototype and Field Tests
### Results  
According to the JJG168-2005 (Verification Regulation of Vertical Metal Tank Capacity Sector/Industry), the maximum measurement tolerance of the radial deviation or radius of each ring is \(\pm\)2.0 mm. It can be seen in Tables 5 and 6 that the deviation of the radial deviation from the actual standard value (radial deviation obtained by tradition) is less than 2.0 mm using the TWCR, both in the internal and external tests.  
The raw field test data of the external measurement test were further processed to develop the volume table. A comparison of the volume data (1 m between the measuring points) acquired by traditional methods and the TWCR is presented in Table 7. It can be seen in Table 7 that,  
\[\text{max}\left(\left|\frac{y_{1}-y_{2}}{(y_{1}+y_{2})/2}\right|\right)=0.0042 \%. \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ring Number**} & **Radial Deviation with** & **Radial Deviation with** & \multirow{2}{*}{**Difference (mm)**} \\  & **Trolley (mm)** & **TWCR (mm)** & \\ \hline
1 & 0.0 & 0.0 & 0 \\
2 & \(-\)0.7 & \(-\)0.2 & \(-\)0.5 \\
3 & \(-\)1.8 & \(-\)1.2 & \(-\)0.6 \\
4 & \(-\)4.3 & \(-\)3.8 & \(-\)0.5 \\
5 & \(-\)10.1 & \(-\)9.2 & \(-\)0.9 \\
6 & \(-\)7.5 & \(-\)6.3 & \(-\)1.2 \\
7 & \(-\)2.7 & \(-\)2.0 & \(-\)0.7 \\
8 & 1.8 & 0.9 & 0.9 \\
9 & 15.5 & 14.2 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Radial deviation with trolley and WCR (externally).  
Figure 15: External Measurement progress with (**a**) a TWCR and (**b**) trolley.  
According to the JJG168-2005 (Verification Regulation of Vertical Metal Tank Capacity Sector/Industry), the expanded uncertainty in the capacity calibration of the tank with volumes greater than 700 m\({}^{3}\) should be less than 0.1%, and the compound uncertainty of two gauges can be expressed as follows:  
\[\sqrt{U_{1}^{2}+U_{2}^{2}}=\sqrt{0.1\%^{2}+0.1\%^{2}}=0.14\%, \tag{15}\]  
and  
\[\max\left(\left|\frac{y_{1}-y_{2}}{(y_{1}+y_{2})/2}\right|\right)<\sqrt{U_{1}^ {2}+U_{2}^{2}}. \tag{16}\]  
Therefore, the results satisfy the requirements of the JJF1033-2016 (Rule for the examination of measurement standards). This tank was also measured by the liquid fill method (a more accurate method), and the measured volume at 19 m was 53,491.026 m\({}^{3}\), which is closer to the result of the TWCR (53,491.121 m\({}^{3}\)) than the result of the trolley (53,492.402 m\({}^{3}\)).  
As it is not required for the operator to climb to the top of the tank and install the pully, the TWCR can reduce the time of the operation by 30%, as recorded, it requires only one operator to perform the capacity calibration, and the risk of operators falling while installing the pulley is removed.
# Augmented Reality-Based Framework Supporting Visual Inspection for Automotive Industry
## 3 The Proposed Cognitive System for Inspection  
### Background  
In this section, we detail the former way of inspection at the target station, which is located at the end of the production line in the automotive industry. The information is displayed through a screen, where the list of defects that the vehicle in the station at that moment has and which should be inspected. Two lists are displayed on this screen, the top one showing the vehicle's defect list, such that defects that have not yet been touched up are marked in red and the ones that are already corrected appear in green color. At the bottom of the screen are the controls made to the vehicle along the line, in green those that have already been carried out and in yellow those that have yet to be carried out. Taking into account the workstation where the AR system has to be implemented, identified as being the most critical post, it is intended to develop an AR application with a tangible user interface that through the display demonstrates what currently appears on the screen (see Figure 2) interactively and dynamically, while reducing the cognitive load necessary for the execution of the operation, so that the operator can have access in the same station to non-conformities, proceed with the correction of anomalies that will be automatically validated as the touch-up is performed, preventing the operation from being completed until all anomalies are corrected. In this way, the time losses identified in the current system will be significantly reduced, while preventing the car from being considered fit for sale. According to the data provided by the automotive industry, at the factory, all workstations have defined ranges, and are mostly characterized by cyclical and repetitive operations. During the cycle time, about 228 s, the operator will have to perform all the tasks necessary for the position he occupies in the assembly of the vehicle. In terms of cognitive load, except for learning in training, the posts that make up the assembly line have a reduced level of demand, as most parts have already been separated previously, with only the control at the post. Therefore, operators are only responsible for checking and assembling parts. This station has been identified as the station for the implementation of AR technology.  
Figure 1: Examples of AR application assisted inspection for: (**a**) Bosh industry [25], (**b**) Volkswagen industry [22], (**c**) under hood inspection [23], (**d**) Spot Welding inspection [24].
## 2 Materials and Methods
### Model Performance Evaluation Indicators  
To evaluate the performance of the proposed model, we adopted the intersection over union (IoU), recall (R), precision (P), F1 score (F1), and BCEDiceLoss (Loss) as evaluation metrics. These metrics were used to quantify the discrepancy between the network's predicted segmentation results and the ground truth annotations.  
The IoU indicates the degree of overlap between the predicted region and the ground-truth region, and its mathematical expression is shown in Equation (14). Recall indicates how many true positive samples are successfully predicted as positive by the model, and its mathematical expression is shown in Equation (15). Precision indicates how many true positive samples are included in the positive category samples. Its mathematical expression is shown in Equation (16). The F1 score comprehensively considers both recall and precision, focusing on both accuracy and coverage. Its mathematical expression is shown in Equation (17):  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{14}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{15}\]  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{16}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{17}\]  
Where TP indicates that the original datum is a positive sample and remains a positive sample after the model's prediction and FN indicates that the original datum is a negative sample and remains a negative sample after the model's prediction, as shown in Table 2.  
BCEDiceLoss is a loss function that combines binary cross-entropy (BCE) and Dice loss. Binary cross-entropy loss is usually used for binary classification tasks. Its expression  
\begin{table}
\begin{tabular}{c c c} \hline  & **Positive Sample** & **Negative Sample** \\ \hline Forecast positive & TP & FP \\ Forecast negative & TN & FN \\ \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
is shown in Equation (18), where \(y_{i}\) is the true label (zero or one) of the \(i\)th sample, \(\hat{y}_{i}\) is the predicted probability of the \(i\)th sample, and \(N\) is the number of samples:  
\[\text{BCE}(y,\hat{y})=-\frac{1}{N}\sum\limits_{i=1}^{N}\left[y_{i}\log(\hat{y}_ {i})+(1-y_{i})\log(1-\hat{y}_{i})\right] \tag{18}\]  
The Dice loss is used to measure the similarity between two sample sets. Its expression is shown in Equation (19), where \(y\) is the binary mask of the true label and \(\hat{y}\) is the predicted binary mask:  
\[\text{Dice Loss}(y,\hat{y})=1-\frac{2\sum\limits_{i=1}^{N}y_{i}\hat{y}_{i}}{ \sum\limits_{i=1}^{N}y_{i}^{2}+\sum\limits_{i=1}^{N}\hat{y}_{i}^{2}} \tag{19}\]  
In the BCEDiceLoss used in this paper, the BCE loss is used to accurately classify each pixel, while the Dice loss is more concerned with the overlap of the overall area, thereby maintaining good performance even in the case of class imbalance. Its expression is shown in Equation (20), where \(\alpha\) and \(\beta\) are the weight coefficients:  
\[\text{BCEDiceLoss}(y,\hat{y})=\alpha\cdot\text{BCE}(y,\hat{y})+\beta\cdot\text {Dice Loss}(y,\hat{y}) \tag{20}\]
# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 2 Related Works on Action Recognition and the Problem of Object Occlusion  
Methods for recognizing human activities are widely applied in various systems and approaches in order to recognize and classify gestures, actions and behaviours. A multitude of applications and activity types results in a variety of features that can be used in the recognition process. However, an exemplary human activity recognition system consists of several common modules that perform motion segmentation, object classification, human tracking, action recognition and semantic description [2]. If a holistic representation is considered, action recognition is then based on the detected objects of interest (foreground binary masks, usually the shapes of human silhouettes) with known locations (trajectory or other motion information). Popular solutions include space-time techniques, where all silhouettes from a single sequence are accumulated and then features are extracted, or shape-based methods where features are extracted from each shape and combined into action representation afterwards.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
### Sensor Network Clustering
The neighbor nodes that receive this information do likewise and transmit it to the next neighboring nodes, and the process continues, until the node meets a neighbor node contained in another cluster. Through this process, each local cluster is formed, and the number of clusters is the same as the number of cluster heads. Thus, choosing the number of CHs is the same as choosing the clustering size. In MOCRN, communication in the sensor network is divided into intra-cluster communication (using a single hop between cluster members and their cluster head) and inter-cluster communication (using multiple hops between cluster heads). MOCRN is a protocol that uses a simple process for the formation of clusters where CHs are selected based on the minimum distance between the nodes, but discounts the residual energy of the selected nodes, which may not have enough energy to receive, aggregate and re-transmit the data to the next CH or the base station. As described above, LEACH, LEACH-C, DECSA and MOCRN are energy-efficient protocols, which have been designed based on an energy-aware, but service-blind routing approach, where the sensor nodes/motes are assumed to have the same sensing and communication capabilities and also provide the same services. These routing algorithms are not fit for the next generation IoT, where the heterogeneity of services will lead to heterogeneous devices with different energy consumption profiles. Such a diversity of energy consumption profiles should be accounted for when designing next generation energy-efficient IoT platforms.
## 3 Case I: Passive Eavesdropper
### BER Performances  
#### 3.5.1 BER at the Legitimate Receiver User A  
The BER performances versus the \(SNR_{A}\) of user A, for different values of the self-interference-to-noise ratio of user A (\(\rho_{AA}\)), are presented in Figure 10a,b for both the without feedback and blind feedback schemes, respectively. We can observe that the self-interference-to-noise ratio also significantly impacts the BER's performance, i.e., the BER increases as the \(\rho_{AA}\) increases, and the increase in the BER is bigger for larger \(SNR_{A}\). It also shows an interesting result that when maintaining \(BER_{A}=10^{-5}\) and increasing the self-interference-to-noise ratio \(\rho_{AA}\) from 0 to 30 dB, the blind feedback scheme needs about 2 to 3 dB in \(SNR_{A}\) to obtain that BER, while the scheme without feedback requires more than 5 dB in \(SNR_{A}\) to achieve comparable results. Therefore, in the passive eavesdropper case, the increase in the self-interference-to-noise ratio has less effect on the blind feedback scheme in the BER performance at the legitimate receiver user A.