esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The first two compression stages are electrically driven (stages 1-2 and 3-4 in Figure 2), while the third stage is driven by the compressor of a turbocharger (stage 4-5). The latter uses the energy of the turbine to generate additional pressure in the air. Between the compression stages there are two heat exchangers (stages 2-3 and 5-6) in which the air, previously heated by the compression processes, is cooled using water at ambient temperature. After the compression stages, there is a regenerative phase prior to the expansion (stage 6-7), which makes it possible to take advantage of the part of the cycle in which the air is at low temperature and pressure to continue cooling it to below ambient temperature. After the regenerator, the air enters the turbine and expands until it reaches its minimum temperature in the cycle, which can be as low as \(-180\)\({}^{\circ}\)C. After the turbine, the cold air is used to cool the thermal load (stage 8-9). Once the thermal load has passed, the air, still at low pressure and below ambient temperature, returns to the regenerator (stage 9-1) to cool the regenerated air before it enters the turbine. On leaving the regenerator, the air is directed back to the first compression stage, closing the loop of the cycle. For the current application, where it is required to generate air jets with high velocities and low temperatures, a partial air extraction is performed at point 7 of the scheme shown in Figure 2. At this point, the air has a high pressure and temperatures below ambient temperature (with values close to \(-80\)\({}^{\circ}\)C). To achieve high velocities in the air jet and maintain low temperatures, this air is expanded through a nozzle and released into the environment. The air extraction is carried out by means of a flexible hose that connects the air outlet point, where the extraction occurs, through a convergent-divergent nozzle. Two of the nozzles designed for extraction can be seen in Figure 3. Different nozzle geometries were 3D printed to vary the mass flow ratio ejected. Figure 1: Installation of the reverse Brayton cycle (RBC). Source: own elaboration, 2023. The objectives of this research are as follows:  
1. To evaluate the low-temperature resistance of materials used in transport vehicles through experimental laboratory tests. 2. To adapt the deep-freezing equipment for experimental tests in the laboratory. 3. To analyse and discuss the data obtained in the experimental tests, assessing the feasibility of the project of using deep-freezing equipment for removing graffiti from railway vehicles. This will consider technical, economic, and environmental aspects, and propose improvements and solutions to the problems identified. Research on green technologies implementable in various sectors also contributes to the 2030 Agenda (SDG3; SDG7; SDG8; SDG9; SDG12; SDG13) and its pact with environmental protection, as well as to the recommendations of the 2030 Agenda for Change, by contributing to the industrial transition.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Physical Unclonable Function  
PUF offers a challenge-response mechanism that outputs a response for a challenge as an input. It can be represented as follows: \(R=PUF(C)\). Exploiting the singularity of the integrated circuit's physical micro-structure in the manufacturing process, it ensures that each PUF is unique. Because the operation of a PUF relies on the intrinsic physical characteristics of the integrated circuit, any endeavor to tamper with the PUF disrupts its functionality, rendering it ineffective [17].
# Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer
## 3 Results and Discussion
### Overall Approach to Performances
In order to maintain the consistency of the wavelength, the wavelength used in the calculation of the astroclimatic parameter was 0.55 microns. The results are shown in Figure 7. The BPNN approach provided more reasonable values, which were close to those measured by balloons, and compared with the results obtained by the BPNN approach, it can be seen that integrated astroclimatic parameters calculated from the HMNSP99 model fluctuated greatly. This is unreliable when reconstructing integrated astroclimatic parameters. Figure 6: Overall statistical analysis of \(\lg C_{n}^{2}\) profiles of 32 balloon samples.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Fault Detection, Diagnosis and MPC Reconfiguration Tests
#### 5.3.1 Scenario 2 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data** & **No Control** & **Normal MPC** & **MPC with h1 Fault** & **Reconfigured MPC** \\ \hline \(N_{ov,1}\) & 0 & 3 & 1 & 1 \\ \(N_{ov,2}\) & 0 & 1 & 1 & 1 \\ \(N_{ov,3}\) & 1 & 3 & 3 & 3 \\ \(N_{ov,4}\) & 3 & 3 & 4 & 3 \\ \(N_{ov,5}\) & 1 & 4 & 4 & 4 \\ \(N_{ov,WWTP}\) & 6 & 5 & 6 & 5 \\ \(T_{ov,1}\) & 0 & 0.0396 & 1.0917 & 0.7417 \\ \(T_{ov,2}\) & 0 & 0.0146 & 0.0063 & 0.0104 \\ \(T_{ov,3}\) & 0.0146 & 0.0437 & 0.0396 & 0.0396 \\ \(T_{ov,4}\) & 0.0771 & 0.0917 & 0.1167 & 0.0917 \\ \(T_{ov,5}\) & 0.0083 & 0.0604 & 0.0583 & 0.0583 \\ \(T_{ov,WWTP}\) & 0.3563 & 0.5854 & 0.5333 & 0.4937 \\ \(V_{ov,1}\) & 0 & 5.3998 \(\times\) 10\({}^{3}\) & 5.0830 \(\times\) 10\({}^{4}\) & 3.5204 \(\times\) 10\({}^{4}\) \\ \(V_{ov,2}\) & 0 & 83.5089 & 47.0085 & 79.0783 \\ \(V_{ov,3}\) & 556.6261 & 1.8417 \(\times\) 10\({}^{3}\) & 1.6621 \(\times\) 10\({}^{3}\) & 1.6522 \(\times\) 10\({}^{3}\) \\ \(V_{ov,4}\) & 7.9044 \(\times\) 10\({}^{3}\) & 1.3097 \(\times\) 10\({}^{4}\) & 2.3892 \(\times\) 10\({}^{4}\) & 1.8166 \(\times\) 10\({}^{4}\) \\ \(V_{ov,5}\) & 314.0450 & 1.2763 \(\times\) 10\({}^{4}\) & 1.2246 \(\times\) 10\({}^{4}\) & 1.3402 \(\times\) 10\({}^{4}\) \\ \(V_{ov,WWTP}\) & 5.9698 \(\times\) 10\({}^{4}\) & 148.6536 & 15.0684 & 1.2201 \(\times\) 10\({}^{3}\) \\ \(V_{ov}\) & 6.8473\(\times\) 10\({}^{4}\) & 2.7935\(\times\) 10\({}^{4}\) & 3.8003\(\times\) 10\({}^{4}\) & 3.4520\(\times\) 10\({}^{4}\) \\ \(Q_{WWTP}\) & 3.2379 \(\times\) 10\({}^{4}\) & 3.5974 \(\times\) 10\({}^{4}\) & 3.4753 \(\times\) 10\({}^{4}\) & 3.5129 \(\times\) 10\({}^{4}\) \\ \(G_{u}\) & 53.9648 & 59.9560 & 57.9210 & 58.5486 \\ \(S\) & - & 6.8276 \(\times\) 10\({}^{10}\) & 6.7398 \(\times\) 10\({}^{10}\) & 3.1712 \(\times\) 10\({}^{11}\) \\ \hline \end{tabular}
\end{table} 
Table 5: System performance in every case: fault in tank 1 level sensor (scenario 2). Considering the main indices (\(V_{\text{ov}}\), \(G_{\text{u}}\) and \(S\)), the results discussion is like the previous section, with the normal MPC controller having the best performance. Furthermore, MPC with the fault considered reduces the system performance, but by comparing with no control case, its performance is much better since \(G_{\text{u}}\) is 58.49% vs. 53.96%, \(V_{\text{ov}}\) is 3.5235 \(\times\) 10\({}^{4}\) vs. 6.8473 \(\times\) 10\({}^{4}\) (m\({}^{3}\)). Finally, by comparing the MPC with a fault with FTCMPC, this one improves the system performance slightly since the total overflow volume is reduced from 3.5235 \(\times\) 10\({}^{4}\) to 3.5162 \(\times\) 10\({}^{4}\) and the degree of WWTP utilization increases from 58.49% to 58.52%, but \(S\) is worse because the system needs higher control efforts.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Chebyshev-ElGamal Public Key Cryptographic Algorithm (CEPKC)  
In [50], [PERSON] et al. utilized the semi-group properties and chaotic characteristics of Chebyshev chaotic mapping to propose a Chebyshev polynomial-based ElGamal-like public key cryptography algorithm, with the ElGamal public key cryptography algorithm as a blueprint. This algorithm does not require the generation of large prime numbers as private keys, and has high computational efficiency. In the algorithm, r and s are large random integers, and \(x\in[-1,1]\). Due to the chaotic characteristics, \(s\) and \(T_{s}(x)\) can be seen as independent random numbers. However, the inverse operation of the trigonometric function definition in the Chebyshev chaotic mapping leads to the ciphertext information being \(c=(c_{1},c_{2})=(T_{r}(x),T_{s}(x))\), from which an adversary can compute:  
Figure 4: Lyapunov Exponent of the Chebyshev Map.  
Figure 5: Information entropy of the Chebyshev map.  
\[r^{\prime}=round\bigg{(}\underset{k\in Z}{min}\bigg{\{}\bigg{\|}\frac{\pm arccos(T_{r} (x))+2k\pi}{arccosx}\bigg{|}\bigg{\}}\bigg{)}, \tag{4}\]  
as \(T_{rs}(x)=T_{sr}(x)=T_{s}(T_{r}(x))=T_{s}(T_{r}(x))=T_{s^{\prime}}(x)=T_{r^{ \prime}}(T_{s}(x))\) and the adversary can recover the plaintext \(m=c_{2}(T_{r^{\prime}}(T_{s}(x)))^{-1}\). Similarly, the adversary can compute \(s^{\prime}\) using Equation (4) to steal [PERSON]'s equivalent private key. To enhance security, [PERSON] et al. extended the definition of Chebyshev polynomials from the real field to the finite field \((Z_{N},+,\times)\), as shown below:  
\[T_{n}(x)=(2 xT_{n-1}(x)-T_{n-2}(x))(modN), \tag{5}\]  
where \(n\geq 2\) and \(N\) is a large prime number. It can be proven that the semi-group properties of Chebyshev polynomials still hold in the finite field \(Z_{N}\). Since \(T_{n}(x)\) is an algebraic expression, Equation (6) holds.  
\[T_{n}(x)(modN)=T_{n}((x(modN)))(modN). \tag{6}\]  
Therefore, the semi-group expression of Chebyshev polynomials in the finite field can be derived, as shown in Equation (7).  
\[T_{r}(T_{s}(x))(modN)=T_{sr}(x)(modN)=T_{s}(T_{r}(x))(modN). \tag{7}\]  
For any Chebyshev polynomial \(T_{n}(x)\) in the finite field \(Z_{N}\), it can also be represented as:  
\[T_{n}(x)=(a_{n}x^{n}+a_{n-1}x^{n-1}+\cdots+a_{1}x+a_{0})(modN). \tag{8}\]  
At the current stage, no inverse function expression for finite field Chebyshev polynomials has been found.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 3 Materials and Methods
### Training and Validation  
The proposed PELM model was trained and validated using a large and diverse dataset compiled from multiple open-access chest X-ray repositories, including Kaggle CXR, Pneumonia CXR, NIH ChestX-ray14, PadChest, and CheXpert. To ensure class balance and clinical relevance, a custom dataset was prepared consisting of 50,000 images split into 40,000 for training, 5000 for validation, and 5000 for testing. The training set includes 20,000 pneumonia, 18,000 normal, and 2000 other findings images, ensuring the model is exposed to real-world variability and non-pneumonia conditions. Other findings images include thoracic pathologies other than pneumonia (e.g., pulmonary oedema, pleurisy, tuberculosis). The addition of these images was performed to increase the model's ability to distinguish pneumonia from other lung abnormalities and to improve generalization performance. The category of other findings images was defined in [PERSON] et al. [28]. Clinically significant and common thoracic diseases were selected. This diverse composition improves generalization and reduces class bias.  
Model training was performed using the Adam optimizer with a learning rate of 0.001, and binary cross-entropy was used as the loss function to address the binary classification objective (pneumonia vs. normal). To prevent overfitting, early stopping was applied based on validation loss, and dropout layers with rates of 0.4 and 0.3 were incorporated into the fully connected layers of the ensemble head. Additionally, a 5-fold cross-validation strategy was implemented to ensure robust performance evaluation and minimize model variance across different data splits.  
During training, metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) were calculated. The validation curves demonstrated strong convergence, and ROC analysis confirmed the model's excellent discriminative ability. Overall, this training and evaluation pipeline ensured that the PELM model achieved high accuracy while maintaining generalization to unseen data, validating its potential for clinical deployment.
## 3 Results and Discussion
### Comparison of Inverted Repeats Region  
Inverted repeats (IR) contraction and expansion events are a major source of chloroproplast sequence variation [33]. IR boundary comparison was performed between two _A. remotilora_ (OP920648 and KP889213). Both IR region sizes ranged from 27,437 to 27,794 base pairs (bp). In the LSC/IR border region, five genes (_trnL_, _trnH_, _pspA_, _rrn16_, and _ycf2_) were found, and four genes (_psaC_, _ndhF_, _ndhE_, and _ndhG_) were found in the SSC/IR border (Figure 5). Among the 10 _ndh_ family genes identified in _A. remotilora_ (Table 1), only the _ndhE_ gene (303 bp length) was found in the SSC/IRa (JSA). In particular, a _ndhE_ pseudogene fragment of 157 bp size was found in the SSC/IRb (JSB) of _A. remotilora_ (KP889213). This pseudogene moved by one base pair from the SSC to the IRb. Therefore, we assumed that the two _A. remotilora_ had sequences for plant species distinction in the JSB border regions.  
Figure 4: Chloroplast nucleotide sequence alignments between _A. remotilora_ (this study, NCBI accession no. OP920648) and another _A. remotilora_ sequence (NCBI accession no. KP889213) using the mVISTA program. Red arrow indicates the location of the _petG_ gene, and gray arrows show the direction and position of each gene. The white areas indicate the dissimilar regions, and gene name is shown at the top.  
Figure 5: Comparative analyses of junction positions in the boundary regions (LSC, SSC, Ira, and IRb) between two _A. remotilora_ (OP920648 and KP889213). The numbers represent the distances or gene length, and gene names are indicated around boxes.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 3 Methodology
### Physical Layer Parameters  
The 38 and 65 GHz bands were analyzed based on the specifications for the physical layer established in the 5G New Radio (NR) standard [25]. The basic modulation scheme was OFDM, and this allowed for different subcarrier spacings [27]. A value of 120 kHz was chosen because this was an indoor environment with short distances, and very high frequencies were used. Furthermore, when the bandwidth (BW) was 400 MHz, the standard only allowed 120 kHz. From the measurements, the exact frequencies were interpolated according to the frequency band, and bandwidth were chosen to exactly fulfil the standard.  
The physical layer (PHY) was studied by focusing on the physical downlink shared channel (PDSCH). Figure 5 shows the block diagram of the physical layer. First, the transport block was generated, whose size depended on the number of subcarriers, the modulation, the \(N\)s, and the code rate associated with the low-density parity check coding (LDPC) channel coder, as defined in [28]. The LDPC base graph was selected to segment the transport block into code blocks. The code blocks were then sent to an LDPC coder and to the rate matching module [29]. Next, all the code blocks were multiplexed and sent to  
Figure 4: Block diagram of the SFBC-HBF technique.  
the modulator. Finally, the modulated data were demultiplexed into _N_s streams [30] to be processed by the algorithms described above.  
The modulation and coding schemes (MCSs) chosen for the study according to the standard [28] are shown in Table 4. For each modulation, the highest coding scheme allowed by the standard was chosen in order to achieve the maximum throughput. Finally, Table 5 shows the bandwidths studied [27], with the number of resource blocks (RBs) and the number of subcarriers used in each one, since each RB has 12 subcarriers.
## 3 Mathematical Modeling  
### Governing Equations  
(1) Mass conservation equation [31]  
\[\frac{\partial\rho}{\partial t}+div(\rho\overline{u})=0 \tag{1}\]  
where \(\rho\) is the fluid density, and \(\overline{u}\) is the velocity vector.  
The ambient fluid density is fitted as a polynomial with temperature according to the empirical equation [32]:  
\[\rho=1001.05-0.0685T-0.0038T^{2} \tag{2}\]  
(2) Momentum conservation equation [31]  
\[\begin{cases}\frac{\partial(\rho u)}{\partial t}+div(\rho u\overline{u})=- \frac{\partial p}{\partial x}+\frac{\partial\tau_{xx}}{\partial x}+\frac{ \partial\tau_{yx}}{\partial y}+\frac{\partial\tau_{yx}}{\partial x}+F_{x}\\ \frac{\partial(\rho v)}{\partial t}+div(\rho v\overline{u})=-\frac{\partial p }{\partial y}+\frac{\partial\tau_{yx}}{\partial x}+\frac{\partial\tau_{yx}}{ \partial y}+\frac{\partial\tau_{yx}}{\partial x}+F_{y}\\ \frac{\partial(\rho w)}{\partial t}+div(\rho w\overline{u})=-\frac{\partial p }{\partial x}+\frac{\partial\tau_{yx}}{\partial x}+\frac{\partial\tau_{yx}}{ \partial y}+\frac{\partial\tau_{yx}}{\partial x}+F_{z}\end{cases} \tag{3}\]  
where \(p\) is pressure, \(u,v,w\) are the components of the velocity vector \(\overline{u}\) in the \(x,y,z\) directions, \(\tau_{xx},\tau_{xy},\tau_{xz}\) et al. are the components of the viscous stress \(\tau\) acting on the surface of the microelement due to the molecular viscosity action, and \(F_{x},F_{y},F_{z}\) are volume forces on the element.  
(3) Energy conservation equation [31]  
\[\frac{\partial(\rho T)}{\partial t}+div(\rho\overline{u}T)=div\bigg{(}\frac{ \lambda}{c_{p}}gradT\bigg{)}+S_{T} \tag{4}\]  
where \(c_{p}\) is the constant pressure of specific heat capacity, \(T\) is temperature, \(\lambda\) is the heat transfer coefficient of fluid, and \(S_{T}\) is the viscous dissipation term.
# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 2 Problem Modeling and Formulation
### Algorithm Stopping Criteria  
The business challenge that FLECO aims to solve, as described in Section 1, requires the swift response of a feasible solution. A feasible solution in this context must satisfy the following requirements:  
* The solution is provided in a timely manner. Since the solution must be discussed in a meeting to reach agreements, it is necessary that the solution is provided to the cross-functional cybersecurity workforce by FLECO within a reasonable timeframe, no longer than 5 min. This requirement has been established by the organization's decision-maker responsible for deploying the CyberTOMP framework. Subsequently, the proposed solution can be deliberated upon amongst various functional domains, ultimately accepted upon consensus, or rejected outright.
* The solution must fulfill all the specific cybersecurity constraints, which is ultimately achieved if \(f1(\overrightarrow{x})=1.0\) as described in Section 2.2.
* The algorithm will terminate when either of the two conditions is met.  
In the event that the algorithm terminates due to time constraints (the limit of five minutes), the population may not have converged, and the resulting solutions may not be feasible. This can occur when the algorithm reaches a stagnation point and is unable to escape it, but it is more likely to happen when the strategic constraints are highly stringent or even contradictory, rendering it impossible to identify a solution that satisfies all of them.  
Furthermore, in the process of designing and developing FLECO, it was determined that the algorithm must be able to run on general-purpose hardware, comparable to the ones employed in the organizations' operational setting, such as a standard laptop or desktop personal computer, rather than on specialized hardware.
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 3 Validation of the Numerical Method
### Reinforcement Effect  
The reinforcement effect is not mentioned in the aforementioned verification. Therefore, the reliability of this method is further examined by comparing with field monitoring data of layered ground by DC reported by [PERSON] et al. [26]. The foundation stratums, from the top down, were sandy soil, embedded weak silt clay layer, and sandy soil, as shown in Figure 5. In this study, the cap parameters of sandy soil used are the same as those of [PERSON] et al. [18; 32]. The relevant parameter of the in-between weak layer was used and selected from those of [PERSON] et al. [17; 31]. The coefficient of permeability of sandy soil and embedded weak layer soil and the bulk modulus of pore water are assumed to be \(1\times 10^{-2}\) m/s, \(1\times 10^{-7}\) m/s, and \(1\times 10^{4}\) kPa, respectively, based on [PERSON] et al.'s numerical analysis [17; 31]. To sum up, the soil parameters used in the numerical simulation are shown in Tables 2 and 3.  
Figure 2: Excess pore water pressure versus time. (**a**) 1500 kN\(\cdot\)m; (**b**) 2500 kN\(\cdot\)m [17].  
The FEM model contained 2000 solid meshes and 2000 fluid meshes, respectively. The whole model was 20 m in width and 30 m in height and divided into three layers. The FEM analysis was performed using a four-node axisymmetric quadrilateral element with reduced integration. The groundwater level was set on ground surface as reported [26]. The vertical boundaries were fixed in the horizontal displacement, horizontal and vertical constraints were fixed at the bottom, the drainage boundary was free on the foundation surface, and the undrained boundary conditions were set at the bottom and side boundaries. Following the field tests, the tamper radius (R) and drop height (H) were kept as 1.2 m and 20 m, respectively. The tamping energy of 3000 kN\(\cdot\)m was simulated, and the blows were 10 at each impact point. A stable time step of \(10^{-6}\) s was used in the explicit analysis.  
In Figure 6, the simulated results and the measured data of DC tests in layered soils are compared, plotting the relative density of the soil against the ground depth. It is shown that the simulated and measured results are in a good agreement, which indicates that the fluid-solid coupling FEM method with the improved cap model is reliable for the analysis of the DC problem in layered soils.  
Figure 4: Comparison with the field results for the case of 2500 kN\(\cdot\)m tamping energy [17]. (**a**) Excess pore water pressure versus soil depth; (**b**) Lateral displacement versus soil depth.  
Figure 3: Comparison with the field results for the case of 1500 kN\(\cdot\)m tamping energy. (**a**) Excess pore water pressure versus soil depth; (**b**) Lateral displacement versus soil depth.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 2 Maritime Scenario and Problem Statement
### Status in the 700 MHz Band  
The main advantages of using a carrier frequency at 700 MHz are:* A low loss propagation and, thus, covering long distances requires less transmitted power [26].
* In the 700 MHz band, the frequency-division duplex (FDD) spectrum usage technique is mainly implemented in today's networks. Accordingly, operating at this band does not impose any synchronization challenges and, thanks to the fact that DL and UL slots are always immediately available, ultra-low latency use cases can be better supported.
* A reduced power consumption of user equipment, leading to it being more suitable for battery-powered devices and IoT.  
Concerning the disadvantages of 700 MHz operation, we can name the following:  
* The available bandwidth is limited in the 700 MHz band.
* UL and DL bandwidths are immutable; thus, there is no flexibility for dynamic bandwidth management, which may be important in certain variable-rate applications.
* The low frequency of operation poses certain hardware design challenges, especially related to the size of certain components which, in particular, makes the implementation of beamforming rather inefficient.
* The low propagation losses, strong diffraction and strong reflection properties of the 700 MHz band present challenges in terms of controlling coverage areas, potentially leading to interference, particularly in regions where other technologies might be still using this spectrum.