esa-sceva/satcom-chunk-collection huggingface dataset:
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 5 Discussion  
The evaluation of the QESIF across multiple dimensions--including attack resilience, latency, throughput, and energy efficiency--demonstrates its comprehensive advantage in securing IoT infrastructures within smart cities. The results validate that the integration of QKD, the Q-IDS, and the hybrid protocol stack plays a non-redundant and critical role in the framework's execution. The ablation study also uncovers that the elimination of any one of these modules translates to measurable loss within core performance metrics, with the largest reduction observed within attack detection with the elimination of the Q-IDS and the largest latency incurred with the elimination of the hybrid communication mechanism.  
The QESIF's seamless movement from a quantum-secured to a classical mode of communications facilitates a balance of resource limits and security needs, a quality that is uniquely beneficial within heterogeneous smart city implementations. The hybrid construction of the protocol minimizes power usage with no loss of cryptointegrity, and the entropy-weighted QKD mechanism is tuned to device levels and environmental noise.  
Also, the design is deployable to practical requirements with minimal device-level hardware modifications using existing IoT protocols. A modular design is made possible on the QESIF to enable scalability within application areas such as transportation, energy, and public safety, where throughput, trust, and latency all have to coexist. Future extensions such as satellite-assisted QKD and blockchain-based audit trails may further strengthen the framework by enhancing geographic coverage and trust transparency. Collectively, these findings confirm the QESIF's applicability as a next-generation security solution for resilient, scalable, and energy-aware smart city infrastructures. While the QESIF maintains compatibility with common IoT protocols such as MQTT and CoAP, the integration of quantum-enhanced mechanisms introduces challenges for cross-vendor interoperability and standardization. The lack of unified quantum APIs, trust negotiation standards, and entropy exchange protocols may hinder immediate large-scale adoption in heterogeneous smart city environments.
## 6 EEG Signal Pre-Processing and Feature Extraction  
### EEG Signal Pre-Processing  
#### 6.1.1 EEG Signal Filtering  
EEG signals exhibit a measurable power spectral density across various frequency bands. Typically, the frequency content of EEG signals spans from 0.5 Hz to around 70 Hz. The frequency bands of EEG signals are standardized, as detailed in Table 3.  
Elevated signal power in the delta band is often associated with brain activity during deep sleep. Recent research has demonstrated that the frequency bands most relevant for emotion recognition, based on various publications, are the theta, alpha, and beta bands [88; 101; 102]. To focus on these specific bands, a second-order band-pass filter is employed to exclude unwanted frequencies.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 3 Results and Discussion
### Statistical Results in 2016
It can be easily seen that the tendency of each accumulated phase shift is quite similar to at least one weather stations' rainfall data. In these cases, the optimal correlation coefficients of a case all exceed 0.96, even some up to 0.99. These results reveal the validity of the data processing algorithm and the reliability of the calculated phase shift.
## 2 Materials and Methods
### Bacteria Isolation and Selection
#### 2.3.2 REP-PCR Genomic Fingerprinting  
Genomic DNA extraction was made with 250 \(\mathrm{\SIUnitSymbolMicro L}\) of a fresh culture in Tryptic Soy Broth (TSB, Sharlau, Barcelona, Spain). Cells were centrifuged for 5 min at 15,680\(\times\)\(g\) and resuspended in 50 \(\mathrm{\SIUnitSymbolMicro L}\) Tris-EDTA (TE) buffer. Cells were lysed with 5 \(\mathrm{\SIUnitSymbolMicro L}\) of Lysozyme (10 mg/mL) and incubated for 1 h at 37 \(\mathrm{\SIUnitSymbolCelsius}\), followed by a 10 min incubation at 65 \(\mathrm{\SIUnitSymbolCelsius}\) with 50 \(\mathrm{\SIUnitSymbolMicro L}\) of Lyss Solution (Genomic DNA purification kit, Fermentas, Baden-Wurttemberg, Germany). Next, 100 \(\mathrm{\SIUnitSymbolMicro L}\) of chloroform was added to the lysate and spun 5 min at 15,680\(\times\)\(g\). The aqueous phase was collected and mixed with 100 \(\mathrm{\SIUnitSymbolMicro L}\) of isopropanol, followed by centrifugation at 15,680\(\times\)\(g\) for 10 min. The resulting pellet was washed with 100 \(\mathrm{\SIUnitSymbolMicro L}\) of 70% ethanol, centrifuged (5 min, 15,680\(\times\)\(g\)), dried and finally resuspended in 50 \(\mathrm{\SIUnitSymbolMicro L}\) of TE buffer and stored at \(-\)20 \(\mathrm{\SIUnitSymbolCelsius}\). Prior to PCR reaction, an agarose gel electrophoresis was performed to verify DNA quality and extraction efficiency.  
The REP-PCR reaction was performed with the following primer sequences: REP1R (5' III ICG ICG ICA TCI GGC 3') and REP2I (5' ICG ICT TAT CIG GCC TAC 3') [32]. A reaction mixture was prepared as follows: 11.15 \(\mathrm{\SIUnitSymbolMicro L}\) of water, 5 \(\mathrm{\SIUnitSymbolMicro L}\) of 5x buffer, 3 \(\mathrm{\SIUnitSymbolMicro L}\) of MgCl\({}_{2}\) (25 mM), 1.5 \(\mathrm{\SIUnitSymbolMicro L}\) of dNTP solution (2 mM of each dNTP), 1.25 \(\mathrm{\SIUnitSymbolMicro L}\) of DMSO, 1 \(\mathrm{\SIUnitSymbolMicro L}\) of each primer (10 mM) and 0.1 \(\mathrm{\SIUnitSymbolMicro L}\) of _Taq_ polymerase. One \(\mathrm{\SIUnitSymbolMicro L}\) of DNA was used as a template. PCR amplification was performed using a MyCycler Thermal Cycler (Bio-Rad, Hercules, CA, USA) with an initial denaturation step at 95 \(\mathrm{\SIUnitSymbolCelsius}\) for 5 min followed by 30 cycles of denaturation at 94 \(\mathrm{\SIUnitSymbolCelsius}\) for 1 min, annealing at 40 \(\mathrm{\SIUnitSymbolCelsius}\) for 1 min and extension at 65 \(\mathrm{\SIUnitSymbolCelsius}\) for 8 min. The final extension was carried at 65 \(\mathrm{\SIUnitSymbolCelsius}\) for 16 min. PCR products were evaluated by ethidium bromide-stained agarose gel electrophoresis and characterized by Dice/UPGMA clustering [33]. Image acquisition was carried using a GelDoc (Bio-Rad, Hercules, CA, USA) imaging system and banding patterns were analyzed with GelCompar II 3.0 (Applied Maths, Kortrijk, Belgium).
## 4 Conclusions  
In this study, a nine degree-of-freedom dynamic model of the feed system is established by deriving the stiffness of the screw shaft and restoring the force functions of the screw nut and bearings. The influence of the excitation amplitude and design parameters on the axial vibration are analyzed. In addition, the effects of the initial contact angle, length of screw shaft and number of loaded balls on the dynamic responses are discussed, and the main conclusions may be summarized as follows:  
(1) The nonlinear contact between balls and raceways for the ball screw and bearing is the main factor leading to the nonlinear vibration of the feed system. With the excitation amplitude increasing, the axial vibration of the feed system exits the softening, hardening type nonlinearity and jump phenomenon in the amplitude-frequency curves.  
(2) The length of the screw shaft is the main parameter affecting the axial vibration characteristics of the feed system. With the increase of the length of screw shaft, the axial stiffness of the feed system is decreased, resulting in the vibration amplitude increasing and the main resonance frequency moving to the low frequency region.  
(3) With the increase of the initial contact angle and number of loaded balls, the axial stiffness increases and the axial vibration nonlinearity decreases for the feed system. Therefore, increasing the initial contact angle and number of loaded balls will improve the stability.  
From the analysis above, the axial vibration of the feed system is affected by the screw-nut, screw shaft and bearing, and has strong nonlinearity behavior in the resonance region. Future work will aim to study the influence of excitation amplitude and assembly error on the coupling vibration of the feed system.  
Figure 19: Vibration responses in the resonance region: (**a**) Time history; (**b**) Spectrum; (**c**) Phase diagram and Poincaré section.  
Figure 18: Amplitude-frequency curves of the feed system for different numbers of loaded balls.  
**Author Contributions:** Conceptualization, C.L. and C.Z.; methodology, C.L.; validation, C.L. and Z.L.; formal analysis, C.L. and S.W.; writing, C.L.; supervision, C.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by National Natural Science Foundation of China (Grant No. 51775094).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that there are no competing interests.
###### Abstract  
Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020-2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from \(10^{-3}\) to \(10^{-6}\). Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional-recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems.  
channel estimation; adaptive modulation; modulation recognition; underwater acoustic communication; machine learning; deep learning
## 3 Results and Discussion
### Processing Method Characterization Based on Producer Information  
First of all, one realized that harvest time does not coincide among producers-- this is reflected upon differences in drupe color and ripeness stage (e.g., A and F in Table 1, separated by one month). It has been reported that the stage of ripeness of _Arbequina_ table olives influences both microbiota and the final sensory characteristics thereof [30]. Therefore, predefinition of the stage of ripeness of _Cobrancosa_ is in order, probably by some easily assessed reference parameter, e.g., the maturity index (MI) elected by [PERSON] et al. [31]; note that this is a simple method, based on assessment of the color of olive skin and pulp (MI values range from 0--very green skin up to 7--purple flesh and black skin). However, looking at Table 1, it appears that most producers harvest the olives in the turning color ripening time.  
According to the information conveyed by the producers--and after careful removal of rotten drupes, stalks, and leaves--the olives are washed and added with spring water to different proportions and kept thereafter in water for 4-6 months. During this period, the table olives are washed periodically, and fresh water is added; the period between renewals of brine ranges, however, from 1 week up to two months (Table 1). This first stage of the  
processing of table olives is unique, and termed sweetening stage by the producers because its major goal is to remove bittermess. Considering that oleuprepin is the dominant bitter compound present in olives, it became important to follow the profile of that (and other) phenolic compounds throughout the process. Meanwhile, the drums are covered or closed, depending on the _modus operandi_ of each producer. After the sweetening stage, the salting stage (as so named by the producers) is in order. During this period, the water is no longer changed until the product is ready for the market; however, salt is gradually added to the brine--up to 7-10% salt content by the moment of selling.  
Temperature is a quite important technological parameter, in that it influences the microbiological and chemical profiles throughout fermentation and, consequently, the sensory properties of the final product [32]. Despite the beneficial effects of controlled temperature upon table olive fermentation, such control is too expensive, and is thus not generally applied in current artisanal manufacture. Although the room temperature in Tras-os-Montes is normally rather low in winter and rather high in summer, a noteworthy temperature amplitude is also observed within each day (Table 1)--which is expected to somehow affect the fermentation process of _Cobrancosa_ table olives.  
After eleven months, table olives are packed in 5-10 kg plastic buckets, after renewing the brine one final time with exchange by another bearing a similar percentage of salt (and devoid of any extra additives or preservatives). Finally, the buckets are sealed and duly labeled for selling.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Semantic Segmentation Architecture
#### 2.2.3 Hyperparameters Configuration  
Hyperparameters such as learning rate, epochs, and batch size should be defined before the training procedure. It is crucial to find the best sets of hyperparameters for a specific dataset as there is no correct answer to what set of hyperparameters works for all datasets. The performance of the neural network is highly dependent on the proper settings of a set of parameters [64]. Learning rate, for example, controls the step size for a model to reach the minimum loss function. A model learns faster with a higher learning rate, but it may miss the minimum loss function, which eventually influences the model result. Besides, the number of epochs defines the number of times the entire training dataset is passed through the neural network during training. Therefore, it is important to tune the hyperparameters to optimal values according to the particular dataset. The tuning of the hyperparameters for each of the networks used in this study was discussed in detail in Section 2.3.1.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 5 Intention Estimation Module
### Relative Speed Computation  
The last step of the proposed IE module is to calculate the relative speeds between the humans and the robot: they represent how fast the humans and robot are moving towards each other (or away from each other), which is crucial for computing the protective distance \(S\), according to ISO/TS 15066 [(4)].  
Human-directed speed, \(v_{RB_{i}}^{t}\), measures how fast the \(i\)-th rigid body is moving towards (or away from) the robot. It is calculated by projecting the rigid body velocity, \(v_{RB_{i}}\), onto the direction from the \(RB_{i}\) to the robot, expressed by the unit vector \(d_{RB_{i}\to R}\). Thus, \(v_{RB_{i}}^{d}\) is the dot product of the rigid body velocity, \(v_{RB_{i}}\), and the direction from the human to the robot, \(d_{RB_{i}\to R}\): it measures how much the human operator velocity is aligned with the  
Figure 5: Human operator (**left**) and robot (**right**) velocities estimated through the LKF.  
direction towards the robot. A positive value indicates that the human is moving towards the robot, while a negative value indicates that they are moving away.  
\[d_{RB_{i}\to R}=\frac{p_{R}-p_{RB_{i}}}{\|p_{R}-p_{RB_{i}}\|},\qquad v_{RB_{i}}^{d }=v_{RB_{i}}^{d}\cdot d_{RB_{i}\to R} \tag{6}\]  
On the other hand, the robot-directed speed, \(v_{R}^{d}\), measures how fast the robot is moving towards (or away from) the human. It is calculated by projecting the robot velocity, \(v_{R}\), onto the direction from the robot to the selected rigid bodies, \(RB_{i}\). The equations are similar to the previous ones:  
\[d_{R\to RB_{i}}=\frac{p_{RB_{i}}-p_{R}}{\|p_{RB_{i}}-p_{R}\|},\qquad v_{R}^{d}= v_{R}^{d}\cdot d_{R\to RB_{i}} \tag{7}\]  
These directed speeds are crucial for determining the protective distance, \(S_{p}\), between each human and the robot, which depends on how fast both the human and the robot are closing the distance between each other. If \(v_{RB_{i}}^{d}\) is large and positive, it means the human is moving quickly towards the robot, which might reduce the time until the robot needs to stop or react. Similarly, if \(v_{R}^{d}\) is large and positive, the robot is moving quickly towards the human, which might reduce the time to avoid the human.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works
### Imaging Photoplethysmography
#### 2.1.1 Video Recording  
Imaging photoplethysmography has mainly been measured with conventional three-band (red-green-blue, RGB) cameras [2]. Monochromatic [8; 38; 40] and near-infrared [41] sensors were also employed in fundamental and early-stage research. [PERSON] et al. demonstrated that five-band cameras outperform traditional RGB cameras in the context of biomedical parameter measurement from image sequences [42]. The researchers also showed that video compression has a negative  
Figure 1: (top) Conventional approach: image processing operations are applied to the video stream to detect pixels or regions of interest (ROIs). The signal is traditionally computed using a spatial averaging operation over the ROI before being processed with spectral or temporal filters. Finally, biomedical parameters like pulse rate are estimated from this signal. (bottom) The approach we propose consists in training an artificial intelligence model using only synthetic data. The input corresponds to a video stream (image sequence). The model predicts a pulse rate for each video patch (25 \(\times\) 25 pixels over 60 frames) and thus produces a map of predictions instead of a single estimation.  
impact on PPG measurement (decrease in signal-to-noise ratio) [43]. Image resolution and sampling frequencies vary greatly even if 640 \(\times\) 480 and 30 frames per second are commonly adopted in practice [6]. Illumination parameters must be considered when PPG signals are measured from image sequences. The methods work well with both natural and/or artificial lighting. They are, in contrast, more effective when illumination is homogeneous and diffuse.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 4 Precise Statement and Solution of the Discrete-Time Control Problem
### Reference Signal in Continuous Time  
As the continuous-time reference signal has the form \(\alpha_{r}(t)=a\sin(\omega_{r}t)\), it can be generated by a dynamical system \(\Sigma_{S}\) (referred to as the _exosystem_), given as follows:  
\[\Sigma_{S}:\quad\left\{\begin{array}{rcl}\dot{r}_{1}(t)&=&r_{2}(t)\,,&r_{1} (0)=0\,,\\ \dot{r}_{2}(t)&=&-\omega_{r}^{2}r_{1}(t)\,,&r_{2}(0)=a\omega_{r}\,,\\ \alpha_{r}(t)&=&r_{1}(t)\,,\end{array}\right. \tag{60}\]  
where \(\omega_{r}>0\) must be known and \(a\in\mathbb{R}\) may be unknown. Introducing \(r(t):=\left[\begin{array}{c}r_{1}(t)\\ r_{2}(t)\end{array}\right]\), we obtain the following:  
\[\Sigma_{S}:\quad\left\{\begin{array}{rcl}\dot{r}(t)&=&Sr(t)\,,&r(0)=r_{0}\,, \\ \alpha_{r}(t)&=&Tr(t)\,,\end{array}\right. \tag{61}\]  
with the state space matrix  
\[\left[\begin{array}{c}S\\ \hline T\end{array}\right]=\left[\begin{array}{ccc}0&1\\ -\omega_{r}^{2}&0\\ \hline 1&0\end{array}\right]. \tag{62}\]  
The characteristic polynomial of \(S\) is given as follows:  
\[\det(\lambda I-S)=\lambda^{2}+\omega_{r}^{2}\,, \tag{63}\]  
with the spectrum (eigenvalues)  
\[\sigma(S)=\{j\omega_{r}\,;-j\omega_{r}\}\subset j\mathbb{R}\,. \tag{64}\]The pair \((T,S)\) is observable, as the observability matrix \(V_{S}\) of \(\Sigma_{S}\) satisfies the following:  
\[\det V_{S}=\det(\left[\begin{array}{c}T\\ TS\end{array}\right])=1\
eq 0\,. \tag{65}\]
## 3 Computational Examples and Analysis
### Case Study II: Tri-Objective Optimization
#### 3.2.2 MOO Problem Formulation for Diatomite-Basalt Fibre Composite Modified Asphalt Mixture  
(1) Objective Function  
Considering the extremely variable climate in the seasonal frozen area in northeast China, high- and low-temperature performances are treated as optimization targets. The objective functions for asphalt mixture containing diatomite and basalt fibre are modelled using GPR based on ML methods. The objective functions of high- and low-temperature performances as well as costs are calculated by the following equations:  
\[\left\{\begin{array}{c}\max\mathbf{F_{1}(X)=\max Y_{4}}\\ \max\mathbf{F_{2}(X)=\max Y_{5}}\\ \min\mathbf{F_{3}(X)=\min(1.35X_{1}+12X_{2}+2.2X_{3})}\end{array}\right. \tag{23}\]  
where \(\mathbf{Y_{4}}\) (\(MS\)) represents the high-temperature performance and \(\mathbf{Y_{5}}\) (\(S_{b}\)) represents the low-temperature performance; \(\mathbf{F_{3}(X)}\) is the cost, the prices per kilogram of diatomite, basalt fibre and asphalt are 1.35, 12 and 2.2, respectively.  
(2) Constraints  
Similarly, following JTG F40-2004 and the previous study [53], the range constraints and volumetric properties constraints for AC-13 specimens modified by diatomite and basalt fibre are summarized in Tables 7 and 8, respectively. The corresponding constraint expressions are written as:  
\[\left\{\begin{array}{c}5\leq\mathbf{X_{1}}\leq 20\\ 0.2\leq\mathbf{X_{2}}\leq 0.5\\ 4.9\leq\mathbf{X_{3}}\leq 5.8\\ 3\leq\mathbf{Y_{1}}\leq 6\\ 65\leq\mathbf{Y_{2}}\leq 75\\ \mathbf{Y