esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Definition of the Scope and System Requirements  
From the analysis of port processes in Ro-Ro terminals, the following problems were identified in traditional systems, both in international literature and during on-site visits to study terminals of the Spanish port system. These inefficiencies are especially relevant in the current context of port digitalization, which is guided by the foundational principles of Industry 4.0--namely, digital integration, real-time decision-making, interoperability, data-driven process optimization, and autonomous system coordination. However, most traditional Ro-Ro terminals still operate under fragmented, reactive systems that fall short of these principles [1; 2].  
The identification of operational inefficiencies was based both on a review of international literature and on observations gathered during technical visits to several Ro-Ro terminals within the Spanish port system. These visits provided first-hand insights into the current limitations of traditional management approaches:  
* Lack of automation in the allocation of spaces, which generates inefficiencies and unnecessary waiting times.
* Non-optimized routes within the terminal, causing longer journeys and higher energy consumption.
* Reactive traffic management, without real-time adjustment mechanisms.
* Manual coordination of shipment, which introduces delays and reduces operational capacity.
* Low data integration and centralized supervision, making strategic decision-making difficult.
* Manual space assignment, without location optimization.
* Uncoordinated flow of vehicles, with inefficient routes.
* Internal congestion in the field due to lack of dynamic regulation.
* Manual boarding processes, generating unnecessary waiting.
* Reactive monitoring, based on late reports instead of real-time data.  
Figure 2: Simplified schematic of the basic operational flow in a roll-on/roll-off (Ro-Ro) terminal. Source: Own elaboration.  
The proposed multi-agent system addresses these challenges by integrating distributed artificial intelligence, enabling decentralized and optimized management of vehicles within the terminal.
## 5 Conclusions  
This study provides valuable insights into the factors influencing HRV in elite athletes and explored its potential as an indicator of sports performance. The analysis of HRV in relation to sleep quality, the menstrual cycle, training loads, and illnesses revealed that sleep quality is the most significant factor influencing HRV, with positive associationsobserved between improved sleep and higher HRV values. While no clear relationship between HRV and competitive performance was established in this study, the observed pre-competition decrease in HRV suggests potential benefits of sympathetic dominance for performance in kayak slalom. These findings align with existing research that shows a decline in HRV before competitions, particularly in endurance sports, where increased sympathetic activation can enhance performance. However, further research with a broader sample and controlled experimental conditions is needed to clarify the potential role of HRV as a predictor of sports performance. HRV monitoring offers a non-invasive, cost-effective, and practical tool for coaches and athletes, enabling daily assessments of an athlete's physiological state. Regularly tracking HRV can help optimize training adaptation, identify signs of fatigue or overtraining, and adjust workloads to enhance recovery and performance. The primary objective of this study was to analyze factors influencing HRV and to determine if HRV can serve as an indicator of athletic performance. Our research indicates that the training load minimally affects HRV, potentially because of successful adaptation and the absence of overtraining. The observed decrease in HRV before competitions suggests a potential advantage of sympathetic dominance in kayak slalom, similar to trends observed in endurance sports. As a case study focusing on a single subject, the results cannot be extrapolated to the general population. Additionally, since HRV is affected by gender and age, there is a necessity for future research to explore a broader sample, including participants of various sexes and age demographics. This longitudinal study was conducted without any interventions during the training process. A larger dataset, more recorded observations, and improved control conditions are necessary to analyze factors influencing HRV, like injuries and illnesses, accurately. Moreover, specific data in this study rely on self-reported metrics and subjective evaluations, such as sleep quality and training loads. Future studies should implement more objective assessment techniques to validate these findings.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 5 Algorithms for VLC-Based IPS
### Machine-Learning-Based Algorithms  
To cope with the vulnerabilities of fingerprinting-based algorithm, ref. [108] presents several machine-learning-based algorithms, which leverage the power of statistical models to learn patterns and relationships from labeled training data. Support vector machines (SVMs), k-nearest neighbors (KNNs), and deep learning (DL) are some of the popular machine learning algorithms used in indoor positioning. The authors in [109] explore the SVM-based algorithm by using variations in the RSS to increase the accuracy and reduce the computation complexity. KNN is a simple algorithm that classifies an object based on its nearest neighbors; see, for example, [106]. In addition, deep-learning-based algorithms have been widely explored in recent years. For instance, refs. [110; 111; 112] propose several DL-based algorithms with diverse sensors.  
These algorithms require substantial amounts of data to learn patterns and make accurate predictions. Acquiring and labeling such data can be time-consuming and costly. Moreover, machine learning algorithms may struggle to generalize well to unseen data if the training data are not representative of the real-world scenarios that they will encounter.  
Machine-learning-based algorithms can overcome several limitations of traditional methods. They can handle possible complex nonlinear properties among various system variables that are often difficult to be handled by traditional methods due to the complexity. In addition, these algorithms can have higher anti-noise ability, e.g., one can reduce the impact of noise on localization performance by training the model on data containing noise.  
Figure 5: Indoor positioning based on the three-dimensional signal fingerprints collected at various locations [107].  
In addition, deep learning algorithms can be used to automatically extract useful features for localization, reducing the effort of manual feature engineering, and discover hidden features that cannot be easily captured by traditional methods. It is also worth noting that machine learning algorithms are often able to adapt to dynamic environments by constantly updating and retraining models, making positioning systems more flexible and effective in practical applications.
# Molecular Insights into the Interactions Between Human Serum Albumin and Phospholipid Membranes
###### Abstract  
In this study, molecular dynamics simulations were employed to analyze interactions between phospholipid membranes and human serum albumin (HSA) in the presence of mono- and divalent cations. Two types of membranes, composed of dipalmitoyl phosphatidylcholine (DPPC) and dipalmitoyl phosphatidylcholine(DPPE), were utilized. The results revealed that both systems exhibited high stability. The DPPE complexes displayed a greater affinity for albumin compared to DPPC. The high stability of the complexes was attributed to a high number of ionic contacts and hydrogen bonds. The presence of mono- and divalent metal cations significantly influenced the membrane's capacity to bind proteins. However, these effects varied depending on the phospholipid composition of the bilayer. The studies confirmed the relatively low ability of DPPC to bind potassium ions, as previously observed by others. Consequently, the DPPC/HSA/K\({}^{+}\) complex was found to be the least stable among the systems studied. While DPPC interactions were limited to HSA domains I and II, DPPE was able to interact with all domains of the protein. Both lipid bilayers exhibited substantial structural changes and characteristic curvature induced by interactions with HSA, which confirms the formation of relatively strong interactions capable of influencing the arrangement of the phospholipids.  
phospholipid membranes; DPPC; DPPE; human serum albumin; intermolecular interactions; hydrogen bonds; ionic interactions; mono- and divalent cations; molecular dynamics +
Footnote †: journal: applied sciences
# SecureVision: An Open-Source User-Customizable Image Encryption Program
## 3 Results and Discussion
### Graphical User Interface  
A graphical user interface (GUI) was developed for the present computer program, which facilitated users to encrypt, decrypt and transfer their images in a very efficient and easy-to-use manner. The GUI implementation of any software leads to a user-friendly working interface. The GUI can be broken down into three main sections, namely: (1) encryption, (2) decryption and (3) transfer to server. A screenshot of the developed GUI is shown in Figure 9.  
The encryption part of the program took eight (8) user inputs, namely, (1) filename of the image that needed to be encrypted, (2) key 1, which was the \(x\) value for the random number generator, (3) key 2, which was value of \(a\) for the random number generator, (4) key 3, which was value of \(b\) for the random number generator, (5) key 4, which was the value of \(deno\) for the random number generator, (6) iteration number for multi-layer merging, (7) delta value for pixel offset and (8) coefficient, which was the value of \(A\) in Equation (2) for histogram shuffling. The GUI read the user inputs and executed the encoder program to encrypt the images with the user input keys.  
The decryption part of the program read the file which stored the user parameters for encryption, with the filename (key name) specified by the user. In the current version of the program, the key file is an ASCII data file with the \".dat\" extension. This key file could be further encrypted with standard encryption techniques and software such as GNU Privacy Guard (commonly known as GPG) [31].  
The last section of the GUI program concerned transfers of files, which employed the secured file transfer protocol (through SSH connection) to copy the encrypted images to a server or computer. The user was required to input the username, server IP address and a directory in their home user folder. The Microsoft Windows version of our program employed the PSCP protocol to transfer the encrypted images to the server. This automatic function would be very useful for quick and secured file transfer to (1) backup servers for further data protection or (2) servers for sharing data in a secured manner with authorized users.  
Figure 9: The developed graphical user interface (GUI) for encryption and decryption images, and for data transfer to server.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids
### Direct Impact Ionization
#### 2.1.2 Electric Field Dependent Molecular Ionization  
Electric field-dependent molecular ionization (field ionization) does not require, in general, the assumption of free electrons and ions occurrence in the space before discharge propagation. In the environment characterized by high values of the electric field intensity, it is possible to separate electrons from inert particles [16; 29]. As a result of this process, free electrons and positive ions appear in the inter-electrode space (right panel of Figure 2). Because of the different mobility of electrons and ions, local variations in density of mentioned particles may occur, leading to the generation and propagation of streamers. Such models are commonly used to describe ionization in transformer oils [30; 31] and water [32]. The basic disadvantage in the practical utility of this theory is the lack of analytical description of the ionization process in liquids. In practice, Zener models are in use, but they are originally used to describe the tunneling process in semiconductor materials [33].  
The ionization process under the influence of the field depends on many factors, such as ionization potential, liquid density, or electric field intensity gradients [16; 29]. This mechanism is characterized by many advantages, such as the lack of necessity to take into account the free energy carriers or pollutants (with lower ionization energy) during the initiation of the discharge. However, it should be mentioned that this type of ionization dominates in areas with very high electric field strengths, such  
Figure 1: Formation of the streamer near the electrode. (**a**) Bubble cluster; (**b**) protrusion; (**c**) gas channels.  
Figure 2: Ionization of atoms under the influence of the field. (**a**) No ionization at low intensity fields; (**b**) ionization in the area of high intensity.  
as in the vicinity of blades or leader's head [16; 34]. Because of the lack of theoretical description of this process for liquid environments, utility of the proposed model is significantly limited.
## 1 Introduction
Metamaterials are composed of structures smaller than the wavelength of the electromagnetic waves they manipulate, allowing them to bend, refract, and even absorb these waves in unusual ways [14]. The earliest form of RIS was the electromagnetic bandgap (EBG) structure, designed to reflect specific frequencies of electromagnetic waves and mainly used to reduce electromagnetic interference in electronic devices [15]. * Around 2012, researchers proposed the concept of using \"smart walls\" composed of metamaterials to improve wireless communication systems [16, 17, 18]. These smart walls could be programmed to selectively block or allow certain frequencies of electromagnetic waves, effectively creating a wireless network with minimal interference [19]. * In 2019, researchers proposed the concept of using reconfigurable surfaces to improve wireless communication in indoor environments [20]. They showed that by using RISs, it was possible to create multiple virtual channels between a transmitter and receiver, effectively increasing the capacity of wireless networks [21]. * Since then, the research on RISs has expanded rapidly, with many researchers investigating different aspects of RIS design, fabrication, and application [22]. Today, RISs are being investigated as a potential technology for improving wireless communication, increasing the efficiency of energy harvesting, and even enhancing imaging and sensing technologies [23,24]. This editorial and brief overview is divided as follows. In Section 2, the working principles of RISs are discussed. Next, in Section 3, the modeling and simulation of RISs are presented. In Section 4, the implementation aspects of RISs are debated. Then, in Section 5, an overview of potential RIS applications is presented. In Section 6, an outline of current RIS challenges and future research directions are listed. Finally, in Section 7, this overview is concluded with a summary of the key findings.
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 4 Semi-Active Control of Suspension Systems
### Modern Control Strategies
#### 4.2.1 Optimal Control  
Optimal control is a widely applied algorithm for determining parameters in linear controlled systems. Prominent optimal control algorithms include Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) control [13; 14]. The core concept of optimal control involves formulating the system's state equations and defining an optimization function for control objectives (typically focusing on ride comfort and stability in suspension systems). [PERSON]'s formula is employed to calculate the feedback gain matrix at the extremal state, which then serves as the controller output to achieve the system's optimal controlled state. This method was initially applied to active suspensions by [PERSON] [52].  
For nonlinear systems, [PERSON] and [PERSON] proposed the fastest gradient control method by transforming the nonlinear suspension system into a time-varying problem of a linear system, based on the improved optimal control theory [53]. [PERSON] and [PERSON] developed a half-vehicle MR semi-active bus suspension model with six degrees of freedom and designed an optimal control strategy utilizing acceleration feedback [54]. [PERSON] et al. demonstrated that when the baseline damping of the semi-active suspension was low, the vibration reduction performance of the LQG-based semi-active suspension could rival that of an active suspension, underscoring the high compatibility of the LQG control strategy with semi-active suspensions [55]. [PERSON] et al. designed an LQR full-vehicle semi-active suspension controller based on a full-vehicle suspension model. Simulation and experimental data showed that the LQR-controlled semi-active suspension significantly enhanced ride comfort [56]. [PERSON] et al. established a half-vehicle suspension model and designed LQR (shown in Figure 10) and PID controllers, finding that the LQR controller outperformed the PID controller in handling transient response and overshoot [57]. [PERSON] and [PERSON] developed an LQR-based adaptive air suspension system and conducted simulation comparisons with the widely used PID controller [58]. The results indicated that the LQR control strategy significantly enhanced ride comfort and reduced the settling time by 85%. In addressing parameter uncertainty in skyhook damper suspension systems, [PERSON] and [PERSON] proposed a skyhook damper based on the LQR algorithm [59]. The principle involves utilizing the LQR control algorithm to calculate the active suspension control force, thereby determining the unknown parameters in the skyhook damper. The results demonstrated superior control performance under random road excitations.  
Figure 10: Schematic diagram of the LQR controller proposed in [57].
# Precise Channel Estimation Approach for a mmWave MIMO System
## 2 mmWave Channel Model
 \tag{4}\]  
Here, \(\rm{F}_{RF}\in\mathbb{C}^{N_{T}\times N_{T}}\) and \(\rm{W}_{RF}\in\mathbb{C}^{N_{R}\times N_{R}}\) are the transmitted and received beamforming matrices, respectively. \(\rm{F}_{BB}\in\mathbb{C}^{N_{T}\times N^{Beam}_{T}}\) and \(\rm{W}_{BB}\in\mathbb{C}^{N_{R}\times N^{Beam}_{R}}\) are the transmitted and received baseband processing matrices, respectively. \(\rm{W}\) is the combiner, such that \(\rm{W}\in\{0,1\}^{N_{R}}\), and \(\rm{F}\) is the precoder, such that \(\rm{F}\in\{0,1\}^{N_{T}}\). We adopted the geometric virtual model (GV) of mmWave MIMO system described in [12, 17]. According to the GV model, channel \(\rm{H}\) is described as  
\[\rm{H}\triangleq\sum_{l=1}^{L_{p}}\alpha_{l}\,a_{R}(\Phi_{R}^{(l)},\theta_{R} ^{(l)})a_{T}^{H}(\Phi_{T}^{(l)},\theta_{T}^{(l)}) \tag{5}\]  
Here, \(\rm{L}_{p}\) is the total number of propagation paths, \(\alpha_{l}\) is the complex channel gain of the \(l\)-th path, which can be obtained from the random complex gaussian distributions, and \(\mathcal{CN}\Big{(}\frac{0.1}{2}\Big{)}\). \(\rm{a}_{T}^{H}\Big{(}\Phi_{T}^{(l)},\theta_{T}^{(l)}\Big{)}\in\mathbb{C}^{N_{T}}\) and \(\rm{a}_{R}\Big{(}\Phi_{R}^{(l)},\theta_{R}^{(l)}\Big{)}\in\mathbb{C}^{N_{R}}\) are the array response vectors (ARV) at the transmitters and receivers, respectively. \(\rm{\Phi}_{T}^{(l)},\theta_{T}^{(l)}\) and \(\rm{\Phi}_{R}^{(l)},\theta_{R}^{(l)}\) are the elevation and azimuth AoA and AoD angles at the transmitters and receivers, respectively. These angles are generated by Laplacian distributions, whose means are uniformly distributed over (0,2\(\pi\)). The ARV of a uniform linear array (ULA) [31, 32] is described by \(\rm{a}(\theta)=\frac{1}{\sqrt{N}}\Big{[}1,\rm{e}^{-\rm{i}\frac{2\pi}{\lambda} \rm{d}\rm{cos}(\theta)},\ldots\rm{e}^{-\rm{i}\frac{2\pi}{\lambda}\rm{(N-1)} \rm{d}\rm{cos}(\theta)}\Big{]}^{T}\). Here, \(\lambda\) is the wavelength, \(\rm{d}\) is the antenna spacing and \(\theta\) is the even function of the ARV. The channel model described in Equation (5) can be further written in matrix form (described in a virtual beamspace representation model [33, 34]) as follows:  
\[\rm{H}=A_{R}ZA_{T}^{H} \tag{6}\]  
Here, \(\rm{A}_{R}\in\mathbb{C}^{N_{R}\times N_{R}}\) and \(\rm{A}_{T}\in\mathbb{C}^{N_{T}\times N_{T}}\) are the unitary matrices with the ARV of the receivers and transmitters [33], respectively, where \(\rm{A}_{R}\triangleq\Big{[}a_{R}\,\left(\Phi_{1},\theta_{1}\right),a_{R}(\Phi _{2},\theta_{2})\ldots a_{R}\Big{(}\Phi_{L_{p}},\theta_{L_{p}}\Big{)}\Big{]}\) and \(\rm{A}_{T}\triangleq\Big{[}a_{T}(\[PERSON]_{1},\theta_{1}),a_{T}(\Phi_{2},\theta_{2 })\ldots a_{T}\Big{(}\Phi_{L_{p}},\theta_{L_{p}}\Big{)}\Big{]}\). By using matrix properties, we can say that \(\rm{A}_{R}^{H}A_{R}=K_{N_{R}}\) and \(\rm{A}_{T}^{H}A_{T}=K_{N_{T}}\), with \(\rm{I}_{N}\) being the \(\rm{N}\times\rm{N}\) identity matrix. In Equation (6), \(\rm{Z}\) is the sparse matrix (which contains few virtual channel gains of greater amplitude) with dimensions \(\rm{Z}\in\mathbb{C}^{N_{R}\times N_{T}}\).
## 2 Materials and Methods
### Data Collection and Preprocessing  
Features acquired were extracted from the datasets of each of the visual and audio models and were then fused in a unified dataset.  
Visual data were harvested from the collection of data from the \"3D Human Pose in The Wild Using IMUs and a Moving Camera\" image dataset. Images were processed with a MediaPipe holistic model. Images were fed into the model, and noise and artifact filtering were applied. Images displayed either an argument between two individuals that escalated or a casual conversation. In each frame, facial features, body stance, and hand gestures were detected, and the corresponding landmarks were later saved in a.csv file along with their corresponding x, y, z coordinates. Imputation techniques were applied with a KNN imputer to handle missing data from frames. Moreover, artifact and noise management were included to unify the resolution of the frames into a single resolution (640 \(\times\) 480 pixels). Lower resolution frames were omitted. Noise reduction and quality control, a fast non-local means denoising algorithm was applied to enhance the quality of the images for a more accurate landmark extraction from MediaPipe. Following data preprocessing, MediaPipe extracted the landmarks that corresponded to specific features [18] (**S1.1**).  
For the audio modalities, the data source was \"Kaggle: Audio-based Violence Detection Dataset\", which contained audio clips from various aggressive and non-aggressive verbal instances from daily events. The audio files were fed into the audio model, which was preprocessed. Filtering and noise techniques, such as MFCCs and pitch filtering, were applied to remove unwanted noise from the data. The final filtered audio files were processed and their features extracted using Root Mean Square energy (RMS) and zero-crossing to describe the time-domain characteristics of the audio, using the extract_temporal_features method [25] (**S1.2**).
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 2 Literature Review
### Fuzzy Logic  
Fuzzy logic is often used to solve problems characterized by subjective uncertainty, ambiguity, and vagueness [48]. It has been widely applied by researchers in construction to incorporate the influence of factors that are linguistically assessed into an uncertain variable quantity [49; 50]. The application of fuzzy logic provides a means of quantifying subjective evaluations and converting this information into a probability distribution function for input into MCS-based risk assessment models. The application of fuzzy logic for the linguistic assessment of root causes of risk factors in onshore wind projects, however, remains relatively unexplored.