esa-sceva/satcom-chunk-collection huggingface dataset:
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 4 Discussion
The precision of our solutions based on SNR show a slight difference with the results in other papers. This may result from the following three points. (1) The GPS elevation of the data used in this paper ranges from 5\({}^{\circ}\) to 12\({}^{\circ}\) and 5\({}^{\circ}\) to 15\({}^{\circ}\), and five-order polynomial fitting method was used to obtain detrended SNR. In contrast, [PERSON] et al. (2016), adopted the measurements from very low elevation angles down to 0.5\({}^{\circ}\) and a three-order polynomial fitting method. (2) In order to improve the accuracy, [PERSON] et al. used two methods for quality control: a normalized spectrogram peak of 1 dB-Hz and a peak-to-noise ratio >3 in LSP; \"nonphysical\" reflector height (RH) peaks were discarded (i.e., RH <3 or >11 m) [PERSON] et al. (2016). (3) In previous research ([PERSON] et al., 2016; 2016), sea level retrievals were corrected with the time derivatives of RH or the height vertical velocity, H'. However, we did not correct the reflector height vertical velocity in this work, which might have caused worse RMS values in comparison with other works in the literature. In general, the constraints of elevation angles, quality of the detrended SNR, amplitudes of spectrogram peaks, and ranges of RH peaks would affect the accuracy of the solutions ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). Still, our purpose of this paper is to verify the possibility of sea level retrievals using the L4 method, and our initial results have strongly proved this feasibility.
## 3 Residue Number System and Multiple Error Correction
### Redundant Residue Number System  
The residual representation of an error-free integer is unique for the range \([0,\ M_{K}-1]\). This range is used for detecting and correcting errors in the RNS. Expanding the code space of the residual representation \(r\) with additional residual bits, we form an \((n,\ k)\)-redundant residue number system (RRNS) with correcting properties.  
An RRNS with \(n=\ k+r\) is based on coprime moduli \((m_{1},m_{2},\ldots,m_{k},m_{k+1},\ldots,m_{n})\) and the full range \(M_{N}=\prod\limits_{i=1}^{n}m_{i}\). The additional moduli, \(m_{k+1},m_{k+2},\ldots,m_{k+r}\), are called the redundant moduli of the RRNS. The range \([0,\ M_{K}-1]\) corresponding to the first \(k\) information moduli from the \(n\)-dimensional vector is called the legitimate range. The range \([M_{K},\ M_{N}-1]\) corresponding to the additional \(r\) redundant (control) moduli is called the illegitimate range. The legitimate range is the required computational range of the number system, while the illegitimate range is necessary for error detection, localization, and correction.  
When an error occurs, an integer \(X=(x_{1},x_{2},\ldots,x_{k},x_{k+1},\ldots,x_{n})\) within the legitimate range is converted to \(X^{\prime}=X+E\), where \(EE\) is the error. The value \(X^{\prime}\) falls into the illegitimate range if  
1. \(m_{k+1},m_{k+2},\ldots,m_{k+r}>m_{i}\), \(\forall i\leq k\),
2. the number of erroneous remainders does not exceed \(r\).  
Thus, to detect an error, we should restore the number \(XX\) from its RRNS representation \((x_{1},x_{2},\ldots,x_{k},x_{k+1},\ldots,x_{n})\). If the resulting value is smaller than the dynamic range of the RRNS (\(X<M_{K}M\)), the number \(X\) contains no errors of multiplicity \(r\) and below; otherwise, the RRNS representation \((x_{1},x_{2},\ldots,x_{k},x_{k+1},\ldots,x_{n})\) of \(XX\) contains 1 or 2 or 3,..., or \(r\) erroneous residual digits.  
The number of erroneous residual digits detected and corrected is determined by the number of redundant moduli added. In the general case without special restrictions, the \((n,\ k)\)-RRNS with \(r=\ n-k\) redundant moduli detects \(r\) and corrects \(t=r/2\) erroneous residual digits [7].
## 4 Implementation of the Proposed Algorithm
### Procedure for Embedding Watermark  
The watermark embedding diagram of this proposed algorithm is showed in Figure 2. The detailed embedding steps are described as follows:  
1. Convert the image watermark into the binary bit stream \(W\ =\ \{w(q),1\leq q\leq L\}\) with the length of \(L\).
2. The carrier audio \(A\) is divided into \(M\) audio fragments \(A_{l}\) (\(1\leq l\leq M\)) with the length of \(N\) after low-pass filtering. \(M\) is the number of the audio fragments, \(M\geq L\).
3. When \(l\) changes from 1 to \(M\), perform the \(r\)-level DWT on each fragment \(A_{l}\) to obtain the wavelet coefficients, and select \(De(r)\) as the embedding frequency-band.
4. Divide \(De(r)\) into the former packet \(De_{1}(r,j)\) and the latter packet \(De_{2}(r,j)\) with the length of \(N/2^{r+1}\) according to Formulas (3) and (4).
5. Perform DCT on \(De_{1}(r,j)\) and \(De_{2}(r,j)\) to obtain \(C_{1}(r,j)\) and \(C_{2}(r,j)\) respectively.
6. Connect \(C_{1}(r,j)\) and \(C_{2}(r,j)\) to form an array \(C(r,n)\) with the length of \(N/2^{r}\).
7. Calculate the average amplitude of \(|C(r,n)|\), \(|C_{1}(r,j)|\) and \(|C_{2}(r,j)|\) according to Formulas (5)-(7).  
Figure 1: Performance evaluation under different embedding depth: (a) evaluation about SNR; (b) evaluation about BER.  
* If \(w(q)=1\), embed one-bit watermark into \(De(r)\) according to Formulas (8) and (9). If \(w(q)=0\), embed one-bit watermark into \(De(r)\) according to Formulas (10) and (11).
* Perform the inverse DCT on \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\) to obtain \(De_{1}^{\prime}(r,j)\) and \(De_{2}^{\prime}(r,j)\).
* Recombe \(De_{1}^{\prime}(r,j)\) and \(De_{2}^{\prime}(r,j)\) into \(De^{\prime}(r,n)\) and perform the inverse DWT to reconstruct the watermarked audio fragment \(A_{1}^{\prime}\).
* Repeat Step 3 to Step 10 until all watermarks are embedded.
* Recombe \(A_{1}^{\prime}(1\leq l\leq M)\) as the watermarked audio signal \(A^{\prime}\).  
Figure 2: The watermark embedding diagram of this proposed algorithm. DWT: discrete wavelet transform; DCT: discrete cosine transform; IDWT: inverse discrete wavelet transforms.
## 3 Experimental Analysis
### Tunnel Cross-Section Point Cloud Data Denoising  
The mobile measurement method generates a detailed point cloud representing the tunnel surface, including various ancillary structures. Effective processing is critical for implementing a robust non-lining removal methodology. The density point cloud outlier removal technique, SOR, was applied to eliminate outliers from the cross-sectional point cloud dataset. Figures 5 and 6 illustrate the single cross-section at different epochs in the same region within the study area, showcasing the results of several processing stages designed to effectively remove non-lining points. These steps enhanced the accuracy and reliability of the cross-section lining point cloud, facilitating advanced analyses such as spatially aware denoising and precise deformation monitoring.  
A new denoising process was introduced, leveraging the density characteristics of point cloud data to enhance the efficiency of tunnel cross-section lining extraction. This step was critical for creating a clean tunnel cross-section dataset, enabling subsequent analysis such as shield ring superposition for ring alignment using the ICP algorithm and deformation analysis.  
Figure 5: MLS point cloud (\(P_{0}\)). (**a**) Raw tunnel cross-section point cloud. (**b**) Cross-section lining point cloud after denoising.  
The tunnel cross-section lining comprises multiple shield rings connected by circumferential joints (seams). Each shield ring was constructed from precast segments joined by radial seams. This study utilized MLS point clouds of the tunnel cross-section lining to isolate and analyze individual shield rings. An extraction algorithm was applied to effectively segment and save the shield rings, numbering them sequentially from 1 to 102 for detailed analysis.
## 2 Materials and Methods  
### Plant Material and Essential Oil Extraction  
The plant material (leaves) of the _R. officinalis_ cultivated species were collected over a 12-month period (February 2022 to January 2023) from Southwest Romania flora (Carcea Village, Doli County, Oltenia region). The study site is situated at an elevation of 181.05 m above the mean sea level and is positioned between the following geographical coordinates: 44.26\({}^{\circ}\) N latitude, 23.90\({}^{\circ}\) E longitude. The plant remained in the flowering stage during the entire harvesting period. All vegetal samples for analysis were collected in the middle of each month from the above-mentioned time interval and were deposited in the Herbarium of the Department of Pharmaceutical Botany, Faculty of Pharmacy, University of Medicine and Pharmacy of Craiova under certificate number ROS-OFF-0222-0123. The study did not involve endangered or protected species. All the leaves from the upper third of the rosemy stems were collected and separated from their young branches manually. The samples were cleaned and naturally air-dried in shaded, cool areas, and then ground to an average size of 0.1 cm for extraction. In a NeoClevenger-type apparatus, 100 g of dried and ground leaves were hydrodistilled for four hours with 1000 mL of distilled water, in a round-bottom flask. The mixture was heated, and the released RoEO was collected in a graded column after passing through a condenser. After hydrodistillation, the extracted RoEOs samples were collected in glass vials, dehydrated on anhydrous sodium, and stored in dark, tightly sealed bottles at 4\({}^{\circ}\)C for further analysis. RoEO amounts were directly measured from the extraction burette. The yield was calculated as the volume (mL) of RoEO per 100 g dried weight (d.w.) of the leaves. EO extraction was carried out as a single batch in the middle of each month over a 12-month period, as mentioned above. A single batch ensured consistency across samples by avoiding potential variability between multiple extractions.  
The RoEO standard sample, referred to as Ro_Tunise, was purchased from Sigma-Aldrich Chemie GmbH (Taufikirchen, Germany). According to the supplier, this EO originates from Tunisia, a region recognized for its significant production of aromatic and medicinal plants, particularly rosemary. The use of this sample provides a standardized and reliable reference for our analysis. It is important to note that the supplier does not provide detailed information regarding the specific period of the year when the raw plant material was collected. Consequently, potential seasonal variations in the chemical composition of the EO cannot be assessed in this study. This limitation is inherent to the use of commercially sourced EO.
## 6 Conclusions  
For the purposes of realizing the real-time monitoring of the foundation displacement of long-span bridges, the GNSS satellite observation stations are established on the foundation platform to obtain the data of foundation displacement. And then the multipath error and random noise of satellite signals with GNSS technology are denoised by SWT and EMD methods. In view of the deviation between the denoised GNSS signals and the precise leveling data, the combination of BP neural network-based algorithm and the construction process data is developed in this paper to establish the non-linear mapping relationship between the GNSS data and the precise leveling data. The case study shows  
Figure 16: Foundation settlements of different points by the proposed method.  
that the proposed method in this paper can meet the requirements of real-time monitoring of foundation displacement of long-span bridges, and the conclusions are shown as follows:  
1. It can achieve desirable effect for using the stationary wavelet denoising to eliminate the multipath error in GNSS signals;
2. Autocorrelation function-based EMD denoising algorithm can effectively process GNSS signals with random noise;
3. Based on measured data in construction process, the nonlinear mapping between the precise leveling data and GNSS signals can be established by the BP neural network;
4. The position and reliability of GNSS measurements can be improved by using the proposed method in this study. The GNSS signal processing method presented in this paper makes the submillimeter monitoring of foundation settlement of long-span bridges come true.  
Conceptualization, Y.Z.; methodology, Y.Z.; formal analysis, M.S.; investigation, D.S.; data curation, D.S.; writing--original draft preparation, Y.Z. and M.S.; writing--review and editing, Y.Z. and M.S.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support received from the National Key Research and Development Plan (No. 2022 YFB2602900), National Railway Group Science and Technology Research and Development Plan (No. SY2021G002), China Academy of Railway Sciences Group Major Project (No. 2021 YJ084) and China Railway Shanghai Bureau Group Co., Ltd. Scientific Research and Development Project (2021142).  
Not applicable.  
Data availability Statement: Data are available upon reasonable request at the Corresponding Authors.  
The authors declare no conflict of interest.
# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 3 Taxonomy of Prompt Attacks  
### Prompt Categories and Their Security Implications  
Prompt engineering plays a crucial role in shaping the behavior and security risks of Large Language Models (LLMs). Different types of prompts influence how LLMs process and generate responses, making them susceptible to various adversarial attacks. The three primary types of prompts are direct prompts, role-based prompts, and in-context prompts. Each of these prompt structures has distinct functionalities and security implications.  
Direct Prompts: These are explicit and structured inputs that directly instruct an LLM to retrieve information or perform a specific task. Direct prompts are commonly used in user queries, automation scripts, and chatbot interactions. While this method enhances adaptability across different contexts, it is also highly vulnerable to adversarial manipulations. Attackers can craft direct prompts designed to bypass security filters, extract sensitive data, or induce harmful responses. For example, an adversary might frame a prompt in a way that exploits the model's knowledge base, leading it to reveal confidential information or generate biased content. This type of attack is particularly relevant to threats targeting Confidentiality in the CIA triad [7].  
Role-Based Prompts: These prompts involve assigning an LLM a specific persona or task-related function in order to guide its responses. Role-based prompts are widely used in AI-powered assistance, customer service applications, and domain-specific language models where contextual expertise is required. While role-based prompting improves task performance and response consistency, it can also be exploited for malicious purposes, as attackers can manipulate assigned roles to coerce the model into performing unintended actions. For instance, an adversary may craft deceptive role-based promptsthat instruct an LLM to act as a malicious advisor to provide security workarounds, generate phishing emails, or spread misinformation. This method is particularly concerning in cases where attackers override system-level constraints, impacting the {Integrity} of model-generated outputs.  
In-Context Prompts: These prompts provide additional examples or contextual information that steer the behavior of an LLM. In-context learning allows a model to adjust its responses based on preceding inputs, which is an effective approach for fine-tuned tasks without requiring retraining. However, this adaptability also introduces critical security vulnerabilities. Adversarial actors can inject misleading examples into the prompt context, influencing the model's decision-making process and generating deceptive or harmful outputs. This type of manipulation can be used to distort facts, fabricate narratives, or generate misleading recommendations, leading to integrity violations. Additionally, excessive in-context input can overload the model, increasing its computational costs and leading to performance degradation that impacts Availability in the CIA triad [22].
## 4 Results
### Effective Stress  
Another significant factor influencing the damping ratio is effective stress. In Figure 7, the minimal damping ratio \(D_{MIN}\), designated at the linear elastic range of soil response, dependant on effective stress (\(p^{\prime}\)) is presented. Conducted research confirmed the linear relationship between these two parameters. The minimal damping ratio decreases with an increase in effective stress. Except for the _K2_ sample, every tested soil obtained the coefficient of determination for linear function \(R^{2}>0.8\). The small value of \(R^{2}\) for the _K2_ sample may be due to the low variability of the \(D_{MIN}\) with the increase of \(p^{\prime}\).  
Figure 6: Examples of damping ratio curves dependant on shear strain for three tested soils: _K2_, _K6_, and _K13_.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline _Test_ & \(p^{\prime}\) & _PI_ & _\(\gamma_{t}^{c}\) (\%)_ & \(\gamma_{t}^{c}\) & _Test_ & \(p^{\prime}\) & _PI (\%)_ & \(\gamma_{t}^{c}\) (\%)_ & \(\gamma_{t}^{c}\) (\%) \\ _No._ & **(kPa)** & **(\%)** & **(\%)** & **No.** & **(kPa)** & **(kPa)** & **PI (\%)** & \(\gamma_{t}^{c}\) (\%)** & **(\%)** \\ \hline _K1_ & 50–200 & 10.60 & 0.0012 & n.e.* & _K9_ & 45–315 & 25.55 & 0.0018 & 0.014 \\ _K2_ & 30–390 & 16.79 & 0.0009 & 0.015–0.023 & _K10_ & 90–315 & 27.44 & 0.0008 & 0.0041–0.011 \\ _K3_ & 35–210 & 8.96 & 0.0008 & 0.007–0.014 & _K11_ & 45–315 & 9.10 & 0.0012 & 0.011–0.016 \\ _K4_ & 60–240 & 6.60 & 0.0005 & 0.0045 & _K12_ & 145–290 & 27.62 & 0.0019 & 0.022 \\ _K5_ & 90 & 22.4 & 0.0012 & 0.03 & _K13_ & 160–320 & 36.68 & 0.0024 & 0.04 \\ _K6_ & 75–415 & 12.03 & 0.0010 & 0.012–0.02 & _K14_ & 95–285 & 37.84 & 0.0011 & 0.007–0.02 \\ _K7_ & 120–410 & 20.11 & 0.0019 & 0.021–0.034 & _K15_ & 55–165 & 14.78 & 0.0013 & 0.013–0.022 \\ _K8_ & 85–310 & 25.11 & 0.0016 & 0.04 & & & & \\ \hline \end{tabular} Notes: \(p^{\prime}\)—mean effective stress, _PI_—plasticity index, \(\gamma_{t}^{c}\)—the nonlinearity threshold strain, and \(\gamma_{t}^{c}\)—degradation threshold strain. \({}^{\star}\) Not examined.  
\end{table}
Table 3: Results of threshold strains \(\gamma_{t}^{c}\) and \(\gamma_{t}^{c}\) investigation of tested soils.  
Effective stress also influences the shape of a damping ratio curve. In Figure 8, the damping ratio curve obtained at different effective stress levels for sample _K8_ is shown. A red arrow indicates an increase in effective stress (\(p\)'). It can be seen that, with an increase of \(p\)', the damping ratio curves move down. Moreover, the zone of nonlinear elastic response of soil also increases. Therefore, the degradation threshold strain (\(\gamma_{t}^{\prime}\)) moves to the larger shear deformation. This phenomenon is presented in Table 3 in the \(\gamma_{t}^{\prime}\) columns.
## 3 Data and Method
### Data
#### 3.1.1 Data Retrieval  
The stock data of the S&P 500 tech stocks primarily originate from multiple publicly accessible financial data websites and application programming interfaces [23], such as Yahoo Finance and Google Finance. Owing to the comprehensive and continuously updated stock trading data these platforms provide, they were chosen as the primary sources for data extraction. Utilizing Python web scraping libraries such as'requests' and 'BeautifulSoup', the required stock data can be periodically retrieved from these websites.  
Initially, hypertext transfer protocol requests are dispatched to the target websites using the'requests' library, obtaining the page content. Subsequently, the retrieved hyperText markup language content is parsed using the 'BeautifulSoup' library to extract relevant stock trading data, including opening price, closing price, highest price, lowest price, and trading volume. Following this method, stock data for the S&P 500 tech stocks spanning nearly 30 years were consistently gathered.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 5 Conclusions  
In this paper, we investigated the effective capacity of downlink satellite networks achieved with NOMA and OMA schemes. In particular, we derived each user's theoretical capacity expressions including in special cases and closed-form situations, each user's capacity difference and the considered system's capacity difference between NOMA and OMA, which enable us to gain further insights into the advantages and disadvantages of NOMA and OMA schemes in satellite communication, and their respective superiority scope. Simulation results have been provided to validate those performance analyses and show that the NOMA scheme outperforms the OMA scheme for User \(c\), who has worse channel quality, only when transmit SNR is small; while for the considered system performance and User \(t\), who has a better link gain, the superiority of the NOMA can be found when transmit SNR is large. Moreover, the influences of key parameters, such as transmit SNR \(\rho\), power allocation factor \(\alpha\), and delay-QoS exponents of users on user selection strategy and system performance are also shown in the simulations.  
Conceptualization, Q.Z. and K.A.; methodology, X.Y. and K.A.; validation, X.Y. and K.A.; investigation, Q.Z. and B.D.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y., K.A., Q.Z. and B.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Guangxi Natural Science Foundation (No. 2020 GXNSFBA15905I), the China Postdoctoral Science Foundation (No. 2020M681457), the Scientific Research Foundation of Beibu Gulf University (No. 2019 KYQD40).  
Not applicable.  
The authors declare that they have no known competing financial interest of personal relationships that could have appeared to influence the work reported in this paper.