esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis
### Offline Password Guessing Attack  
When a participant's smart card is lost or stolen, an attacker can try to brute force the owner's password to log in to the system. However, in this study, the smart card does not required entering or storing any password, it only needs biometric characteristics through fuzzy extraction, and does not directly store any private keys. Therefore, the attacker cannot obtain the biometric characteristics of the smart card owner via a smart card and will not be able to apply the fuzzy extractor to pass the password verification at any phase of our proposed scheme. Therefore, our proposed scheme can resist offline password guessing attacks.
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 2 Preliminaries  
### Ambtc  
Absolute Moment Block Truncation Coding (AMBTC) [25] efficiently improves the computation time of Block Truncation Coding (BTC) and improves the image quality over BTC. The basic configuration of one block in AMBTC is two quantization levels and one bitmap, while one block is compressed by preserving the moment. Here, the two quantization values are obtained by calculating the higher mean and the lower mean of each block. For AMBTC compression, the grayscale image is first divided into (\(k\times k\)) blocks without overlapping, where \(k\) can determine the compression level by (\(4\times 4\)), (\(6\times 6\)), (\(8\times 8\)), etc. AMBTC adopts block-by-block operations. For each block, the average pixel value is calculated by:\[\bar{x}=\frac{1}{k\times k}\sum_{i=1}^{k^{2}}x_{i} \tag{1}\]  
where \(x_{i}\) represents the \(i\)th pixel value of this block with a size of \(k\times k\). All pixels in this block are quantized into a bitmap \(b_{i}\) (zero or one); that is, if the corresponding pixel \(x_{i}\) is greater than or equal to the average (\(\bar{x}\)), it is replaced with \"1\", otherwise it is replaced with \"0\". Pixels in each block are divided into two groups, \"1\" and \"0\". The symbols \(t\) and \(k^{2}-t\) refer to the numbers of pixels in the \"0\" and \"1\" groups, respectively. The means \(a\) and \(b\) of the two groups indicate the quantization levels of the groups \"0\" and \"1\". The two quantization levels are calculated by Equations (2) and (3).  
\[a=\left\lfloor\frac{1}{t}\sum_{x_{i}<\bar{x}}x_{i}\right\rfloor \tag{2}\]  
\[b=\left\lfloor\frac{1}{k^{2}-t}\sum_{x_{i}\geq\bar{x}}x_{i}\right\rfloor \tag{3}\]  
where \(a\) and \(b\) are also used to reconstruct AMBTC.  
\[b_{i}=\begin{cases}1,&\text{if }x_{i}\geq\bar{x},\\ 0,&\text{if }x_{i}<\bar{x}.\end{cases} \tag{4}\]  
\[g_{i}=\begin{cases}a,&\text{if }b_{i}=0,\\ b,&\text{if }b_{i}=1.\end{cases} \tag{5}\]  
The bitmap is obtained from Equation (4), and the compressed block is simply uncompressed by using Equation (5); that is, the compressed code unit, \(trio(a,b,BM)\), may be obtained by using Equations (2)-(5). The image block is compressed into two quantization levels \(a\), \(b\), and a Bitmap (BM) and can be represented as a \(trio(a,b,BM)\). A BM contains the bit-planes that represent the pixels, and the values \(a\) and \(b\) are used to decode the AMBTC compressed image by using Equation (5).  
**Example 1**.: _Here, we describe the encoding and decoding procedure of one block of a grayscale image using an example. Figure 1a is a grayscale block, and the mean value of the pixels is 106. By applying Equations (2)-(4) on (a), we can obtain the bitmap as shown in (b) and two quantization levels (\(a=102;b=107\)). The basic unit of each block is \(trio(a,b,BM)\) = (102, 107, 010111111011001). Using the information of the \(trio\) and Equation (5), the decoded grayscale block in (c) is reconstructed._  
Figure 1: An example of AMBTC: (**a**) a natural block; (**b**) a bitmap block; (**c**) a reconstructed block. BM, Bitmap.
## 2 Background and Methods
### Examined Countries  
The questionnaire was completed in three countries: Hungary, Romania and Austria (Figure 1). The three countries have different historical pasts. Thus, roughly simplified, Austria can be considered a \"western\", Hungary a \"central\", and Romania an \"eastern\" type of country. This difference is reflected in the GDP per capita and even in the length and quality of roads. These characteristics make these three countries suitable for comparison and let us make a recommendation for a one-size-fits-all tool for everyone.  
Figure 1: The three countries examined: Austria, Hungary and Romania.
## 2 Materials and Methods
### Hard Clustering for Covariance Matrices  
One of the most commonly used hard clustering algorithms is the K-means algorithm, which aims to minimize the within-cluster sum of squares (WCSS). For a set of covariance matrices \(A=\{\mathbf{A}_{1},\ldots,\mathbf{A}_{n}\}\), the WCSS is given by [19]:  
\[\text{WCSS}=\sum_{j=1}^{n}\sum_{i=1}^{c}D(\mathbf{A}_{j},\bar{\mathbf{A}}_{i})^{ 2}. \tag{1}\]  
The K-means algorithm for clustering covariance matrices is shown in Algorithm 1.  
```
Input: set of \(n\) covariance matrices \(\mathbf{A}\), number of clusters \(c\in\mathbb{N}\), and an initially empty vector \(\mathbf{y}\) to store cluster labels. Output: the final cluster centres \(\bar{\mathbf{A}}_{i}\), and cluster labels \(\mathbf{y}_{j}\)
1 Randomly initialize the centres of the clusters \(\bar{\mathbf{A}}_{i}^{(0)}\). Let \(r=0\). while\(\mathbf{y}_{j}^{(r)}\
eq\mathbf{y}_{j}^{(r-1)}\)do
2 Set \(r=r+1\).
3 Calculate \(D(\mathbf{A}_{j},\bar{\mathbf{A}}_{i})\), for \(i\in\{1,\ldots,c\}\), and \(j\in\{1,\ldots,n\}\) (using Table 1).
4 Assign each \(\mathbf{A}_{j}\) to its nearest centre; store cluster label of \(\mathbf{A}_{j}\) in \(\mathbf{y}_{j}\).
5 Update the centres \(\bar{\mathbf{A}}_{i}^{(r)}\) (using Table 2). Return\(\bar{\mathbf{A}}_{i}\) and \(\mathbf{y}_{j}\) values.
```  
**Algorithm 1**K-means Algorithm
# Optimizing the Sodium Hydroxide Conversion Using Regression Analysis in CSTR
## 1 Introduction
The batch reactor performance was studied using regression analysis for saponification of ethyl acetate [25] and maximal conversion of 0.995 realized under optimum conditions reactant concentration and agitation rate. An ethyl acetate saponification reaction in a tubular reactor was studied using factorial design [26], and the reaction order was determined as nearly equal to 2. In the current work, the multiple regression was applied for examining the CH\({}_{3}\)COOC\({}_{2}\)H\({}_{5}\) hydrolysis using continuous stirred tank reactor (CSTR). The investigation was unambiguously dedicated to the process of upgrading reactants conversion to ethanol and sodium acetate. The novelty of the current work is that it optimizes the formation of sodium acetate and ethanol, applying step-wise regression applying polynomial models. The methodology comprises of two steps: experiments designed to remove the insignificant factors and RSM. The overall performance of CSTR was analyzed in terms of conversion with respect to sodium hydroxide (X\({}_{\text{a}}\)). All the potential factors were supposed to have an influence on system performance. The authors chose to study the key and interaction influence of temperature X\({}_{1}\) (\({}^{\circ}\)C), stirrer speed X\({}_{2}\) (rpm), feed rate X\({}_{3}\) (mL/min), and rector volume X\({}_{4}\) (L). The experimental outcomes [27] were chosen as a base to perform and carry out the optimization analysis. After the trial for screening of the first phase, feed rate and stirrer speed were established as important predicting variables and optimal outputs of these factors were calculated through a 2 nd order polynomial.
###### Abstract  
The effect of hybrid carbon fillers of multi-walled carbon nanotubes (CNT) and carbon black (CB) on the electrical and morphological properties of polystyrene (PS) nanocomposites were systematically investigated in microinjection molding (\(\upmu\)IM). The polymer nanocomposites with three different filler concentrations (i.e., 3, 5 and 10 wt %) at various weight ratios of CNT/CB (100/0, 30/70, 50/50, 70/30, 0/100) were prepared by melt blending, then followed by \(\upmu\)IM under a defined set of processing conditions. A rectangular mold insert which has three consecutive zones with decreasing thickness along the flow direction was adopted to study abrupt changes in mold geometry on the properties of resultant microparts. The distribution of carbon fillers within microparts was observed by scanning electron microscopy, which was correlated with electrical conductivity measurements. Results indicated that there is a flow-induced orientation of incorporated carbon fillers and this orientation increased with increasing shearing effect along the flow direction. High structure CB is found to be more effective than CNT in terms of enhancing the electrical conductivity, which was attributed to the good dispersion of CB in PS and their ability to form conductive networks via self-assembly. Morphology observations indicated that there is a shear-induced depletion of CB particles in the shear layer, which is due to the marked difference of shear rates between the shear and core layers of the molded microparts. Moreover, an annealing treatment is beneficial to enhance the electrical conductivity of CNT-containing microparts.  
microinjection molding; hybrid fillers; multi-walled carbon nanotubes; carbon black; conductive polymer composites; microstructure +
Footnote †: journal:
## 3 Results and Discussion
### Diet Reconstruction
We considered five possible source contributions to the diet after adjusting their mean isotopic values by one trophic level, namely C3 plants (\(\xi^{13}\)C = \(-23.9\)%, \(\xi^{15}\)N = 5%), terrestrial animals (\(\xi^{13}\)C = \(-19\)%, \(\xi^{15}\)N = 8%), freshwater fish (\(\xi^{13}\)C = \(-18\)%, \(\xi^{15}\)N = 12%), marine fish (\(\xi^{13}\)C = \(-11\)%, \(\xi^{15}\)N = 13%) and C4 plants (\(\xi^{13}\)C = \(-8.9\)%, \(\xi^{15}\)N = 8.2%). In the first column, we present the results of the samples, excluding the outliers (T6 and T24 KT), while in columns two and three we present the results for the outliers T6 and T24 KT, respectively. We observed that both package results are in good agreement on the T6 outlier's diet source contribution, reinforcing the previous assumption that this individual had no C4 contribution. For the T24 KT outlier, IsoSource did not succeed in evaluating the percentages and the results from FRUITS show a greater contribution of plants, both C3 and C4, in the diet, than of animals. For the rest of our samples, the two packages do not agree on the contribution percentages of terrestrial animals, freshwater and C4 plants. IsoSource suggested a diet based mostly on freshwater fish, with an insignificant contribution of C4, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Samples Excluding**} & \multicolumn{2}{c}{**T6 Outlier**} & \multicolumn{2}{c}{**T24 KT Outlier**} \\  & \multicolumn{2}{c}{**Outliers**} & & & & \\ \hline \multirow{2}{*}{**Source**} & **IsoSource** & \multirow{2}{*}{**Fruits \%**} & **IsoSource** & \multirow{2}{*}{**Fruits \%**} & **IsoSource** & \multirow{2}{*}{**Fruits \%**} \\  & **\%** & & & **\%** & & **\%** \\ \hline C3 & 19.6 & 20.8 & 59.4 & 59.4 & - & 28.6 \\ \hline Terrestrial Animals & 15.1 & 24.6 & 4.3 & 3.6 & - & 27.8 \\ \hline Freshwater & 43.2 & 22.8 & 30.8 & 32 & - & 3.7 \\ \hline Marine & 18.6 & 17.9 & 4.9 & 3.7 & - & 4 \\ \hline C4 & 3.6 & 13.7 & 0.7 & 0.9 & - & 35 \\ \hline \multicolumn{6}{c}{_Errors_} \\ \hline \hline \multicolumn{6}{c}{**Samples Excluding the**} & \multicolumn{2}{c}{**T6 Outlier**} & \multicolumn{2}{c}{**T24 KT Outlier**} \\  & **NoSource** & \multirow{2}{*}{**Fruits \%**} & **IsoSource** & \multirow{2}{*}{**Fruits \%**} & **IsoSource** & \multirow{2}{*}{**Fruits \%**} \\  & **\%** & & & **\%** & & **\%** \\ \hline C3 & 9 & 12 & 3 & 2 & - & 13 \\ \hline Terrestrial Animals & 10 & 17 & 3 & 3 & - & 19 \\ \hline Freshwater & 14 & 16 & 5 & 3 & - & 2 \\ \hline Marine & 13 & 12 & 4 & 0.1 & - & 2 \\ \hline C4 & 2 & 9 & 0.8 & 0.7 & - & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IsoSource and Fruit results. but returned almost the same probability for marine and terrestrial animal consumption. On the other hand, FRUITS suggests a more balanced diet combining all the sources.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 4 Experiments  
### Experimental Settings  
We selected publicly available brain DWI image data from the literature [28]. By reducing the dimensionality of the DWI image data, we obtained approximately 80,000 2 Dslice images. All slice images were cropped to \(96\times 96\), 64,000 slices were chosen as the training set, and 16,000 slices were chosen as the test set. For the experimental settings, we used the Adam optimizer with a learning rate of 0.0001 and betas of (0.5, 0.99). The number of epochs was set to 100 with a batch size of 32. The watermark was a binary image of size \(24\times 24\). The reversible watermarking method utilizes difference expansion, and the image hashing is computed using the MD5 algorithm. The model parameters and sizes of the robust watermarking network framework are presented in Table 1.  
The robustness of a watermarking algorithm refers to the accuracy of the watermark information extracted from the attacked protected image; the higher the accuracy, the better the robustness of the algorithm. We use the bit error rate (BER) to evaluate the robustness of the algorithm. The formula of BER is shown in the following,  
\[BER=\frac{B_{e}}{N_{h}\times N_{w}},BER\in[0,1] \tag{8}\]  
where \(B_{e}\) is the number of watermarked bits for error detection; while \(N_{h}\times N_{w}\) is the total number of bits of the binary watermarked image. To evaluate the difference in visual quality between the watermarked image \(I_{e}\) and the cover image \(I_{o}\). We used the peak signal-to-noise ratio (PSNR) as a measure. The higher the PSNR value, the higher the quality of the \(I_{e}\) image and the PSNR definition can be expressed as follows,  
\[PSNR=10\bullet\log_{10}(\frac{MAX^{2}}{MSE}) \tag{9}\]  
where \(MAX\) is the maximum greyscale order of the image; \(MSE\) is the mean squared difference between the \(I_{o}\) and \(I_{e}\) images.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
### Related Work  
Chirp modulation of acoustic signals under water have gathered attention since the early 2000s [39], and is researched continuously by groups around the world ever since. While the achievable data rate is severely limited compared to narrow-band schemes [5], CSK will be especially of interest, when the data amount is low and the channel inhibitions are strong. [PERSON] and [PERSON] [37] present a performance evaluation of CSK in additive white Gaussian noise (AWGN) environments and for more realistic underwater models. [PERSON] et al. propose similar methods for acoustic aerial communications (AAC). A low-cost underwater acoustic modem is proposed by [PERSON] et al. [17], which uses FSK instead of CSK. Symbol synchronization is a large issue underwater due to Multi-path and Doppler effects, [PERSON] et al. [40] therefore propose a self-synchronization method for CSK-type communication. [PERSON] and [PERSON] [41] subjoin methods of code division multiplexing to chirp communication to enhance stealthiness. The Fractional Fourier Transform (FrFT) is employed by [PERSON] et al. [42] to enable a multiuser communication system.  
[PERSON] et al. [20] iterate on the multiuser possibility by proposing several sets of orthogonal chirp waveforms. Interference cancellation is crucial for underwater communication, this topic has been addressed by [PERSON] [43]. In the more recent work of [PERSON] et al. [44], the authors explore the parameter space of chirp spread spectrum (CSS) methods in context of long range communication.  
The performance of our approach in this work has partially been reported in [36]. The scope of this contribution is to report on the details of our system's inner structure and underlying algorithms. This investigation zooms in on the modulation and demodulation part of typical basic elements of digital communication systems [45], hence, expects coded input and will return still coded output. Consequently, additional forward error correction coding is likely to improve the estimation of the overall system [46], but is not part of this investigation.
###### Abstract  
The 5-Sit-to-stand test (5 STS) is used for lower limb muscle power (MP) determination in field /clinical setting. From the time taken to perform five standing movements and three partially verified assumptions (vertical displacement, mean concentric time, and mean force), MP is estimated as the body's vertical velocity x force. By comparison with a gold standard, laboratory approach (motion capture system and force plate), we aimed to: (1) verify the assumptions; (2) assess the accuracy of the field-estimated MP (MP\({}_{\text{field}}\)); (3) develop and validate an optimized estimation (MP\({}_{\text{field-opt}}\)). In 63 older adults (67 \(\pm\) 6 years), we compared: (i) estimated and measured assumptions (2-WAY RM ANOVA), (ii) MP\({}_{\text{field}}\) and MP\({}_{\text{field-opt}}\) with the reference/laboratory method (MP\({}_{\text{lab}}\)) (2-WAY RM ANOVA, Pearson's correlation coefficient (r), Bland-Altman analysis). There was a significant difference between estimated and measured assumptions (\(p<0.001\)). Following the implementation of the optimized assumptions, MP\({}_{\text{field-opt}}\) (205.1 \(\pm\) 55.3 W) was not significantly different from M\({}_{\text{lab}}\) (199.5 \(\pm\) 57.9 W), with a high correlation (r = 0.86, \(p<0.001\)) and a non-significant bias (5.64 W, \(p=0.537\)). Provided that corrected assumptions are used, 5 STS field test is confirmed a valid time- and cost-effective field method for the monitoring of lower limbs MP, a valuable index of health status in aging.  
Keywords:sit-to-stand test; muscle power; aging +
Footnote †: journal: Applied
# Control-Based 4D Printing: Adaptive 4D-Printed Systems
## 2 Controllable 4D-Printed Systems
The sensitivity of the snap-through energy of these mechanisms could be controlled by specific features of 3D printing [27; 34; 58]. In addition to these printed tunable parameters, a bistable shape-memory strip was also developed exhibiting thermally sensitive volume changes to the surrounding. Various configurations of these constructs resulted in multi-trigger multi-state mechanisms [59]. A programmable, reversible and rapid buckling-induced 4D-printed active skin was developed, that could be controllably actuated in response to uniaxial tensile loading [60]. Variable stiffness is another recently developed technique for controlling the compliance of 4D-printed systems by heterogeneous SMPs. In other words, the adjustable stiffness of these structures introduces passive sensing that regulates the stiffness of the body according to environmental variations, e.g., humidity and temperature. A sweating actuator was 4D-printed by poly-N-isopropylacrylamide (PNIPAm) and covered with a microporous polyacrylamide (PAAm) dorsal layer. This system operates based on thermoregulation, that is, below the glass transition (T\({}_{\text{g}}\)) temperature the pores are sealed and the actuator is rigid, while above T\({}_{\text{g}}\), the dilation of the pores causes the sudden decrease in stiffness to soften the stiffness of the fluidic elastomeric actuator [61] (Figure 2c). Bioinspire microneedle arrays with backward barbs were 4D-printed to provide the more sustained use of minimally invasive microneedles for drug delivery and biosensing applications [62]. The 4D-printed system is privileged for the light sensitive barbs that adjust their stiffness based on the environmental changes, for pain-free and ease-of-use functions. The autonomous adjustment of materials' stiffness could be implemented through various techniques, including thermal stiffness control, acoustic-based control, jamming-based control either in pneumatic or fluidic medium, and electrically or magnetically viscosity-based control [63]. These techniques have shown a great compliance control in enabling the manipulation and grasping of fragile objects [64]. A finger-like soft actuator was 4D-printed using a dielectric elastomer (DE) with an FDM-based 3D printer. The system was modeled via the finite element model (FEM) and used as a soft robotics gripper [65]. Figure 2: _Cont._
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
## 2 Literature Review
### History and Current State of RAG  
The field of NLP has seen significant advancements with the emergence of LLMs like the GPT and LLama series, as well as models like Gemini [2; 10; 11]. Despite their successes, these models still need to cope with challenges such as outdated knowledge and a lack of domain-specific expertise [12; 13]. One major issue is the generation of incorrect information, known as \"hallucinations\", especially when faced with queries beyond their training data [3; 14]. These limitations highlight the need for additional safeguards before deploying LLMs in real-world settings [6].  
A solution to address these challenges is RAG, which integrates external data retrieval into the generative process. RAG, pioneered by [PERSON] et al. [5] and [PERSON] et al. [4], revolutionizes generative tasks within the LLM domain. In RAG, LLMs initially query external data sources to gather relevant information before generating text or answering questions. This retrieval step ensures that the subsequent generation phase is grounded in the retrieved evidence, which significantly improves the accuracy and relevance of the output [8; 15].  
By dynamically retrieving information from knowledge bases during inference, RAG effectively tackles issues such as factual inaccuracies or \"hallucinations\" [9]. The adoption of RAG in LLMs has rapidly gained momentum, and it has become a central approach to enhance the capabilities of chatbots and make LLMs more suitable for practical applications [8].