esa-sceva/satcom-chunk-collection huggingface dataset:
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 2 The Beam-Like Model
### Kinematics of Frames and Beams  
The proposed beam-like model (BLM) is able to represent 3D structures (Figure 2a) by means of a 1D beam element (Figure 2d). With reference to the \(k\)-th storey of the building in the \(\mathrm{X},\mathrm{Y}\) plane, shown in Figure 2b, the centers of mass (CM) and stiffness (CS) will be assumed as not coincident to each other, thus inducing a torsional behaviour of the structure. It is worth noting that, at each floor, the CM point is placed on the global vertical axis (Z) and, consequently, the CS coordinates coincide with the CS-CM eccentricity \((e_{x},e_{y})\). Each inter-storey is modelled by a beam segment with uniform stiffness properties, distributed masses \(m_{x,k},\ m_{y,k}\) and second order moment \(l_{0,k}\). Concentrated values for the masses \(M_{x,k}\) and \(M_{y,k}\), as well as the second order moment \(\widetilde{l}_{0,k}\), are applied at the end of each beam segment in order to simulate the presence of the beam and floor masses and applied loadings.  
In order to simplify the description of the kinematics of the multi-storey frame, the hypothesis of rigid floors and inextensible columns have been assumed. Figure 2b sketches the \(k\)-th storey and indicates the displacements \(u_{x,k}(t)\), \(u_{y,k}(t)\) and the rotation \(\delta_{z,k}(t)\) of the CM of the \(k\)-th rigid floor. According to the hypothesis of rigid floors, very often adopted in the literature, the end of each column, which has the coordinates \((x_{i,k},y_{i,k})\) at each \(k\)-th floor level, has the following displacement components:  
\[\begin{split} u_{x,k}(t)&=u_{x,k}(t)+\delta_{z,k}(t )y_{i,k}\\ u_{y,k}(t)&=u_{y,k}(t)-\delta_{z,k}(t)x_{i,k}\end{split}\, \tag{1}\]  
At this stage it could be anticipated that an original strategy to take into account also the out of plane floor deformability is proposed and discussed in the next sub-section.  
Each building inter-storey \(k\) is modelled by means of an equivalent beam segment having the same length \(h_{k}\) and uniform properties (Figure 2c).  
Displacements in the \(x\) and \(y\) direction and rotations of the beam-like are assumed as continuous functions of the abscissa \(z\) and denoted, respectively, as \(u_{x}(z,t),\ u_{y}(z,t),\ \delta_{z}(z,t)\).  
The complete non-uniform beam-like model is obtained by the sequence of all the beam segments with uniform properties each, as shown in (Figure 2d).  
Figure 2: Conceptual representation, starting from (a) the 3D structure; (b) the generic kth floor (or inter-storey) up to the (c) sub-beam element; and the (d) the proposed beam-like model.
## 1 Introduction
In this paper, we provide a comprehensive review of the latest results on the development of silicon-based nonlinear optical devices for all optical processing of complex modulation formats achieved by our research group through extensive collaborations with various academic partners namely University of Pavia, CEA-Leti, University of Athens and the Technological Educational Institute of Athens. The paper is organized as follows: Section 2 is devoted to the discussion of amorphous silicon based devices, while Section 3 focuses on silicon germanium devices. Section 4 is dedicated to new CMOS compatible materials that are currently being studied in our group and can serve as a platform for future efficient optical nonlinear devices. Finally, conclusions are drawn in Section 5.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 2 Materials and Methods
### Controller Architecture  
The controller operations for a given time step are as follows. First, a trajectory is generated between the current system state and the set point. This trajectory is used to determine the current desired velocity and acceleration. Based on a system dynamics model, the desired velocity and acceleration are used to calculate a control input to the system that will achieve the desired velocity and acceleration. The inputs are applied to the system, and then measurements of the system state are fed into a state estimator, and the estimated system state is used to optimally update the estimates for the parameters of the system dynamics model. Then this new system state estimate is used to generate a new trajectory, and the process repeats.
## 3 Post-Layout Simulation Results
### Operational Amplifier  
In this section, results of post-layout simulation of the FDOA used in 2 nd order filter are presented. Gain and phase response under nominal bias conditions of the LP and HP path FDOA is shown in Figure 9. The LP path FDOA has 43.1 dB gain, a phase margin of 50.8 degrees, and a bandwidth of 6.27 MHz, which equals a GBWP of 1.26 GHz. The power consumption is 2.32 mW from 1.2 V supply. Similarly, the HP path FDOA has 51.3 dB gain, a phase margin of 19.1 degrees, and a bandwidth of 7.59 MHz, which equals a GBWP of 2.22 GHz. The power consumption is 7.64 mW from 1.2 V supply.  
FDOA gain-bandwidth product, maximum passband gain, in-band output-referred third-order intercept point (OIP3) and power consumption at different bias control values are shown in Figures 10 and 11. By changing the value of the bias control, power consumption can be used as a trade-off to change the GBWP of FDOA and optimize performance of the filter for different bandwidth settings. For LP and HP paths, respectively, the GBWP can be changed from 0.36 GHz to 2.7 GHz and from 0.85 GHz to 4.4 GHz. The change of bias has a small impact on other FDOA parameters. For LP path, the gain varies from 38 dB to 44.6 dB, the OIP3 from 22.4 dBm to 21.15 dBm, while for HP path, the gain varies from 49.8 dB to 51.3 dB, the OIP3 from 25.4 dBm to 22.4 dBm across the bias control range.  
Figure 10: Gain–bandwidth product (GBWP) and maximum gain response at different bias control values of the fully differential operational amplifiers used in 2 nd order filter: (**a**) low frequency path (LP); (**b**) high frequency path (HP).  
Figure 9: Gain and phase response under nominal bias conditions of the fully differential operational amplifiers used in 2 nd order filter: (**a**) low frequency path (LP); (**b**) high frequency path (HP).
## 4 Multi-Objective Parameter Tuning
### Pareto Optimality  
The definition of Pareto optimality, and by extension, the Pareto-optimal set, is based on the concept of non-dominance. Suppose two parameter configurations \(\theta_{1}\) and \(\theta_{2}\) with respective performance vectors \(\mathbf{C}(\theta_{1})=\mathbf{C}_{1}=\left[C_{1}^{1},C_{1}^{2},\dots,C_{1}^{n}\right]\) and \(\mathbf{C}(\theta_{2})=\mathbf{C}_{2}=\left[C_{2}^{1},C_{2}^{2},\dots,C_{2}^{n}\right]\). Configuration \(\theta_{1}\) dominates configuration \(\theta_{2}\) (denoted \(\theta_{1}\prec\theta_{2}\)), if and only if \(C_{1}^{i}\leq C_{2}^{i}\)\(\forall i\in\{1,2,\dots,n\}\), and \(\exists j\in\{1,2,\dots,n\}\) such that \(C_{1}^{j}<C_{2}^{j}\).  
Now suppose a subset \(\bar{\Theta}\subset\Theta\) where \(\forall\bar{\Theta}\in\bar{\Theta}\), \(\
exists\theta\in\Theta\setminus\bar{\Theta}\) such that \(\theta\prec\bar{\theta}\). Such a set \(\bar{\Theta}\) contains non-dominated (or Pareto-optimal) parameter configurations. It is known as a Pareto-optimal set. The corresponding objective vectors of the configurations in this set, when plotted in objective space, will form a Pareto front. An example of a Pareto front is shown in Figure 1. Here, the red data points are the set of non-dominated points, while the blue dots are all points that are dominated by at least one point in the Pareto optimal set.
## 9 Conclusions  
This paper proposes a stacking model based on the single, ensemble, and deep ML models combined with hyper-parameter tuning to enhance each model's prediction performance. This stacking method greatly improves the forecasting accuracy and reliability, and the stacking model has an R2 of 0.98 and a CoV of 0.147 in the test set, and it is much better than other independent ML models (R\({}^{2}\) = 0.88-0.95, CoV = 0.179-0.651). The following conclusions are mainly obtained:  
1. In the single model, DT achieves an R\({}^{2}\) of 0.94 for the test set, showing good trend-capturing ability. Among the ensemble models, Gradient Boosting performs the best and is comparable to DT in terms of RMSE and R\({}^{2}\) and significantly outperforms other ensemble methods, such as RF and Bagging. DNN shows the strongest predictive power among all models, with the lowest RMSE (263) and a test R\({}^{2}\) of 0.95, far outperforming the majority of ensemble models.
2. Among the traditional analytical models, the Hwang and Lee model shows the best prediction performance with an RMSE of 291.1, MSE of 68,166.2, MAE of 172.1, and an R\({}^{2}\) of 0.87, demonstrating a good ability to explain data variability. Compared to ML methods, such as DNN, RF, and Gradient Boosting, the model is able to achieve a better balance of accuracy and interpretability than traditional analytical models.
3. The stacking model has an RMSE of 128.2 on the test set, which is significantly more accurate in terms of prediction compared to other independent ML models (263-335.7). Second, the MSE of the stacking model in the test set is 1644.4, which is much lower than those of other independent ML models (69,173.2-145,930), showing its superior fitting ability and sensitivity to data variability. The stacking model has a test set MAE of 65.8, which is also better than those of other independent ML models (107.7\(-\)264.6), indicating its ability to provide more accurate predictions. Further, the stacking model's R\({}^{2}\) reaches 0.98, showing its good ability to capture variations and trends in the data, while the other models' R\({}^{2}\) is generally lower than 0.95. Finally, the stacking model's CoV stays low for both the training set and the test set, reflecting its stability (0.013 and 0.147 for the training and test sets, respectively); the stacking model shows better consistency performance, which is significantly better than other independent ML models (R\({}^{2}\) = 0.88\(-\)0.95, CoV = 0.179\(-\)0.651).  
Methodology, S.T. and X.N.; Investigation, S.T.; Resources, Y.W.; Data curation, Y.W.; Writing--original draft, S.T.; Writing--review & editing, X.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 2 Omni-Directional SGM with Reliable Cost Propagation
### Cost Volume Update Scheme
When \(\omega\) is a large number, we have \(\varphi(\mathbf{p})\approx 1.0\), which means we utilize the normalized aggregated cost as the input of next pass, it is similar to the strategy used in Ref. [33]. However, it will result in the wide spread of unreliable matching cost, deteriorating the quality of the disparity image. With this cost volume update scheme, the formulas of our algorithm in each direction are slightly different from that provided in Section 2.2. The initial cost volume \(C(\mathbf{p},d)\) in Equations (6)-(8) should be replaced by \(\tilde{C}(\mathbf{p},d)\). Suppose the size of input image is M\(\times\)N, and the disparity space is D. Traditional SGM only takes the message from pixel on the same scan line into account, so that the computational complexity of traditional SGM is \(O(MND)\). MGM utilizes the information from pixels visited in previous scan line, and the computational complexity of MGM is two times of that for SGM. Our method needs to compute the outputs from all three child nodes, since the computational complexity is three times of that for SGM. However, both SGM and our method can be implemented in parallel taking advantage of FPGA, while MGM can only be implemented in a raster order as it introduces dependency along the neighboring scan lines. A comparison of SGM, MGM and our method on computational complexity, parallelization, and pixels under consideration are presented in Table 1. Our method takes pixels in the whole image into account with little extra computational cost, and still can be implemented in parallel.
## 6 Safety Mechanisms Enabled by AI Agents
### Redundancy and System Resilience  
In autonomous maritime operations, where long missions unfold far from technical support, system failure is not an option. To safeguard against breakdowns and preserve mission continuity, AI agents actively manage multiple layers of redundancy and fail-safe logic, ensuring the vessel can maintain safe operation even in degraded or partially impaired conditions.  
Redundancy: Hardware and Software  
Redundancy begins at the hardware level, where critical subsystems--such as navigation units, power supplies, propulsion controllers, and communication channels--are duplicated or diversified. The AI agent continuously monitors system health via diagnostics and sensor feedback. If anomalies are detected (e.g., signal dropout, overheating, delayed response), the agent seamlessly switches to backup modules without requiring manual intervention [116].  
On the software side, agents supervise multiple algorithmic instances and models (e.g., backup route planners, independent sensor fusion pathways) to ensure that a fault in one logic stream does not compromise the system's overall functionality. This logical redundancy is especially vital for protection against software bugs, sensor spoofing, or cybersecurity incidents.  
For instance, if the main navigation module's depth sensor fails while entering a narrow channel, the agent reroutes the decision flow through alternative sonar input, validates it against historical maps, and maintains safe course until manual recalibration is possible.  
Fail-Safe Protocols: Intelligent Degradation  
When redundancy is no longer sufficient, e.g., in cases of simultaneous failures or critical unknowns, the agent activates fail-safe modes. These are predefined, rigorously tested emergency procedures designed to bring the ship into a safe operational state.  
Fail-safe strategies include the following:  
* Entering controlled drift or \"hold position\" mode;
* Gradually reducing propulsion while broadcasting distress to surrounding vessels;
* Autonomously navigating to the nearest safe harbor or anchor zone.  
In hybrid crewed-autonomous systems, the agent can also signal for manual override or activate beacon systems to enable external rescue or remote control handover.  
Proactive Resilience Management  
AI agents do not just react to failures; they anticipate them. Through predictive analytics and multi-sensor diagnostics, agents identify degradation trends early (e.g.,power fluctuations, vibration anomalies, thermal stress) and prepare the system for graceful degradation rather than catastrophic collapse.  
Maritime resilience means not just surviving a failure but navigating through it, preserving core functionality and decision-making capability under pressure.
## 5 Perceptual Audio Features Extraction Techniques
### Cepstral Domain Perceptual Features
#### 5.6.2 Autoregression-Based Cepstral Features  
A common trait of autoregression-based cepstral features is that linear predictive analysis is incorporated within the cepstral-based framework. This group of features includes perceptual linear prediction, relative spectral-perceptual linear prediction and linear prediction cepstrum coefficients, which are described next.  
* **Perceptual Linear Prediction:** or PLP for short, this feature represents a more accurate representation of spectral contour by means of a linear prediction-based approach that incorporates also some specific human hearing inspired properties like use of a frequency Bark-scale and asymmetrical critical-band masking curves, as described by [PERSON] [241]. These features, were later revised and improved by [PERSON] _et al._[242] for speech recognition purposes and recently applied to baby crying sound events recognition by [PERSON] [201].
* **Relative Spectral-Perceptual Linear Prediction:** also referred to as RASTA-PLP, this is a noise-robust version of the PLP feature introduced by [PERSON] and [PERSON] [243]. The objective is to incorporate human-like abilities to disregard noise when listening in speech communication by means of filtering each frequency channel with a bandpass filter that mitigate slow time variations due to communication channel disturbances (e.g., steady background noise, convolutional noise) and fast variations due to analysis artifacts. Also, the RASTA-PLP process uses static nonlinear compression and expansion blocks before and after the bandpass processing. There is a close relation between RASTA processing and delta cepstral coefficients (_i.e._, first derivatives of MFCC), which are broadly used in the contexts of speech recognition and statistical speech synthesis. This features have also been applied for audio-based surveillance systems by [PERSON] _et al._[24].
* **Generalized Perceptual Linear Prediction:** also denoted as gPLP, is defined as an adaptation of PLP originally developed for human speech processing to represent their vocal production mechanisms of mammals by substituting a species-specific frequency warping and equal loudness curve from humans by those from the analyzed species (see the work by [PERSON] _et al._[32; 33]).
## 2 Materials and Methods
### Morphometric Analysis  
The CT images were transferred to a computer for morphometric assessment. Measurements were obtained from CT transverse images using standardized soft tissue window settings (window width WW: 250-450; window level WL: 30-50). All measurements were performed between L1 to L7 at mid-vertebral body level. This segment was chosen due to the scarce morphometric information of components of the vertebral canal at this level in dogs. All image data were imported and recorded into the Osirix(r) DICOM viewer (Pixmeo Inc., Version 3.9.4., 32 Bit, Bernex, Switzerland), a software for morphometric analysis. Anatomical parameters were measured at the mid-level of the vertebral body of the lumbar segment (L1-L7). Measurements were taken in millimeters in the axial planes and were collected three times by the same observer. The following anatomical parameters were determined considering the anatomical structures shown in Figure 2:  
* cross sectional area of the vertebral canal (mm\({}^{2}\));
* cross sectional area of the dural sac (mm\({}^{2}\));
* total area of the IVVP (area of the right and left IVVP component) (mm\({}^{2}\));
* area of the epidural space: vertebral canal area minus the area occupied by the dural sac (mm\({}^{2}\));
* percentage of the IVVP occupying the vertebral canal;
* percentage of the dural sac occupying the vertebral canal;
* percentage of the IVVP within the epidural space;
## 2 Materials and Methods
### Measurements  
#### 2.3.1 Thermal Stability Measurements  
Thermogravimetric analysis (TGA) is a common method to test the thermal stability of surfactants. The instrument used in this experiment was an HTG\(-\)3 thermogravimetric analyzer. The argon flow rate was 50 mL\(\cdot\)min\({}^{-1}\) and the heating rate was 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\).
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks
### Cognitive Radio Functions
#### 2.1.4 Spectrum Mobility  
The event of spectrum handoff or mobility is when the conditions of the channel in use by the CR node degrade due to interference or outage in the channel or node movement or PU appearance, and the CR node must change its frequency of operation so that the communication continues in a different free part of the spectrum [55]. The different layer protocols of the network stack, such as protocols at the transport layer closely coupled with the lower level spectrum detecting, neighbor disclosure in a link layer and routing protocol, need to adapt to the channel transmission parameters to execute spectrum handoffs efficiently [42; 55].
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 4 Empirical Analysis
### Comprehensive Evaluation of US Air Traffic Network Reliability  
The fuzzy set weights of the CORN, TTRN, and CARN are calculated based on the Delphi method and converse generator. The results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index\(\backslash\)Month** & **September** & **October** & **November** \\ \hline \(\mu_{\text{cap}}\) & 0.8684 & 0.8674 & 0.8627 \\ \(\sigma_{\text{cap}}\) & 0.0244 & 0.0238 & 0.0275 \\ \(R_{\text{cap}}\) & 0.8440 & 0.8436 & 0.8352 \\ \hline
**Index\(\backslash\)Month** & **December** & **January** & **February** \\ \hline \(\mu_{\text{cap}}\) & 0.8342 & 0.8499 & 0.8549 \\ \(\sigma_{\text{cap}}\) & 0.0251 & 0.0269 & 0.0297 \\ \(R_{\text{cap}}\) & 0.8091 & 0.8230 & 0.8252 \\ \(\overline{R}_{\text{cap}}\) & 0.8300 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The capacity reliability of the air traffic network (CARN) of the US air traffic network.  
Figure 5: Flight schedule on 15 January of ATL airport.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Fuzzy Set Weights** \\ \hline CORN & (0.155, 0.046, 0.0179) \\ TTRN & (0.345, 0.034, 0.0147) \\ CARN & (0.500, 0.050, 0.0003) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fuzzy set weights of the connectivity reliability of the air traffic network (CORN), TTRN, and CARN.  
Then, the actual uncertainty transformation models of the CORN, TTRN and CARN can be obtained based on the converse generator, seen in Table 9.  
The comprehensive evaluation uncertainty transformation model \(RC\) is calculated based on Equation (16):  
\[RC=\sum_{i=1}^{3}R_{i}\times W_{i}=(0.7955,0.0920,0.0190) \tag{17}\]  
The membership fuzzy sets are shown in Figure 6. Fuzzy sets with \".\" correspond to five rules, and fuzzy sets with \"o\" correspond to the reliability of the US air traffic network.  
By comparing the fuzzy sets, we find that the most similar fuzzy set to the reliability fuzzy set of the US air traffic network is the relatively reliable fuzzy set, indicating that the reliability of the US network is relatively high. Although the CORN of the US air traffic network is low, the total reliability of the US air traffic network is high. Thus, we can conclude that the air traffic organization and management in the United States is efficient.  
We also evaluate the reliability of the US air traffic network during peak hours (10:00-14:00) and nonpeak hours (19:00-23:00). The comprehensive evaluation uncertainty transformation model of peak hours is (0.7335, 0.1041, 0.0082), and the uncertainty transformation model of nonpeak hours is (0.8376, 0.0832, 0.0096). It can be seen that the reliability of the network in non-peak hours is higher than that in peak hours. This is because the air traffic demand is much higher and the flight delay is much larger during peak hours. The disorder of air traffic in peak hours is much larger than that in nonpeak hours.