esa-sceva/satcom-chunk-collection huggingface dataset:
## Proposed System
### Performance and Evaluation
It introduces features like:  
a) High accuracy voltage detection for each cell  
* Overcharge threshold 3.6 V-4.6 V with accuracy: \(\pm\)25 mV (+25 \({}^{\circ}\)C) / \(\pm\)40 mV (-40 \({}^{\circ}\)C to + 85 \({}^{\circ}\)C)
* Overcharge hysteresis 0.1 V with accuracy: \(\pm\)50 mV
* Over-discharge threshold 3.6 V-4.6 V with accuracy: \(\pm\)80 mV
* Over-discharge hysteresis 0 V/0.2 V/0.4 V with accuracy: \(\pm\)100 mV  
b) Three grades voltage detection of discharge overcurrent  
* Discharge overcurrent 1 0.025 V-0.30 V (50 mV step);
* Discharge overcurrent 2 0.2 V/0.3V/0.4V/0.6 V;
* Short circuit 0.8 V/1.2 V.  
c) 3/4/5/6 cells protection enable  
d) Supports external bleeding for balance  
Figure 5: Block diagram of BM3451. Figure 6: BM3451 with three-cell application with balance function. * Over-temperature protection
* Setting of output delay time
* Overcharge, over-discharge, discharge overcurrent 1 and discharge overcurrent 2 protection delay time can be set by external capacitors. * Controlling the state of charge or discharge by external signals
* The maximum output voltage of CO/DO:12V
* Breaking wire protection
* Low power consumption
* Operation mode (with Temp protection) 25 \(\upmu\)A typical;
* Operation mode (without Temp protection) 15 \(\upmu\)A typical;
* Sleeping mode 6 \(\upmu\)A typical. Another important component for the design of the 3S BMS type is the switching control block, where it was based on the usage of the STD95N3L (LH6) Power MOSFET. It has the required specifications and features for the development of the used BMS proposed in this study, as:  
* Extremely low on-resistance RDS(on);
* Low gate drive power losses;
* High avalanche ruggedness. Due to the new gate structure based on the 6 th generation of design rule of STs, STripFETTM technology, the resulting power MOSFET exhibits the lowest RDS(on), which makes it suitable for a demanding DC-DC converter application as BMS, where high power density has to be achieved. The switching-time test circuits used for the designing of the improved simulator MTC are shown in Figures 7 and 8. Further details are explained in Appendix A, respectively in Figures 11-12.
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
## 3 IoT Monitoring Solution
### Hardware  
With regard to hardware components, the selected sensors as well as the calibration process and the prototype encapsulation are now described. In fact, the selected sensors provide an analog output necessary to calibrate them, in our case by using the Transmille 3000A Series calibrator. The sensors are in charge of monitoring PV installations under the IEC-61724 requirements. The IEC-61724 standards were previously proposed to analyze PV system performance under a variety of climates [53]. In our solution, PV electrical data and weather parameters are gathered to estimate PV operating conditions and exchange meteorological and electrical data. More specifically, the ACS758 current sensor [54] and the YHDC HV25 voltage sensor were selected.
## 3 Results and Discussion  
### Strong Dynamic Load Temporal Classification and Spatiotemporal Distribution  
In the previous studies mentioned [1; 2; 3; 4; 5], numerous scholars have analyzed the types of mine tremors from both macro-triggering and micro-fracture perspectives. In this section, the spatiotemporal distribution of dynamic load monitoring at Gu Cheng Coal Mine was analyzed to identify regions of strong dynamic load aggregation. Through the micro-seismic monitoring system, Gu Cheng Coal Mine can monitor micro-seismic signals across the entire mine. The statistical analysis was conducted on dynamic load events with energy greater than \(10^{3}\) J monitored from January 2019 to February 2019 at the 3310 working face. The spatial distribution of mine tremors is illustrated in Figure 7, where green dots represent \(10^{4}\) J energy events, and pink dots represent \(10^{3}\) J events.  
From Figure 7, it can be observed that during the mining period from January to February 2019, the 3310 working face was significantly affected by mining activities. Dy  
Figure 6: Specific implementation plan process.  
Figure 7: Planar distribution map of working face dynamic loading events with energy greater than \(10^{3}\) J, where green dots represent \(10^{4}\) J energy events and pink dots represent \(10^{3}\) J events.  
namic load events were concentrated within a 100 m range from the working face and within the wide coal pillars. Among them, strong dynamic loads with energy greater than \(10^{3}\) J mainly were clustered near the 3310 return airway, and there were also a few strong dynamic load events near the intersection of faults F18-2 and F18-0. Overall, dynamic load events were more densely distributed in the roof and coal pillar areas. During the mining period of the 3310 working face, there was no mining activity in the surrounding mining areas. However, a few strong dynamic load events also occurred in the F18 area, which is relatively far from the working face, indicating possible fault activation in that area. The three types of strong dynamic loads can be verified in Figure 7, highlighting the results of their distribution locations.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Main Error
#### 2.1.4 Other Errors  
Other errors include time delays introduced by firmware and other instruments, such as cables, which can be corrected.  
Since both receivers received eLoran signals at the same location, we assumed that the propagation path correction errors were consistent for both. Therefore, this study primarily focuses on analysing the control errors at the transmitter and broadcaster.
## 2 Background and Related Work
### SOA vs. MSA  
Considering the above, the previously cited studies reveal different eHealth applications, based on SOA or MSA, that focus on one or several of the software attributes of interoperability, maintainability, reusability and scalability.  
Both architectures have their strengths and weaknesses, according to the needs of the software and implementation [62]. SOA focuses mainly on the sharing of services in relation to the processes or capabilities of an organization [50; 63]. In this sense, SOA can refer to a broad accumulation of knowledge over the last two decades [62]. Regardless, this approach presents many issues when correcting errors or adding new functionalities, since the design of the application is very complex and time consuming. In addition, scalability in SOA is generally not possible or requires significant effort to be achieved. Consequently, organizations are beginning to migrate to other architectural solutions such as MSA [64; 65].  
MSA allows efficient development, implementation and maintenance of services compared to SOA. In fact, since the services are autonomous, they can be developed, maintained and tested independently, which facilitates their automation and continuous deployment (e.g., DevOps practices) [21; 50; 66]; however, such practices entail complexities inherent to a distributed computing environment in relation to the isolation, availability, security and transaction of services [33; 50], which in turn causes a fundamental problem known as granularity (i.e., if a microservice needs to decompose or merge) [21; 51].  
Based on these considerations, the implementation of SOA or MSA depends on the nature of the target system to be developed. Thus, for the development of our eHealth application, it is of utmost importance to contrast the performance between SOA and MSA based on the concept of versatile high-performance [21], since it is necessary to meet the needs of the environment of health organizations, focused on horizontal scalability. Quicker and more accurate real-time responses to demand peaks are thus enabled [22]. Additionally, it is necessary to allow the hierarchy and consolidation of a patient's clinical information, including what is necessary not only for generating standard reports but also for allowing the integration of recommender systems, to support decision making with consistent and truthful parameterized indicators of the data collected [21].
## 5 Conclusions  
In this paper, we presented a stream cipher based on a coupled chaotic map lattice to overcome the weakness of the original CML. We equipped a counter to eliminate the disadvantage of coupled map lattices. In addition, we employed two different chaotic maps, and a compound structure to promote the security of the chaotic stream ciphers.  
We analyzed some known attacks, carried out balance performance analysis, and presented the experimental results of the statistical random number tests under FIPS PUB 140-1 and NIST SP800 to verify its security. It can resist the known attacks, such as brute-force attack, chosen ciphertext attack, and guess-and-determine attack, etc. In the balance performance analysis, the keystreams of 0 and 1 are almost balanced. In the differential cryptanalysis, the difference rates are at least 46.23%. It shows that the proposed schemes have high key sensitivity. The experimental results show that the proposed chaotic stream cipher keystreams of 0 and 1 are almost balanced. For the statistical test of FIPS PUB 140-1, all the pass rates of the proposed chaotic stream ciphers are 100%. For the pass rates of the statistical test of NIST SP800-22, the proposed chaotic stream ciphers with improved CML and compound CML are at least 95% and 97%, respectively.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study.  
**Acknowledgments:** The author wishes to thank [PERSON] for his important assistance and the anonymous referees for their valuable and useful comments that have enriched this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Methodology** & **Application** \\ \hline Ours & Original and counter & Stream cipher \\ [PERSON] et al. [18] & Non-adjacent CML & Image encryption \\ [PERSON] et al. [7] & CML and DNA sequence & Image encryption \\ [PERSON] et al. [16] & Intermittent jumping CML & Image encryption \\ [PERSON] et al. [17] & Non-adjacent CML & Image encryption \\ [PERSON] et al. [20] & Tent-multi dynamic piecewise CML & Image encryption \\ [PERSON] et al. [10] & CML and algebraic computation & Stream cipher \\ [PERSON] et al. [12] & CML and discretized map & Stream cipher \\ [PERSON] et al. [11] & CML, tent chaos and sine function & Stream cipher \\ [PERSON] et al. [25] & One-way CML and nonlinear transformation & Stream cipher \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison between different CML methodologies and applications.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Power Distribution Network
#### 5.2.4 Critical Load  
An essential distinguishing feature of resilience is the ability to establish a differentiation between loads. For instance, in electrical networks, groups of customers are prioritized during emergencies, and will be spared from load shedding strategies due to their relative importance compared to other loads. Analyses in [128; 135] assign weights to loads based on the priority they have during the load-shedding procedure or the restoration phase in case of a strong event which affects even critical nodes. Resilience evaluation is however done on impact over the entire network. Works such as [124; 127; 131] take it a step further by evaluating the resilience metrics for the whole system on the one hand, then on the other hand only for critical loads, giving a deeper insight into the network dynamics during the event. Finally, frameworks in [136; 137; 138; 126] focus mainly on the critical load, as priority rankings are considered during curtailment and recovery stages, and resilience metrics quantify the impact in critical units.
## 4 Results and Discussion  
### Model Validation  
The absorber test rig is operated at specified conditions for 15 min, which gives results in time-dependent values for each measured parameter (i.e., temperature, pressure, and gas concentrations). The standard deviation that displays the amount of variation of each measured parameter is then calculated to estimate the random error. The systematic error of the measurement devices is constant throughout the experiments and is, therefore, not additionally shown in this chapter. Generally, the measurement uncertainty of directly measured values (e.g., pressure, temperature, and flue gas concentrations) depends only on the relative uncertainty of the measuring devices and is given by the relative error. For indirectly measured parameters or calculated values (e.g., volumetric flow rate, where the pressure difference and temperature are applied for its calculation), the Gaussian error propagation method is applied, with the assumption of normally distributed uncertainties. In this work, the volumetric concentrations are detected using the gas analysis unit, and the maximum relative error for CO\({}_{2}\) in the different process streams is approximately 3%.  
Figure 5 shows a comparison between the outlet volume fraction of CO\({}_{2}\) obtained by the experimental model (including the random error bars) and by the Aspen PLUS simulation model as a function of pressure at the inlet gas flow rates of 15 and 20 Nm\({}^{3}\)/h. It can be noted from Figure 5 that there is an agreement between the simulation results and experimental data at 15 Nm\({}^{3}\)/h. There is good agreement between the results up to 0.24 MPa at an inlet flow rate 20 Nm\({}^{3}\)/h; the deviation between the results increase when the pressure is increased between 0.24-0.3 MPa at the inlet gas flow of 20 Nm\({}^{3}\)/h.  
For assessing the accuracy of the rate-based simulation model, the relative error is calculated between the concentrations estimated using the rate-based simulation model and those obtained from the experimental model. The relative error values are shown in Table 2. As can be seen in Table 2,  
Figure 5: Effect of pressure on the outlet volume fraction of CO\({}_{2}\) at the inlet gas flow rate of 15 (**a**) and 20 Nm\({}^{3}\)/h (**b**). The error bars are obtained by the standard errors of measurement.  
there is good agreement between the experimental model and the Aspen PLUS simulation model obtained, with a maximum relative error of 2.27%.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 4 Experimental Results
### Performance Comparison Results  
Figure 5 provides a visual comparison of the image enhancement results achieved with the proposed OSE and with the other state of the art methods: the 3-D block-matching algorithm (BM3D), the supervised learning-based method DnCNN, and the unsupervised learning-based method DRGAN. It can be observed that all methods achieved satisfactory speckle reduction performance, but only OSE was effective in also removing the blurring effects on the image details, as shown in the selected magnified areas.  
The data in Table 2 summarizes the quantitative performance metrics obtained for the four different enhancement methods. OSE improved PSNR from 8.94 to 26.71 and SSIM from 0.34 to 0.81, outperforming all the other methods in terms of both denoising and deblurring.  
Table 3 shows the mean processing time of the methods assessed for 10,000 \(\times\) 1024 pixel images. Considering this data, we can see that although BM3D provides good image enhancement results, it takes much more time than the other methods to process the OCT images. In addition, we can note that DnCNN performed better than DRGAN but was slower. Furthermore, as explained earlier, DnCNN is a supervised learning method that requires a well-paired set of images for training. On the other hand, OSE provided top image enhancement performance with the best processing speed when the computations were performed on a GPU. It achieved a mean processing rate of 8.3 fps on the GPU.  
Figure 5: Sample images from the image enhancement performance study comparing the proposed OSE with the state of the art methods BM3D, DnCNN and DRGAN. The image areas marked in red are magnified and shown as inset pictures to facilitate the visual assessment of the different algorithms.
###### Abstract  
Objectives: This study investigates the prevalence of Spheno-Occipital Synchondrosis (SOS) and sella turcica morphometry (STM) association with different phenotype factors related to ectopic eye tooth/teeth (EET) using cone beam computed tomography (CBCT) imaging. Methods: This comparative retrospective study analyzed 252 CBCT images. Subjects in the EET group consisted of 197 CBCT images with the phenotype factor in number, i.e., unilateral (13, \(n=62\); 23, \(n=59\)) or bilateral (1323, \(n=76\)) and matched control (\(n=55\)). SOS and STM were investigated using 3D OnDemand Software. Seven parameters of STM were measured. To test the association X2 used for SOS prevalence, disparities in STM were tested using ANOVA and post hoc Tukey test. Results: The prevalence of unfused SOS was 48.4% and 16.1% in the 1323 and control groups, respectively. Fused SOS prevalence was quite similar in all four groups. Phenotype factor in number, i.e., unilateral 13, 23, or bilateral EET group showed significant disparities in six out of seven parameters (\(p=0.044\) to \(p\geq 0.001\)). Additionally, phenotype factor in position, i.e., occlusal, buccal, or palatal group showed insignificant disparities (\(p=0.463\) to \(p=1.00\)). Conclusion: SOS in the ectopic eye tooth group (EETG) was prevalent in the 1323 and buccal position groups. Differing from previous two-dimensional (2D) studies, there were statistically significant disparities in all seven measured variables of STM among control, and three different phenotype factors in numbers were revealed using three-dimensional (3D) CBCT imaging.  
Spheno-Occipital Synchondrosis; sella turcica; Ectopic Eye Tooth; CBCT
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 2 Materials and Methods
### YOLOLite-CSG  
This study proposes a lightweight crop pest detection method based on convolutional neural networks. Firstly, a detection method named YOLOLite with fewer parameters and less computation is built by optimizing the structure of YOLOv3. Secondly, k-means++ is adopted to generate the prior boxes and improve their degree of matching. Then, the classic residual blocks for processing the feature maps with high channel numbers are replaced by the lightweight sandglass blocks to reduce the number of parameters and computations in the detection method. Finally, coordinate attention is introduced into the classic residual block and the lightweight sandglass block to guide the feature extraction process. The final method is named YOLOLite-CSG, and its structure is shown in Figure 8.  
Described above are the core methods of our crop pest detection research. The complete architecture for crop pest detection using this method is shown in Figure 9, divided into seven steps. The first step is to capture pest images. Pest images can be manually captured using a camera, or by using a drone or sticky trap. The second step is to build the dataset. This step includes selecting the pest images, performing data augmentation, and labeling the data. These above two steps determine the quality of the final dataset used for training our method and are crucial for the later pest detection results. The third step is the generation process of the prior boxes. Different datasets contain different sizes of pests, which have different requirements of the prior boxes. The prior boxes must be generated to match the dataset. The fourth step is to set the hyperparameters. In order to obtain global optimal results, reasonable hyperparameters should be set in this step, such as input resolution and batch size. The fifth step is to train our method. This step trains the proposed method in this paper using the constructed dataset, predefined prior boxes and hyperparameters. Our method learns the knowledge and information contained in the dataset through the training process. The sixth step is to perform pest detection. The trained model can be deployed in an unmanned aerial vehicle (UAV) or sticky trap to detect crop pests. The seventh step is to analyze the detection results. The detection results should be carefully analyzed in order to develop a reasonable pest control plan.  
Figure 8: The structure of YOLOLite-CSG. The detailed structures of Bottleneck-CA and LightSandGlass-CA are shown in Figure 7b,c.
# Adaptive Global Fast Sliding Mode Control for Steer-by-Wire System Road Vehicles
## 1 Introduction
In [30; 31; 32] a fixed gain based full-state nonlinear observer is designed to estimate the longitudinal, lateral, and yaw velocities of the vehicle. However, for good estimation performance the observer gains must be tuned to a wide range of driving conditions. In order to reduce the burden of gain tuning from a-nonlinear observer [33], employed linear matrix inequality based convex optimization to obtain the gains of reduced order observer for estimating vehicle velocities. In [34] the authors implemented an adaptive gain based sliding mode observer to estimate the battery's charging level and health in electric vehicles. In this paper first, we have established the adaptive sliding mode observer (ASMO) and the Kalman filter (KF) to simultaneously estimate the vehicle states and cornering stiffness coefficients by using the yaw rate and the strap down [35] lateral acceleration signals. Then, based on the simultaneously estimated dynamics, the two-fold adaptive global fast sliding mode control (AGFSMC) is designed for SbW system vehicles, considering that the steering parameters are unknown. In the first fold, estimated dynamics-based control (EDC) is utilized to stabilize the impact of self-aligning torque and frictional torque. In the second fold, the AGFSMC is developed to estimate the uncertain SbW parameters and eliminate the effect of residual disturbance left out from the EDC. The adaptation capability of the proposed scheme not only intelligently handles the tire-road environmental changes, but also adapts the system parameters and sliding gains according to the different driving conditions. Finally, for avoiding overestimations of parameters and gains, discontinuous projection mapping [36] is incorporated to stop the estimation and adaptive mechanism as the tracking error converges to the designed dead zone bounds [37]. In the simulated results section, the comparative study will show the effectiveness of the proposed AGFSMC scheme, which ensures that the tracking error and sliding surface converge asymptotically to zero in a finite time. The rest of the paper is structured as follows: In Sections 2 and 3, vehicle dynamics modeling and SbW system modeling with external disturbance are discussed. In Section 4, the ASMO and KF are established to estimate the vehicle states and parameters. In Section 5, the AGFSMC scheme is developed for the SbW system and the convergence analysis with bounded conditions is discussed in detail. Section 6 describes the simulation results and findings to validate the proposed scheme, followed by the last section that concludes the paper.