esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Surface Accuracy and Topography Analysis
### Combined Rough-Fine Grinding of CMA Moulds  
Figure 11a shows the topography of the CMA mould after rough grinding. It presents that subsurface microcracks dominated material removal in the rough grinding process. The rough grinding stage observed several micro-cracks, fracture spalling, and fractured pits in the SiC phase. Some fractured pits are connected end-to-end to form a continuous fracture zone, caused by the increased feed rate and large depth of the cut. Figure 11b illustrates the appearance of the CMA mould after fine grinding. The machined surface is mainly composed of ductile grooves and striped residue. Most of the grooves are distributed on the grinding surface along the grinding direction, while a small amount of flake fragmentation and large pits exist in the SiC phase. The striped fracture and ductile grinding surface are significantly improved because of the large wheel rotational feed rate and small cut depth, resulting in the decrease in the maximum undeformed chip thickness. The material removal mechanism involved plastic deformation, brittle fracture, and brittle plastic mixing. To further improve the surface quality of CMA moulds, it is necessary to polish them again to eliminate the broken layer.  
Figure 10: Microscopic morphology after texturing of (**a**) the metal-bonded grinding wheel and (**b**) the resin-bonded grinding wheel.  
Figure 9: Analysis of the profile of diamond wheel after texturing.  
Figure 12 shows LSM photos of the CMA mould and the fine-grinding profile accuracy of the CMA mould. The excellent surface topography of the CMA mould was machined using a rough-fine grinding strategy. After grinding, the topography of the formed microstructure array was uniform and smooth. From the result, the P.V. value of the sampling length is 6.7 \(\upmu\)m, the error value of the sampling length near the middle of the sinusoidal contour, corresponding to the profile error curve of the resin-bonded diamond wheel. Hence, the precision of the CMA profile is highly dependent on the precision of the grinding wheel profile. The profile error of the grinding wheel is directly recorded on the CMA's surface.
## 3 Effect of Axial Joint Transfixion Degree on Dynamic Strength
### Peak Strength  
Figure 4 shows the relationship between the peak stress of the specimen and the number of cycles. As shown in Figure 4, under the action of similar impact loads, the peak strength with the increase of the number of shocks is generally characterized by a trend of first increasing and then decreasing. The average peak strength of the no-joint specimen was 66.62 MPa. The 20% to 100% axial joint transfixion degree specimens' average peak strength were 74.30 MPa, 69.47 MPa, 64.30 MPa, 58.60 MPa, and 52.17 MPa, respectively, and the average decay rate was 5.53 MPa. The peak intensity ratios of jointed specimens and non-joint specimens were 111.54%, 104.28%, 96.52%, 87.97%, and 78.31%, respectively. It can be seen that the peak stress of joint specimens with the increase of transfixion degree shows a significant linear downward trend, and the reason for the trend relationship is that under the action of external loads, the failure of the joint rock mass will first produce stress concentration at the end of the joint, which will cause the nascent crack to occur and extend along the tip of the primary joint by combining with the internal damage imaging of the specimen. When the transfixion degree is small, due to the small volume of water-saturated freezing of the specimen, the expansion path of the main crack is extended, and the destructive resistance is increased, these factors resulted in the limited weakening of the peak strength of the specimen. As the transfixion degree increases, the destructive resistance becomes smaller and smaller, and the energy required for the expansion of the main crack decreases accordingly, so the peak stress will further decrease rapidly. By the peak strength relationship of the specimens 20% > 40% > 0% > 60% > 80% > 100%, it can be seen that the water-saturated freezing effect increases the strength of the specimen and exceeds one-half of the length of the joint of the specimen weakens the strength of the specimen.  
By compared the dynamic characteristics of similar specimens in the literature [36], it can be seen that under the same impact pressure, the cement mortar structure subjected to dynamic load should have better dynamic performance after the water-saturated freeze pre-conditioning process.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 3 Proposed FVT-FCM Algorithm
### Summary of the Proposed FVT-FCM Algorithm  
The proposed method is an iterative optimization algorithm for fuzzy membership. The best classification result is obtained by defuzzifying the optimal fuzzy membership. The process of the proposed FVT-FCM algorithm can be summarized as follows:  
S1 Parameter initialization; include the number of polygons \(m\), the number of classes \(k\), the regulatory factors \(q\) and \(R\), the fuzzy factors \(\lambda_{1}\) and \(\lambda_{2}\), the pixel level fuzzy membership \(\mathbf{\mu}^{(0)}\), the polygon level fuzzy membership \(\mathbf{W}^{(0)}\), and iteration indicator \(t=0\);  
S2 calculating the balance coefficient \(\eta\);  
S3 calculating \(\mathbf{C}^{(t)}\) by Equation (37);  
S4 calculating cluster center \(\mathbf{V}_{l}\)\({}^{(t)}\) by Equation (39);  
S5 calculating the spatial distance between pixels and fuzzy polygons \(d_{\tilde{y}}\)\({}^{(t)}\) by Equation (22);  
S6 calculating the polygon level distance \(D_{jl}\)\({}^{(t)}\) using Equation (23);  
S7 updating \(\mathbf{W}^{(t)}\) using Equation (34);  
S8 updating \(\mathbf{\mu}^{(t)}\) using Equation (30);  
S9 repeating S3-S8 until the objective function converges. When the fuzzy membership \(\mathbf{\mu}^{(t)}\) and \(\mathbf{W}^{(t)}\) no longer change, it can be considered that the algorithm has converged.
## 3 Results and Discussion
### Multivariate Analysis
#### 3.2.2 Partial Least Square Discriminant Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pre-Processing Method** & **Modeled Class** & **Cherimoya** & **Sousop** & **Pineapple** \\ \hline \multirow{8}{*}{**First derivative of Savitzky-Golay**} & Sensitivity (Cal) & 0.974 & **1.000** & **1.000** \\ \cline{2-5}  & Specificity (Cal) & 0.972 & 0.987 & **1.000** \\ \cline{2-5}  & Sensitivity (CV) & 0.897 & 0.972 & **1.000** \\ \cline{2-5}  & Specificity (CV) & 0.958 & 0.987 & **1.000** \\ \cline{2-5}  & Class. Err (Cal) & **0.0267094** & 0.00666667 & 0 \\ \cline{2-5}  & Class. Err (CV) & **0.0721154** & 0.020556 & 0 \\ \cline{2-5}  & RMSEC & **0.26207** & 0.197014 & 0.198749 \\ \cline{2-5}  & RMSECV & **0.311281** & 0.252941 & 0.209272 \\ \cline{2-5}  & Bias & \(-0.00915475\) & 0.00408662 & **0.00506812** \\ \cline{2-5}  & CV Bias & **0.00775709** & \(-0.0145767\) & 0.00681962 \\ \cline{2-5}  & \(\text{R}^{2}\) Cal & 0.699008 & **0.822952** & 0.819861 \\ \cline{2-5}  & \(\text{R}^{2}\) CV & 0.595265 & 0.717618 & **0.800737** \\ \hline \multirow{8}{*}{**Secondderivative of Savitzky-Golay**} & Sensitivity (Cal) & 0.548 & **1.000** & 0.964 \\ \cline{2-5}  & Specificity (Cal) & 0.906 & 0.831 & **1.000** \\ \cline{2-5}  & Sensitivity (CV) & 0.548 & **0.960** & 0.929 \\ \cline{2-5}  & Specificity (CV) & **0.887** & 0.831 & 1.000 \\ \cline{2-5}  & Class. Err (Cal) & **0.272976** & 0.0847458 & 0.0178571 \\ \cline{2-5}  & Class. Err (CV) & **0.28241** & 0.104746 & 0.0357143 \\ \cline{2-5}  & RMSEC & **0.397685** & 0.343363 & 0.242586 \\ \cline{2-5}  & RMSECV & **0.400828** & 0.351006 & 0.244279 \\ \cline{2-5}  & Bias & **0.0964188** & \(-0.131557\) & 0.0351384 \\ \cline{2-5}  & CV Bias & **0.0958097** & \(-0.13477\) & 0.0389599 \\ \cline{2-5}  & \(\text{R}^{2}\) Cal & 0.366397 & 0.538207 & **0.740784** \\ \cline{2-5}  & \(\text{R}^{2}\) CV & 0.356427 & 0.518102 & **0.740114** \\ \hline \multirow{8}{*}{**Thirdderivative of Savitzky-Golay**} & Sensitivity (Cal) & 0.821 & 0.944 & **1.000** \\ \cline{2-5}  & Specificity (Cal) & 0.889 & 0.813 & **0.987** \\ \cline{2-5}  & Sensitivity (CV) & 0.821 & 0.917 & **1.000** \\ \cline{2-5}  & Specificity (CV) & 0.875 & 0.813 & **0.973** \\ \cline{2-5}  & Class. Err (Cal) & **0.145299** & 0.121111 & 0.00666667 \\ \cline{2-5}  & Class. Err (CV) & **0.152244** & 0.135 & 0.0133333 \\ \cline{2-5}  & RMSEC & **0.348922** & 0.32159 & 0.208805 \\ \cline{2-5}  & RMSECV & **0.361874** & 0.336387 & 0.234444 \\ \cline{2-5}  & Bias & \(-0.00584702\) & 0.000421702 & **0.00542532** \\ \cline{2-5}  & CV Bias & \(-0.00528552\) & \(-0.00200115\) & **0.00728667** \\ \cline{2-5}  & \(\text{R}^{2}\) Cal & 0.465947 & 0.528059 & **0.801175** \\ \cline{2-5}  & \(\text{R}^{2}\) CV & 0.425925 & 0.486149 & **0.749947** \\ \hline \hline \end{tabular}  
* Cal: calibration; CV: cross-validation; RMSEC: root mean square error calibration, RMSECV: root mean square error calibration of cross-validation.  
\end{table} 
Table 5: _Cont._Figure 6: ROC curves for the first derivative of the Savitzky–Golay pre-processing method.
## 2 Methodology
### Node Generation Algorithm  
[PERSON] et al. (2017) pointed out that when the discretization (cloud of nodes) becomes more regular, the obtained results become more logical [21]. Furthermore, to avoid the diffraction caused by the difference between the node and the interface positions, it is necessary to adjust the node position at the inner part of the interface and the boundary, so that the nodes are located at the interface and the boundary. In this study, we proposed an algorithm combining the Poisson disk node generation algorithm and the centroid Voronoi adjustment algorithm, to obtain a reasonable node distribution according to the velocity model.  
1. Randomly Distributed Node Generation The Poisson disk node generation algorithm proposed by [PERSON] et al. (2015) was used [28]. In the two-dimensional case, the calculation steps for the random node generation algorithm (Figure 2) are as follows: 1. Set the horizontal positions (potential dot positions, PDPs) of the initial nodes, and randomly set the vertical coordinates; 2. Out of all the current PDPs, find the PDP closest to the bottom of the model, and classify this PDP as the determined dot position (DDP). The newly determined DDP is at the center of the circle, and the discrete distance set by the position model parameters is the radius (noting the radius is a function of velocity [29]); 3. No other center may appear inside the circle, so all PDPs except the DDP within the circle are removed; 4. Determine the two PDPs closest to the DDP at the circle center, make a circle through these two PDPs with the DDP as the center, and select multiple (we used five) new PDPs at equal angles on the arc between the two DDPs; 5. Then, select the PDP closest to the bottom (excluding the cycled PDP and adding a new PDP) and repeat steps (b)-(d) until the cycle for all points in the calculation area is completed. At this point, uniformly and randomly distributed node coordinates will have been obtained in the computing area.  
Figure 2: Generation process for the Poisson disk algorithm [28]. The red solid point is the DDP, and the black hollow points are PDPs.
## 2 System Model
### Power Consumption Model
#### 2.2.1 Power Amplifier Power Consumption Model  
Current cellular standards use orthogonal frequency division multiplexing (OFDM) as a downlink transmission scheme, and there is no doubt that LSASs should use OFDM, because it has very high spectral efficiency and is robust against multipath fading. However, OFDM has a very high peak-to-average power ratio, which seriously reduces the PA efficiency [20]. If we assume 10 MHz bandwidth with 1024 subcarriers, the peak-to-average power ratio of an OFDM signal is higher than 11 dB with 0.1% distortion probability or complementary cumulative distribution function. This means, without using digital pre-distorter or peak-to-average power ratio reduction schemes, we should choose Input-Back-Off of more than 11 dB for proper operation. We also assume that we use Class-B PAs, which have 78.5% efficiency. The Class-B PA efficiency, \(\eta(\%)\), can be represented as follows [21; 22]:  
\[\eta(\%)=\frac{\pi}{4p}, \tag{9}\]  
where \(p\) is the square-root of Input-Back-Off, \(\sqrt{IBO}\). The simple relationship between \(P_{tx}\) and \(P_{PA}\) can be represented as:  
\[P_{tx}=\eta P_{PA}. \tag{10}\]
## Improving Placement Accuracy
### Hybrid Target Method  
This study used a session state world-tracking approach to supplement the 'Image Target' method of AROS_\(\alpha\). Specifically, the ARKit platform provides 'ARWorldMap,' a technique that uses the details of the user's physical space to determine the device's position and orientation, as well as any anchor objects added to the session that can represent detected real-world features or virtual content. ARWorldMap first identifies the physical features around the user, after which the information is used to anchor the AR model to an identical location in the same direction ([[https://developer.apple.com/documentation/arkit/arworldmap](https://developer.apple.com/documentation/arkit/arworldmap)]([https://developer.apple.com/documentation/arkit/arworldmap](https://developer.apple.com/documentation/arkit/arworldmap)) (accessed on 5 May 2022)). The physical features and topographical objects are obtained from point clouds collected from the LiDAR attached to the iPad device. In effect, these are used to create multiple anchor points. Moreover, ARWorldMap stores spatial measurement data on the device, so the anchors can be used for quick re-projections in the same location.  
Figure 9 shows ARWorldMap integrated with the 'Image Target' method to improve placement accuracy. The AR model is first created using a QR code that contains the projection information. Simultaneously, ARWorldMap is executed, and topographical objects are identified using LiDAR. If the user moves, the LiDAR continues searching for characteristic points that will serve as new anchors. This prevents even the smallest displacement of the model, thus ensuring that it stays fixed even if the QR code is not visible on the AROS_\(\beta\) screen.  
Figure 9: Schematic of the ARWorldMap-based hybrid target method.
## 1 Introduction
Our key contributions can be summarized as follows:  
* We propose a _formally verified_ certificate-based user authentication method that does not require additional secure hardware (e.g., ARM TrustZone) to mitigate offline password guessing attacks. The user's sign key is encrypted and the encrypted key can only be decrypted when the primary device successfully communicates with a network-connected secondary device that assists in the decryption process. We proved that the proposed protocol is secure using ProVerif that is an automatic cryptographic protocol verifier in the formal model. * We demonstrate the feasibility of the proposed certificate-based authentication method by implementing a prototype with popularly used cryptographic algorithms (AES-256, RSA-3072, and ECDSA-256). With this prototype, we analyze its communication overhead and demonstrate that the execution time overhead of the signing key recovery process is acceptable (0.039 s for RSA-3072, 0.073 s for ECDSA-256 on average compared with the total execution time of the conventional system) for the industrial controller application. The rest of this paper is organized as follows. In Section 2, we provide an overview of the related work. In Section 3, we explain the conventional authentication method with a digital certificate. In Section 4, we describe possible attack scenarios against the authentication method. In Section 5, we present a certificate-based user authentication method using a secondary device. In Section 6, we analyze the security of the proposed system using a well-known verification tool called ProVerif. In Section 7, we evaluate the performance of our prototype implementation. Finally, the conclusions of the study are provided in Section 8.
## 4 Conclusions  
In conclusion, this paper investigated the improvements in launching nanosatellites in lower inclination orbits for IoT communications missions in the near-equatorial regions. The simulation results proved that the use of low-inclination orbits will reduce the number of satellites in the constellation. Comparing it with the SSO, the findings of the simulation showed that the coverage percentage of the near-equatorial region is higher for satellites with low inclination angles. Moreover, while SSO provides global coverage, the coverage time for each region is almost identical and low, and the revisit time is high. Additionally, the number of accesses to the ground station is lower. On the other hand, for an IoT communication mission, a satellite with an inclination of 24\({}^{\circ}\) will provide higher coverage time and more coverage area to the region covering from 0\({}^{\circ}\) to \(\pm\)27\({}^{\circ}\) latitude. In addition, the revisit time is lower and the number of accesses to the ground station is higher. The IoT terminal design used allows any sensor to be connected to collect data and forward it instantly to the satellite when in view. Unfortunately, due to power constraints, the limitations on packet size apply and less data can be sent out of the terminal. From the link budget simulation, we can conclude that a higher spreading factor offers a higher and positive link margin, while lower spreading factors offer a higher data rate and require less airtime.  
Furthermore, the use of the lean satellite standard reduces the monetary and time cost of the development of satellites, allowing developing countries in the near-equatorial regions to design and deploy IoT applications using LEO satellites across various industries, such as telemedicine, agriculture and the enabling of smart cities. Finally, future research could focus on exploring other regions and new IoT designs, and also, expanding the scope of the study to include real data from launched satellites in lower inclination orbit.  
Conceptualization, Z.H. and S.A.B.; methodology, Z.H. and A.S.; software, D.R., S.A.B. and A.S.; validation, D.R., S.A.B., Z.H. and A.S.; formal analysis, S.A.B.; investigation, S.A.B., Z.H. and D.
## 2 Description of the Method
### Development of the Warning System
#### 2.2.1 Synthesis  
At first, the user decides whether to feed the network the entire database or a selected subset of sequences. Often, the whole database is very large, which would make it affordable to perform several training subsets with a growing number of elements, until the last added sequences do not significantly modify the resulting SOM. The final subset is used as the training set. The training is performed by means of a hierarchical clustering procedure (Figure 2). At the root of the procedure, an SOM is trained on the whole training set, which is split into a set of clusters. The coordinates of the barycenter of the cluster are the connection weights linked to the corresponding output. The vector of such weights is referred to as a prototype. The training process moves the prototypes toward the barycenter of different clusters. In general, the patterns belonging to different clusters are differently scattered (see Figure 3). As a measure of the scattering of a cluster, the maximum difference between patterns and prototype is assumed.  
Figure 1: Schematic flowchart of the warning system.  
The aim of the hierarchical clustering procedure (see Figure 2) is to split the clusters until their scattering is lower than a preset threshold. To this purpose, clustering is preliminarily performed on the entire training set, and the obtained clusters are subdivided into three subsets, as follows:  
* Clusters A: the scattering is lower than the threshold;
* Clusters B: the scattering is larger than the threshold;
* Clusters C: empty.  
Clusters A and their related prototypes are stored for successive assembly in the final network (assembling). Each Cluster B is reprocessed separately, giving rise, in general, to A, B, and C new clusters. Clusters C are eliminated. The procedure ends when no more Clusters B are found, such that only Clusters A remain. The prototypes of Clusters A are collected into a final unique SOM, which is the core of the monitoring system.  
A preliminary choice of a warning criterion is required before entering the monitoring phase. The present method may be applied regardless of the warning criteria assumed. To assess the effectiveness of the method in the present work, two warning criteria are considered and used in the application example of Section 3. The first one is based on the incidence of patterns within the clusters, with the low-incidence clusters being labeled warning clusters. In fact, the rare occurrence of a behavior can be assumed as an index of  
Figure 3: Scattering of patterns belonging to a cluster.  
Figure 2: Hierarchical clustering procedure.  
anomaly. The second criterion refers to a past noticeable event that labels as warning the cluster which it belongs to. The monitoring strategy based on the rare behavior criterion is referred to as Strategy 1, while that based on an event classified as noticeable is referred to as Strategy 2.  
The labeling procedure of the clusters is performed before the monitoring phase. The labels are stored in the database.