esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Iterative Process to Obtain the Pressure Loss Coefficients per Unit Length
### Application of the Compact Model with a Random Case  
In order to check the correct working of the compact model and that the correlations are able to supply coefficients that approximate the flow pattern correctly, a simulation of a new detailed model case was performed. The modeling strategy is the same as that  
Figure 9: Flow magnitudes for the outlet cross-section of the domain obtained via Idelchick vs. the flow magnitudes obtained via the detailed models.  
presented in [PERSON] et al. [17]. The geometric and operation parameters corresponding to this new case are presented in Table 6. Figure 10 presents the results obtained by the compact model (employing the pressure loss coefficients given by the correlations), by the detailed model, and by the coefficients provided by the Idelchick handbook [20]. As the figure shows, the prediction given by the compact model is closer to the detailed one than the prediction using the Idelchick handbook. The fit for the minimum and maximum axial velocities is better with the compact model that is introduced in this work, as is the adjustment for the tangential velocity. As the Idelchick handbook does not include pressure loss coefficients perpendicular to the flow direction, more tangential flow passes the screen when solving the flow by the coefficients given in this handbook. Therefore, the compact model presented in this work that consists of three directional pressure loss coefficients logically approximates a three-dimensional flow better.  
It is necessary to note that the new case is supposed to be between those cases for which the Idelchick handbook is expected to provide a solution, as the level of the porosity of the screen is fixed to its higher value (see Table 6). This way, the value for the coefficient perpendicular to the flow direction tends towards a lower value. Therefore, through this new case, apart from showing that the compact model functions well, it was demonstrated that even for cases with high porosity, these correlations work better than the modeling currently employed.  
Figure 10: Axial and tangential velocity fields (m/s) for the outlet cross-section of the domain for the new case obtained by the compact model, by the detailed model and by Idelchick.
# Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure
## 3 Results and Discussion
### First Experiment with MaxSpeed 50 km/h and 40 km/h  
For the 12 spatiotemporal scenarios, the 14 cells, and the 28 days, we had a total of 14*12*28 = 4704 measurements. In Table 2, we show the results for one day (3 August 2013) with _MaxSpeed_ = 50 km/h and in Table 3, with _MaxSpeed_ = 40 km/h. ND stands for No Data; this means that there were not enough trajectories (less than 11) for a cell in a specific spatiotemporal scenario. Note that all the measurements for cells 3 and 4 are equal to ND. We discuss the results of the first experiment together with those of the second one in the next Section 3.2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{TempSeg} & \multicolumn{8}{c}{**Cell**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline
**(000; 02;000)** & ND & 0.471 & ND & ND & 0.089 & 0.268 & 0.606 & ND & 0.153 & 0.331 & 0.438 & ND & ND & 0.485 & ND & ND \\
**(02;00; 04;00)** & 0.048 & 0.405 & ND & ND & 0.157 & 0.331 & 0.569 & ND & ND & 0.423 & 0.569 & ND & ND & ND & ND & 0.601 \\
**(04; 00; 06;00)** & 0.539 & 0.514 & ND & ND & 0.069 & 0.405 & 0.680 & ND & ND & 0.421 & 0.534 & 0.247 & ND & ND & ND & ND \\
**(06; 00; 08; 00; 00)** & ND & 0.568 & ND & ND & 0.091 & 0.374 & 0.586 & 0.264 & 0.571 & 0.438 & 0.627 & ND & ND & 0.622 & ND & ND \\
**(08; 00; 00; 100; 00)** & 0.409 & 0.782 & ND & ND & 0.083 & 0.396 & 0.659 & ND & 0.399 & 0.424 & 0.474 & 0.345 & 0.107 & 0.320 & 1 & ND \\
**(100; 100; 1200)** & 0.176 & 0.587 & ND & ND & 0.094 & 0.381 & 0.734 & 0.408 & 0.441 & 0.452 & 0.528 & 1 & ND & 0.352 & 0.090 \\
**(12; 100; 1400)** & 0.064 & 0.660 & ND & ND & 0.068 & 0.404 & 0.691 & 0.507 & ND & 0.500 & 0.505 & ND & ND & ND & ND & ND \\
**(14; 100; 1600; 1600)** & 0.514 & 0.627 & ND & ND & 0.828 & 0.436 & 0.727 & ND & ND & 0.458 & 0.494 & ND & ND & 1 & 0.587 & ND \\
**(16; 160; 1800)** & ND & 0.570 & ND & 0.169 & 0.455 & 0.738 & ND & 0.889 & 0.455 & 0.504 & ND & ND & 1 & ND & ND \\
**(18; 100; 200; 2000)** & ND & 0.594 & ND & ND & 0.085 & 0.414 & 0.619 & 0.055 & 0.456 & 0.398 & 0.458 & ND & 0.537 & 0.220 & ND & ND \\
**(200; 2200; 0)** & 0 & 0.274 & ND & ND & 0.101 & 0.364 & 0.646 & 0.206 & 0.500 & 0.362 & 0.419 & 0.504 & ND & 0.313 & 0.435 & ND \\
**(22; 00; 000; 0)** & 0.449 & ND & ND & 0.112 & 0.271 & 0.598 & 0.138 & 0.277 & 0.320 & 0.374 & 0.257 & ND & 0.528 & 0.413 & ND \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated speed limit rates for 3 August 2013 with _MaxSpeed_ = 50 km/h.
# High-Sensitivity Displacement Sensor Using Few-Mode Optical Fibers and the Optical Vernier Effect
## 4 Results and Discussion
### OVE Analysis (Spectral Shift and Slope)  
The spectral shift of the envelopes as a function of linear displacement was subsequently analyzed. To ensure a fair comparison, all devices were calibrated over a displacement range of 0 to 15 mm, with intervals of 2.5 mm. The resulting spectral shifts are illustrated in Figure 9, while the corresponding sensitivities are displayed in Figure 10. The experiment numbers 1 through 5 from Table 1 correspond to Figures 9a-e and 10a-e, respectively.  
These results demonstrate that the lengths of the _FMF\({}_{Ref}\)_ and _FMF\({}_{Sen}\)_ significantly impact the amplification of the OVE, as well as the sensor's usable range and sensitivity. With each incremental increase in _FMF\({}_{Ref}\)_ and _FMF\({}_{Sen}\)_ lengths, there is a corresponding amplification in the _FSR\({}_{envelope}\)_, enhancing the detection system's sensitivity, as seen in Figure 10a-e. However, as the _FSR\({}_{envelope}\)_ increases significantly, the complete range of spectral shift becomes too large to capture within the optical spectrum analyzer's (OSA) measurement window, as shown in Figure 9a-e. This limitation means that a higher sensitivity comes at the cost of a reduced measurement range. For instance, in Figure 9b, the range is reduced by 2.5 mm (from 0-12.5 mm), while in Figure 9c, a 5 mm reduction (from 0 to 10 mm) is observed. In Figure 9e, the range is further reduced by 10 mm (from 0 to 5 mm).  
Figure 9: The spectral shift of the envelopes as a function of linear displacement from table 1: (**a**) experiment 1, (**b**) experiment 2, (**c**) experiment 3, (**d**) experiment 4 and (**e**) experiment 5.  
As highlighted in the previous discussion, sensitivity increased as the lengths of \(FMF_{R\!\!f}\) and \(FMF_{Sen}\) extended, with values ranging from 9.36 nm/mm in Figure 10a to 39.07 nm/mm in Figure 10e. Each device displayed similar slopes between the envelopes, suggesting that the FMF provided a highly uniform refractive index and light dispersion characteristics. Furthermore, all devices exhibited an R\({}^{2}\) value of 0.99 or higher, indicating a high degree of linearity. This high linearity is crucial for sensor development as it confirms the system's consistency, accuracy, reproducibility, and ease of calibration.
## 3 Automatic Procedure to Design the Location of Control Points in the Geodetic Network  
For the establishment of a geodetic network, we must define which points of the network will have their coordinates previously determined in the desired reference system. These points are called control points, or constraint points. These points that allow the other points of the geodetic network to be linked to a reference system. Therefore, it is essential to define the location of these control points at the design stage of a geodetic network. the proposed automatic method here focuses on designing of the geodetic networks in terms of high reliability. Under the present proposal, the quality criterion to be considered during the design stage is based on the smallest value of the maximum influence of an outlier on the coordinates of the network (i.e., maximum external reliability). The method does not depend on the real measurements values but only on the model design, i.e., the network geometry and covariance matrix. The computations can be performed as follows:  
1. Defining a significance level \(\alpha\) and the type II error \(\beta\) in order to compute the non-centrality parameter. Here, we use the recursive algorithm based on the work by [PERSON] and [PERSON] [33], namely bisection algorithm, in order to obtain the non-centrality parameter for one degree of freedom, i.e., \(\lambda_{q=1}\). Typically a value of the level \(\alpha=0.001\) and \(\beta=0.2\) is adopted (see, e.g., [4]).
2. Defining a geodetic network configuration as well as the uncertainties of the observations, i.e., the design matrix \(A\) and the covariance matrix of the observations \(Q_{e}\), respectively. The covariance matrix of the observations \(Q_{e}\) may consist of random effects and the uncertainties associated with the correction of systematic effects. The latter follows from the instrument precision, measurement techniques and field condition. In this step, the design matrix and covariance matrix are conditioned to the position of the control point (or by the combination of control points) in the network. It is important to mention that the design matrix defined must have a minimum configuration to avoid rank deficiency [47].
3. Computing the MDB for each observation according to Equations (7)-(9).
4. Computing the external reliability according to Equation (10).
5. Computing and store the maximum external reliability according to Equation (11).
6. Checking whether all the points (or all combination of points) of the network were configured as control point. If not, select a new control point (or new combination of control points) and return to Step 3. Otherwise, the algorithm selects the configuration of the network that has the lowest value of the maximum external reliability. Important to mention that matrix \(A\) is modified when a new point (or a new combination of points) is selected as the control.  
The proposed procedure is summarised as a flowchart in Figure 1.  
Figure 1: Flowchart of the automatic method proposed to design the location of control points in the geodetic network.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Air and Space  
There are many objects over the water surface that can be used for networking in oceans. The typical one is satellites, which are the only available solution to provide reliable Internet services to cover the ocean. However, its high cost makes it difficult to become popular especially among the ordinary users as discussed in Section 4.2. At much lower attitudes, planes and helicopters can provide powerful communication occasionally to bypass users, and drones as well as balloons near the water surface may also provide limited communication services subject to their energy constraint.  
In between, a quasi-stationary aerial platform called High-Altitude Platform (HAP) can be deployed in the stratosphere 17-22 km above the Earth's surface [27]. It can be used to provide more cost-effective communication services with a coverage radius up to 100 km, which depends on the altitude and elevation angle. More discussion can be found in Section 4.5.
## 7 Defenses against Privacy Attacks  
Privacy attacks in FL pose significant risks to the confidentiality of sensitive data. To safeguard against these threats, a variety of defense mechanisms have been developed. The upcoming subsections will evaluate these strategies, highlighting methods such as differential privacy, Secure Multi-party Computation, and homomorphic encryption. We will examine how these techniques protect individual data contributions from inference and reconstruction attacks, ensuring that privacy is maintained throughout the FL process.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Defense Framework** & **Definition** & **Implementation Stage** & **References** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Detect and Remove} & Identifies and eliminates malicious updates or compromised clients using anomaly detection or statistical analysis to protect the global model’s integrity. & Pre-aggregation & [61; 82; 83; 84; 85; 86; 87; 88; 89] \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Adversarial Training} & Models are trained using adversarial examples to enhance robustness against attacks on data and model updates. & Pre-aggregation & [91; 93] \\ \hline \multirow{3}{*}{3} & \multirow{2}{*}{Model Pruning} & Reduces model size by removing unnecessary parameters, making it harder for adversaries to inject malicious updates while improving efficiency. & Pre-aggregation & [102; 103] \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Byzantine/Reliable Aggregation} & Aggregates updates consistent with the majority to filter out outliers from potentially compromised clients. & In-aggregation & [77; 78; 104; 105; 106; 107; 108; 109] \\ \cline{1-1} \cline{3-5} \multirow{2}{*}{5} & \multirow{2}{*}{Robust Client Selection} & Selects clients based on reliability, reducing the impact of malicious or unreliable clients on the global model. & Pre-aggregation & [110; 111; 112] \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Data and Update Analysis} & Analyzes local data and model updates to detect inconsistencies or suspicious behavior indicative of adversarial attacks. & Pre-aggregation/Post-aggregation & [113; 114; 115] \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Blockchain} & \multirow{2}{*}{Atds transparency, immutability, and auditability to FL using blockchain, preventing tampering and ensuring trustworthy aggregation.} & Post-aggregation & [122; 123; 124] \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Incentive Federated Learning} & Rewards client participation, encouraging honest contributions and dissuading malicious actions. & Pre-aggregation & [131; 132] \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Regularization} & Mitigates overfitting by adding constraints to model updates, improving generalization and reducing vulnerabilities to attacks. & Pre-aggregation & [137; 138] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data and model defense frameworks for FLEach approach offers unique advantages and challenges, contributing to a robust privacy defense framework for FL systems.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Data-Driven Objective
#### 3.4.4 Optimiser Level  
Integrating physical constraints into machine learning models presents significant optimisation challenges [150; 151]. In PISMs, the total loss function is typically a weighted sum of multiple physics-informed loss components, i.e., Equation (4), where \(\omega_{j}\) represents the weight assigned to each loss term. The presence of physics-based loss terms often leads to highly complex loss landscapes, affecting convergence efficiency. As a result, optimisation becomes the primary bottleneck in training PISM models. One of the primary difficulties in physics-informed optimisation is the imbalance in loss magnitudes.  
Equation (4). Total loss  
\[\textit{Loss}_{total}=\omega_{1}\cdot\textit{Loss}_{1}+\cdots+\omega_{n}\cdot \textit{Loss}_{n} \tag{4}\]  
with:  
\(\textit{Loss}_{total}:\) Loss function on which model is trained.  
\(\omega_{i}\cdot\textit{Loss}_{i}:\) Weighted loss function for each physics-informed loss component.  
A naive approach for the loss function of Equation (4) is to set fixed weights; however, different loss components often have vastly different magnitudes. For example, PDE residuals may be orders of magnitude smaller than data loss, causing the optimiser to focus disproportionately on minimising data error while neglecting physical constraints. This imbalance can result in overfitting to data while violating fundamental physics. To address this issue, adaptive weighting techniques dynamically adjust loss term weights during training. Several approaches have been proposed [152]:  
* Gradient-Based Reweighting: The model rescales loss weights based on the magnitude of their gradients, ensuring that all terms contribute equally to optimisation [153].
* Uncertainty-Based Weighting: Weights are adjusted according to the confidence or uncertainty in each term, giving lower weights to noisier loss components [100; 154].
* Multi-Objective Optimisation: Reformulating training as a multi-objective problem allows for an automatic trade-off between competing loss components [155; 156].
* Gradient Normalisation: Rescales gradients to balance optimisation across all terms, preventing any single loss component from dominating [157].
* Hybrid training scheme: It is based on an initial first-order optimisation like ADAM, followed by a second-order optimisation [86].
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 1 Introduction
### Air-gap Covert Channels  
Air-gap isolation has always been considered as a simple and effective means for blocking intrusion, which places important computers in isolated environments without any physical connection to public networks, in order to prevent hackers from damaging or infiltrating them. However, many researchers have posed challenges to such methods. Security researchers have proposed different types of air-gap channels that can breach the air gap and establish communication with computers within an organization. These methods create invisible bridges to bypass the air-gapped system. These publicly available methods can be broadly categorized as follows: electromagnetic channel [4], thermal channel [5], acoustic channel [6], power line channel (PowerHammer) [7], and optical channel [8].
## 2 Materials and Manufacturing of Steel/GFRP Hybrid Panels
### Surface Deformation of Steel Panels  
The bond strength between the steel panel and the GFRP panels is an important factor in determining the bond characteristics and durability of a hybrid panel [1; 7]. Several methods have been used to increase the adhesive force by pre-processing the surface of the steel panel [1; 7]. A common strategy is the use of a high strength resin combined with mechanical deformation and/or adhesion-enhancing treatment of the steel surface [28; 29; 30; 31; 32]. Previous surface modifications have included scratching the steel surface, drilling holes through the steel layer, and the application of a silica sand coating [1]. The highest bond strength was obtained with the silica sand coating. Therefore, this study makes use of a similar coating to enhance the mechanical adhesion and a high-strength vinyl ester resin to enhance the chemical adhesion between the hybrid layers.  
Figure 2(a) shows specimens with no surface processing (control) and Figure 2(b) shows specimens in which the steel surface was sand-blasted with silica particles [1; 7].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Material**} & \multicolumn{5}{c}{**Physical and Mechanical Properties**} \\ \cline{2-6}  & **Yield Strength** & **Elastic Modulus** & **Ultimate Strain** & **Density** & **Diameter** \\  & **(MPa)** & **(GPa)** & **(\%)** & **(g/mm\({}^{3}\))** & **(\(\upmu\)m)** \\ \hline \hline
\begin{tabular}{c} Vinyl ester resin \\ E-glass \\ \end{tabular} & 90 & 3.4 & 5.2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of glass fiber and matrix resin [1].  
Figure 3: Surface deformation of steel panels.
## 4 Results and Discussion
### Creation and Validation of the Basis Set of the High-Dimensional Space of Protein Dynamics  
The representative proteins of the nine clusters are identified by the PDB codes: 3D23, 1 HPG, 2 YOL, 1 VCP, 3 QO6, 1L1J, 1 WXR, 4 JCN, and 4I8H. Their structures are represented in Figure S6. Protein 1 GDQ was chosen as the reference structure of the whole dataset, against which the other representatives are dynamically aligned prior to lattice interpolation of their normal modes (see Section 3). In the latter, the oriented protein modes are placed and interpolated on a cubic lattice, orthonormalized, and finally, ordered. The interpolation on the grid allows us to easily compare the dynamics of any pair of proteins, irrespective of the number of residues. For instance, modes from proteins with a different number of C\({}_{\alpha}\) cannot be directly compared in terms of scalar products, while different vector fields on the grid have the same dimensionality.  
We investigated the quality of the orthonormalized modes as a basis set for the dynamics of the whole dataset, by computing the overlap between the spaces given by the protein modes and by the basis. To this aim, the RMSIP was computed between the space spanned by the first five modes of each protein in the dataset (after their interpolation on the lattice) and the first \(n\) components of the basis. For each protein, the components of the basis are ordered so as to maximize the RMSIP with the protein modes. The resulting RMSIP for each protein is plotted in Figure 6a as a function of the number \(n\) of basis vectors considered for the calculation of the RMSIP. From the distribution of the values attained when using the full basis set (45 vector fields), the RMSIP is greater than 0.5 for \(\approx\)94% of the proteins, showing in those cases a good agreement between the dynamics of the protein and the one expressed by the basis [83]. The agreement is excellent (RMSIP \(>\) 0.7) for \(\approx\)61% of the proteins; therefore, we can conclude that the identified basis is indeed able to describe with good generality the large-scale conformational dynamics of the dataset. For each protein, we also computed the normalized RMSIP, by dividing each value of the RMSIP with the value obtained with the use of the full basis set. The normalized RMSIP curves show that, for each dataset member, as few as 15 basis components are sufficient to reproduce 80% of the dynamics that would be attained with the use of the full basis set (Figure 6b); however, such components differ from protein to protein, meaning that there are no vector fields in the basis that can be considered more essential than others. This suggests that a further reduction in the dimension of the basis set would lead to a loss of generality in the description of the dynamics of this class of proteins.
## 4 Analysis on the Impact of Confined Water Approaching Construction on the Existing Station
### Effect of Embedded Depth of Diaphragm Wall on Station Deformation  
Based on above analysis, this section adopts the method of variables control to study the effect of different embedded depths on the deformation of the station, the simulation includes the following three conditions.  
Condition I: the embedded depth of the diaphragm wall is 30 m, with only partition in confined water (V);  
Condition II: the embedded depth of the diaphragm wall is 36 m, inserted into the confined water (VI), without partition;  
Condition III: the embedded depth of the diaphragm wall is 40 m, with partition in confined water (VI).  
The deformation curves of the existing station under different construction conditions are shown in Figure 8.  
According to Figure 8, the floating of existing stations decreases as the embedded depth of the diaphragm wall increases. With the addition of the wall depth, the groundwater seepage descends and the wall deformation decreases. When the diaphragm wall embedded depth is 40 m, the floating wall and surrounding surface deformation are effectively controlled under the barrier effect of the wall on ground water, thus alleviating the impact on the existing station.