esa-sceva/satcom-chunk-collection huggingface dataset:
# Extracting the Secrets of OpenSSL with RAMBleed
## 4 Algorithm for Recovering RSA Private Keys
### [PERSON] et al.'s Algorithm  
The algorithms presented by [PERSON] et al. (2016), [PERSON] et al. (2017), and [PERSON] et al. (2012) can be utilized to recover the correct private key from a corrupted secret key. Prime numbers \(p\) and \(q\), which constitute the RSA private key under attack are read using RAMBleed, contain different proportions of bits that should be zeros but are ones, and vice versa. The manner in which this error occurs is similar to the error model assumed in the algorithm proposed by [PERSON] et al. This algorithm takes the erroneous \(p\) and \(q\) as input and reconstructs the most likely \(\hat{p}\) and \(\hat{q}\) in groups of \(t\) bits beginning from the least significant, using the following process. The likelihood measure is defined as the probability that an error occurs at a given rate of bit reversal in a candidate series that matches the input series. [PERSON] et al. calculated this likelihood by determining the bit-flip error rates from 0 to 1 and from 1 to 0 using \(\alpha\) and \(\beta\), respectively.  
**step 1.**: Make empty sequences \(\hat{p}\) and \(\hat{q}\);
**step 2.**: Extend \(\hat{p}\) by \(t\) bits and determine \(\hat{q}\) using a relational expression that must be satisfied by the public key, \(p\) and \(q\). Thus, \(2^{t}\) pairs of \((\hat{p},\hat{q})\) are candidates;
**step 3.**: Calculate the likelihood of each candidate;
**step 4.**: Retain \(L\) candidates in descending order of likelihood. Execute step 2 for the remaining \(L\) candidates.  
This operation is repeated until the required number of bits is obtained, and \(L\) candidate secret keys (\(\hat{p}\), \(\hat{q}\)) with the highest likelihood are selected. The larger the values of \(\alpha\) and \(\beta\), the larger the values of \(t\) and \(L\)are required, which increases the time required for restoration, and decreases the accuracy of restoration.
## 4 Discussion  
The predictions of the four models for the prediction results of DO, pH, and turbidity in the Huangyang Reservoir reflected the trend of change, and the accuracy of the prediction was higher at the beginning, while the accuracy of the models' predictions became worse after the prediction time step became larger. In the later prediction period, the differences in the predictions of water quality index concentrations among the four models become larger, and the ARIMA model predicts a poor trend, and the information obtained does not have much value. The Informer model obviously has a higher degree of overlap than the LSTM model in both short- and long-term prediction, so the Informer model is significantly better than the LSTM model. Compared to the other three model, in terms of high overlap between predicted values and ability to use actual data, the Linear models work better and are of great reference value.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Informer**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**ARIMA**} \\ \hline \multicolumn{2}{c}{**Metric**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**MAE**} \\ \hline \multirow{3}{*}{DO} & 96 & **0.05479** & **0.15481** & 0.06586 & 0.18727 & 0.07077 & 0.17953 & 0.25039 & 0.49658 \\ \cline{2-9}  & 336 & **0.10412** & **0.25121** & 0.11539 & 0.26084 & 0.20794 & 0.32633 & 0.54285 & 0.69872 \\ \hline \multirow{3}{*}{pH} & 96 & **4.49666** & **0.87898** & 4.63165 & 1.15597 & 5.84864 & 1.47656 & 6.85472 & 3.54125 \\ \cline{2-9}  & 336 & **5.67115** & **1.30048** & 5.76526 & 1.36833 & 5.77852 & 1.43615 & 7.85423 & 3.58954 \\ \hline \multirow{3}{*}{Turbidity} & 96 & **0.63564** & **0.37395** & 1.66478 & 0.58936 & 0.75222 & 0.41665 & 0.94851 & 0.56314 \\ \cline{2-9}  & 336 & **1.38009** & **0.56380** & 1.47716 & 0.58168 & 1.58312 & 0.58772 & 1.65789 & 0.78954 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Univariate long-sequence time-series forecasting results. The best results are highlighted in bold.  
Figure 6: Comparison of pH for each model at different time steps.  
Figure 7: Comparison of Turbidity for each model at different time steps.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
###### Abstract  
In this paper, we introduce a two-party quantum private comparison (QPC) protocol that employs single photons as quantum resources and utilizes rotational encryption to safeguard the privacy of the inputs. This protocol enables two parties to compare their private data without disclosing any information beyond the outcome of the comparison. The participants' private data are encoded as single photons, which are encrypted using a rotational encryption method. These encrypted single photons are then transmitted to a semi-honest third party (TP), who conducts single-particle measurements to determine if the users' private data are equal and subsequently announces the results to the participants. By harnessing the principles of quantum mechanics, we ensure robust protection against potential eavesdropping and participant attacks. In contrast to numerous existing QPC protocols that rely on multi-qubit or d-dimensional quantum states, our method exhibits superior efficiency and practicality. Specifically, our protocol achieves a qubit efficiency of 50% by using two single photons to compare one bit of classical information, and single photons are easier to prepare than multi-qubit and d-dimensional quantum states.  
quantum cryptography; quantum private comparison (QPC); single photons; semi-honest third party (TP); rotational encryption +
Footnote †: journal: Applied sciences
## 5 Results
### Finite Element Analysis  
#### 5.2.1 Analysis Setup  
For LSA (linear static analysis) using the FEM (finite element method), we used a Siemens NX Nastran Solver. The following assumptions are made to simplify the analysis:  
* Connector caps and profile caps are considered to have minimal impact on the structure and were excluded from the analysis model (See Figure 22).
* The connection between the profile and the glass is assumed to be well adhered to by the EVA foam weather strip, and the glue contact condition is applied.
* The steel floor structure part is excluded from the analysis model, and boundary conditions are applied to the profiles in contact with the floor structure and the M8 screws that connected them to the floor structure. The boundary conditions for the FEA were set as follows:
* DoF 1 to 6 boundary conditions are applied to the M8 screws that connect the profiles in contact with the floor structure, fixing them to the position.
* DoF 3 to 5 boundary conditions are applied to the profiles in contact with the floor structure, restricting the Z-direction movement. The types of elements used in the analysis are shown in Figure 26.  
The number of elements by type is listed in Table 1.  
Figure 26: Element types: (**a**) Profile: 3D 10-node tetrahedron elements; (**b**) Nodal connector: 2D 4-node quadrangle and 3-node triangle elements (mid-surface); (**c**) End cap: 2D 4-node quadrangle and 3-node triangle elements (mid-surface); (**d**) Bolt: 1D beam and RBE2 (rigid body element, type 2).  
Figure 25: Assembly detail.  
The material properties used in the FEA are as shown in Table 2.  
FEA was conducted under three unilateral load condition cases, as shown in Table 3.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection
### Semantic Validation with NLP  
The extracted message is processed using the spaCy library to validate its linguistic coherence in Spanish and to determine whether it aligns with the expression in Equation (13), where \(T\) represents the message extracted from the image.  
\[V(T)=\left\{\begin{array}{ll}1,&\text{if $T$ contains valid entities or structures;}\\ 0,&\text{if $T$ it is not an understandable text.}\end{array}\right. \tag{13}\]  
Subsequently, the grammatical structure, the presence of named entities, and the frequency of words are analyzed according to a reference dictionary. To consider the message as valid \(V(T)=1\), the following condition must be met:  
\[E_{T}+S_{T}+P_{T}>\tau \tag{14}\]  
where \(\tau\) denotes the minimum linguistic validity threshold required to consider a message as meaningful. In this work, it was set to 0.3. This evaluation considers features such as the presence of structured sentences (\(S_{T}\)), named entities (\(E_{T}\)), and the frequency of Spanish words (\(P_{T}\)), verified through natural language processing techniques.  
The choice of \(\tau=0.3\) was determined by preliminary evaluations of a sample of 100 messages extracted from steganographic images. Several threshold values ranging from 0.1 to 0.6 were tested, and this value yielded the best trade-off between accepting coherent messages and filtering out semantically meaningless content. Messages classified as valid under this criterion showed consistent grammatical structure, named entities, and dictionary-based word frequency, with minimal inclusion of random or truncated strings. While this threshold is task-specific, it proved effective in reducing false positives during the semantic validation stage.  
Additionally, based on experimental observations during the validation process, it was noted that messages shorter than 10 characters generally lacked sufficient syntactic structure or semantic content to be reliably validated. Therefore, this value can be considered a practical lower bound for the minimum detectable message length in the current CNN-NLP configuration. Messages below this threshold frequently failed to meet the combined linguistic criteria shown in Equation (14) and were classified as incoherent or invalid.  
In brief, the developed model implements a specialized architecture for detecting LSB steganography in digital images, combining image processing techniques, deep convolutional neural networks, and semantic validation through natural language processing.
## 3 Results and Discussions
### Exploring the Mechanism of Manganese Sorption Using DX
#### 3.3.2 Isothermal Sorption Model  
The sorption experimental data in this study were described using the Langmuir and Freundlich model sorption isotherms (Figure 9). The calculated constant parameters and correlation coefficients for each isotherm model are shown in Table 4. The R\({}^{2}\) for the Langmuir models were 0.917 and 0.938 at 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, but only 0.875 at 30 \({}^{\circ}\)C. Therefore, the Langmuir model can only be used to describe the sorption of Mn(II) in  
Figure 8: Kinetic equation fitting curve for manganese sorption.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **R\({}^{2}\)** \\ \hline Pseudo-first-order & k\({}_{1}\) = 0.165 & 0.886 \\ Pseudo-second-order & k\({}_{2}\) = 0.055 & 0.952 \\ Elovich & a = 162.099; b = 2.545 & 0.994 \\ Intraparticle diffusio & k\({}_{\text{d}}\) = 4.584; c = 0.055 & 0.570 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and regression coefficients of the sorption kinetic model.  
DX under certain temperature conditions. The R\({}^{2}\) of the [PERSON] models were all greater than 0.936, indicating that the model is applicable to describe the sorption process at all temperature conditions, proving that the sorption process is a multilayer sorption system [48; 49]. Meanwhile, the adsorbent sorption strength (n) was much greater than 1 at all temperatures, explaining the easy sorption characteristics of DX for Mn(II).
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Terrain Model Generation  
To extract a terrain model from a surface model, man-made structures and vegetation have to be removed from the given input DSM. As depicted in Figure 1, the workflow for DTM generation comprises two main steps: determination of bare-earth points and interpolation of all other points and according post-processing. As no state-of-the-art method was found that delivered satisfying DTMs at fast runtimes, a novel method named _multi-directional slope dependent (MSD)_ DTM generation is presented in this section. Our Matlab source code is available at GitHub: [[https://github.com/rolandperko/dsm2](https://github.com/rolandperko/dsm2) dtm]([https://github.com/rolandperko/dsm2](https://github.com/rolandperko/dsm2) dtm).
# Characterization and Use of Char Produced from Pyrolysis of Post-Consumer Mixed Plastic Waste
## 4 Conclusions  
The effectiveness of chars from the pyrolysis of non-recyclable mixed plastics from municipal solid waste for the removal of lead from aqueous medium was investigated. Several characterization analyses were performed including elemental analysis, thermal decomposition and immediate analysis, HHV determination, BET surface area, total pore volume, average pore size and pore distribution analysis, FTIR and XRF analysis. The properties and sorption capacity of chars varied with pyrolysis temperature. The highest lead adsorption capacity was attained by the char resulting from the pyrolysis at 550 \({}^{\circ}\)C. The maximum lead adsorption capacity (Sips isotherm) was 61.3 mg/g at pH of 5.  
Conceptualization, M.A.M.-L. and M.C.; methodology, G.B.; software, M.A.M.-L. and A.P.; formal analysis, M.A.M.-L., A.P. and M.C.; investigation, A.P. and G.B.; resources, G.B.; data curation, M.A.M.-L., A.P. and M.C.; writing--original draft preparation, A.L.; writing--review and editing, M.A.M.-L. and M.C.; supervision, M.A.M.-L. and M.C.; project administration, M.A.M.-L. and M.C.; funding acquisition, M.A.M.-L. and M.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**[PERSON]**} & \multicolumn{2}{c}{q\({}_{\rm m}\), mg/g} & b, L/mg & r\({}^{2}\) & \(\sum({\rm y}-{\rm y}_{\rm cal})^{2}\) \\ \cline{2-6}  & 40.767 & 0.129 & 0.992 & 3.011 \\ \hline \multirow{2}{*}{**[PERSON]**} & \(K_{\rm F}\), mg/g-(L/mg)\({}^{1/n}\) & n & r\({}^{2}\) & \(\sum({\rm y}-{\rm y}_{\rm cal})^{2}\) \\ \cline{2-6}  & 5.653 & 1.699 & 0.992 & 3.356 \\ \hline \multirow{2}{*}{**Redlich–Peterson**} & A, L/g & B, ((L/mg)\({}^{8}\)) & g & r\({}^{2}\) & \(\sum({\rm y}-{\rm y}_{\rm cal})^{2}\) \\ \cline{2-6}  & 8.550 & 0.617 & 0.66 & 0.994 & 2.395 \\ \hline \multirow{2}{*}{**Sips**} & q\({}_{\rm m}\), mg/g & b, L/mg & n & r\({}^{2}\) & \(\sum({\rm y}-{\rm y}_{\rm cal})^{2}\) \\ \cline{2-6}  & 61.301 & 0.094 & 1.266 & 0.995 & 2.175 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fit parameters of the experimental results to the different equilibrium models.  
**Funding:** This work has received funds from the project PID2019-108826 RB-I00/SRA (State Research Agency)/10.13039/501100011033.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 4 Experimental Setup
### Implementation Details  
For the prediction of discharge capacity using the C-SDFormer model, we established a standardized dataset for network training, represented as \(D=\{(X(1),C(1)),(X(2),C(2)),\cdots,(X(\tau),C(\tau))\}\), where \(C(t)\) denotes the discharge capacity value, and \(X(t)\) signifies the input vector or scalar at time step \(t\). The input \(X(t)\) is \(X_{VNT}(t)=[V(t),N(t),T(t)]\), where \(V(t)\), \(N(t)\), and \(T(t)\) correspond to voltage, the communication count, and temperature at time step \(t\). To enable the model to grasp the temporal relationships among the input variables, we employed a sliding window algorithm with a window size of 24 h on the input data, with the subsequent 10 min being predicted as the output.  
Data that did not reach the primary point, contained numerous missing values, or exhibited excessive noise were not used in the discharge profile data. Moreover, we excluded three test sets from the training process for model evaluation. The remaining data were divided into a training set (80%) and a validation set (20%). We implemented early stopping to combat overfitting, terminating training when the validation loss exhibited no improvement for 10 epochs, with a maximum limit of 100 epochs. The model was trained using the Adam optimizer and the mean absolute error (MAE) loss function, employing a batch size of 64 and a learning rate of 0.0001.  
The C-SDFormer model was implemented in Python (version 3.8.11) using the PyTorch deep learning framework (version 2.2.1). Our experiments were conducted on a Linux-based system (Ubuntu 20.04) equipped with CUDA 12.1 and an NVIDIA GeForce RTX 3090.
# Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO
###### Abstract  
With the rising levels of atmospheric CO2, electrochemistry shows great promise in decarbonizing industrial processes by converting CO2 into valuable products through scalable and sustainable technologies. In this framework, the present study investigates the solar-driven CO2 reduction toward carbon monoxide, achieved by the integration between the electrochemical reactor and dye-sensitized solar cells (DSSCs), both in experimental and modeling perspectives. COMSOL(r) Multiphysics 6.3 was used to develop a detailed finite element method model of the electrochemical cell integrated with a photovoltaic module, validated with the experimental results that demonstrated a strong correlation. A 2D model was designed, incorporating cathode and anode regions divided by an ion-exchange membrane. The model includes platinum foil and silver nanoparticles as catalysts for the oxygen evolution reaction and CO2 reduction reaction, respectively. Integration with the fundamental equations of the DSSCs was simulated to analyze the solar-driven CO2 reduction behavior under solar irradiance variations, offering a valuable tool for optimizing operating conditions and predicting the device performance under different environmental conditions. The integrated device successfully produces CO with a faradaic efficiency of 73.85% at a current density of J = 3.35 mA/cm\({}^{2}\) under 1 sun illumination, with the result validated and reproduced by the mathematical model. Under reduced illumination conditions of 0.8 and 0.6 suns, faradaic efficiencies of 68.5% and 64.1% were achieved, respectively.  
CO2 validation; carbon monoxide; electrochemical reactor; finite element method modeling; dye-sensitized solar cell; PV-driven CO2 reduction
###### Abstract  
Fifth-generation (5G) sensor networks are critical enablers of Industry 4.0, facilitating real-time monitoring and control of industrial processes. However, significant challenges to their deployment in industrial settings remain, such as a lack of support for interoperability and manageability with existing industrial applications and the specialized technical expertise required for the management of private 5G sensor networks. This research proposes a solution to achieve interoperability between private 5G sensor networks and industrial applications by mapping Yet Another Next Generation (YANG) models to Open Platform Communication Unified Architecture (OPC UA) models. An OPC UA pyang plugin, developed to convert YANG models into OPC UA design model files, has been made available on GitHub for open access. The key finding of this research is that the proposed solution enables seamless interoperability without requiring modifications to the private 5G sensor network components, thus enhancing the efficiency and reliability of industrial automation systems. By leveraging existing industrial applications, the management and monitoring of private 5G networks are streamlined. Unlike prior studies that explored OPC UA's integration with other protocols, this work is the first to focus on the YANG-OPC UA integration, filling a critical gap in Industry 4.0 enablement research.  
Industry 4.0; OPC UA; YANG; 5G sensor network; private 5G
## 5 Conclusions  
This research conducted real-time uniaxial compression simulations under high temperatures using a PFC-GBM constructed based on laboratory experiments. The stress-strain relationship, crack propagation, and evolution of acoustic emission activity and energy with temperature were analyzed. The main conclusions obtained are as follows:  
1. During uniaxial loading, intergranular tensile cracks in granite initiate first and dominate throughout the entire process, with shear cracks appearing after the peak strength point and being minimal in number. As the temperature increases, the total number of microcracks gradually increases, becoming significant after 400 \({}^{\circ}\)C, and reaching the maximum development rate at 600 \({}^{\circ}\)C.
2. Combining SEM (scanning electron microscopy) results, the distribution of microcracks in granite at different temperatures reveals that the damage under uniaxial compression is mainly controlled by mica and feldspar. As the temperature rises, the internal crack distribution in granite becomes more uniform, with edge cracks rapidly developing to connect with the main crack to form a fracture network, indicating a transition to ductile failure characteristics.
3. With increasing temperature, acoustic emission events begin to occur during the initial compression stage, and their distribution becomes wider near the peak strength region with a decrease in amplitude. Multiple peaks in acoustic emission events gradually  
Figure 14: Boundary and frictional energies and the relationship between the ratio of them for granite damage at different temperatures.  
appear in the post-peak stage. Concurrently, both dissipated energy and boundary energy gradually decrease, and the energy consumption ratio increases, indicating more severe thermal damage to the granite.  
Conceptualization, K.L. and F.Z.; formal analysis, K.L.; data curation, K.L.; writing--original draft preparation, K.L.; writing--review and editing, K.L. and F.Z.; supervision, F.Z. The results were discussed and conclusions were drafted jointly by all authors. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the National Natural Science Foundation of China (Grant No. 51979100).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.