esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Mobile Money Access  
In Uganda, mobile money applications have a standardised interface across the seven MMSPs. Varieties of acceptance technologies are available to mobile money merchants to access services from the servers, which include the following:  
* Unstructured Supplementary Services Data (USSD): This is a communications protocol used by mobile communication technology in mobile networks to send texts between mobile phones and an application program without internet access [21]. It is session-based and interactive, but much faster since it involves simple operations that are handset-independent [39]. All seven MMSPs in Uganda, namely, MTN Uganda, Airtel, UTL, Africell, M-Cash, Ezeey Money, and Micropay, work by initiating a session between the mobile phone and the server through dialling a USSD code, for example, by dialling \"165# in MTN to display the mobile money menu, followed by a  
Figure 1: Mobile money system (MMS) architecture.  
series of steps to accomplish a transaction [2]. There are two modes of USSD operations, namely, the mobile-initiated operation and the network-initiated operation [39].
* SIM Toolkit (STK): This is the portion of the GSM standard that enables the SIM to initiate activities to further exploit SMS by providing value-added services such as mobile banking [41]. GSMA [42] added that the STK is a set of applications that run on the SIM card and interact with the mobile device. Mobile operators use STK to present information about their services to subscribers [41]. The mobile money transaction is achieved when the mobile money platform breaks down the transaction into a series of logical steps by using STK and then reassembles the different steps into a complex statement, which is transmitted to the server via SMS [2].
* SMS: This is a technology used by mobile subscribers and mobile telephones to exchange alphanumeric messages [43]. All the mobile money platforms in Uganda use SMS to send confirmation or notification messages for the success or failure of a transaction to the user [2; 39].  
Mobile money platforms in Uganda have a simple interface made of a basic menu that is accessed using the USSD protocol. Mobile money users can send electronic money from their accounts to other users by interacting with the USSD interface and telecommunication company [2]. The telecommunication company sends a confirmation message to both the sender and the recipient about the money. The mobile money confirmation SMS contains details about the transaction identifier (ID), date and time of the transaction, amount sent, the recipient's name and phone number, the fee charged for sending the money, the reason for sending the money, and the available balance in the sender's account.
## 5 Conclusions  
This study introduced the atmospheric CO\({}_{2}\) fertilization effect and canopy-to-leaf CO\({}_{2}\) concentration gradients into the CASA model to improve the estimation of GPP. The validation results indicated that the improved GPP estimation model performed well and the error between estimated and observed GPP values was reduced by 18.69% compared to the traditional model. Then, the long-term time series Qinghai-Tibet Plateau GPP dataset was generated based on the improved GPP estimation model, which was capable of identifying ecological security issues on the Qinghai-Tibet Plateau with more specificity. It is a fact that the model showed clearly that there is a significant augmented photosynthetic capacity of vegetation with rising temperature and atmospheric CO\({}_{2}\) concentration, testified by an overall increase in GPP. Furthermore, this analysis identified NDVI, temperature, and atmospheric CO\({}_{2}\) concentration as the three primary factors exerting substantial influence on GPP. In conclusion, the improved GPP estimation model provided researchers with insights into the CO\({}_{2}\) fertilization on vegetation growth, and the long time series, spatio-temporally continuous, high-precision GPP dataset of the Qinghai-Tibet Plateau produced using the proposed model in this study provided data support for the analysis of carbon source and sink.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16111856/s1](https://www.mdpi.com/article/10.3390/rs16111856/s1)]([https://www.mdpi.com/article/10.3390/rs16111856/s1](https://www.mdpi.com/article/10.3390/rs16111856/s1)), Table S1: Information of the validation sites used in this study.  
Conceptualization, K.J.; methodology, J.L.; software, J.L.; validation, J.L., L.Z. and G.T.; formal analysis, J.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, K.J., W.Z., Y.L., Y.Y. and X.Z.; visualization, J.L., L.Z. and G.T.; supervision, K.J.; project administration, K.J.; funding acquisition, K.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Second Tibetan Plateau Scientific Expedition and Research Program (grant number 2019 QZKK0405), the National Natural Science Foundation of China (grant number 42192581 and 42171318), and Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products (grant number OF202308).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Optimal Tracking and Disturbance-Rejection
### The Second-Order Filter  
The closed-loop transfer function \(G_{\text{CL}}\) from the reference to the process output is the following:  
\[G_{\text{CL}}(s)=\frac{G_{F}\Big{(}K_{I}+K_{P}s+K_{D}s^{2}\Big{)}G_{P}}{s+(K_{ I}+K_{P}s+K_{D}s^{2})G_{P}}. \tag{28}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(b\) & \(K_{P}\) & \(K_{I}\) & \(K_{D}\) \\ \hline
0 & 0.93011 & 0.23327 & 0.85427 \\
0.2 & 0.93383 & 0.2486 & 0.85466 \\
0.5 & 0.85191 & 0.19158 & 0.84114 \\
0.7 & 0.75396 & 0.11619 & 0.8251 \\
0.9 & 0.64303 & 0.03635 & 0.80683 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The PID\({}_{\text{b}}\) controller parameters.  
Figure 6: The reference filter with the PID controller and the process.  
The purpose of the reference filter \(G_{F}\) is to make the reference following transfer Function (28) as closely as possible to the desired function with the PD controller (\(b=1\)):  
\[G_{CL1}(s)=\frac{(K_{P1}+K_{D1}s)G_{P}}{s+(K_{P1}+K_{D1}s)G_{P}}. \tag{29}\]  
where \(K_{P1}\) and \(K_{D1}\) represent the controller Parameters (25) of the PD controller. The structure of reference filter \(G_{F}\) is chosen as follows:  
\[G_{F}(s)=\frac{1+b_{F1}s+b_{F2}s^{2}}{\left(1+\frac{K_{D}}{K_{I}}s+\frac{K_{D }}{K_{I}}s^{2}\right)}. \tag{30}\]  
The denominator of the Filter (30) is chosen to cancel the controller zeros in the closed-loop transfer Function (28), similar to the approach proposed by [PERSON] and [PERSON] [106]. The numerator of the filter (parameters \(b_{F1}\) and \(b_{F2}\)) is then calculated by equating the first two characteristic areas (\(A_{0}\) and \(A_{1}\)) of the closed-loop transfer Function (28) (using Expression (8) with \(G_{CL}\) instead of \(G_{P1}\) in the Expression (7)) with the equivalent areas of the desired closed-loop transfer function \(G_{CL1}\) when using the PD Controller (29). The following filter parameters are thus obtained:  
\[b_{F1}=\frac{K_{P1}}{K_{I}}-\frac{1}{A_{0}K_{P1}}, \tag{31}\] \[b_{F2}=\frac{\left(A_{0}{}^{2}K_{D}K_{P1}{}^{2}+A_{0}K_{P1}{}^{2 }-A_{0}K_{P}K_{P1}+A_{0}K_{KD1}-A_{1}K_{I}K_{P1}+K_{I}\right)}{A_{0}{}^{2}K_{ K}{P_{1}}^{2}}.\]
## 1 Introduction
TVPDI [28] is used to monitor wet and dry conditions in China. In addition to these drought indices, [PERSON] and [PERSON] et al. [29] used GRACE gravity and PCR-GLOWB model data to quantify the deficiencies of terrestrial water stocks (LWS) and groundwater stocks (GWS) in Peninsula India, providing new insights into the groundwater monitoring framework. Natural habitats vary greatly throughout an area. Tibet, which is situated at a high altitude in the QTP, is significant as a \"river source\". The principal rivers of China originate there. The environment of Tibet is very vulnerable because of its unique geological, geographical and meteorological factors [30]. As a result, natural calamities, including droughts, storms and frosts, frequently affect Tibet. Therefore, further studies are needed on the features of recent droughts in Tibet and how they relate to climatic variables. Many research studies have contributed to drought resistance on the QTP. [PERSON] et al. [31] established a PDSI reconstruction model from 1440 to 2007 in the southeastern QTP. They found that temperature changes in this region may be related to SST changes in the North Pacific and Atlantic oceans. [PERSON] et al. assessed the candidacy of various probability functions for SPI and SPEI for more than 500 stations in China, including the QTP. [PERSON] et al. analyzed the elevation dependence in SPEI index by using gauged-based time-series data of precipitation and temperature at 274 meteorological stations scattered over the QTP [32; 33]. These meteorological drought indexes based on atmospheric precipitation and evapotranspiration can monitor droughts at a specific time better, but in some regions, soil moisture is not completely controlled by precipitation and evapotranspiration [34]. TVDI contains factors that increase or decrease moisture in the atmosphere, but lacks information about moisture in the soil. Therefore, it is unknown whether TVDI can keep track of the agricultural drought in southeastern Tibet. On the other hand, southeastern Tibet has a small and irregularly distributed number of weather stations. There may be uncertainties caused by large spatial variability, due of the restricted ground observations, considerable regional variability and demographics connected to drought [35]. In this paper, soil moisture was added to TVDI, named Temperature-Veggetation-Soil Moisture-Precipitation-Dryness Index (TVMPDI), based on multi-dimensional Euclidean distance, considering the influence of atmospheric, soil and vegetation moisture during agricultural drought. The objectives of the study are to evaluate the performance of TVMPDI in drought monitoring, and then analyze the spatial and temporal variation characteristics of TVMPDI in southeast Tibet. We also compare the monitoring effects of TVMPDI and TVPDI on meteorological drought and agricultural drought, respectively, and analyze the factors controlling agricultural drought in different regions.
## 3 Results and Discussion
### Univariate Analysis  
The univariate calibration curves achieved using the emission areas of the elements C, Mn, and P featured a poor linearity with too low (R \(<\) 0.90) correlation coefficients, which affected the accuracy and the precision of the prediction and the robustness of the model. Differently, the univariate calibration and cross-validation curves constructed using the areas of the emission lines of Si I at 288.158 nm and Ti II at 334.903 nm and 334.940 nm, which were those that presented the best results in the calibration, versus the reference XRF concentrations were more satisfactory (Figure 5). In the case of Ti, the two emission lines considered were overlapping, and a unique area was calculated which included both lines. An R coefficient of 0.95 for Si and 0.86 for Ti and an RMSE of 237.05 for Si and 126.10 for Ti were achieved for calibration with a MAPE of 18.04% for Si and 19.40% for Ti for validation. Although these values could be considered satisfactory, they would have limited application in real cases, i.e., univariate calibration curves could be used only for a rapid estimation of some elements in the samples but not for an accurate estimation of unknown samples.  
Figure 4: Typical LIBS spectrum acquired with the _NanoLIBS_\(h\)LIBS instrument for a pig iron sample.  
Furthermore, building univariate calibration curves by plotting LIBS emission line areas vs. percentage of weight concentration might introduce matrix effect errors because the same numerical concentration in different matrices might correspond to a different weight concentration on dependence on the stoichiometry of the sample. Thus, the univariate model yielded an unsatisfactory outcome, therefore it was discarded, and the PLS multivariate calibration approach was attempted to improve the analysis of LIBS spectra in both broad spectral range and selected emission lines to possibly allow the technique to be used in industrial application. In multivariate analysis, the choice of weight or number concentration for the reference samples does not make a difference, because all emission lines are analysed simultaneously.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Image Recognition Accuracy Evaluation
This result indicates that the BoW model has some generalization ability for images under different movement modes, which is beneficial for general image recognition tasks. However, for line-scan carriage images, this generalization is undesirable because the goal is to distinguish images based on different movement modes. The fundamental reason for this phenomenon is that the BoW model represents images as unordered collections of feature words, losing spatial information among features. The analysis of the experimental results in Table 2 further verifies that the BoW model is unsuitable for recognizing line-scan carriage images. Table 3 presents the accuracy comparison between our method and multi-class neural network methods. The six multi-class network models used for comparison are representative of current image classification methods, exhibiting high recognition accuracy on multiple datasets as detailed in Section 2. Compared with the results in Table 2, the multi-class neural networks significantly outperform the BoW method in overall recognition accuracy. The recognition accuracy distribution is consistent with that of the BoW method, with high accuracy for categories with consistent images (e.g., BDL1, C70, NX70A, and C64K). However, for inconsistent image categories, the recognition accuracy still falls short of practical application standards. Comparing the accuracy performance on the two datasets in Table 3 also shows that the multi-class network models perform significantly better on the 19-category dataset than on the 69-category dataset and outperform the BoW results in Table 1. This indicates that the generalization ability of multi-class network models far exceeds that of the BoW  
\begin{table} \begin{tabular}{c} **Resmel** \\ **[**13**]** \\ \end{tabular}   
model, primarily due to the enhanced feature representation capabilities of neural networks trained on the carriage dataset. However, the significant accuracy improvement also reveals the limitations of multi-class network models in the 76-category line-scan carriage image recognition task. Despite extensive training, they cannot accurately recognize carriage movement modes for inconsistent image categories. However, accurate identification of reference images for fault analysis is crucial in train fault detection. Combining the accuracy statistics from Tables 2 and 3, our method achieves 100% Top-1 Accuracy on both datasets, significantly outperforming other methods. Neither the BoW model nor the deep learning-based multi-class network models effectively recognize line-scan carriage images under different movement modes. In contrast, our method can accurately recognize carriage types and further distinguish carriage movement modes, ensuring the accurate acquisition of reference images for fault analysis.
## 3 Methods
### Fixed Parameters Method  
Table 5 delineates this set of fixed BRDF model parameters for the green, red, and near-infrared (NIR) bands of the Landsat satellites. Given that the Jilin-1 GP01/02 satellites share identical band settings with both Landsat and Sentinel-2 satellites, it was feasible to apply this same set of fixed parameters for related calculations.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 2 Background and Motivation
### Non-Stationary Data  
Non-stationary data [5; 6] refers to time series data in which the statistical properties, such as the mean, variance, and covariance, change over time. In the context of time series forecasting, non-stationary data pose significant challenges because models trained on stationary assumptions may fail to capture the evolving nature of the underlying patterns, especially when real-world events cause sudden bursts or shifts in trends.  
In cloud demand forecasting, the changing trends in cloud resource demand are highly dynamic, and data characteristics evolve over time, making direct prediction challenging for model convergence. After analyzing trend variations in the data, the capacity variation of resource pools can be divided into two phases: the resource delivery phase and the resource maintenance phase. The resource delivery phase is characterized by a rapid increase in resource utilization, and its usage curve accurately reflects the user demand for resources, making it the main focus of capacity forecasting. In contrast, during the  
Figure 2: Few-shot problem.  
resource maintenance phase, administrators intervene to control resource allocation and release, causing resource usage during this phase to deviate from actual demand. As a result, capacity forecasting must focus on predicting demand within the resource pools that are in the resource delivery phase. This imposes higher requirements on the model's ability to extract time series features and distinguish between phases.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
###### Abstract  
The point-to-point quantum key distribution (QKD) system is limited by the transmission distance. So, the wide area QKD network with multiple endpoints is the research focus of this study. The relay-node scenario and key relay protocols provide the solutions to the QKD network. The early key relay protocols require the relay nodes to be reliable. Once the relay nodes become compromised, the whole network is insecure. In this paper, we extend the chain structure of the public-XOR(exclusive OR)-key scheme with two endpoints to the complex network with multiple endpoints. The relay nodes in our scheme do not need encryption actions, decryption actions, or storage XOR keys, which simplifies the system compared with other key distribution schemes based on trusted relay nodes. Our scheme not only improves the practical performance and simplifies the system's complexity, but it also ensures that the security is not reduced. Specifically, we rigorously demonstrate that an eavesdropper can never access the key shared by the users of the network as long as the process of generating XOR keys and destroying the original keys is secure. In addition, we discuss the information leakage of the practical QKD network from the perspective of the unicity distance.  
Keywords:quantum key distribution; key distribution network; relay node; unicity distance +
Footnote †: journal: Accepted: 6 March 2019; Published: 14 March 2019
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 2 Related Work  
Several researchers have studied the grid resilience corresponding to severe weather events. The linear regression model-based prediction approach was presented in [16, 17]. They tried to forecast the number of damages caused by strong wind or storm events. Various statistical methods, such as regression tree and multivariate regression, were used to predict power outage durations during a hurricane [18]. [PERSON] et al. [19, 20] developed a linear model-based power outage prediction method using hurricane variables and environmental variables at a given location. [PERSON] et al [25, 26] presented a power-outage estimation model using a similar approach with [PERSON] et al [19, 20] but with additional exploratory variables. [PERSON] et al. [27] introduced a generalized hurricane outage prediction model by considering the additional explanatory variables, such as land cover, soil moisture, and hurricane indicators. Recently, the power grid component state prediction method during hurricane events was presented in [28, 29]. Logistic regression and support vector machines were used to model the power component state during the hurricane.  
Most previous studies focused on hurricane events and their developed models relied on several grid vulnerability variables and specific hurricane indicators. However, not all power companies have detailed data about the power outage caused by weather events and it can be expensive to gather extensive datasets. Recent studies tried to predict the grid component state during the hurricane event based on a few weather variables. However, these researches used artificially generated samples rather than real power outage data. Therefore, the previous studies have limited its scalability to other weather events and real applications.  
The objective of this study was to characterize the power outage caused by heavy rain and to predict grid states during a heavy-rain event based on real data. To be scalable and applicable in reality, the common weather variables were used to develop a prediction model. Therefore, it can be easily extended to other disruptive weather events.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### Further Optimizations
#### 3.6.2 Optimization II: Multiple Ranging Sequences  
The second optimization reduces the amount of sub-GHz synchronization beacons by introducing multiple ranging sequences with the same anchor nodes within a superframe. All slots of the superframe--except the sync slot--is repeated several times (Figure 5b). This is only possible if the selected set of anchors does not change during the superframe duration. Similarly, the long-range sub-GHz reporting messages can also be combined, reducing the number of report slots, but resulting in longer duration between range reports. The number of slots is reduced from \(k(3+2n)\) to \(1+n+k(2+n)\), where k is the number of sequences.