esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 5 Results and Analysis
### Encryption Quality Analysis  
#### 5.1.1 Key Space Analysis  
The proposed encryption algorithm utilizes two keys, \(k_{1}\) and \(k_{2}\), in its permutation and confusion stages, respectively. Both keys consist of a control parameter \(c\) and an initial condition \(x_{0}\). The key space of the algorithm is determined by the range of possible values for \(c\) and \(x_{0}\). It is safe to say that, even with the fastest computers available today, the time required to brute-force a 256-bit key would be much greater than the estimated age of the universe. For example, consider a 64-bit key, a typical Intel(R) Core(TM) i7 computer with a clock speed of 1.8 billion cycles per second would take approximately \(5.8\times 10^{16}\) years to perform a brute-force attack on a 64-bit key. This demonstrates the importance of selecting strong cryptography keys in order to ensure the security and integrity of sensitive information.  
Further, both keys are designed to have a large key space in the floating-point representation format, which ensures that the encryption algorithm is secure against key brute-force attacks. The use of different keys for the permutation and confusion stages further increases the security of the encryption algorithm by making it more difficult for an attacker to crack the encryption open and gain access to the message.
## 4 Methodology
### Pseudo-wells and Synthetic Seismic Generation  
Valid elastic property distribution is always constrained by a number of objective data, more precisely, well data. For studied reservoir porosity prediction is based on artificial well catalog workflow and AVO scenarios.  
Pseudo-wells are created to calculate Rock Physics properties of Well-B changing porosity and reservoir thickness (Figure 5). Porosity is varied with a range from 20% to 40%, while thickness is varied from 5 to 15 m. Reservoir mineralogy and saturation are based on Well-B petrophysical analysis. The process results in 78 pseudo-wells with associated pseudo-logs.  
AVO modeling was performed to create synthetic seismic gathers for each pseudo-well based on created logs. The synthetic gathers were calculated using the P-wave reflection coefficients obtained from the Zoeppritz equations [23] and a wavelet extracted from actual seismic data. Synthetic gathers generated from 12 pseudo-wells are shown in Figure 6.  
The synthetic gathers were processed using the same methodology used for processing actual seismic data, resulting in the creation of a series of angle stacks with the same angle range. To obtain the same set of input data, pre-stack seismic inversion is performed on synthetic AVO gathers. These synthetic angle stacks were used as inputs for the neural network analysis.
# Factorial Design Analysis for Localization Algorithms
###### Abstract  
Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (\(\eta\)) and the noise level (\(\sigma_{dB}\)) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the \(2^{k}\) factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (\(\eta\)) and noise level (\(\sigma_{dB}\)) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.  
localization; WSN; \(2^{k}\) factorial; MSE +
Footnote †: journal: Applied Sciences
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 4 Analysis of Horizontal Rotational Stability
### Displacement Load Variation of Hydro-Pneumatic Springs  
Assuming a counterclockwise direction is positive, it can be inferred from the structural characteristics of the suspension system that during horizontal rotation, the displacement loads of the hydro-pneumatic springs are symmetrically distributed about the upper and lower sections. Therefore, only the displacement load variations of the upper three hydro-pneumatic springs need to be analyzed. As shown in Figure 9, during rotation, the middle hydro-pneumatic spring (OB) does not generate rotational torque to affect the suspension stability. The two hydro-pneumatic springs on the left and right do generate such torque. Suppose that the wheelset rotates counterclockwise by an angle \(\theta\), where \(h_{c}\) represents the vertical distance between the upper and lower ball joints of the hydro-pneumatic spring, \(s_{0}\) represents the initial length of the hydro-pneumatic spring, \(r_{c}\) represents the distance from the lower ball joint to the center of rotation, and \(l_{c}\) represents the horizontal projection distance from the upper ball joint to the center of rotation.  
Figure 8: Schematic of horizontal rotation.  
Figure 9: Simplified model of horizontal rotation for the upper three hydro-pneumatic springs.
## 2 Related Work
[PERSON] et al. [21] discussed the technological aspects of vehicle insurance telematics and the use of smartphones as a platform for vehicle insurance telematics [22]. Finally, on the commercial side, several approaches have been presented, TrueMotion [23] presented a smartphone app that monitors driving behavior using sensors in the smartphone. The app then gives an overall safety score based on the habits behind the wheel. Later, TrueMotion developed an app called \"Mojo\" that provides feedback and incentives to help users reduce distracted driving. The app provides an overall score that characterizes how distracted a user is while driving. The app runs in the background on a driver's phone and uses sensor-based algorithms to capture and break down distracted driving into three categories: typing, handheld calls and hands-free calls [24]. DriveWell is an app developed by Cambridge Mobile Telematics (Cambridge, MA, USA) that uses phone sensors to records trip data and using telematics and machine learning infer key metrics about vehicle mileage, road types, speed, acceleration patterns, phone distraction, and collisions. Contrary to TrueMotion, DriveWell can aggregate the data and provide a driver/passenger classification. Moreover, this app showed a 40% reduction in phone usage once the app is used [25]. Similar to DriveWell, Flowo Score is an app developed by The Floow Limited (Yorkshire, UK), which uses the phone sensors and telematic data, to score speed, smoothness of driving, distraction and time for the driver; this app is mainly used to aid insurance companies with pricing their policies and predicting risk for each driver more accurately; the app also provides tips, education, and coaching, in order to improve the score and drive more safely. A software developer kit (SDK) is also available to provide data collection, storage, scoring, etc. and generate custom solutions [26].
# Decarbonizing European Industry: A Novel Technology to Heat Supply Using Waste and Renewable Energy
###### Abstract  
This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 \({}^{\circ}\)C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.  
decarbonization; heat demand; high-temperature heat pumps; PCM-TES system; SUSHEAT
## 3 Results
### MLP-Only Model for LSP  
The MLP-only model with the conventional gradient descent algorithm was used to carry out LSP for comparison. The optimal number of neurons in the hidden layer was determined to be 21 according to the minimum prediction error method [62]. All the other parameters of the MLP-only model were set to default values and/or determined using default methods. The landslide susceptibility map produced by the MLP-only model is shown in Figure 6b and was also divided into very low (27.7%), low (22.9%), moderate (22.7%), high (16.9%), and very high (9.9%) susceptibility levels using the natural interval point method (Table 2).  
Figure 6: Landslide susceptibility maps respectively produced by PSO-MLP (**a**), MLP-only (**b**), BPNN (**c**), and IV (**d**) models.
## 5 Experimental Results
### Integrity and Efficiency Analysis  
**Integrity:** The integrity requirement refers to the watermarking scheme's ability to minimize false alarms. DICTION achieves this through its generative adversarial network (GAN) strategy. We trained the projection model \(\text{Proj}_{\theta}\) to map the activation maps of the watermarked target model \(M^{\text{wat}}\) to the intended watermark and those of the original model \(M\) to a random watermark \(b_{r}\). This approach enables DICTION to effectively meet integrity requirements.  
**Computational efficiency:** We analyzed the overhead incurred by the watermark-embedding process in terms of computational and communication complexities. DICTION introduces no communication overhead for watermark embedding since the process is conducted locally by the model owner. The additional computational cost depends on the target model topology \(M^{\text{wat}}\), projection model \(\text{Proj}_{\theta}(\cdot)\), and the number of epochs required for watermark embedding while maintaining high accuracy.  
Our experiments show that 30 epochs are sufficient for \(\text{Proj}_{\theta}(\cdot)\) convergence with satisfactory target model accuracy. The projection model topology is modest compared to the target model, enabling efficient watermark embedding. Table 7 provides computation times for the model training and watermarking processes. DICTION incurs reasonable overhead, with a maximum of 30 epochs for all benchmarks, suggesting high efficiency.  
**Complexity comparison:** Our embedding complexity remains higher than the Uchida [45] and DeepSigns [51] methods because their projection functions are based solely on secret matrix multiplication. However, our method has similar complexity to ResEncrypt [55], as both train an additional model (\(\text{Proj}_{\theta}(\cdot)\) and MappingNet, respectively), which is less expensive than RIGA [57] since we do not require a third model to preserve weight distribution.
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Encryption  
Vote encryption can be done using ElGamal public-key encryption. Each voter encrypts the memo field using publicly known Encryption-Key, while the decryption is possible only with corresponding private Decryption-Key published after the end of elections. Unfortunately, this scheme produces ciphertext two times larger than the key size. The ciphertext is stored in the memo field, which is bounded to 32 bytes that mean we are limited to use 16 bytes (128 bits) keys, that are far from secure. Instead of doing asymmetric encryption, we can leverage the fact that the memo is encoded within the Stellar transaction. Therefore we have always access to the sender (voter) public key. We can use Elliptic Curve Diffie-Hellman (ECDH) to derive the symmetric encryption key--unique for each sender (voter)--and use AES-256-CTR for symmetric encryption. In elliptic curve cryptography, the private key is a random \(n\)-bit integer where \(n\) is the key size.  
Let us call the private key \(k\) and his corresponding public key represented by the point on the curve \(K\), (where \(K=k*G\) and \(G\) is known constant point on the curve). If we take two key-pairs: voter \((k_{v},K_{v})\), and ballot-box \((k_{b},K_{b})\), then using ECDH we can derive both encryption and decryption keys \((E,D)\) using formulas:  
\[E =k_{v}*K_{b} \tag{1}\] \[D =k_{b}*K_{v}. \tag{2}\]  
The keys are equal because  
\[k_{v}*K_{b}=k_{v}*k_{b}*G=k_{b}*k_{v}*G=k_{b}*K_{v}. \tag{3}\]  
During the voting, each voter uses publicly available \(K_{b}\) in conjunction with its \(k_{v}\) to encrypt its memo field. After the end of the voting, organizers publish corresponding \(k_{b}\), which in combination with \(K_{v}\), is used to decrypt each vote memo field. This way, we achieve asymmetric encryption, where each voter uses a different symmetric key.  
With this schema, our system relies on a central authority--the organizers--that promise to reveal the decryption key after the end of the election. To mitigate this issue, we can split the decryption-key \(k_{b}\) using [PERSON]'s Secret Sharing [40] among multiple trusted parties (e.g., candidates). The results can be decrypted only when the majority of them reveal their parts altogether as [PERSON]'s Secret Sharingallows split the key into \(m\) parts, where just \(n\leq m\) parts are sufficient to reconstruct the key. This way, we decentralize the trust placed on the central authority. Although not perfect, because the majority of the key-part holders can conspire and use it together to get the insight into the current state of the results, or even not reveal them at all--blocking the voting results. Our system requires trust in the majority of key-holders.
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 5 Numerical Experiments
### Model 1. Periodic Signal
#### 5.1.2 Least-Square Method Results for the Model (16)  
For simplicity, we assume that \(m=m_{1}=m_{2}\). Table 2 demonstrates the dependence of the residual \(\varepsilon_{N}\) on the parameters.  
All calculations for least-square method were performed in MATLAB. Overdetermined matrix is solved using the lsqminorm function. lsqminnorm solves the linear equation \(AX=B\) and minimizes \(\|AX-B\|\). The function uses the complete orthogonal decomposition to find a low-rank approximation of a matrix. It also should be noted that all the integrations during calculation were carried out analytically and did not introduce additional error in the results. Residual error for fixed \(m=5\) and \(k=(m+m^{2})\times 5\) is presented in Figure 9. Residual error for fixed \(m=7\) and \(k=(m+m^{2})\times 5\) is presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(m=3\) & \(m=5\) & \(m=7\) \\ \hline \(k=(m+m^{2})\times 2\) & \(8.07\times 10^{-4}\) & \(4.92\times 10^{-10}\) & \(2.50\times 10^{-16}\) \\ \(k=(m+m^{2})\times 5\) & \(8.07\times 10^{-4}\) & \(3.90\times 10^{-10}\) & \(1.50\times 10^{-16}\) \\ \(k=(m+m^{2})\times 10\) & \(8.07\times 10^{-4}\) & \(4.90\times 10^{-10}\) & \(2.87\times 10^{-15}\) \\ \hline \end{tabular}
\end{table}
Table 2: Dependence of the residual \(\varepsilon_{N}\) on the values \(m\) and \(k\).  
Figure 8: Residual for \(m=10\).  
Figure 9: Residual for \(m=5\) and \(k=(m+m^{2})\times 5\).
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 4 Results  
### Oedometric Response  
Knowing the load-settlement relationship of the Martian simulant shed light on the design of a Rover or excavation tools [29]. So far, it is clear that there is a lack of data on the compression index (\(C_{\text{c}}\)) and swelling index (\(C_{\text{s}}\)) of Martian simulants in the literature.  
Figure 4: Interface direct shear apparatus: (**a**) loading frame, (**b**) bottom shear box and (**c**) upper shear box.  
The void ratios as functions of the effective vertical stresses are plotted in Figure 5. From the straight-line part of the compressibility curve, the compression index (\(C_{\rm c}\)) is determined as:  
\[C_{\rm c}=\frac{e_{1}-e_{2}}{\log\sigma^{\prime}{}_{v2}-\log\sigma^{\prime}{}_{ v1}} \tag{2}\]  
where \(e_{1}\) is the void ratio at effective vertical stresses of \(\sigma^{\prime}{}_{v1}\), and \(e_{2}\) is the void ratio at effective vertical stresses of \(\sigma^{\prime}{}_{v2}\). The swelling index (\(C_{\rm s}\)) of the JEZ-1 simulant can be obtained from the data on the unloading line:  
\[C_{\rm s}=\frac{e_{1}-e_{2}}{\log\sigma^{\prime}{}_{v2}-\log\sigma^{\prime}{}_ {v1}} \tag{3}\]  
The \(C_{\rm c}\) is 0.131 and the \(C_{\rm s}\) is 0.030, which are calculated with the oedometric data of JEZ-1 in Figure 5. Both compression and swelling indexes of the JEZ-1 are quite low, indicating that it is a less compressible and lower swelling soil.
## 4 Result
### Comparative Analysis of LVT, PT, and PAUT for BVID Detection  
Table 4 presents a visual comparison of defects identified using LVT, PT, and PAUT (shown as C-scan images). For LVT and PT, the thermal images were captured from the front surface of the samples, the same side where the impact was applied. In contrast, the C-scan measurements were taken from the back surface of each specimen. The comparison of defect images from PT, LVT, and PAUT (C-scan) shows clear differences in the appearance and shape of BVID. In PT images, defects generally appear as two distinct dark lobes, particularly in samples with lower impact energy. These lobes are well separated and show a sharp contrast against the background. In LVT images, the defects are typically seen as a single, more continuous region, either darker or brighter than the surrounding area, often with sharper outlines and more compact shapes compared to PT.  
C-scan images, obtained from PAUT, consistently show a single rounded or elliptical defect area. The defect shapes in C-sans are generally smooth, centred, and compact, with no splitting or multiple lobes observed. In most cases, the VT results more closely resemble the C-scan images, both in terms of shape and position. PT often shows more visual complexity, with multiple lobes or elongated patterns, while C-sans and LVT images indicate a single, centralised area of damage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **LVT** & **LVT Image** & **PT** & **PT Image** \\ (Type + J) & **SNR** & & **SNR** & \\ \hline Thick 4.0 & not detected & not detected & not detected & \\ \hline Thin 12.0 & 33.15 & & 38.63 & \\ \hline Thick 8.0 & 22.31 & & 42.31 & \\ (A) & & & \\ \hline Thick 8.0 & 17.23 & & 35.01 & \\ (B) & & & \\ \hline Thick 12.0 & 29.90 & & 41.07 & \\ \hline Thick 20.0 & 37.04 & & 49.16 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample (Type + J)** & **LVT Image** & **PT Image** & **C-scan Image** \\ \hline Thin 2.5 & & \\ Thin 4.0 & & \\ Thin 8.0 (A) & & \\ Thin 8.0 (B) & & \\ Thin 12.0 & & \\ Thick 8.0 (A) & & \\ Thick 8.0 (B) & & \\ Thick 12.0 & & \\ Thick 12.0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Qualitative comparison of defect visibility using LVT, PT, and PAUT (C-scan) imaging (only upper-positioned samples are included).
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
## 3 SOA Design, Modelling and Analysis
### Equivalent Cavity and Reflectivity Design  
A multi section cavity analysis has been performed to optimize the SOA model by incorporating flip chip technique assembly constraints in terms of its effective reflectivity. We took into account the multi-cavity effect due to discontinuities in the hybrid integrated SOA-SOI chip by using effective mirror model and scattering-matrix analysis [43]. In Figure 5, an equivalent mirror cavity is depicted, which replaces the SOI waveguide passive section by an effective mirror with reflectivity \(\mathbf{r}_{\text{eff}}\). In particular, the reflectivity from the spot-size converter taper between the SOA and SOI waveguide and from the input/output GC reflectivity have been considered in order to compute the effective complex facet reflectivity \(\mathbf{r}_{\text{eff}}\) as:  
\[\mathbf{r}_{\text{eff}}=\mathbf{r}_{\text{tap}}+\frac{\mathbf{t}_{\text{tap}}^ {2}\mathbf{r}_{\text{gc}}\mathbf{e}^{-2\widehat{\beta}\widetilde{\mathbf{ \rho}}_{\text{P}}\mathbf{L}_{\text{P}}}}{1+\mathbf{r}_{\text{tap}}\mathbf{r}_ {\text{gc}}\mathbf{e}^{-2\widehat{\beta}\widetilde{\mathbf{\rho}}_{\text{P}} \mathbf{L}_{\text{P}}}} \tag{7a}\] \[\widetilde{\beta_{P}}=\left(\frac{2\pi}{\lambda}\right)\mathbf{n}_{eff,P}+j \cdot 0.5\cdot\alpha_{P} \tag{7b}\]  
where \(r_{tap}\) and \(t_{tap}\) are the reflection and transmission coefficient at the spot-size converter taper part respectively, \(r_{gc}\) is reflection at GC, \(\widetilde{\beta_{p}}\) is the complex propagation constant, \(n_{eff}\) is the effective index and \(\alpha_{P}\) is passive cavity internal loss. This yielded the value for the residual facet reflectivity \(\mathbf{R}=|\mathbf{r}_{\text{eff}}|^{2}\) (assuming \(\mathbf{r}_{\text{eff1}}=\mathbf{r}_{\text{eff2}}\)) of the SOA that needs to be considered in Equation (1) for ensuring a reduced ripple amplitude of 0.3 dB. Figure 6 shows the contour plot for the calculated RFR versus GC and tapered section reflectivities. The plot has been obtained using SOA with an active region length of 1600 \(\upmu\)m assembled to the SOI waveguide with 2600 \(\upmu\)m passive cavity length from each side.  
Figure 5: (**a**) Multi-section cavity model; (1): input grating couplers (GC) equivalent mirror (2): silicon on insulator (SOI)–SOA taper section (**b**) equivalent cavity.