esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Observation Simulations of GMR, GeoSTAR, and GIMS
### Experimental Results
The predicted tracks began to diverge from the best track after 6 h, and from 18 h onward, the track forecasts given by the three DA experiments were consistently better than those given by CTRL. Of the three DA experiments, from 18 h onward, the predicted track of DA-GeoSTAR was consistently the closest to the best track, followed by DA-GMR, and the track forecast given by DA-GIMS still had the largest deviation. Table 9 presents the average typhoon track forecast performance of the different experiments. Due to the limited accuracy of WRF, we have rounded the results to 10 km. Compared with CTRL, for _Bualoi_ and _Hagibis_, DA-GMR improved the average 72-h typhoon track forecast accuracy by 24% and 43%, DA-GeoSTAR by 33% and 50%, and DA-GIMS by 10% and 29%, respectively. Table 10 presents the 72-h typhoon track forecast performance of the different experiments. Compared with CTRL, for _Bualoi_ and _Hagibis_, DA-GMR improved the average 72-h typhoon track forecast accuracy by 27% and 50%, DA-GeoSTAR by 27% and 68%, and DA-GIMS by 3% and 21%, respectively. CTRL without any data assimilation generated the worst results, DA-GeoSTAR obtained the best performance, followed by DA-GMR, and DA-GIMS had the largest track error in the three DA experiments. The consistent results in both Typhoons _Hagibis_ and _Bualoi_ suggest general positive impacts on typhoon forecasts after assimilating the simulated GEO-MW observations of GMR, GeoSTAR, and GIMS in the OSSE experiments. The difference among the typhoon track forecast performances of the three assimilation experiments may be related to the observed brightness temperatures errors of GMR, GeoSTAR, and GIMS. As described in Section 4.3, for opaque channels 4-6 and 8 used in assimilation experiments, the observed brightness temperature error is more from the random noise of the system. GeoSTAR, as a stationary SAIR, has the longest integration time in the simulation configuration, resulting in the smallest system noise. From Table 5 and Figure 9, it can be seen that GeoSTAR had the smallest overall brightness temperature error for channels 4-6 and 8, followed by GMR, and GIMS had the largest error. The relatively large brightness temperature error of GIMS mainly came from the shorter integration time due to array rotation, and the error introduced by the interpolation of the UV plane visibility may also slightly degrade the quality of the reconstructed brightness temperature. Figure 15: The predicted track of Bualoi initialized from 06:00 UTC on 22 October 2019 (**a**) and track forecast errors (**b**). The red curve in (**a**) represents the best typhoon track data.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 3 Proposed Clock Recovery Circuit and Method
### Circuit  
In the receiver, a clock recovery scheme which decouples the intrinsic VCO jitter bandwidth requirement from the clock recovery overall bandwidth is used. To do so, we are segregating our circuit into two loops: digital PLL, with high bandwidth in the forward path to filter as much as possible VCO noise, and another loop is governed by the software controlled \"Digital LPF\" and interacts with the  
Figure 5: VCO to output transfer functions.  
Figure 6: Clock recovery circuit used in proposed method.  
\"Numerically Controlled Oscillator\" by adjusting phase delay. The architecture is presented in Figure 6. The parameters of the digital or analog PLL, represented as \"Numerically Controlled Oscillator\", VCO gain/PD gain/loop damping and bandwidth are chosen in a way to guarantee a stable operation around the desired sampling frequency and adequate transient time.
## 2 System Description  
The proposed grid-connected PV inverter system setup contains a control system, a solar panel, a single-phase inverter and a load. The control system has several submodules, including MPPT and current-control logic functions, Park and Inverse Park transforms, grid synchronization, and pulse width modulated (PWM) signal generator [14]. The block diagram of the PV system for both approaches investigated is presented in Figure 1[14].  
Solar panel blocks have two inputs: irradiance value and temperature. The output goes into the MPPT block (Inc or fuzzy logic), and its output provides a voltage reference for the inverter. Continuously, the signal obtained connects to the PI controller (classic or fuzzy). Finally, the latter gives the current in an adjusted form. This current, namely Id_ref, is the reference for the d-axis of the Park transformation, responsible for controlling the active power of the system.  
Additionally, Park transformation generates Id and Iq (feedback current) from the inverter's current I_inv. Feedback currents Id and Iq have to be subtracted from the reference currents Id_ref and Iq_ref. One of the reference currents, Iq_ref, is set to zero to avoid reactive power in a photovoltaic system. The obtained signals are fed into the PI controllers, then signals are amplified and integrated within the signal error to fulfill the equality conditions with the reference currents. The control currents obtained are sent to the inverse Park transformation and later modeled by the PWM generator. Henceforward, the generator's output sends the command pulses to the inverter [14].  
The inverter feeds current into the grid via an RL interconnection load, including the effects of the utility source impedance. Finally, a phase-loop (Classic PLL or SOGI-PLL) synchronizes the current injected by the inverter with the grid voltage. The sinusoidal current injected by the inverter is synchronized in phase and frequency with the grid'svoltage. In this way, the inverter is current-mode controlled to obtain the maximum useful power and deliver it to the grid [14].
## 6 Experimental Results
### Experimental Results  
The fault estimation results are shown in Figure 8.To reduce the high-frequency chattering and noises of fault estimation of \(\hat{\phi}\) in Equation (8) before it is used in the AFTC scheme, a simple low-pass filter was adopted as:  
\[\hat{\phi}_{k}^{\mathit{fitted}}=(1-v)\hat{\phi}_{k-1}^{\mathit{fitted}}+v \hat{\phi}_{k} \tag{44}\]  
where \(\hat{\phi}_{k}^{\mathit{fitted}}\) and \(\hat{\phi}_{k}\) are the output and input, respectively, of the low-pass filter at the \(k^{th}\) step.  
The low-pass filter allowed the signal from the fault estimation (FE) to become smoother and was suitably applied to the AFTC schemes. However, the fault estimation also increased the time delay of the feedback to the controller. To ensure that the smoothness and time delay were acceptable, \(v=0.05\) was selected. Unlike in the simulation, the real system included large uncertainties, so upper and lower thresholds were used to detect faults. In Figure 9, the error tracking trajectory is presented. These results show that before five seconds, the error values at each joint were similar. However, after five seconds, due to the effects of synchronization control, the errors at each joint were different. Unlike in the simulation results, these experimental results show that AFTC S-TSMC1 is more effective than AFTC S-TSMC2. In a real system with large uncertainties and noise, the coupling position  
Figure 8: Fault estimation results with a single joint fault occurs at Joint 2. The aqua and pink dashed lines are the upper and lower thresholds, respectively.  
error in AFTC S-TSMC1 with the integral term may affect how uncertainties are handled. In general, both proposed AFTCs can reduce the picking phenomenon, but the effect of each controller is different, and depends on its knowledge of the system.
# Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming +
###### Abstract  
Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. We investigate in this article the employment of Genetic Programming (GP) for evolving Boolean functions with large non-linearity by examining the search space consisting of Walsh transforms. Especially, we build generic Walsh spectra starting from the evolution of Walsh transform coefficients. Then, by leveraging spectral inversion, we build pseudo-Boolean functions from which we are able to determine the corresponding nearest Boolean functions, whose computation involves filling via a random criterion a certain amount of \"uncertain\" positions in the final truth table. We show that by using a balancedness-preserving strategy, it is possible to exploit those positions to obtain a function that is as balanced as possible. We perform experiments by comparing different types of symbolic representations for the Walsh transform, and we analyze the percentage of uncertain positions. We systematically review the outcomes of these comparisons to highlight the best type of setting for this problem. We evolve Boolean functions from 6 to 16 bits and compare the GP-based evolution with random search to show that evolving Walsh transforms leads to highly non-linear functions that are balanced as well.  
artificial intelligence; cybersecurity; cryptography; evolutionary computation; evolutionary algorithms; genetic programming; stream ciphers; spectral inversion; Boolean functions; non linearity; Walsh transform +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON] [PERSON]; [PERSON] Evolution of Walsh Transforms with Genetic Programming. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation GECCO '23 Companion, Lisboa, Portugal, 15-19 July 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 2386-2389.
## Materials and Methods
### Sensory Analysis  
Quantitative descriptive analysis of EVOO samples was performed by the Panel for sensory analysis of VOO of the Institute of Agriculture and Tourism in Porec (Croatia), accredited for VOO sensory analysis according to the EN ISO/IEC 17025:2007 standard and authorized by the Croatian Ministry of Agriculture for official VOO testing from 2012, and recognized in continuation by the IOC from 2014. The panel consisted of eight assessors (4 female, 4 male, average age 39) trained and accredited for VOO sensory analysis according to the International Olive Council (IOC) method adopted by the European Commission Regulation [1]. As well, all the tasters have had long-terminvolvement in EVOO research and have gained large experience in Croatian monovarietal EVOO sensory analysis. Qualitative (selection of descriptors/attributes by consensus and standardization of vocabulary) and quantitative (intensity of perception) criteria of the tasters were attuned by audibly tasting representative samples of Croatian monovarietal EVOOs through several preliminary training sessions. The panel agreed that the sensory attributes which best describe the investigated monovarietal EVOOs were the same for all varieties and among those commonly perceived in EVOO, but differed with respect to the ratios of their intensities. The panel used a modified profile sheet expanded with particular positive odor and taste attributes, which were quantified using a 10 cm unstructured intensity ordinal rating scale from 0 (no perception) to 10 (the highest intensity). For evaluating general quality attributes, a 10-point overall structured rating scale from 0 (the lowest quality) to 10 (the highest quality) was applied. For overall quality evaluation, VOOs were graded with points from 1 (the lowest quality) to 9 (the highest quality). Before each session, the tasters attuned their criteria with respect to the intensities of the perceived sensory attributes by tasting the same standard reference VOO sample, a blend characterized by all the selected sensory attributes/descriptors. According to the sensory analysis, all the investigated samples were classified as EVOO (no defect, fruitiness \(>\) 0).
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 1 Introduction
### Communication between Devices  
In this area, we have studied possibilities and technologies enabling the communication of smart devices through the local server to the cloud server. The aim of this analysis isto choose appropriate communication protocols as well as cloud technologies. An extensive survey of software-defined networking and related technologies and services can be found in [5].  
Several recent results concerning this topic have been studied. The factory simulation [6] represents communication from the production level equipped with a PLC to the local server (KEPServerEX) and then to the cloud server (ThingWorx). Thus, the augmented reality application is fed with data from the cloud. This solution uses the Ethernet and OPC UA protocols for communication and two servers (a local KEPServerEX and a Thingworx cloud server). The control computer with the SCADA system receives data from the PLC device via the OPC UA protocol, which are then sent to the KEPServerEX and from this local server finally to the cloud server Thingworx via an Ethernet protocol and back to the AR device on the local network.  
Another experimental system was an energy trading platform with peer-to-peer communication [7]. This project is based on remote access and the remote control of the devices. The implementation uses the MQTT protocol for peer-to-peer communication, and two local servers are employed (an MQTT broker using Node-RED and a web user). Communication is initiated by the user's login.
## 3 Developments of CIM
### Developments of CIM Across Countries
#### 3.1.1 Current Status of CIM Applications in Various Countries  
With the rapid acceleration of urbanization, a growing number of countries are embracing CIM as a strategic tool to enhance the construction and management of smart cities. As an integrated and intelligent solution, CIM significantly enhances the urban management efficiency and bolsters the sustainable development capabilities across diverse domains, such as infrastructure management, environmental monitoring, and traffic optimization. The depth and scope of CIM applications vary across different countries, reflecting their distinct priorities and technological advancements in the pursuit of intelligent urban development. Globally, the application of CIM is increasingly permeating the diverse facets of urban management. Leading the charge are countries such as Singapore and the United  
Figure 2: Annual publication trends of City Information Modeling (CIM) research.  
States, which have demonstrated significant advancements in areas like smart city development, traffic management, and environmental monitoring. China has also achieved notable progress in digital city governance, with major metropolitan hubs like Shanghai, Beijing, and Shenzhen actively deploying CIM across a wide range of application scenarios [27]. In Europe, nations including Germany, the Netherlands, and France are prioritizing sustainable development, energy efficiency, and disaster management, leveraging CIM as a foundational tool to achieve these objectives. Furthermore, countries such as Japan, South Korea, Canada, and the United Arab Emirates are integrating CIM into their respective technological ecosystems to address the multifaceted urban challenges.  
Table 2 below illustrates the contributions of various countries to the CIM framework across the key application areas, including smart cities, traffic management, environmental monitoring, energy management, real estate, and disaster management. Each country's involvement is marked with a check or a cross, indicating the extent of their contribution or application in each domain. The classification is based on an extensive review of the academic literature, government policies, industrial reports, and case studies from authoritative sources. Countries with active implementation of CIM-related projects, significant policy support, or substantial research output in a given domain are marked with a check, while those with limited or no major contributions are marked with a cross. This classification helps to identify the trends in technological adoption and progress across different regions, providing valuable insights for future research, policy development, and the potential for collaboration in these critical sectors.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Data Collection  
The first step corresponds to a literature review and gathering the database from diverse sources, including industry databases, research publications, and laboratory experiments. Collecting the database is the primordial step in building the predictive model, as the ANN will learn from these data. If it presents an inadequate representation of the problem, the model cannot predict the property effectively, thus reducing the model's reliability. Furthermore, for the model to be representative, there must be a sufficiently large amount of data to ensure diversity. In this step, it is necessary to recognize which relevant input features potentially impact the model's output response. After this process, all the information must be inserted into a CSV or Excel file.
## 4 Methodology
### Decision Tree Classifier
This deduction derives from the inherent characteristics of the original dataset, which predominantly consists of unique samples. Consequently, when partitioned, a portion of these unique samples from the training set migrates to the test set. In such circumstances, the application of SMOTE assumes significance, augmenting both the sample count within the dataset and diversifying the dataset itself by introducing synthetic data. In Table 2, we observe the following performance metrics: precision, recall, F1 score, accuracy, and both weighted and unweighted (macro) mean, which compute the average of precision, recall, and F1 score. Notably, in the case of the weighted sum, this computation incorporates the support, representing the sample count, for each category. The classifier accurately predicts only the samples belonging to categories 1 and 6. Category 1 exhibits uniform categorical values across its samples, facilitating the classifier's ability to discern the underlying pattern within this category. On the other hand, category 6 samples share a common value for the attribute \"Pain Intensity\". Figure 2: Decision tree classifier for a 30% test set splitting. The gini value [34] at each node evaluates how well a decision tree node separates the data into different categories. Its values range from 0 to 1. A value of 0 indicates a perfect separation, meaning all the samples at one node belong to one category. A value of 1 indicates an even distribution of samples among the different categories. Figure 2 refers to the root node of the decision tree in Figure 2; samples are categorized as category 6 when the \"Pain Intensity\" attribute value exceeds 2.5. Conversely, category 4 exhibits the lowest F1 score among the categories due to limited diversity among its samples, resulting in an inadequate number of distinct training samples for the classifier. In contrast, the remaining classes benefit from an adequate sample size, resulting in commendable classifier performance, with the potential for further enhancement.
## 4 Different Landslide Prediction Methods
### Probabilistic Models for Landslides  
It is recommended that parameters are regarded as random variables for the purposes of probabilistic analysis to account for any uncertainties that may be involved with their calculation. Additionally, the probability density function (PDF) and the statistical characteristics (such as the standard and mean deviation) of unknown variables are computed based on the available laboratory or field data. For models for which deterministic analysis is feasible, Monte Carlo simulations are one of the most frequently utilized techniques of probabilistic analysis, and they may be used for any model for which deterministic analysis is conceivable [47]. It is believed that Monte Carlo simulations are a more complete probabilistic analysis method because they incorporate all random variables, along with the probability of failure that is determined by reliability analysis [48; 49].  
The Umyeon area served as a representative case study for urban landslides in South Korea, and Monte Carlo simulation was used to examine the relationship between landslide location, altitude, slope aspect, particular catchment area (SCA), soil depth, bulk modulus, cohesion, angle of friction, hydraulic properties, and rainfall amount. A deterministic analysis was also carried out to provide a basis of comparison, as shown in Figure 14. This methodology was used in conjunction with a physically based model approach; nevertheless, in this case of probabilistic analysis, the parameters were not treated as random variables [50].  
Figure 13: TRIGRS model used for rainfall-induced landslide analysis [43].  
Several researchers have used probabilistic analytic techniques to evaluate physically based models when conducting their investigations. However, since the hydrogeological model was not utilized in these studies, it was assumed that the groundwater level remained constant across the whole study area in these calculations. The probabilistic approach treats the input data as unknown parameters, with uncertainties in their determination taken into consideration. The statistical characteristics of the input parameters (e.g., probability density function, mean, and standard deviation) are then computed using the available data, and the likelihood of failure is calculated using these random variables [50]. Because the correctness of the statistical description of input unknown parameters has a major effect on the results of probabilistic analysis, the validity of the input data should be mathematically verified by a sufficient number of samples. That is, a significant amount of reliable data is needed to conduct the probabilistic analysis and calculate the uncertainty properly [52].
## 3 Results and Discussion
### Flexural Strength Flexural (FS)  
Flexural load curves for the kaolin clay and sulfate-bearing soil along with the calcium-based stabilizer (L) and non-calcium-based stabilizer (M) with the inclusion of RHA, GGBS, and GGBS-RHA ratios of 10% and 20% after curing for 7 and 28 days, are shown in Figures 18 and 19. The kaolin clay curve exhibited a flexural strength of 121.44 kPa after curing for 28 days. In contrast, the curves produced by the M stabilizer mixtures showed a slight improvement with and without the presence of sulfate, which is a direct measure of the flexural strength of the specimens. When compared to specimens treated with the L stabilizer, the flexural strength was less with the presence of sulfate. Furthermore, using 10% and 20% of the M-RHA, M-GGBS, and M-GGBS-RHA stabilizers resulted in a significant increase in the peak flexural load. This effect was especially relevant regarding the 20% M-GGBS content. The flexural strength of the M-GGBS mixture with 10% and 20% content reached 1270.37 and 1922.55 kPa after curing for 28 days, respectively, gaining values 303.4% and 302.9% higher than those of the L stabilizer (314.89 and 477.11 kPa) using 10% and 20% mixtures, respectively, after 28 days of curing.