esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Evaluation Metrics  
The statistical metrics used to evaluate the performance of the classifiers are here reported. In the context of binary classification, it is usual for a prediction of the system to be called \"positive\" or \"negative\". In our case study, the label \"positive\" refers to a student who has passed the course, while the \"negative\" label is associated to a failure. Once positive and negative classes have been defined, it is possible to understand if the prediction made by a classifier is correct or not by computing:  
* false positive (FP): student labeled as fail and wrongly classified as pass;
* false negative (FN): student labeled as pass and wrongly classified as fail;
* true positive (TP): student labeled and correctly classified as pass;
* true negative (TN): student labeled and correctly classified as fail.  
The set of these indicators is typically organized in a matrix called the confusion matrix. Based on these parameters, three main metrics were extracted:  
* Accuracy: measures the relationship between correct predictions and the total number of instances evaluated. Let n be the number of instances evaluated, with n = TP + FP + TN + FN \[\text{Accuracy}= \frac{\text{(TP+TN)}}{\text{n}}\]
* Sensitivity (Rate of true positives): measures the proportion of positively identified positives (i.e., the proportion of those who have some condition (affected) who are correctly identified as affected by the condition).  
\[\text{Sensitivity}= \frac{\text{TP}}{\text{(TP+FN)}}\]
* Specificity (Rate of true negatives): measures the proportion of negatives that are correctly identified.  
\[\text{Specificity}= \frac{\text{TN}}{\text{(FP+TN)}}\]
## 3 Absorption Characteristics
### Electric Field, Magnetic Field, and Surface Current Analysis  
Figures 13 and 14 depict the e-field (V/m) and h-field (A/m) distributions of a proposed MMA at a normal incidence angle of 0\({}^{\circ}\). This electromagnetic activity can also be used to determine the absorbance mechanism. The electromagnetic properties of the absorber are increased and disturbed in a new area. Moreover, electromagnetic behavior is greatly impacted by the metamaterial property. The e-field and h-field characteristics of the wavelengths at 450 nm, 557 nm, and 650 nm are discussed. The correlation between electromagnetic performance and metamaterial characteristics can be shown in Equations (9) and (10).  
\[B=\mu_{eff}\ \mu_{0}H \tag{9}\]  
\[D=\varepsilon_{eff}\ \varepsilon_{0}E \tag{10}\]  
where \(\varepsilon_{eff}\) = effective permittivity, \(\mu_{0}\) = permeability of free space, \(\varepsilon_{0}\) = permittivity of free space, \(\mu_{eff}\) = effective permeability, E = the electric field intensity and H = magnetic field intensity, B = the magnetic flux density, and D = the electric flux density. The highest e-field (4.24 \(\times\) 10\({}^{7}\) V/m) is attained in the patch's bounding area, whereas the h-field maximum intensity (1.28 \(\times\) 10\({}^{5}\) A/m) appeared in the patch's metal region. The non-metallic portion of the dielectric medium has a strong e-field, while the SRR's left and right arms have a strong magnetic field. The e-field intensity is lowered at 450 nm, as shown in the absorber. This results in a lower 90% absorbance at 450 nm and 650 nm, respectively, instead of a higher 99.99% absorbance at 557 nm. On the other hand, Figure 15 depict the surface current (A/m) distributions of a proposed MMA at a normal incidence angle of 0\({}^{\circ}\). It would seem that the surface current transfer at 450 nm was followed by a surface current that was much stronger when it reached 557 nm independently. Figure 15c demonstrated that the surface current dispersion was extremely substantial at 557 nm. In each of the three stages, the rear layer exhibited lower energization levels than the front layer.  
Figure 12: The proposed MMA bending effect (**a**) TE mode (**b**) TM mode.
## 3 Proposed Method
### Shape Recovery  
To recover the 3D face shape, motivated by the method in [55], we train a cascade coupled-regressor \(\{R_{Id}^{k}\}_{k=1}^{K}\), \(\{R_{Exp}^{k}\}_{k=1}^{K}\) to reconstruct the 3D shape based on the detected 2D landmarks \(U^{*}\) on the input image. Here, 3D face shape is disentangled as  
\[S=\overline{S}+\Delta S_{Id}+\Delta S_{Exp}, \tag{10}\]  
where \(\overline{S}\) is the mean 3D shape of frontal pose and neutral expression, termed pose-and-express-normalized (PEN) 3D face shape, \(\Delta S_{Id}\) is the difference between the subject's PEN 3D shape, and \(\overline{S}\), \(\Delta S_{Exp}\) is the deformation in \(S\) caused by expression.  
Given a 3D face shape and the corresponding 3D landmark vertices \(D\), its 2D landmarks \(U\) at the same pose as the input face is obtained through weak perspective projection \(M\). Note that the 3D-to-2D projection matrix is computed via least squares such that the projection of the \(D\) is as close as possible to the ground truth landmarks \(U^{*}\), i.e.,\[M=((D)^{T}D)^{-1}(D)^{T}U^{*}. \tag{11}\]  
The core idea of this method is to assume that there is a \"relationship\" between the difference on two 3D face shapes and the difference on their corresponding 2D landmarks, which can be learned from training samples. Thus, the reconstruction can be described as:  
\[\begin{array}{ll}S^{k}=S^{k-1}&+R_{Id}^{k}(U^{*}-U^{k-1}))\\ &+R_{Exp}^{k}(U^{*}-U^{k-1})),\end{array} \tag{12}\]  
where \(S^{k}\) is the reconstructed 3D face shape after \(k\) iteration, \(S^{0}=\overline{S}\).  
To train the coupled-regressors, we synthesized 1000 face images under various poses and expressions based on the resampled 3 DMM. The ground truth \(\Delta S_{Id}\) and \(\Delta S_{Exp}\) of these images were recorded during the synthesis. In this study, we cascaded five pairs of identity and expression shape regressors, i.e., \(K=5\).
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 3 Rotordynamic Coefficient Identification Based on Orbit Decomposition Method
### Elliptical Orbit Decomposition  
Equation (1) gives the mathematical expression of the elliptical whirl orbit. It can be rearranged as:  
\[\left\{\begin{array}{l}X=\frac{a+b}{2}\cos(\Omega t+\alpha)+\frac{a-b}{2}\cos(- \Omega t+\alpha)\\ Y=\frac{a+b}{2}\sin(\Omega t+\alpha)+\frac{a-b}{2}\sin(-\Omega t+\alpha)\\ \end{array}\right. \tag{7}\]  
Equation (7) shows that the elliptical whirl orbit can be decomposed into two circular whirl orbits. One is whirling in the same direction as the rotor's spin direction (forward) and the other is whirling opposite to the shaft rotation (backward). Figure 4 shows this decomposition and this decomposition is defined as:  
\[\left\{\begin{array}{l}X=a\cos(\Omega t)\cos(\alpha)-b\sin(\Omega t)\sin( \alpha)\\ Y=a\cos(\Omega t)\sin(\alpha)+b\sin(\Omega t)\cos(\alpha)\\ \end{array}\right.=\left\{\begin{array}{l}X_{f}=\frac{a+b}{2}\cos(\Omega t +\alpha)\\ Y_{f}=\frac{a+b}{2}\sin(\Omega t+\alpha)\\ \end{array}\right.+\left\{\begin{array}{l}X_{i}=\frac{a-b}{2}\cos(-\Omega t +\alpha)\\ Y_{b}=\frac{a+b}{2}\sin(-\Omega t+\alpha)\\ \end{array}\right. \tag{8}\]  
where \((X_{f},Y_{f})\) are the \(x\) and \(y\) direction rotor displacements of the forward circular orbit whirl model, and \((X_{b},Y_{b})\) are the \(x\) and \(y\) direction rotor displacements of the backward circular orbit whirl model.
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 4 Use Case Validation
### Mobile App  
The mobile app guides inspectors during the on-field operations, using a wizard-like navigation UI that allows selecting the machine to check, provide specific metadata (e.g., inspection date, machine-hours, next-inspection date, considered checkpoints), and record the measurements collected with the inspection toolkit and the observations about the asset status. Upon finishing the inspection, the mobile app allows reviewing the collected data before submitting it to the cloud module for storing and processing the inspection data. The mobile app requires network access (e.g., 3G/4G/5G, WiFi) to perform this operation; however, if no connection is available, the mobile app can locally store the data of several inspections.  
The UI/UX of the mobile app has been designed by following the hierarchical data model of machines. First, each machine has multiple systems (e.g., engine); then, each system may have multiple subsystems (e.g., fuel subsystem); finally, each subsystem may have multiple checkpoints (e.g., fuel tank). On average, each machine has around 30-35 subsystems, and each subsystem has four checkpoints (deviation from 2 to 4) equally divided into measures and observations. Thus, an inspection contains, on average, between 140 to 160 checkpoints. Table 1 presents the checkpoint distribution per type of machine.
## 4 The EDBA Simulation Program and Results of the \(Rh\) Optimization
### The EDBA Functionalites
A requested bandwidth for the third ONU3 unit is followed with starting cycle times \(SC_{i}\) representing finished times \(FC_{i}\) of the ONU1 unit for three wavelengths and with the ONU2 unit for one wavelength. Moreover, the 12 KB requested bandwidth is reported with the 0.12 Gbit/s data rate. Again, starting cycle times \(SC_{i}\) are ordered from the smallest to the highest as at previous units. Based on the Equation (2), a difference between the values of neighboring starting times of wavelengths \(\mathit{diff}(i)\) is calculated. At the ONU3 unit, the  
Figure 4: The allocated bandwidth of three wavelengths for the ONU1 bandwidth request with the addition of guard times. Figure 5: The allocated bandwidth of four wavelengths for the ONU1 and ONU2 bandwidth requests with the addition of guard times. situation is the same as at the ONU1 unit where a difference _dif_(_i_) is equal to zero because all adjacent wavelengths have identical starting cycle times. The assigning of wavelengths for the ONU3 unit depends on the iteration based on the Equation (3), and other wavelengths will be added until a condition in the Equation (4) will be satisfied. Subsequently, the requested bandwidth is uniformly divided into three wavelengths (Figure 6). The data transmission from the ONU3 unit takes 266.7 us (at the 0.12 Gbit/s data rate and the 2 \(\times\) 1 us \(GT\) guard time) for each of the three wavelengths. The fourth ONU4 unit has the 60 Mbit/s data rate with the 12 KB requested bandwidth. The starting cycle times of wavelengths \(SCi\) are ordered from the smallest to the highest, and the finished cycle times \(FC_{i}\) of ONU3 and ONU2 units are the starting times of the ONU4 unit. The assigning of wavelengths for the ONU4 unit is the same as for the ONU3 unit because a difference _dif_(_i_) is equal to zero (except the last one). The assigning of wavelengths depends on the iteration based on Equations (3) and (4). By this way, a requested bandwidth will be assigned on the first three wavelengths (Figure 7). The data transmission from the ONU4 unit takes 533.3 us (at the 0.06 Gbit/s data rate and the 3 \(\times\) 1 us \(GT\) guard time) for each assigned wavelength. Figure 6: The allocated bandwidth of four wavelengths for the ONU1, ONU2 and ONU3 bandwidth requests with the addition of guard times. Figure 7: The allocated bandwidth of four wavelengths for the ONU1, ONU2, ONU3 and ONU4 bandwidth requests with the addition of guard times. The EDBA algorithm indicates a characteristic of both the Water-Filling and First Fit algorithms. Compared to the Water-Filling Algorithm, the EDBA algorithm can save six \(GT\) guard times. Compared to the First Fit Algorithm, the EDBA algorithm can effectively utilize available wavelengths.
# Federated Learning for Edge Computing: A Survey
## 5 Applications
### Finance  
The financial sector is also one of the sectors where the highest possible security of private client data is required. Thanks to banking, individual customers can now own their banking information, which is crucial for developing a new ecosystem of data markets and financial services. For banking, FL needs to be modified and improved to address severalreal-world issues, including managing user incentives, limiting user access to personal data, limiting user scope, and limiting user time.  
[PERSON] et al. [122] introduce the use of FL in open banking, where they focus on and discuss statistical heterogeneity, model heterogeneity and access limits within the banking system. [PERSON] [123] describes the problems in the bank loan approval process. Many times, this process has to be approved manually, and banking institutions do not have enough data to automate these processes and create machine learning models to make these decisions. The lack of data causes inaccurate models that are unusable in the real world. In this case, FL can solve the problem of data scarcity, as more financial institutions could be involved in training a global model. In [124], they analysed and presented the credit risk assessment process. The aim is to propose a model using FL to predict the risk arising in the case of credits. Currently, the Chinese bank WeBank [125] is trying to create various systems and processes that would simplify the bank's decision-making. The bank uses various types of encryption in network communication to prevent and reduce the risk of data leakage. They have implemented the FedRiskCtrl mechanism [126]. This FL mechanism evaluates loan applications for small and medium-sized enterprises.
## 3 Results and Discussion
### Compressive Strength Results  
As in the case of tensile strength, the most profound influence of activated fly ash was observed in the early compressive strength results (Figure 7a). With an increase in fly ash, the binder mass specimens with untreated fly ash showed a decrease in the 2-day compressive strength results. The activation of fly ash by sieving with the 125 or 63 \(\upmu\)m sieve enabled utilizing up to 25% of fly ash in the binder without a negative influence on the 2-day compressive strength results. With the 90-day results, no detrimental effect was observed with the addition of up to 15% of either FA(B)125 or FA(B)63 fly ash (Figure 7b).  
Figure 6: The (**a**) 2-day and (**b**) 90-day tensile strength test results of mortars with the addition of sieved fly ash (FA(B)63 or FA(B)125) compared with the results of mortars with untreated fly ash (FA(B)N) (ANOVA 95% CI in the whiskers).  
The control specimen result for the 2-day compressive test was 22.8 MPa, and for the 90-day compressive strength test, it was 59.3 MPa.  
The dynamics of the increase in the compressive strength for specimens with a 15% amount of FA(B)63 fly ash in the binder mass was similar to the compressive strength increase in the control specimens (Figure 8). The difference between the 90-day strength of mortars with the addition of 15% b.m. of FA(B)125 and 15% of FA(B)63 was not statistically significant (Figure 7b). Despite not meeting the 90-day SAI criteria (Table 5), substantial strength growth was observed at 28 days and 90 days of hydration for both types of sieved fly ash.  
Figure 8: Strength development of mortars with 15% addition of sieved fly ash (FA(B)63 or FA(B)125) compared with the results of mortars with 15% addition of untreated fly ash (FA(B)N).  
Figure 7: The (**a**) 2-day and (**b**) 90-day compressive strength test results of mortars with the addition of sieved fly ash (FA(B)63 or FA(B)125) compared with the results of mortars with untreated fly ash (FA(B)N) (ANOVA 95% CI in the whiskers).  
The development of compressive strength of cement mortars modified with activated fly ash observed in the study can help formulate practical conclusions: the addition of up to 15% b.m. of fly ash sieved through either a 63 \(\upmu\)m or 125 \(\upmu\)m sieve can give similar mechanical properties to control specimens (Figure 8). However, despite the higher compressive strength results, sieving biomass fly ash through a 63 \(\upmu\)m sieve must be economically justified because less than three-quarters of the initial material is utilized. The oversized residue would require further management in different class composites or different industries. Considering the above, the utilization of a 125 \(\upmu\)m sieve might be enough for the potential technological installation activating biomass fly ash. Considering the variance in the chemical composition of fly ash, the utilization rate of both FA(B)63 and FA(B)125 fly ash can be increased further by combining it with active pozzolanic material [14; 23; 41].
## 3 Results
### Verification of the a Priori Model
Figure 10: OADEV at 9000 s of CAST-A (C20), SECM-A (C30), and SECM-B (C43) satellites with respect to the \(\beta\) angle. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sat. Type** & **Strategy** & **Along (cm)** & **Cross (cm)** & **Radial (cm)** & **3D (cm)** & **OADEV (s)** \\ \hline \multirow{4}{*}{CAST-A} & ECOM & 20.5 & 7.3 & 5.4 & 22.5 & \(2.47\times 10^{-14}\) \\ \cline{2-6}  & ECOM+G1 & 21.8 & 9.1 & 6.7 & 24.5 & \(2.67\times 10^{-14}\) \\ \cline{2-6}  & ECOM+J1 & 21.8 & 8.2 & 6.0 & 24.0 & \(2.36\times 10^{-14}\) \\ \hline \multirow{4}{*}{CAST-B} & ECOM & 27.2 & 8.8 & 8.5 & 29.9 & \(2.75\times 10^{-14}\) \\ \cline{2-6}  & ECOM+G1 & 28.5 & 10.3 & 9.6 & 31.8 & \(3.43\times 10^{-14}\) \\ \cline{2-6}  & ECOM+G2 & 28.3 & 10.2 & 9.5 & 31.6 & \(3.40\times 10^{-14}\) \\ \cline{2-6}  & ECOM+J1 & 27.5 & 8.9 & 8.6 & 30.2 & \(2.81\times 10^{-14}\) \\ \cline{2-6}  & ECOM+J2 & 27.4 & 8.8 & 8.5 & 30.1 & \(2.75\times 10^{-14}\) \\ \hline \multirow{4}{*}{SECM-A} & ECOM & 30.2 & 6.3 & 5.3 & 31.3 & \(2.36\times 10^{-14}\) \\ \cline{2-6}  & ECOM+G1 & 29.6 & 7.1 & 5.2 & 30.9 & \(2.12\times 10^{-14}\) \\ \cline{2-6}  & ECOM+J1 & 29.0 & 6.4 & 5.2 & 30.2 & \(1.95\times 10^{-14}\) \\ \hline \multirow{4}{*}{SECM-B} & ECOM & 34.5 & 10.3 & 9.4 & 37.2 & \(3.53\times 10^{-14}\) \\ \cline{2-6}  & ECOM+G1 & 33.8 & 10.8 & 9.1 & 36.6 & \(3.41\times 10^{-14}\) \\ \cline{2-6}  & ECOM+J1 & 33.2 & 10.1 & 9.1 & 35.8 & \(3.18\times 10^{-14}\) \\ \hline \end{tabular}
\end{table} 
Table 5: DBD and OADEV at OADEV at 9000 s for different POD strategies.
## 4 Implementation Scenario and Conceptual Framework
### Accuracy of NTCA Using Different ML Models  
We evaluated our ML algorithm for the NTCA designs. We split the collected data into training (80%) and test (20%) sets. The classification accuracy is considered by various factors, such as the dataset size and the number of features.  
For the sake of generating a generic result that is consistent across time and multiple trials in a given environment for a given model, we ran the NTCA algorithms several times, running our models 10 times. We calculated the percentage of error made by the classifier agent for a number of classification trials. We evaluated the mean and variance in terms of accuracy for different NTCA using various ML models. The result is presented in Table 3.  
Figure 14: Comparison of NTPA CPU usage.  
Figure 13: Comparison of NTPA memory usage.  
According to the table, we can understand that the DT-based NTCA is the most robust and accurate of all the classifier agents. This is because it has the smallest variance while the algorithm is run multiple times.
## A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery
###### Abstract
The existing fire detection algorithms can be classified as two categories: fixed-threshold algorithms and contextual algorithms [14-17]. A fixed-threshold algorithm will be set before judging whether the pixel is a fire one or not in the algorithm. The image is processed pixel by pixel by this method. On the contrary, the contextual algorithm is more flexible in the judgment of a fire pixel. First, it distinguishes the potential fire pixels based on a fixed threshold. Second, the statistics from ambient pixels are computed. Finally, the fire pixels from potential fire pixels are confirmed based on the ambient pixel statistics. Many contextual fire detection algorithms have been developed for the AVHRR, MODIS, ATSR, GOES and VIRS sensors [18-25]. The MODIS fire algorithm has been developing and increasing in maturity. In the previous version it had some disadvantages [19, 22, 26], such as the \"hole\" which was found in cases where the brightness temperatures of neighboring non-fire pixels were high enough to cause a centre pixel containing a hot and/or large fire not to be detected. Meanwhile, false detections may occur over some deserts and bare soils. The detection algorithm was improved greatly in the MODIS Fire Product version 4 [10]. Furthermore, in this version, the MODIS algorithm has considered the effect of coastlines, and includes a formula for the sun glint [10]. Based on the advantages of the MODIS algorithm, HJ-1B data is expected to be more sensitive to smaller and cooler fires than MODIS or AVHRR due to the finer spatial resolution. Further, AVHRR/MODIS fire detection algorithms may not be appropriate because of the channel differences. Modifications or new algorithms are necessary. To demonstrate the potential ability of active fire detection and seek a suitable algorithm for the new high spatial resolution infrared sensor, simulated HJ-1B images were used in this paper. After a brief description of the HJ-1B sensor payloads, the simulation method of HJ-1B data which include RED, NIR, MIR and TIR channels is introduced. Fires of various sizes and temperatures in a wide range of terrestrial biomes and climates were generated in the simulated images. The MODTRAN 4 atmospheric model was used to model various atmospheric conditions. Considering the characteristics of HJ-1B sensor, which is much sensitive to smaller and cooler fires due to its fine spatial resolution, a contextual fire detection algorithm was proposed and its performance was tested using the simulated HJ-1B images. Probabilities of successful fire detection, commission error and omission error were quantified as functions of fire temperature and fire area in various atmospheric conditions.