esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 2 Proposed Solution
### Description of ML Models and Performance Metrics  
In this work, machine learning was employed to predict solar irradiance, addressing a regression task aimed at forecasting a continuous variable. In line with this framework, tree-based and ensemble learning models were utilized [26; 27; 28]. A description of the models used, which are initialized with a random state of 42 to ensure reproducibility, is provided below.  
1. MLP: A Multi-Layer Perceptron regressor with a neural network architecture consisting of three hidden layers, each containing 100 neurons. The model is set to run for a maximum of 10,000 iterations.
2. Decision TreeRegressor: A decision tree regressor that uses a tree-like model of decisions to predict a target value.
3. RandomForest: An ensemble learning method that constructs a multitude of decision trees during training and outputs the mean prediction of the individual trees.
4. ExtraTrees: An ensemble learning technique similar to the RandomForest, but it uses the entire dataset instead of a bootstrap sample and selects split points at random.  
Figure 2: The proposed framework for GH prediction.  
GradientBoosting: An ensemble technique that builds trees sequentially, each one correcting errors made by the previous ones.
* CatBoost: A gradient boosting regressor that handles categorical features natively and is designed to be fast and accurate. The model has its verbosity set to 0 to suppress output during training.
* LightGBM: A gradient boosting framework that uses tree-based learning algorithms. It is designed to be efficient and fast, especially with large datasets.
* XGBoost: An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.  
While \(R^{2}\) and RMSE metrics are commonly used in regression tasks, and MAE to some extent, other metrics can also be significant. The performance metrics used in this work are depicted in Table 4.
## 4 OWC Technologies for the 5G, 6G, and IoT Solutions
### Fulfilling the Network and Infrastructure Characteristics
The latency is calculated for transmission during backhaul connectivity. Hence, the FSO network will be an excellent complementary solution of wired and microwave/mmWave systems to support high-data-rate communications in the 5G and 6G networks. Figure 7: High-capacity backhaul connectivity for a remote hill, a remote island, and a remote city. _Green Communication_: Green communication can be achieved by several factors, such as energy awareness in network deployment, choice in communication devices, and design in the communication network protocols. Hence, the green aspects of the future 5G/6G and IoT networks require an energy-efficient communication that can be effectively realized by increasing the use of LED-based OWC technologies. The OWC technologies will support a large portion of the entire wireless data volume. A large amount of energy can be saved if a large portion of indoor users use the OWC networks based on LEDs that are also used for illumination. Moreover, the OWC system can serve for the purpose of energy harvesting (EH). It has been demonstrated that solar cells can be integrated into VLC links to function both as energy harvesters and as optical receivers [53]. Hence, the OWC systems contribute to a built-up green communication system that is one of the most important characteristics of the 5G/6G and IoT networks. _Tactile internet support_: The International Telecommunication Union defines tactile internet as the future internet network that combines ultra-low latency with extremely high availability, reliability, and security. The tactile internet will be the next evolution of IoT to encompass human-to-machine and machine-to-machine interactions [54; 55; 56; 57]. The OWC technologies have the ability to support the tactile internet. Our previous work in [58] introduced human bond communication (HBC) to enable continuous bidirectional communication among multiple users. _Intelligent transportation_: Vehicular communication is an essential part of the modern era that promises to provide ubiquitous connectivity with ultra-reliable and low-latency connectivity [59]. V2X communications improve road safety, traffic efficiency, and availability of infotainment services [60]. The dedicated short-range communication (DSRC) technology, which works in a 5.9 GHz band, is being widely used to support V2X communications, specifically those focusing on vehicular safety applications [61]. The mmWave bands are also attractive for V2X communications to support Gigabits per second date rates, which cannot be achieved in the DSRC [61]. Moreover, the OWC technologies have potential to support a reliable connectivity in LOS conditions. VLC and LiFi can support short-distance inter-vehicle communications, whereas OCC can support communication over a 60 m distance [62]; FSO can support even longer distance communication.
# Why Do Tethered-Bilayer Lipid Membranes Suit for Functional Membrane Protein Reincorporation?
## 4 Different Types of Tethered-Bilayer Lipid Membranes
### Peptide-tBLMs or Pep-BLMs
Hence, the proximal layer is monospecific and only formed by one lipid species, which could restrict both the dynamic behavior of the tethered lipid bilayer and the capacity of the membrane protein to be reinserted; the composition of the bilayer in terms of length and/or unsaturation of the fatty acyl chains on one hand, and the nature of the phospholipid polar head on the other hand, being essential for ensuring successful functional reincorporation of the membrane proteins. In summary, tBLMs were originally developed for reinsertion of transmembrane proteins in view of their use as a recognition element for biosensing applications [41]. As seen above, a number of membrane proteins has been reinserted in tBLMs. Most of them are small pore-forming peptides such as _gramicidin_, _melititin_, _alamethicin_, _valinomycin_, _cholesterol-dependent cytoplasins_ (CDCs), A\(\beta\) oligomers, which spontaneously insert into the preformed membrane from an aqueous solution, but larger transmembrane proteins are rarer; they concern _porins_ or large oligomeric bacterial toxins such as _a-hemolysin_, receptors e.g., _integrins_, _nicotinic acetylcholine receptor_, _H\({}^{*}\)-ATPases_ or redox proteins such as _cytochrome b\({}_{5}\)_, _cytochrome b\({}_{3}\)_, _cytochrome c oxidase_ or _CymA_, a menaquinol-7 dehydrogenase, which, except _a-hemolysin_, _H\({}^{*}\)-ATPases_ and _CymA_, do not possess bulky extra-membrane domains.
## 4 Mathematical Modelling and the IA-SPM Algorithm
### The IA-SPM Model Based on the Markov Chain  
Assuming that the cognitive user detects the channel at the beginning of each sensing period, the system state can be modeled as a discrete time Markov chain. The system state is represented by \((i,j)\), where \(i\) indicates the status of the authorized user. When \(i=1\), it indicates that the PU exist. Otherwise, the PU does not exist. \(j\) indicates the serial number of the silent period. \(p_{i}\) and \(q_{i}\), respectively, represent the false alarm probability of system states \((0,j)\) and \((1,j)\). Since the false alarm probability and false dismissal probability is a random process, the SU's detection process satisfies the Markovian nature. It has the properties  
\[\left\{\begin{array}{ll}p_{ij}\geq 0,&q_{ij}\geq 0;&i,j\in I\\ \sum_{j\in I}p_{ij}=1,&\sum_{j\in I}q_{ij}=1;&j\in I\end{array}\right. \tag{1}\]  
We define the K-step transition probability as \(P_{ij}(K)\).  
Assuming that the probability of using the frequency band by the PU is \(\theta\) and the probability of releasing the frequency band by the PU is \(\rho\), the state transition of the system can be obtained by the state transition diagram which is shown in Figure 4. In this system model, \(\lambda(i,j)\) denotes the stationary probability of the state \((i,j)\).  
From Figure 4, we can get the state transition equations  
\[\lambda_{(0,0)}=(1-\beta)q_{0}\lambda_{(0,0)}+(1-\beta)(1-q_{1})\lambda_{(0,1) }+\rho\lambda_{(1,0)} \tag{2}\]  
\[\lambda_{(0,j)}=(1-\beta)q_{i-1}\lambda_{(0,i-1)}+(1-\beta)(1-q_{i+1})\lambda_ {(0,i+1)}+\rho\lambda_{(1,i)} \tag{3}\]  
\[\lambda_{(0,k)}=(1-\beta)q_{k-1}\lambda_{(0,k-1)}+(1-\beta)q_{k}\lambda_{(0,k )}+\rho\lambda_{(1,k)} \tag{4}\]  
\[\lambda_{(1,0)}=\beta\lambda_{(0,0)} \tag{5}\]  
\[\lambda_{(1,j)}=(1-\rho)\lambda_{(0,j-1)}+\beta\lambda_{(1,j)} \tag{6}\]  
\[\lambda_{(1,k)}=(1-\rho)\lambda_{(1,k-1)}+\beta\lambda_{(0,k)}+(1-\rho) \lambda_{(1,k)} \tag{7}\]  
Figure 4: The state transition equation.  
The stability probability of each state can be obtained by the above flow Equations (2)-(7). The normalized equation can be expressed as  
\[\sum_{i=0}^{k}\left(\lambda_{(1,i)}+\lambda_{(0,i)}\right)=1 \tag{8}\]
The studies, which were conducted within the framework of the EU Project \"Quiet City Transport\", showed that loudness, sharpness, roughness, impulsiveness, and relative approach are important psychoacoustical properties to describe the perceived annoyance of the pass-by sounds [21; 22; 23; 24; 25; 26]. The studies of [PERSON]'s team showed that particularly the sensation dull/shrill, which can be characterized by the total energy of tonal components within critical bands from 16 to 24 banks, can describe urban road single-vehicle pass-by noise annoyance [27; 28]. The aim of this study is to find perceptually relevant measures for the characterization of the noise annoyance caused by pass-by noise of passenger cars, including conventional, hybrid, and electric cars with and without an acoustic vehicle alerting system (AVAS) and signal. A real-life traffic pass-by situation, which is an acceleration from idle to 50 km/h, was selected for this study. The consideration of the hybrid and electric passenger cars including AVAS is one of the important novel aspects of this study. The selected pass-by situation differs from the existing literature [21; 22; 23; 24; 25; 26; 27; 28]. To achieve the above-mentioned aim,the exterior noise signals of passenger cars, such as diesel, gasoline, hybrid, and electric, were recorded. Then, the recorded signals were analyzed, and psychoacoustical metrics were calculated. In parallel, a listening test was conducted to evaluate the perceived annoyance. The results of the psychoacoustical analyses and the annoyance evaluation test were correlated.
## 5 Case Studies: Validating the CILS Framework
### Case Study One: Busuu
#### 5.1.5 Conclusions  
Applying the CILS within Busuu represents a paradigm shift in how language learning platforms can operate at the intersection of technology and cultural education. This collaboration has enhanced the efficacy of language learning for users and set new benchmarks for integrating AI into educational technologies.  
The success of the CILS in Busuu underscores the potential of AI to revolutionize educational experiences by making them deeply personalized and culturally relevant. Learners with better language and cultural understanding are more effective communicators, which is increasingly important in our globally connected world. The findings from this integration also suggest directions for future research, particularly in developing more advanced AI algorithms that can further enhance the personalization and cultural adaptability of language learning platforms.  
The positive outcomes from the Busuu case study indicate that similar models could be effectively applied across different educational platforms and learning environments. In this study, users of Busuu reported a 22% improvement in language proficiency after three months of consistent use, as measured by standardized language tests. In total, 85% of users felt more confident in their language skills. These concrete results suggest that educational technology developers can draw on the lessons learned from this implementation to explore new ways of leveraging AI to meet the diverse needs of learners worldwide. The ongoing evolution of AI capabilities promises even greater advancements, making it an exciting time for innovators and educators aiming to transform educational landscapes around the globe.
# On Applications of Spiking Neural P Systems
## 1 Introduction
SNPS models are Turing complete. Furthermore, SNPS have established themselves as a very popular direction of research in the last few decades, where the computing power of different variants of SNPS and the use of SNPS models in solving many problems in computing as well as in real-life applications have been investigated extensively. Many variants of SNPS have been introduced by incorporating features from the biological neurons such as asynchronous systems [8], astrocytes [9], rule on synapses [10], communication on request [11; 12], synapses with schedules [13], structural plasticity [14], weighted synapses [15], inhibitory synapses [16], anti-spikes [17], etc. These models have also been used in solving problems related to real-life applications, such as fault diagnosis of power systems [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31], pattern recognition [32; 33; 34], computational biology [35], performing arithmetic and logical operations and hardware implementation [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47], solving computational hard problems [18; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64], computing morphisms [65; 66], biochip design [67], programming for logic controllers [68], etc. In this paper, we study the SNPS models and their applications. We also give comparisons of the models, their structures and workings. Furthermore, we study their advantages and disadvantages and the implementation of these models in hardware. Finally, we discuss some methodologies to extend the works present in the literature. The main motivation to prepare this survey is as follows:  
1. To give an updated and comprehensive survey of the SNPS models, their structures and workings and their applications;
2. Study comparison of these models while solving these problems along with their advantages and disadvantages;
3. Study implementations of these models in hardware;
4. Introduce some new ideas to expand the scope of applications of SNPS models. The main contributions of this work are as follows:  
1. Listing a majority of the SNPS models used for solving problems in fault diagnosis, pattern recognition, computational biology, intrusion detection, computing morphism, performing arithmetic and logical operations. Additionally, their use in solving computationallyhard problems, the construction of \(\mu\)-fluidic biochig design and programming for PLC (programmable logic controller);
2. Studying a comparison of these models;
3. Studying their advantages and disadvantages in solving problems;
4. Discussing a possible extension of these models in applications. The paper is organized in the following manner: Section 2, discusses the structure of the SNPS model; in Section 3, we study the applications of SNPS models; Section 4 is conclusive in nature.
## 3 Analysis of Results for Optimal Model
### Torque Characteristics of Six- and Three-Phase Winding  
It is possible to switch to three-phase motors with a six-phase asymmetric winding structure [29]. As shown in Figure 1, in the case of a six-phase asymmetric winding motor in which two three-phase windings are arranged with phases shifted 30 degrees from each other, if a fault occurs in one phase of the inverter or motor, it becomes a three-phase motor in the case of both of the other connected phases being opened. Figure 9 shows the torque curve for one cycle for six- and three-phase operation when the rated current was 10 A, analyzed using FEM. During six-phase operation, the average, maximal, and minimal torque values were interpreted to be 17.5, 18.1, and 17.0 Nm, respectively, and the ripple factor (RF) was calculated to be 6.3%. During three-phase operation, the average, maximal, and minimal torque values were interpreted to be 8.2, 8.7, and 7.8 Nm, respectively, and RF was calculated to be 11%. Therefore, the average torque during three-phase use decreased to 47% of the average output torque when all six phases were used, and the RF increased from 6.3% to 11%.  
Additionally, load analysis was performed at a speed of 480 r/min to investigate the torque characteristics with respect to current. Since torque analysis through forced current injection is not affected by speed, it was analyzed by only increasing the current from 2 to 10 A. The analytical results are presented in Table 6 and show that as the current increased, the difference between output torque and maximal or minimal torque increased as well, and the RF showed a tendency to decrease.  
Figure 8: Comparison of inductance curves according to currents using LPM and FEM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(L_{q}\) **(LPM)** & \(L_{q}\) **(FEM)** & \(L_{d}\) **(LPM)** & \(L_{d}\) **(FEM)** \\ \hline Max. value [mH] & 3.82 & 3.86 & 6.75 & 6.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of maximum inductance using LPM and FEM.  
In Table 6, most RF values were large, which is analyzed as the effect of concentrated winding. In general, the air-gap flux density distribution is a square or trapezoidal waveform in the concentrated winding, which has large spatial harmonics, causing a large torque ripple. To reduce torque ripple, optimization methods of rotor and stator shapes have been proposed [32; 33], but in this study, the optimization of design parameters to reduce the torque ripple of FSCW (fractional slot concentrated winding) was not considered.  
For reference, the RF was calculated to be large at 2 A because the average torque was small, and therefore the difference between minimal and maximal torque was relatively large. However, the magnitude of its difference was the smallest.
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## Security Algorithms with Lightweight Security
### System Topology
#### 5.1.1 Certification Authority  
In our architecture, a certification authority is an entity that is connected to the PUBS and secondary user base station (SUBS). The CA will act as a trust repository for every node in the mHealth network for trust calculation, and it issues certificates for all members. Both SUBS and PUBS have full trust on CA.
## 3 MPPT Control Algorithms
### Fuzzy Logic for MPPT
Figure 13: (**a**) Input variable (E) membership function, (**b**) Input variable (CE) membership function and (**c**) Output variable (\(\Delta\)D) membership function. The last step is defuzzification, when the fuzzy output values are modified by their membership function, resulting in Crisp Outputs. For this system the defuzzification technique used is center of gravity method, resulting in the output duty cycle. This technique when applied to the already presented rule system, gives the duty cycle change based on equation results (9). \[\Delta D=\frac{\Sigma_{j=1}^{N}\mu\Delta D_{j}-\Delta D_{j}}{\Sigma_{j=1}^{N} \Delta D_{j}} \tag{9}\]  
The value of Equation (9) represents the change of the previous duty cycle, in order to adjust the duty cycle value according to the system needs. The final duty cycle will generate a PWM (Pulse Width Modulation) signal, to control the IGBT switch of the boost converter. The change of duty cycle values is more versatile compared to the traditional P&O, where the step size is fixed, so it is expected that this controller will adapt faster to the changing environment conditions and achieve more stable parameters at steady state conditions. A general control rules overview of the proposed FLC controller is presented in Figure 14.
## 3 Results and Discussion
### Inference Time and Practical Considerations for Deployment  
In addition to classification accuracy and generalization, inference time is a critical factor in determining model suitability for real-world deployment. It should be noted that the reported inference time does not account for segmentation operations such as GrabCut-Watershed. Moreover, depending on the image domain (original or preprocessed) and experimental setup (same-domain or cross-domain evaluation), the inference time varied significantly. These variations were analyzed to better reflect real-world application scenarios where preprocessing steps may or may not be included.  
As summarized in Table 2, Model 2 exhibited the shortest pure model inference time (0.037 s/image), benefiting from the visual simplicity of pre-segmented inputs. Model 1 recorded a slightly slower inference time (0.055 s/image), but required no additional preprocessing, making it operationally more self-contained. Among the cross-domain settings, Model 1-CDE achieved a strong balance of performance and speed, with the fastest overall inference time (0.040 s/image) and an accuracy of 82.77%. In contrast, Model 2-CDE showed the worst performance across all metrics, with both the longest inference time (0.053 s/image) and the lowest accuracy (34.15%), underscoring its lack of generalizability and computational inefficiency when tested on raw images.  
These findings emphasize not only the importance of raw image-based models in terms of classification performance but also their advantage in achieving a favorable trade-off between accuracy and inference speed. In particular, Model 1-CDE demonstrates that models trained on diverse visual features can generalize well even under domain shift, while maintaining efficient inference performance. This highlights the practical benefit of training on raw data in scenarios where real-time decisions must be made under varying visual conditions.  
From a practical standpoint, inference times between 0.04 and 0.06 s/image for monitoring plants, as observed in Models 1 and 1-CDE, fall within the generally acceptable range for near-real-time applications in industrial plant factories [13]. In such environments, image acquisition and decision-making cycles often occur at intervals of several seconds to minutes. Therefore, the observed inference times indicate that raw image-based models are suitable not only in terms of accuracy and generalization but also in meeting operational requirements for deployment in real-world automated cultivation systems.