esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Sites and Database Description
### SAR Signal Sensitivity Analysis  
In this section, the sensitivity of SAR signal to soil moisture (_mv_) in the VV and VH polarizations was analyzed for different NDVI ranges using the Tunisian and French databases together (Figure 2). Figure 2 was created using the French database in addition to the Tunisian database. The results show that for an NDVI between 0.1 and 0.75, the radar signal in both the VV and VH polarizations (incidence angle approximately 39\({}^{\circ}\)) increases as soil moisture increases (_mv_ between 5 and 40 vol %). The sensitivity of the radar signal to soil moisture is approximately 0.16 dB/[vol %] for the VV and 0.13 dB/[vol %] for the VH, with an NDVI between 0.1 and 0.75. The decrease in the radar signal sensitivity to soil moisture is observed in Figure 2 when the NDVI increases for the NDVI values between 0 and 0.75. Beyond 0.75 (vegetation height \(>\) 70 cm), the radar signal in the VV and VH tends to become saturated for an _mv_ between 5 and 40 vol %. The saturation is related to the high attenuation of the radar signal by the well-developed vegetation cover. Accordingly, only database elements with NDVI values lower than 0.75 will be used to validate our inversion approach for soil moisture estimates because the penetration of the radar signal in the C-band seems very weak for NDVI values higher than 0.75.  
The decrease in the radar signal sensitivity to the soil moisture when the vegetation cover increases was reported in the studies of [PERSON] et al. (2016) and [PERSON] et al. (2016). In [PERSON] et al. (2016), the sensitivity of the radar signal at the C-band (VV polarization and high incidence angle) is 0.11 dB/[vol %] and 0.05 dB/[vol %] for a grassland biomass lower and higher than 1 kg/m\({}^{2}\), respectively. Moreover, the results showed that the sensitivity of the radar signal in the VV polarization to the soil moisture was higher than that in the VH polarization (0.08 dB/[vol %] and 0.01 dB/[vol %] for a grassland biomass lower and higher than 1 kg/m\({}^{2}\), respectively).
## 4 Security Notions
### Publicly Verifiable Property
#### 4.3.1 Spatial Verification  
Based on the public communication flow, any node in the neighborhood can gain the encrypted message formed as \(x^{i}_{(j,d)}+r^{i}_{(j,d)}\) from the pairwise nodes, and compute its local aggregation formed as \(LS(i,d)\) and \(LT^{i}_{(j,d)}\), and thus the total aggregation \(AS_{d}\) and \(AT(i,T)\) for the neighborhood can be computed and compared with the reported result from the GW. If the result is questionable, the user can report directly to the CC. With such a supervision, the CC can detect the fraudulent profile of the malicious GW.
# Defect Prediction for Capacitive Equipment in Power System
## 4 Experiments
### Defect Prediction Model Based on RF
Finally, the defect occurrence prediction model demonstrates optimal performance with the following parameter settings: 'N_estimators' = 110 and 'Max_features' = 15. On the other hand, for the defect level prediction model based on RF, the optimal parameter configuration is found to be 'N_estimators' = 150 and 'Max_features' = 17.
## 3 Results
### Case Study: Control of Laboratory Hybrid System
Cyber-physical characteristics of production parts that undergo changes at various operation situations are described by time-driven control models, while the hybrid timing control of operations is described by event-driven models. We propose that manufacturing parts are characterized by cyber-physical states (e.g., velocity, position, pressure, temperature, etc.) subject to time-driven dynamics, and by temporal states (e.g., operation start and stop times) subject to event-driven dynamics. A hybrid system model for control of manufacturing processes represents the behavior of dynamical systems where the states can evolve continuously as well as instantaneously. Such systems arise when control algorithms that involve digital smart devices are applied to continuous-time systems, or due to the intrinsic dynamics (e.g., mechatronic systems with bumps, electrical and mechanical devices for switching control). Hybrid control may be used to improve performance and robustness compared to conventional control (PID), and hybrid dynamics may be unavoidable due to the interplay between digital and analog components of a complex manufacturing system. An important class of discrete-event systems in practice are automated storage and retrieval systems (AS/RS). There is a big demand for new modeling and control methods of these systems in automotive industry in Slovakia. AS/RS are an important part of logistics. Presently, economic activity across continents is gaining unprecedented dimensions. The consumer lifestyle of most of the population requires production of large quantities of goods for a short time. The advantage of AS/RS consists especially in saving the human capital, and higher reliability. There is a big demand for new modeling and control methods of these systems in automotive industry in Slovakia (Volkswagen, Jaguar Land Rover, Peugeot-Citroen, KIA). The research about AS/RS, their examples and modeling of such systems using Petri nets can be found in [13; 14]. Figure 12:0 Control scheme for hybrid system using PN2 ARDUINO. The Smart Industry concept is a national initiative in Slovakia to transform and strengthen the industry using the Industry 4.0 methodology based on the latest research activities at universities and in companies mainly in automotive industry. Research and applications in this field are oriented mainly on small and medium-sized enterprises; one of important features is design of their optimal architectures and hybrid control of individual sub processes. An example of reference architecture of a workplace in which production is realized with minimal human intervention is shown in Figure 12:1
## 3 Results and Discussion
### Performance of the DNN-Bernoulli Model for FSI Simulation  
Various glottal shapes are extracted from the converged Bernoulli-FEM FSI results within one vibration cycle. The number of extracted shapes is 92 in this case. The input features \(x\) listed in Table 2 are extracted from these shapes. The corresponding output target \(\mathbf{y}\), that is, the target value of \(f_{r}\) is obtained from the N-S solution by Equation (7). The whole data set \((\mathbf{x},\mathbf{y})\) is fed into the same neural network as described in Section 2.2.3 to train and evaluate the DNN model.  
The history of 5-fold cross validation results is plotted in Figure 15. The scatter plot of the performance of the trained model on the test set is illustrated in Figure 16. MAE on the training set, validation set and test set are 0.0620, 0.0551 and 0.0393, respectively. The relative MAE divided by the mean ground-truth value of \(\mathbf{y}\) on the test set is around 4.0%.  
Figure 14: Surface pressure contour and distribution (divergent-convergent).  
Figure 15: Five-fold cross validation results (FSI shape).  
With the trained model, the DNN-Bernoulli model is coupled with the FEM solver to conduct FSI simulation of the vocal fold vibration. Time history of the flow rate predicted by the DNN-Bernoulli model is compared with that obtained by the Bernoulli model in Figure 17. It can be observed that the maximum value of flow rate is reduced from around 235 mL/s to 135 mL/s with the present model due to the inclusion of the viscous loss. To evaluate if the DNN-Bernoulli model provides a more accurate prediction on the pressure pattern inside the glottis, we extracted the pressure patterns in two representative convergent and divergent glottal shapes from the DNN-Bernoulli results and compared that to those obtained by the Bernoulli and N-S models using the same glottal shapes. The results are illustrated in Figures 18 and 19, respectively. From these figures, we can see that the pressure distribution along the inferior-superior direction of the vocal folds can be well predicted by the present model. Compared with the Bernoulli model, the additional CPU time required for the present DNN-Bernoulli model during prediction is almost negligible.  
Figure 16: Performance of the trained model on the test set (FSI shape).  
Figure 17: Comparison of the flow rate obtained by the DNN-Bernoulli and Bernoulli model.
## 1 Introduction
In [24], an extended recursive least squares algorithm was proposed to identify recursively both the system parameters and the dynamic disturbance and improved the estimation accuracy of time-invariant system parameters. Few studies have been conducted to improve the resonance-suppression ability of the MISP. The study in [4] analyzed the coupling effect of a dual-stage system on resonance suppression ability. A dual-stage stabilization platform is based on the isolation of the payload by a magnetic bearing from the gimbal system. It can provide fine pointing with a high closed-loop bandwidth, while the gimbal system follows the payload to maintain the gap of the magnetic bearing. The main contributions of this study are as follows. First, the factors influencing the resonance of the MISP are analyzed. Second, an improved RLS algorithm based on the error power for the variable forgetting factor is proposed to estimate the resonance frequency online. Third, an adaptive filter combined with the improved RLS algorithm is designed to suppress mechanical resonance under the dual-stage stabilization system of the MISP. The remainder of this paper is organized as follows. The MISP structure is introduced in Section 2, where the relationship between the resonance of the MISP and the gear transmission is given. In Section 3, an adaptive resonance suppression method is presented, including the improvement in the forgetting factor of the RLS algorithm and an adaptive notch filter. In Section 4, the feasibility of the proposed method for MISP variable-frequency resonance suppression is validated through simulation. Finally, Section 5 concludes the study.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 2 Mathematical Model
### Orbit Maneuver Detection
Therefore, we utilized the unstable state of a satellite during the maneuvering period to  
Figure 2: The schematic diagram of the required orbit for orbit maneuver detection. The green rectangle with a solid border represents the high-precision orbit one day before orbit maneuvering, while the green rectangle with a dashed border represents the orbit predicted by orbital integration. The blue rectangle with a solid border represents the high-precision orbit one day after the orbit maneuvering, while the blue rectangle with a dashed border represents the orbit predicted through reverse orbital integration. The orange rectangle represents the satellite orbit estimated by using the SRIF method. achieve real-time detection for the end time of maneuvering. This method's schematic diagram is shown in the following Figure 3.  
In Figure 3, the blue line represents the orbit arc before orbit maneuvering; the orange line represents the satellite orbit estimated using the SRIF method; and the red line represents the orbit during orbit maneuvering period. They can all be obtained in real time. The black dashed line, red dashed line, and green dashed line represent the orbit difference before the orbit maneuvering, during the maneuvering period, and after maneuvering, respectively. \[\Delta\dot{\textbf{{X}}}_{ACR}^{i}=\Delta\textbf{{X}}_{ACR}^{i}-\Delta \textbf{{X}}_{ACR}^{i-1} \tag{8}\]  
where \(\Delta\dot{\textbf{{X}}}_{ACR}^{i}\) represents the difference between the epochs of the orbit difference; \(i\) denotes the epoch. As is well known, satellites remain in a stable state during non-maneuver periods. Therefore, the orbit differences do not undergo jumps during non-maneuver periods. The difference between the two orbits before orbit maneuvering is small, and the epoch difference of the orbit difference tends to be zero. After the end of the orbit maneuver, the orbit difference gradually increases, but the difference between the epochs of the orbit difference tends to stabilize and absolute values become positive. During the orbit maneuver adjustment period, the epoch-to-epoch difference in the orbit difference shows irregular jumps. Based on this characteristic, the real-time detection of the end time of orbit maneuvers can be achieved. The orbit can be estimated in real time based on the SRIF algorithm and each epoch only costs 3 s. As long as epoch-wise orbits are obtained, we can then determine whether maneuvering happens immediately. This approach is necessary for the processing of maneuvered satellites in real-time processing as mis-detecting will cause filter divergence.
# Effect of Laser Surface Treatment on the Corrosion Resistance of Zircaloy-4 at High Temperature
## 2 Experimental Procedures  
Plate-like Zircaloy-4 (Zr-1.34 Sn-0.19 Fe-0.11 Cr, wt.%) with a thickness of 1 mm was used in this study. The surface of the material was treated with a Nimma-900 pulsed laser with a laser voltage of 700 V, a frequency of 10 HZ and a laser energy of 208 mJ. A schematic diagram of the LST process is shown in Figure 1. The spot size of the laser was \(\sim\)0.8 mm. To prevent the specimen from oxidizing during the LST process, Ar gas was continuously blown onto the surface of the specimen at a flow rate of 10 L per minute (LPM) as a protection gas. During the process of LST, the laser beam and Ar gas were fixed, but the moving rate of the specimen along \(X\)-axis was 3.628 mm/s, while the moving step of the specimen along \(Z\)-axis was 0.5 mm. The laser-treated plate-like specimen was cut into 15 mm \(\times\) 10 mm sheet-like specimens. Next, samples of 800#, 1000#, and 1200# sandpaper were used to remove the cutting marks. Then, ultrasonic cleaning was used to clean the specimens. The surface morphologies of the specimens were observed using a NOVEL optical microscope (Jiangnan Yongxin, Nanjing, China).  
A thermogravmetric analysis (NETZSCH STA 449F) instrument was used to carry out the corrosion test for the 15 mm long specimens in steam at 1100 \({}^{\circ}\)C and a heating rate of 20 \({}^{\circ}\)C/min. At the furnace temperature of 350 \({}^{\circ}\)C, a gas mixture containing steam with a mass flow rate of 2 g/h and Ar gas with a volume flow rate of 170 mL/min was injected into the furnace. After the furnace temperature reached 1100 \({}^{\circ}\)C, the specimens were held at this temperature for 2 h. The corrosion test was then completed, and the furnace was cooled down to room temperature at a rate of 10 \({}^{\circ}\)C/min. Specimens heated to 600 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C in this instrument, according to the above corrosion process, were identified to study the mechanism behind the effect of LST on the corrosion resistance of Zircaloy-4. X-ray diffraction (XRD, Bruker D8, Brooke Scientific Instruments, Darmstadt, Germany) was used to detect the phases on the LST layer and the phases in oxide films. The 20 angle range was 20-90\({}^{\circ}\), and the scanning step was 0.02\({}^{\circ}\). The cross-sectional morphologies of the LST Zircaloy-4 specimen and the oxide films of the LST and the NLST Zircaloy-4 specimens, as well as the element distribution of the treatment layer, were observed using a scanning electron microscope (SEM) and a transmission electron microscope (TEM), respectively. Prior to observing the cross-sectional morphology of the LST layer using SEM, the specimen was pickled in a mixed solution of 10%HF + 30%HNO\({}_{3}\) + 30%H\({}_{2}\)SO\({}_{4}\) + 30%H\({}_{2}\)O (in volume). The cross-sectional oxide specimen for TEM was fabricated using a focused ion beam (FIB, Helios Nanolab 600i, FEI Company, Hillsboro, OR, USA) system and observed via TEM (FEI Tecnai F20, FEI Company, Hillsboro, OR, USA).
# A Survey of LoRaWAN for IoT: From Technology to Application
## 2 Introduction to LoRa and LoRaWAN
### LoRaWAN MAC Layer
#### 2.2.2 Network Joining Mechanism and Security Context  
The LoRaWAN standard specifies how an end node can join the network. Each end device that joins the network needs to be personalized and activated. End devices can be activated over the air (OTAA) or by personalization (ABP). Several keys and identifiers are needed for the joining procedure, as well as for all communication during one session. All the required keys and identifiers are shown in Table 1. The session keys are valid only for a single communication session between an end device and the network server/application server. It is desirable that session keys are updated by a rejoining procedure before the saturation of packet counters [11] to avoid any possible replaying attack. When using the OTAA procedure, an end device is personalized with keys and identifiers that are saved before the procedure starts as shown in Table 1. The end node can send join or re-join requests, whereas the network server will reply with join-accept messages. A join-request message will include the DevEUI, JoinEUI IDs as well as 2 bytes of a DevNonce, which is a counter-based nonce. The join-request is not encrypted, but the message integrity check (MIC) is calculated using NwkKey. The network server will respond with a join-accept message if the device is permitted to join the network. The join-accept message will contain DevAddr and 3 bytes of JoinNonce among other communication parameters. The JoinNonce is an incremental number that will not repeat. The join-accept message is encrypted using NwkKey (if it is a reply to join-request message) or  
Figure 4: ADR mechanism algorithm implemented in the end node. JSEncKey (if it is a reply to rejoin-request message). Its MIC is verified using JSIntKey. The join-accept message is accepted if the MIC is verified and the JoinNonce is greater than the last stored one. Finally, the FNwkSIntKey, SNwkSIntKey and NwkSEncKey are derived using NwkKey, while AppSKey is derived using AppKey. At the end, the end device will send a MAC command to the network server to confirm switching to the new security context. The new security context will be used once the network server acknowledges this MAC command. An end device has the possibility to send rejoin-request messages during its communication with the network. This will give the possibility for renewing the security context, changing the DevAddr by the network and changing of the join server. In Figure 5, the packet flow for OTAA activation is shown.
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 2 Preliminaries  
### I-Vector  
Traditional speaker embeddings were mostly based on linear generative models in statistics [2; 14]. The i-vector [14] is one of the speaker embeddings based on factor analysis and has been the state-of-the-art speaker embedding in ASV until the development of deep learning. It is a set of factors that explain the total variability of the Gaussian mixture model (GMM) supervector [19]. In the i-vector framework, an utterance-dependent supervector \(\mathbf{M}\) is represented by the following:  
\[\mathbf{M}=\mathbf{m}+\mathbf{T}\mathbf{x} \tag{1}\]  
where \(\mathbf{m}\) is an utterance-independent supervector (generally, a universal background model [20] supervector), \(\mathbf{T}\) is a low-rank rectangular matrix that defines a total variability subspace, and \(\mathbf{x}\) is a set of latent variables that follows a standard multivariate normal distribution \(N(0,\mathbf{I})\). In practice, however, i-vectors exhibit non-Gaussian behavior [21; 22]. In most cases, a simple length normalization [23] is applied to i-vector to reduce the non-Gaussian behavior.  
The i-vector extractor (corresponding to the total variability matrix \(\mathbf{T}\)) cannot be directly estimated to discriminate between classes because it is trained in an unsupervised manner. It means that the i-vector may have unnecessarily large within-class variability. Therefore, it is necessary to compensate for the within-class variability of the i-vector for robust performance [24]. The PLDA has been dominantly used to compensate for the within-class variability, and the i-vector/PLDA had been the state-of-the-art option in the field of ASV.
## 4 Results
### Polygon Overlay Analysis Difference Operator  
Figure 7 shows the results obtained when applying the polygon overlay analysis difference operator to the different data sets using the different data division methods, including the running time, acceleration ratio, and load balancing index.  
According to the multicore parallel computing results for the difference operator based on the land use patch data for China, the serial running time of the data is approximately 94,917 s. Figures 6 and 7 show that the calculation results are similar when performing intersection operations and difference operations on these data. When the number of threads is four, the traditional polygon number-based data partitioning method can achieve a better acceleration effect and better load balancing than the shape complexity-based data partitioning method designed in this paper. When the number of threads is 8, 12, or 16, the proposed data partitioning method can yield substantially improved results. Compared with that achieved with the traditional data partitioning method, the calculation speedup increased by 34%, 24%, and 29%, respectively, and the load balancing index is also significantly reduced. When using the data partitioning method proposed in this paper to perform multicore parallel difference calculations with 16 threads, a 32-fold speedup can be achieved, and the running time is only 2966 s.  
Figure 7 also shows the multicore parallel computing results for the difference operator based on the land use data for several counties in Ningxia in 2009. The serial algorithm execution time on these data is 461 s. Figures 6 and 7 show that when either the intersection operator or the difference operator is applied to these data, both data partitioning methods improve the running time compared to that of the serial algorithm, and their improvement effects are roughly the same. However, the data partitioning method designed in this paper achieves better balancing of the multicore parallel difference calculations than does the traditional data partitioning method. The maximum load balancing index is only approximately 0.3, whereas the load balancing index of the traditional partitioning method exceeds 2.  
According to Figure 7, when the multicore performs the Vatti algorithm difference operator in parallel, with the increase of the complexity of vector data, the acceleration ratio of the data partitioning method based on shape complexity proposed in this paper is significantly improved compared with the data partitioning method based on the number of polygons. Regardless of whether the data complexity is improved, the load balancing performance of this method can be significantly improved compared with the traditional method.