esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Obstacles and Challenges in Evolving V2X Communication
### Further Physical Layer Security Approaches
#### 5.3.2 Physical Layer Key Generation  
The secrecy of the communication system can be provided through an adequate encryption method, which is complicated enough to decipher. However, such methods require demanding computational resources that are not available in the case of small-sized, mobile devices, as they are usually designed for low energy consumption as well (e.g., internet of things or wearable devices). These applications require alternative methods to create a secret key for communication secrecy. One of these methods is the physical key generation, where the key is calculated from the individual and mutual movement trajectories of the devices, which is measured via the received signal strength (RSS). This method assumes that specific devices have similar movement properties, which makes them unique, unlike the possible eavesdropper [103].  
Physical layer security applications have drawn attention recently due to their low computational demands and applicability for modern wireless multi-antenna systems. There are several secrecy flaws that are observed and well studied. However, for the applicability to the new MIMO and 5G systems, these methods require modifications or even the introduction of novel ones, as such methods are still scarce. After the overview of the recent and perspective sight methods, it seems that several aspects are not studied yet, requiring further research [109]:  
* The effect of the eavesdroppers' mobility;
* Multi-user MIMO scenarios;
* Backscatter communication perspectives;
* An eavesdropper with an MIMO antenna [110].  
As it seems, published methods for providing physical security have considered only a section of possible threads, raising the importance of investigating new scenarios, e.g., a system of several participants and eavesdroppers using MIMO antennas, and advanced algorithms, i.e., artificial intelligence, to cause malfunction or gain information. The summary of the introduced risks are presented in Table 6.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
### Clustering Algorithms  
Clustering algorithms have been applied in many fields. For example, some basic algorithms such as from reference [11] and reference [12] are provided as meta-algorithms, and others have been used in software engineering as well [13]. However, the most recently used fields are related to self-organizing networks such as wireless sensor networks (WSN) or ad-hoc networks such as Refs. [14; 15; 16]. These studies noted the utility of managing clustering when combined with a mesh network. In other words, it has a smaller controller load than central management, but flooding occurs more often than for a fully-connected mesh network. Taking advantage of clustering on the ad-hoc network claimed in these papers, we note that clustering can have similar advantages over V2X mesh networks that share the basic characteristics of mesh networks.  
In particular, VANET based on a clustering algorithm is suitable for reducing the load of the central control unit, which will increase during the IoT era, and will reduce the data redundancy on the network [17]. Distributed clustering is also applicable to location-unaware WSN, and VANET belongs in this category [18].  
However, the existing clustering algorithms based on the current traffic environment are not optimized for autonomous driving when the accurate path of the vehicle can be known beforehand [19].  
Therefore, this paper proposes a clustering algorithm that is specialized for autonomous driving environment.
## 4 Stability Evaluation
### Operating Pressure Scheme and Evaluation Method
#### 4.2.2 Assessment Criterion  
Numerous studies have indicated that for salt-cavern gas storage facilities to maintain safety and stability, the caverns need to meet the requirements of sealing performance, stability, and usability, which necessitates a reasonable safety evaluation method [34; 35]. After long-term exploration, a set of stability evaluation indicators has been introduced to assess the stability of the cavern's surrounding rock during injection and extraction operations, including displacement, plastic zone, volume shrinkage rate, expansion safety factor, and equivalent strain [36; 37]. Regarding plastic damage, it is essential that there are no large connected plastic regions in the surrounding rock of the cavern wall. In terms of volume deformation, the design requirements for salt-cavern gas storage vary by country. In Germany, the cavern volume shrinkage rate should not exceed 20% after 30 years of operation, while in France, the limit is set at 30% over the same period [38]. In China, considering the creep inhibition effect of mudstone interlayers, the volume shrinkage rate of salt-cavern gas storage is evaluated based on the French volume deformation requirements. For the expansion safety factor, local damage is considered to have occurred when it drops below 1.5. A value lower than 1.0 indicates destruction, and a value below 0.6 implies collapse [39]. The equivalent strain, used to evaluate the rock mass's creep damage, should not exceed 3% after 30 years of operation [40; 41].
## 4 Discussion
### Comparison with Results from Other Studies  
Comparison of our results with those of other studies on the assessment of respectfulness is difficult because no open data sets exist. The closest study to our work [23] has approached the task of predicting respect in a different manner. However, the order of the quality of our models can be compared with those in other studies using text analysis. In this context, our observations align with those from other studies. In [65] the researchers demonstrate that a pretrained BERT model provides superior results to those of TF-IDF models in several text classification tasks. A survey of text classification algorithms [66] has also ranked deep learning methods ahead of TF or BoW feature extraction techniques. Another study [67] has investigated two classification tasks and found that when LSTM is considered as the method for creating entity-level embeddings, the quality of the language model used for embedding tokens plays an important role, wherein older methods such as Glove, Flair, and Elmo [68] are outperformed by the more recent BERT and RoBERTa techniques.
## 2 Materials and Methods
### Results
In the disordered situation, it is clear that performance improved significantly from the complex condition to the simple condition on accuracy, \(\Delta\text{AIC}=-3.1\), LLR \(\chi^{2}\) (1) = 5.137, \(p<0.05\); and response time, \(\Delta\text{AIC}=-1233\), LLR \(\chi^{2}\) (1) = 1236.93, \(p<0.001\), such that simple and disordered bi-symbols were more easily processed and recognized than complex and disordered bi-symbols, which is consistent with prior studies' results. The result in ordered situations was the opposite,  
Figure 5: (**a**) Mean accuracy performance of subjects over six rounds; error bars indicate \(\pm 1\) standard errors. (**b**) Mean reaction time of subjects over six rounds; error bars indicate \(\pm 1\) standard errors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Effects** & **AAIC** & **LLR \(\chi^{2}\) (1)** & \(p\) **Value** \\ \hline \multirow{6}{*}{Reaction time} & round & \(-225.2\) & \(265.19\) & \(<\)0.001 \\  & order condition & \(-106.9\) & \(108.89\) & \(<\)0.001 \\ \cline{1-1}  & complex & \(-1463.4\) & \(1465.34\) & \(<\)0.001 \\ \cline{1-1}  & condition & \(-1580.8\) & \(1584.76\) & \(<\)0.001 \\ \cline{1-1}  & four types & \(-248.1\) & \(250.11\) & \(<\)0.001 \\ \cline{1-1}  & complexity & \(-248.1\) & \(250.11\) & \(<\)0.001 \\ \cline{1-1}  & order & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of linear mixed-effects regression analysis on reaction times. subjects performed basically the same between complex and simple bi-symbols on recall accuracy, \(\Delta\)AIC = 2, LLR \(\chi^{2}\) (1) = 0.0073, \(p\) = 0.932; and reaction time, \(\Delta\)AIC = \(-\)778.4, LLR \(\chi^{2}\) (1) = 780.4, \(p\) = 0.44. These results are consistent with our hypothesis that the effects of complexity on user's perception were mitigated when the stimuli were highly ordered.
## 1 Introduction
The overall aim of this study was to investigate the utility of MODIS-derived spectral indices for inferring photosynthetic activity and detecting seasonal transitions in a southern boreal coniferous forest. The specific objectives were:  
1. To derive phenological parameters from a 13-year NDVI and PRI time series based on MODIS MAIAC-corrected data and validate the observations with estimates from in-situ ecosystem gas exchange measurements. 2. To compare the performance of MAIAC data in predicting phenology against the standard MODIS NDVI product. 3. To assess the relative utility of NDVI versus PRI as optical indicators of seasonal variations in vegetation photosynthetic activity.
## 8 Conclusions  
The article provides a comparative analysis of the optimized designs of the traction ferrite-assisted synchronous reluctance machine (FaSynRM) and the traction ferrite-assisted synchronous homopolar machine (FaSHM) in a subway train drive with a power rating of 370 kW. The machines have an approximately equal outer diameter of the stator lamination. When optimizing the machines, the train movement cycle is considered. To reduce the computational burden during optimization, the computationally efficient Nelder-Mead method is used. Moreover, each time the optimization function is called, the characteristics of one of these machines are calculated only at 6 operating points, from which, using quadrature formulas, the integral characteristics of the machines can be calculated throughout the whole moving cycle.  
The goals of machine optimization are to reduce losses in the train moving cycle, the maximum current of the semiconductor inverter, and the torque ripple. In addition, the goal of optimization is to prevent irreversible demagnetization of the ferrite magnets in the rotor.  
For the FaSHM, compared with the FaSynRM, the losses are reduced by 3.4%, and the maximum current of the traction inverter is reduced by 1.54 times. For the FaSynRM, compared to the FaSHM, the active core volume is higher by 3.9%. However, for the FaSynRM, the mass of active materials is reduced by 7.4%, and the cost of active materials is reduced by 29% due to the smaller mass of magnets. Also, the FaSynRM has less risk of irreversible demagnetization of the ferrite magnets in the rotor.  
In general, it can be concluded that the machines in question have comparable performance. However, the FaSHM has better field weakening capability, which reduces the maximum current, power, and cost of inverter power modules, while the FaSynRM requires fewer permanent magnets at the same torque density and is more resistant to irreversible demagnetization, which reduces its cost and improves reliability.  
Future work will involve conducting a comprehensive comparison of the FaSHM with other types of electrical machines utilized in subway drives and other applications.  
Conceptual approach, V.D. and V.P.; data curation, V.D. and V.K.; software, V.D. and V.P.; calculations and modeling, V.D., V.K. and V.P.; writing--original draft, V.D., V.K. and V.P.; visualization, V.D. and V.K.; review and editing, V.D., V.K. and V.P. All authors have read and agreed to the published version of the manuscript.  
The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors thank the editors and reviewers for careful reading and constructive comments.  
The authors declare no conflict of interest.
## 5 Conclusions  
This study established an S-LCMV method that reduces the number of constraints to below the DoFs in a ULMA system. The correlation between the PEC signal power and Legendre polynomial stages revealed sparsity of the signal with respect to the Gauss-Legendre stage. The S-LCMV provides an adequate number of DoFs under the imposed constraints, making it possible to accurately obtain valid weights, thus mitigating the influence of TRDs at different array elements. In addition, the number and locations of the casing-thickness changes were evaluated using the CFAR indicator, enabling rapid and intuitive nondestructive evaluations for downhole casings. Simulation and field experiments confirmed the superiority of S-LCMV over LCMV, which not only reduced the computational cost of the weights, but also improved the computational speed and accuracy in spite of the deviation of the borehole axis from the true axis. In future work, we will design an improved CFAR detector that accurately detects both the number and location of targets in borehole applications.  
Conceptualization, L.Y.; Methodology, B.D.; Software, L.Y.; Validation, J.D.; Formal analysis, Y.Z.; Investigation, J.D.; Resources, B.D.; Data curation, Y.Z.; Writing--original draft preparation, J.D.; Writing--review and editing, J.D.; Visualization, L.Y.; Supervision, Y.Z.; Project administration, B.D.; Funding acquisition, B.D. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by the National Natural Science Foundation of China [grant numbers 51974250, 61901371]; the Youth Science and Technology Nova Project in Shaanxi Province, China [grant number 2020K]XX-018]; the Natural Science Basic Research Program of Shaanxi [grant number 2024]C-YBMS-522]; the Youth Scientific Research and Innovation Team Construction Plan Project of Xi'an Shiyou University [grant number 202 QNKYCXTD02]; and China Postdoctoral Science Foundation [grant number 2020M683541].  
Not applicable.  
All the details of this work including the data and algorithm codes are available by contacting the corresponding author: [EMAIL_ADDRESS].  
The authors would like to thank the reviewers for their helpful suggestions, which have considerably improved the quality of the manuscript.  
The authors declare no conflicts of interest.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.7 Security  
With the development of MSA and distributed architecture, security has started to be a more important topic because each microservice exposes a new entry point to both the internal and external sides. This situation brings with it a lot of security problems as long as it is not addressed.  
The first problem is the access control mechanism in MSA. With the access control, the resource that is intended to be accessed is either restricted or not allowed. However, applying access control in the MSA is difficult to apply in a monolithic architecture because of its distributed nature.  
Furthermore, building a comprehensive framework to establish security between microservice is tough work to achieve because it is very difficult to integrate and use unusable and non-lightweight frameworks comfortably.  
Another important issue is the monitoring of the network traffic and running some security rules defined according to requirements.
## 8 Results Analysis  
### Model Prediction Accuracy Analysis  
In this model, 75 sets of finite element simulation data were selected using random sampling, with 55 sets used as the training set and 20 sets as the testing set. The wall thickness uniformity corresponding to different parameter combinations was calculated and compared with the simulated wall thickness uniformity, as shown in Figure 19. From the figure, it can be seen that the RBF neural network's prediction for both the training set and the testing set fits well with the actual values.  
To verify the accuracy of the model's predictions, the coefficient of determination (\(R^{2}\)) was calculated for both the training set and the testing set, yielding values of 0.899 and 0.871, respectively. This indicates that the RBF neural network model has a high level of accuracy in predicting and analyzing wall thickness uniformity. Additionally, the mean squared error (_MSE_), root mean squared error (_RMSE_), and mean absolute error (_MAE_) were calculated, as shown in Table 10.  
Figure 18: The flow chart of the PSO algorithm for parameter combination optimization.  
Based on the above analysis, it can be concluded that the RBF neural network model provides a highly accurate prediction of the relationship between outer die taper angle, drawing speed, friction coefficient, and wall thickness uniformity.
## 2 Background  
### Principal of Using CMOS Sensor in OCC  
An image sensor is classified by two main technologies: one is CCD supporting global shutter, and the other is CMOS supporting rolling shutter, as we can learn from [20; 21; 22]. The construction process of an image using the global shutter effect is shown in Figure 1. All the pixels on the sensor are exposed simultaneously, and the readout data are accessed at the end of exposure. With an RSE-based camera, the captured image is generated row by row of pixels, which means that the pixels exposure and data readout are performed sequentially by each row in a CMOS sensor. All scanlines captured at different exposure times are stitched into an image at last, as shown in Figure 2. Utilizing an RSE-based camera, changing states of light source will be captured in one image. However, if the transmitter, comprised of RGB-LED in our work, is modulated at a higher-speed, ISI will be introduced probably. The main incentive of ISI is that there are overlaps between the exposure time of each row, and two states of light source will be captured in them, which will result in an invalidation of pixel-row per symbol in a large proportion of rows. Additionally, there is a processing gap time between the constructions of two adjacent frames when no row can be activated during this period. In [23; 24], a special packet design and packet combination are proposed to solve the problem in which the signal emitted during the processing gap-time of the CMOS sensor cannot be captured. However, finding a more effective scheme to mitigate intense ISI in higher-speed signal transmission is still an impediment for OCC technology development.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 2 Materials and Methods
### Research Methodology
#### 2.3.2 Calculation of the Cumulative Effect  
The cumulative effect of drought on vegetation was also treated by first removing outliers that might interfere with the results, and then calculating the correlation coefficients through the time series of the IDRISI software [30; 74]. To calculate the cumulative effect, the accumulation of the SPEI for 1 to 12 months was required; this is different from the lagged effect calculated using only SPEI01. Therefore, the Pearson coefficients of the correlation between growing season GPP values and month-by-month SPEI values on a 1-12 month time scale from 2000 to 2018 were used as candidate indicators of the cumulative effect of drought on vegetation in this study (Equation (3)). The largest correlation coefficients (i.e., Rmax_cum) and the corresponding month scales were considered the cumulative effect of drought on vegetation (Equation (4)). For example, if the correlation coefficient between monthly GPP and the SPEI for the previous 4 months is the largest, then a cumulative time scale of 4 months indicates that the current vegetation GPP is most affected by the previous cumulative 4 months of drought.  
\[\mathrm{r_{m}=corr(GPP,mSPEI)\ 1\leq m\leq 12,} \tag{3}\]  
\[\mathrm{Rmax\_cum=max(r_{m})\ 1\leq m\leq 12,} \tag{4}\]  
where \(\mathrm{r_{m}}\) is the coefficient of the correlation between GPP and the SPEI with a cumulative effect of m months; m is the cumulative total of m months, ranging from 1 to 12; GPP is the monthly GPP for the growing seasons in 2000-2018; mSPEI is the cumulative SPEI for 1-12 months; and Rmax_cum is the maximum value of \(\mathrm{r_{m}}\).  
The steps for calculating the lagged and cumulative effects of drought on vegetation GPP are shown in Figure 2.
## 3 Methodology and Data Sources
### Model Formulation
#### 3.5.2 Evaluating the Contribution of SOB-Caused Inflow PM\({}_{2.5}\) Concentration
According to Equation (9), the contribution of the PM\({}_{2.5\text{-inflow}}\) concentration (\(C_{n\_{r}\text{-}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{} \text{}\text  
with  
\[\alpha_{\tau 1}=\frac{1}{\frac{v_{d}\tau_{1}}{H_{rc}(\tau_{1})}+1}-\frac{1}{\tau_{ 1}2^{\left[\frac{v_{d}}{H_{rc}(\tau_{1})}+\frac{1}{\tau_{1}}\right]^{2}}}=\frac{ H_{rc}(\tau_{1})}{v_{d}\tau_{1}+H_{rc}(\tau_{1})}-\frac{H_{rc}(\tau_{1})^{2}}{ \left[v_{d}\tau_{1}+H_{rc}(\tau_{1})\right]^{2}} \tag{11}\]  
where \(H_{rc}(\tau_{1})\) is the urban PBLH during \(\tau_{1}\), that is, during the first hour since the air mass flowed into the city. Furthermore, the increase in the urban hourly average PM\({}_{2.5}\) concentration during \(\tau_{1}\) caused by PM\({}_{2.5\text{-inflow}}\)\(\overline{C_{rc}(\tau_{1})}_{C_{n_{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\texttext  
The fluctuation in hourly PM\({}_{2.5}\) concentration during PEs was significant, as shown in Figure 6. PE I and PE III were characterized by three increase processes, and the duration time of these two PEs was longer, at 35 and 40 h, respectively. For PE II and PE IV, the duration time was shorter (both 11 h); PE II had a bimodal distribution, whereas PE IV had only one peak value. In addition, the peaks of PE I, PE II, and PE III were seriously polluted levels, among which PE II had the highest peak value of 598 \(\upmu\)g m\({}^{-3}\), and the highest pollution level during PE IV is classified as heavily polluted. From the meteorological conditions during the PEs, the peaks of PM\({}_{2.5}\) concentration generally occurred when the urban PBLH level was low. However, not all the peak values of the PEs occurred under static or low wind speed conditions; sometimes, the high PM\({}_{2.5}\) level occurred when urban wind speeds were high. The difference in relative humidity during PEs is obvious, with higher humidity levels and even precipitation during PE I, but lower average relative humidity levels for PE II, PE III, and PE IV. \begin{table}
\begin{tabular}{c c c c c} \hline
**PE** & **Start** & **End** & **Duration Time** & **PM\({}_{2.5}\) Peak Value** & **Highest Pollution Level** \\ \hline PE I & 20:00 on November 16 & 7:00 on November 18 & 35 h & 270 \(\upmu\)g m\({}^{-3}\) & Seriously polluted \\ PE II & 20:00 on 7 April & 7:00 on 8 April & 11 h & 598 \(\upmu\)g m\({}^{-3}\) & Seriously polluted \\ PE III & 19:00 on 8 April & 11:00 on 10 April & 40 h & 251 \(\upmu\)g m\({}^{-3}\) & Seriously polluted \\ PE IV & 18:00 on 20 April & 4:00 on 21 April & 11 h & 223 \(\upmu\)g m\({}^{-3}\) & Heavily polluted \\ \hline \end{tabular}
\end{table} 
Table 1: PM\({}_{2.5}\) pollution episodes in Changchun during the SOB season of 2020–2021. Figure 5: Calendar plot of hourly PM\({}_{2.5}\) concentrations of Changchun during the SOB season of 2020–2021.