esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme  
In this section, we review Chuang and Lee's trust-extended authentication scheme (TEAM) [15]. In their scheme, the vehicles are classified into three types, namely, law executors (LEs), mistrusted vehicles (MVs) and trusted vehicles (TVs), as shown in Figure 1. An LE, such as a police vehicle, is treated as permanently trusted and plays a role similar to that of a mobile authentication server (AS). When a normal vehicle is authenticated successfully, it is deemed to be trusted, otherwise, it is treated as mistrusted. A TV will turn into an MV once the lifetime of its key has expired. To ensure the security of communication, an OBU can obtain service from providers only if it has been authenticated successfully.  
TEAM consists of eight procedures: registration, login, password change, general authentication, trusted-extended authentication, secure communication, key update and key revocation. Before each vehicle joins the network, its OBU performs the registration procedure to register itself with the AS. The login procedure is performed when a vehicle intends to access service from the vehicular ad hoc network. After successfully completing the login procedure, the OBU checks its authentication state. If the vehicle is an MV, it needs to perform either the general authentication procedure or the trust-extended authentication procedure; it will then turn into a TV once it has been authenticated successfully and has obtained an authenticated key. Then, it can play the role of an LE to authenticate other mistrusted OBUs via the trust-extended authentication procedure. Two trusted vehicles can perform the secure communication procedure to interact with each other. A trusted vehicle can choose to perform the key update procedure with an LE when its key is approaching expiration. Otherwise, the state of the TV changes to mistrusted when the lifetime of the key has expired.  
The OBU of each vehicle is equipped with secure hardware, including a tamper-proof device (TPD) and an event data recorder (EDR). The TPD hinders an attacker from obtaining information from the OBU. Recording important data, such as public parameters, preloaded secret keys, times, and locations, is the responsibility of the EDR. In addition, each vehicle is synchronized via a GPS device. Finally, each vehicle periodically broadcasts a hello message with its authentication state (mistrusted or trusted). The related notations are briefly defined in Table 1. The details of the TEAM protocol follow.
## 7 Conclusions  
In this paper, we analyzed micro-Doppler data collected by a custom-designed radar system utilized in different scenarios with a variety of targets, such as UAVs and birds. A dataset was created based on the micro-Doppler images obtained, and classification results were achieved with high confidence. Different characteristics of target detection were explored, including the landscape and the behavior of the target. Using a custom-built 10-GHz CW radar, over 700 images were collected and used for target distinction. For classification between UAV sizes, an accuracy of 90% was observed, whereas for the binary classification of two classes, namely birds and UAVs, an accuracy of 96% was obtained. More specific classification among the five classes showed an accuracy of 85%.  
Some limitations of our work include the fact that the bird data were obtained under non-natural conditions because the birds were caged and could not be observed in full flight. In addition, the data were limited to the observation angles that were available at the test site. Furthermore, drone data were acquired under limited conditions of flying towards and away from the radar, but not performing maneuvers. Future work should address some of the limitations stated above.  
Future work in this field would also include methods for automating a method for detecting targets with a low-powered SDR, then notifying and turning on the 10-GHz radar to collect the micro-Doppler signature. This would allow for more efficient automatic target recognition.  
Another avenue for future work would involve the distinction of UAV motion as the target rolls and pitches in different directions. Due to the cluttered motions in the varying velocities, it would be of interest to design an algorithm for separating these rotor velocities and characterizing the overall target motion. Detecting micro-Doppler signatures in urban or cluttered areas would also be beneficial.  
Using micro-Doppler images, classification efforts regarding UAV motions and target recognition can be confidently characterized.  
Conceptualization, R.M.N. and R.B.; methodology, B.T.; software, B.T.; validation, R.M.N., B.T. and R.B.; formal analysis, R.M.N., B.T. and R.B.; investigation, B.T.; resources, R.M.N. and R.B.; data curation, B.T.; writing--original draft preparation, R.M.N.; writing--review and editing, R.M.N., B.T. and R.B.; visualization, B.T. and R.B.; supervision, R.M.N.; project administration, R.M.N. and R.B.; funding acquisition, R.M.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by U.S. Office of Naval Research, grant number N00014-20-1-2064. We appreciate the assistance provided by the following individuals at Shaver's Creek: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
Figure 27: 5-class confusion matrix. In the class description labels, “B\({}_{-}\)”, “M\({}_{-}\)”, and “S\({}_{-}\)” refer to big, medium, and small, respectively.
## 1 Introduction
In all of the above studies, the accuracy of the extraction results depended on the resolution of the remote-sensing images [25]. Therefore, due to the popularization of high-resolution remote-sensing images, we selected these as the data source for abandoned farmland data extraction. [PERSON] et al. and [PERSON] et al. chose GF-1 and GF-6 as the data sources in their studies, and the overall accuracy was higher than for middle- and coarse-resolution-image-based results [26; 27]. However, due to the low temporal resolution of these images, it is often difficult to obtain continuous remote-sensing images that can cover the whole study area, making it difficult to construct a time series dataset. To address this problem, spatial-temporal resolution fusion techniques are widely used, which can be mainly categorized into methods based on pixel unmixing (MMT [28], STRUM [29], etc. ), methods based on weighting functions (STARFM [30], ESTARFM [31], etc. ), and algorithms combining the two methods (FSDAF [32], EFSDAF [33], etc.). Pixel unmixing often requires high-resolution land classification data, while enhanced algorithms based on STARFM usually require multiple pairs of high- and low-resolution image pairs, and when faced with the rainy season, it can be difficult to collect data. However, the STARFM algorithm only needs a pair of images of high and low spatial resolution obtained at similar times to predict high-spatial-resolution images at another time, which is less demanding than other methods and, thus, has been widely used to construct time series datasets for analysis. [PERSON] et al. used STARFM to fuse MODIS and Landsat images to improve the accuracy of the replanting index extracted using time series analysis [34]. [PERSON] et al. and [PERSON] et al. used a STARFM-fused dataset to estimate the biomass trend of alpine grassland and monitor the dynamics of forest destruction and regeneration [35; 36]. [PERSON] et al. fused GF-1 and MODIS data to differentiate between cultivated and abandoned farmland and to further differentiate crop types [37]. The above research applications show that the STARFM algorithm can reduce the difficulty of time series dataset construction when selecting high-resolution remote sensing images as a single source and can enhance the accuracy of the extraction results through time series analysis. To address the problems with the methodology mentioned above, this study took western Minichi County as the study area and supplemented the time stamp without GF-6 data covered by STARFM-fused data, which alleviated the problem of no data coverage in the growing and non-growing stages of some crops that would have resulted in reduced accuracy. Then, the NDVI time series analysis was combined with overlay analysis to extract abandoned farmland data from 2021 and 2022 in the study area, and a distinction was made between seasonal and permanent abandonment. Finally, the features of the abandoned farmland were analyzed according to the extraction results.
# A High-Sensitivity Magnetic Field Sensor Based on PDMS Flexible Resonator
## 3 Results and Discussion
### Position of PDMS Flexible Resonator Coupling  
We analyze the position of PDMS flexible resonator coupling, and due to the compression of the magnetic field on the resonator, there are differences in stress and strain at different coupling positions. We simulate and calculate the magnetic sensing sensitivity corresponding to different coupling positions. Figure 6a shows the magnetic field sensitivity at coupling positions of 0.3 h, 0.35 h, 0.55 h, 0.6 h, 0.65 h and 0.7 h, where h is the height of the resonator. It can be seen that the magnetic field sensitivity gradually increases with the upward shift of the coupling position. Figure 6b quantitatively describes the differences in sensitivity at different positions, and the sensitivity at the 0.7 h coupling position is 113% higher than that at the 0.3 h coupling position. The closer the coupling position is to the location where the force is applied, the greater the deformation, and the greater the magnetic field sensitivity as the coupling position increases. The reason for choosing 0.7 h as the upper limit of the coupling position is that in the actual measurement process, a too-high coupling position makes it difficult to obtain a good resonant spectrum.  
Figure 4: Preparation of PDMS flexible resonator.  
Figure 5: Schematic diagram of the experimental setup.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 4 Experiments and Results
### Results and Analysis
#### 4.2.3 Scenario 3: High Complexity  
The cubic expansion of the state space (_cell size_ = 3) resulted in the most pronounced performance differences between _XSQ-Learning_ and _Q-Learning_. _XSQ-Learning_ reached its first stable maximum reward at an average of 80.70 test points, requiring only 43,525 steps. In contrast, _Q-Learning_ not only failed to match this level of performance--achieving a significantly lower average of 52.85 test points--but also required an average of 71,750 steps to do so, representing 58.30% more steps. Furthermore, in 2 out of 20 trials, _Q-Learning_ did not reach a stable maximum reward at all. Among the remaining similarity-based variants, _QL-SmoothActions_ outperformed _Q-Learning_ in both the reward obtained and the number of steps required. However, compared to the proposed _XSQ-Learning_, _QL-SmoothActions_ achieved a lower average reward and required 32.32% more steps to converge. Meanwhile, _QL-SmoothObservations_ and _QS-Learning_ completely failed to achieve a stable maximum reward in this scenario, further underscoring the challenges of uncontrolled or misapplied similarity propagation in complex environments.  
Notably, the stability of _XSQ-Learning_ was especially evident in this setting: the absence of variance shadows in Figure 2c after convergence indicates highly consistent performance across all random seeds. This strong scalability suggests that _XSQ-Learning_'s adaptive similarity mechanisms become increasingly effective as the state space grows in size and complexity.
# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 3 Feature Shift Networks
### Transfer Methods between Different Distributions  
One of the most popular approaches is domain alignment, which aligns the feature distributions across the source domains in order to better apply predictive models trained on the source domain to the unknown target domain. The reasoning behind this approach is as follows: if a feature learned on the source domain is insensitive to distributional offsets between the source domains, then, for that feature, it is also relatively robust to distributional offsets on the unseen target domain. Currently, most of the methods related to domain generalization are developed based on the idea of domain alignment, and such methods are generally based on aligning the edge distribution \(P(X)\), aligning the class-conditional distribution \(P(X|Y)\), or aligning the posterior distribution \(P(Y|X)\).  
A general approach, in the scenarios discussed in this section, leads to information waste. Therefore, new domain generalization methods and processes are needed to retain and learn these regularities when we have seen the actual regularities between domains.  
**New Approach.** In the scenario discussed in this section, each source domain and target domain label correspond to an actual working condition of the bearing, whereas the numbers (0, 1, 2, etc.) represent its physical meaning. Thus, an increase in the numbering of domain labels numbered 0, 1, and 2 represents an increase in their corresponding bearing loads, and with a linear relationship between the numbers, it is natural to expect that the feature spaces of their corresponding domains also have some relationship with each other. Extending this to unseen domains, if the target domain numbers also have actual physical meaning accordingly, then the target domain feature space should also have some kind of relationship with certain source domain feature spaces. The fact that the domain numbering adds physical meaning and that the target domain domain numbering is known is the biggest difference between the scenarios in this chapter and the domain generalization problem. Therefore, the traditional method of domain generalization through domain feature space alignment does not make any use of this added information, resulting in its underutilization. In order to make full use of the regular information between domains, this chapter proposes a new feature shift network FSN, which is optimized for this special domain generalization scenario with additional information and improves the practical effect of domain generalization.
## 5 Implementations
### The ChaCha Accelerator Core  
The architecture of the accelerator core for ChaCha used in the SoC was also written in Verilog HDL and connected using the same principle. The connection diagram between ChaCha core and the SoC bus is shown in Figure 9. This implementation provides some configurable elements, such as the round number, the optional 128-bit key input, and the direct encryption output of a 512-bit message block (XORed with the 512-bit block of the keystream). The accelerator core can be accessed at the address of 0x0200 in the CSR peripheral bus. The offset addresses are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Address** & **Description** \\ \hline Configuration & 0x00 & Configuration of encryption or decryption \\ Key Input & 0x01–0x03 & 80-bit key input register \\ Data Input & 0x04–0x05 & 64-bit data input register \\ Cipher Output & 0x06–0x07 & 64-bit cipher output register \\ \hline \hline \end{tabular}
\end{table}
Table 2: Internal register address mapping for the PRESENT-80 core.  
Figure 8: The PRESENT-80 accelerator architecture with bus interface.  
Figure 9: The ChaCha accelerator architecture with bus interface.
## 3 Tokenizer Performance--Models, Dataset, and Performance Measure Insights
### State-of-the-Art Multilingual, Slavic or Serbian-Specific Models
As shown in [15], XLM-RoBERTa outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks and performs particularly well on low-resource languages, which is the reason it has been selected for this research. Model capabilities were achieved through the trade-offs between positive transfer and capacity dilution and the performance of high and low resource languages at scale. The XLM-RoBERTa vocabulary contains 250,002 tokens. The NER4 Legal_SRB [19] is a LLM-based solution for Named Entity Recognition (NER) in the case of legal documents written in Serbian language. It leverages pre-trained BERT, which had been carefully adapted to the specific task of identifying and classifying specific data points from textual content. The model was trained on a novel dataset including court rulings and achieved a mean F1 score of 0.96 on cross-validation tests using a manually labeled dataset. Additionally, the authors presented results on examples of intentionally modified text inputs to confirm the model's robustness. NER4 Legal_SRB contains a WordPiece vocabulary with a 32,000 token vocabulary size.
## 5 Conclusions  
This paper presents a modification on the modal-based ground motion selection (MGMS) procedure for more proper consideration of the modal response characteristics of the RC shear wall structures in the input ground motions (IGMs) selection procedure. The first two modes dominated the dynamic behaviour of most shear wall structures with a symmetric plan. Therefore, the proposed MGMS procedure for the wall structures considers the impact of frequency contents combinations in the time domain (FCCTD) of IGMs on the seismic demand of building structures with the interaction of the first two modes. The capability of the MGMS in selecting more proper IGMs for the NLRHA of RC shear wall structures was verified with a comprehensive case study, where three reinforced concrete shear wall structures with different heights are studied. The conditional mean spectra (CMSs) and ASCE 7-16 design spectra (DSs) were adopted to select eight overall sets of twenty seed IGMs. Mean seismic demands of the RC shear walls computed using the whole set of seed IGMs were employed as the reference demands for the comparison. Two spectrum-matching based IGM selection methods that select IGMs whose response spectra have the closest matching concerning the mean spectrum of the set were adopted to investigate the MGMS procedure's capability to improve the reliability and reasonability of the NLRHA. These two IGM selection procedures are uniformly weighted closest spectra matching procedure (UW-CM) and variably weighted closest spectra matching procedure (VW-CM). From the comparison of the results, the following conclusions can be drawn:  
1. Considering the modal response characteristics of RC shear wall structures, the MGMS procedure for shear wall structures presented led to a more reliable computation of seismic demands than the original version of the MGMS procedure. 2. Compared with the CM procedure with uniformly weight value for all the period points in computing difference between the response spectra of IGMs and mean spectra of the set (UW-CM), the CM procedure adopting variable weight value (VW-CM) procedure can ensure a more reliable and reasonable computation of seismic demands of RC shear wall structures. 3. Compared with the VW-CM procedure, the presented MGMS procedure could noticeably improve the reliability and reasonability of the computed seismic deformation, including the floor displacement and inter-story drift ratio, and achieve similar reliability and reasonability in calculating the seismically induced force. 4. Since the MGMS procedure just requires conducting NLRHA of equivalent single-degree-of-freedom systems, the computational consumption is minor. Taking advantage of high efficiency and great effectiveness in improving the reliability and reasonability of the NLRHA, the MGMS is an excellent supplement to the seismic design codes of practice's IGM selection procedure for the NLRHA of wall structures. **Conflicts of Interest:** The author declares no conflict of interest. **Nomenclature**  
The following acronyms and symbols are used in this paper.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
###### Abstract
The main motivation behind this paper is to provide a basis of PPP understanding for young researchers and developers, so they can understand the concepts behind these open-source PPP tools and even go further to develop their own implementations. While this article draws from the first author's PhD thesis on integrating PPP and inertial technologies [21], it is more to the point, emphasizing the granular implementation of standard PPP accompanied by hands-on examples. Additionally, it incorporates contemporary references of PPP applications, the latest developments in the field, and recent updates on PPP correction formats and data sources. The rest of this paper is organized as follows. Section 2 delves into the PPP error sources and their mitigation strategies. Subsequently, Section 3 presents an overview of various PPP models, laying emphasis on the dual-frequency (DF) variants. Section 4 shares empirical results derived from a standard DF-PPP application using both static and kinematic data. Concluding the paper, Section 5 encapsulates the challenges linked with PPP and offers potential solutions.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 1 Introduction
In [23], the received signal strengths were predicted for an environmental wireless sensor network by using several candidate machine learning algorithms, including Adaboost, RF, ANN, and KNN. Among these methods, RF showed the highest accuracy in the considered environment, achieving a significant reduction in the average prediction error compared to the empirical models. From the perspective of feature reduction, the authors used a variety of manifold learning methods to reduce the original feature dimension to two dimensions to establish a path loss model in [24]. The diverse application scenarios in the 5G era pose a great challenge to the channel models. A flexible modeling framework should be built to satisfy the requirements for the applications at new frequencies and in new propagation environments. As mentioned above, machine-learning-based methods are able to provide a tradeoff between accuracy and complexity of the path loss models. Nevertheless, machine learning is a data-hungry technique whose performance heavily depends on the amount and quality of the training data. Due to the high cost of conducting measurements, the path loss dataset is always far from the concept of \"big data\" which can be easily obtained on the Internet or Internet of Things (IoT). Especially when new scenarios or new frequencies are put into use, it is difficult to collect enough data for the path loss prediction in a short time. Therefore, data expansion solutions are also proposed in this paper to fill the research gap. The major contributions of this paper can be summarized as follows. 1. The basic principle and procedure of the path loss prediction based on machine learning are presented. Some crucial issues such as data collection, data preprocessing, algorithm selection, model hyperparameter settings, and performance evaluation, are discussed. 2. In order to obtain enough data for machine-learning-based models, two mechanisms are proposed to enlarge the training dataset by taking full advantage of the existing data and the classical models. Data transferring is considered in both the scenario dimension and the frequency dimension. 3. Different machine learning algorithms are employed to validate the proposed methods based on the measured data. Both outdoor and indoor scenarios are taken into account and measured data are used to verify the feasibility of the machine-learning-based predictors. The remainder of this paper is structured as follows. The procedure of machine-learning-based path loss prediction is presented in Section 2. Section 3 introduces some representative machine learning methods for regression task, including ANN, SVR, and decision tree. In Section 4, data expansion solutions are proposed and verified with the measured data in an outdoor urban scenario and an indoor aircraft cabin scenario. In Section 5, some issues for future path loss prediction based on machine learning methods are discussed. At last, conclusions are drawn in Section 6.