esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods
### Goodness-of-Fit and Model Selection Criteria  
After estimating the parameters of the probability distribution model, it is necessary to assess the goodness-of-fit (GOF) of this model. The GOF criteria show how well the selected model fits the wind speed data, and they also indicate the applicability of the model to describe the behaviour of the data at given locations. Assessing the performance of different probability distribution models is necessary to provide more accurate information about their performance and to compare these models among themselves. Finding a more precise model helps to better understand the wind speed at a given location. In this study, we applied commonly used GOF tests--the Kolmogorov-Smirnov (\(KS\)) test and the Anderson Darling (_AD_) test. Further, the information criteria, namely Akaike's information criterion (_AIC_) and Bayesian information criterion (_BIC_), the root mean square error (_RMSE_), and the coefficient of determination (\(R^{2}\)) were used. Employed GOF tests and model selection criteria are briefly described below. The GOF tests were used to decide whether the data followed the specified theoretical distribution. The Kolmogorov-Smirnov (_KS_) test exploits the CDF of the probability distribution. The _KS_ test statistic represents the largest vertical difference between the theoretical and the empirical CDF:  
\[D=\max_{1\leq i\leq n}\left[\left|\hat{F}\big{(}x_{(i)}\big{)}-\frac{i-1}{n} \right|\right|\left|\frac{i}{n}-\hat{F}\big{(}x_{(i)}\big{)}\right|\right] \tag{10}\]  
where \(\hat{F}(x)\) is the estimated cumulative distribution function, \(x_{(1)},\ x_{(2)},\ \ldots,\ x_{(n)}\) are observations in ascending order, i.e., \(x_{(1)}\leq x_{(2)}\leq\ \ldots\leq x_{(n)}\), and \(F_{n}(x)=\frac{1}{n}\ \sum_{l=1}^{n}I\Big{(}x_{(i)}\leq x\Big{)}\) is the empirical distribution function, where \(I\Big{(}x_{(i)}\leq x\Big{)}\) is an indicator function assuming the value 1 if \(x_{(i)}\leq x\) and 0 otherwise. The null hypothesis that the data follow the distribution under test, is rejected at the chosen significance level \(\alpha\) if the test statistic \(D>D(\alpha)\), where \(D(\alpha)\) is a critical value of the _KS_ test. The smaller the value of the test statistic \(D\), the better the fit. The Anderson-Darling (_AD_) test is a modification of the _KS_ test. This test is considered to be a better GOF test because it gives more weight to the tails of the distribution than the _KS_ test does. According to studies [65; 66], the AD test was ranked as one of the most powerful tests for detecting deviations from the Weibull and the extreme value distributions. The _AD_ test statistic is defined as follows:  
\[A^{2}=-n-\sum_{i=1}^{n}\frac{2i-1}{n}\Big{[}\ln\Big{(}\hat{F}\big{(}x_{(i)} \big{)}\Big{)}+\ln\Big{(}1-\hat{F}\Big{(}x_{(n+1-i)}\Big{)}\Big{)}\Big{]}. \tag{11}\]  
The null hypothesis that data follow the specified distribution, is rejected at the significance level \(\alpha\) if the test statistic \(A^{2}\) is greater than the critical value of the _AD_ test. Again, the smaller value of the test statistic \(A^{2}\) indicates a better fit.
## 5 Experiments and Discussion
### Experimental Datasets and Settings  
#### 5.1.1 Datasets  
We evaluated our model on two real datasets: PHEME (English dataset, mainly from international Twitter) [47] and CED (Chinese dataset, mainly from the domestic Sina platform) [48]. The PHEME dataset contains three types of labels: true, fake, and uncertain. The CED dataset contains only true and fake labels. Because CED lacks the basic information of the users participating in a discussion, we collected basic information on the users participating in discussions by designing a web crawler (since some of the participating accounts have been cancelled, we only collected nine types of meta-features of about 460 thousand related accounts, including gender, location, description, message, followers, friends, etc. The values of the cancelled accounts' multiple meta-features are given as 0). Table 1 provides the detailed statistics of datasets.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
## 5 Conclusions  
A design technique was proposed for tilting dual CP focused beams in specular directions using VRT with rotating reflectarray elements printed on a parabolic reflector fed by a single dual CP feed horn.  
A rotating reflectarray element to apply VRT made of a conductive cross embedded in a grounded multilayer substrate was proposed. The reflectarray element was optimized to provide few calls to a full-wave analysis routine in the design process, and very linear phase curves and low cross-polarization level were obtained.  
To demonstrate the multi-beam capacity, the parabolic reflectarray was illuminated with three feeds, and six main spots were obtained with angular separation less than 0.56\({}^{\circ}\) and cross-polarization level below 35 dB with respect to the maximum of radiation.  
Conceptualization, F.C.; methodology, F.C.; software, A.S. and I.G.; validation, A.S., R.F., and LG.; formal analysis, F.C.; writing--original draft preparation, A.S.; writing--review and editing, R.F.; visualization, R.F.; supervision, F.C. and I.G.; project administration, F.C. funding acquisition, F.C.  
Figure 7: Elevation cuts of the (**a**) co-polar and (**b**) cross-polar radiation patterns for three positions of the feeder and for two orthogonal circular polarizations. The directions of the beams were for \(\theta_{\text{beam,1}}=\pm 0.28^{\circ},\pm 0.83^{\circ}\), and \(\pm 1.36^{\circ}\). LHCP: left-handed circular polarization.  
**Funding:** This research was funded in part by the Spanish Ministry of Economy and Competitiveness, Projects Ref. TEC 2017-89456-R with the support of FEDER funds, TEC2016-75103-C2-1-R and DI-14-06818, and by the European Space Agency (ESA) under contract 4000117113/ 16/ NL/AF.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Model Building  
The ARIMA model is a popular and widely used statistical method for time-series forecasting. It attempts to extract the time-series patterns hidden within the data through autocorrelation and differencing, using these patterns to forecast future data. However, the ARIMA model does not perform well with non-smooth and nonlinear data. To address this issue, deep learning methods have been introduced for time-series prediction. Traditional RNNs have limitations in handling long-term dependencies, while LSTM can effectively address this issue. Nevertheless, LSTM may result in the loss of features from long time series, limiting their ability to extract features effectively. To enhance the efficiency of predicting long time sequences, the Informer model has been developed. Informer is based on the attention mechanism of Transformer, ensuring effective feature learning while reducing computational complexity through probabilistic sparsification. This technique enhances the sequence prediction speed and accuracy of the model. However, Informer only utilizes positional encoding and labeled embedded subsequences, which, although helpful in retaining some ordering information, may lead to the loss of temporal information due to the replacement invariance of the self-attention mechanism. In order to simplify these problems, the LTSF-Linear model can be used for multi-step prediction to achieve the same predictive effect without the need for complex computations, remaining fast even when handling large datasets.
## 2 Miniaturization of the Microwave System
### Microwave Transmission System Miniaturization Design
#### 2.2.1 Conventional Length Waveguide Plus Transition Waveguide  
The electromagnetic simulation outcomes corresponding to scheme, which involves conventional length waveguide plus transition waveguide, are exhibited in Figures 4a and 5's plan 1 curves. The simulation results about the electric field distribution of the entire system are as follows: when the power is one watt, the microwave energy coupled into the discharge chamber through the microwave window, and a maximum electric field of 1661 V/m can be formed in the discharge chamber, but the highest electric field of the entire microwave system is indeed primarily focused inside the transition waveguide, which leads the plasma to migrate towards the waveguide section and might result in the power blockage [16]. The scattering parameter S11 and the transmission efficiency of the system in the 2-3 GHz frequency range,  
Figure 5: The transmission performance of the three schemes: (**a**) is the distribution of the scattering parameter S11 in the 2\(-\)3 GHz frequency band; (**b**) is the 2\(-\)3 GHz frequency band distribution of transmission efficiency into the discharge chamber.  
respectively, are shown as plan 1 curves in parts (a) and (b) of Figure 5. At 2.45 GHz, the S11 parameter is only \(-0.826\) dB, and the power transmission efficiency is just 13.4%. The above demonstrates that the system's impedance is incompatible. As a result, this matching strategy needs to include a three-pin tuner or it will fail to meet the requirements of efficient microwave transmission.
## 3 Synergetic Optimization Operation Model of PV and FID  
### Objective Function  
Multi-objective optimization can provide a comprehensive optimal solution for problems, which is adopted in this paper. The optimization objectives of distribution network  
Figure 1: The reactive power output ranges of the PV inverter for five modes.  
operation include reducing network loss and operation cost, improving power quality, balancing load, increasing the ratio of clean energy, and so on. Among them, three objective functions of voltage deviation (\(F_{1}\)), network loss (\(F_{2}\)), and the ratio of PV abandonment (\(F_{3}\)) are selected. The objective function is defined as follows:  
\[\min f=(F_{1},F_{2},F_{3}) \tag{7}\]  
1. Voltage is one of the most important quality indicators for a distribution network. The voltage quality is closely related to a network's safe operation, network loss, and consumers' electricity utilization. The line drop, the load fluctuation, and the connection of PV increase the difficulty in maintaining qualified voltage. Thus, minimizing voltage deviation is chosen as an objective function, representing the power quality. The voltage deviation is defined as follows: \[F_{1}=\sum\limits_{i=1}^{n}\left|\frac{V_{i}-V_{iN}}{V_{iN}}\right|\] (8) where \(V_{i}\) is the actual voltage of node \(i\). \(V_{iN}\) is the rated voltage of node \(i\). \(n\) is the quantity of the nodes.
2. Network loss is one of the most important economic indicators for a distribution network. By active and reactive power optimization, network loss can be minimized. The network loss is defined as follows: \[F_{2}=\sum\limits_{ij\in\Omega_{0}}\left(\frac{\left(P_{ij}\right)^{2}+\left( Q_{ij}\right)^{2}}{\left(V_{i}\right)^{2}}R_{ij}\right)\] (9) Therein, \(P_{ij}\) and \(Q_{ij}\) are the active power and the reactive power flowing through branch \(ij\), respectively. \(R_{ij}\) is the resistance of branch \(ij\). \(\Omega_{0}\) is the set of all branches.
3. Environmental friendliness is an important requirement for sustainable development. PV belongs to clean energy and the permeability of PV continually increases. Large amounts of active power injections will cause voltage rise. Voltage regulation can be realized by the curtailment of PV active power output. However, the curtailment of PV active power will not only reduce the consumption rate of PV but also decrease the benefit of PV systems. To balance voltage quality and PV consumption rate, minimizing the ratio of PV abandonment is chosen as an objective function. The ratio of PV abandonment is defined as follows: \[F_{3}=\frac{\sum\limits_{i=1}^{m}P_{\text{MPPT},i}-\sum\limits_{i=1}^{m}P_{ \text{PV},i}}{\sum\limits_{i=1}^{m}P_{\text{MPPT},i}}\] (10)  
Therein, \(\sum\limits_{i=1}^{m}P_{\text{MPPT},i}\) is the sum of all PVs' active powers of MPPT. \(\sum\limits_{i=1}^{m}P_{\text{PV},i}\) is the sum of all PVs' active power outputs.
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 4 Spinel Ferrite-Ferroelectric Coaxial Fibers
### Nickel Zinc Ferrite-PZT Core-Shell Fibers  
Zinc-substituted nickel ferrite, Ni\({}_{1-x}\) Zn\({}_{x}\) Fe\({}_{2}\)O\({}_{4}\) (NZFO), and ferroelectrics are well-studied composite systems in the form of thin films, thick films, nanopillars, etc., due to their large magnetostriction and piezomagnetic coupling coefficients [3; 65; 73; 74]. Studies on coaxial fibers of NZFO-PZT were reported very recently by Ge et.al. [59]. Core-shell fibers of NZFO (x = 0.1-0.5)--PZT, prepared by electrospinning with a diameter in the range 1-3 \(\upmu\)m and annealed at 700-900 \({}^{\circ}\)C, were free of impurity phases and had uniform core and shell structures, as seen in electron and scanning probe microscopy images [59]. Ferromagnetic ordering of NZFO was confirmed by FMR and by magnetization measurements. A gyromagnetic ratio \(\gamma\) = 2.7-3.1 GHz/kOe and effective magnetization 4\(\pi\)M\({}_{\text{eff}}\) = 0.9-2.9 kG were estimated from FMR profiles, as shown in Figure 11a. The saturation magnetizations for the fibers were in the range M = 5-24 emu/g. The magnetostriction \(\lambda_{11}\) was measured along the H direction, with a strain gate on the discs of the fibers. Profiles of \(\lambda_{11}\) vs. H, as in Figure 11b,c for NZFO (x = 0.2)-PZT showed hysteresis and the saturation values of \(\lambda_{11}\) varied from \(-\)5 ppm to \(-\)11 ppm, depending on the x-values [59].  
Figure 10: The fractional change in the remnant polarization as a function of static field H for discs of fibers of (**a**) BTO core–NFO shell and (**b**) NFO core–BTO shell. Low-frequency MEVC vs. H for films of fibers of (**c**) BTO core–NFO shell NFs and (**d**) NFO core—BTO shell [69].  
The strength of ME coupling in the composites was measured by H-induced variation in \(\rm P_{r}\) and low-frequency DEVC. Estimated \(\rm\Delta P_{r}/P_{r}\) from P vs. E under H, as in Figure 12a,b, in discs of the fibers of NZFO (x = 0.3)-PZT, showed evidence of a giant ME coupling [59]. A \"memory effect\" was seen in the data, characterized by an increase in the magnitude of \(\rm\Delta P_{r}/P_{r}\) to 80% when H was increased from 0 to 5 kOe and this value was retained when H was decreased back to zero. A similar behavior was evident for samples with either a ferrite core or a PZT core. Low-frequency DEVC measured in fiber discs showed a rapid increase in H, as shown in Figure 12c, and the maximum DEVC values ranged from 6 to 35 mV/cm Oe and were an order of magnitude smaller than reported values for thick film NZFO-PZT [65].
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Analysis
#### 5.1.3 Effect of Different Positions on Experimental Results  
According to the characteristics of the CSI signal, it is sensitive to changes in position. In order to investigate the effect of location on recognition rate, the experiment was explicitly designed so that the experimenter's gesture direction was fixed and only the experimenter's location was changed, as shown in Figure 14. In our proposed method, sample data of six gestures were collected from each position and used to recognize sample data from each location. The final comparison results are shown in Figure 15.  
Figure 14: Experimental position design. The numbers in the diagram represent the position of the personnel.  
Figure 13: The effect of cross-targeting on gesture recognition: (**a**) Effect of different experimenters on gesture recognition. (**b**) Effect of the same experimenter on different gestures recognition.  
As seen from the experimental results in Figure 15, position changes significantly impact the CSI signal, resulting in different levels of accuracy at each position. Location 1 has low accuracy because it is next to the conference table and the furthest away from the device, which creates more multi-path effects, resulting in the lowest accuracy. Position 3 has the highest accuracy because it is closer to the transceiver and has fewer multi-path effects. Positions 4 and 2 are equally accurate, and both are affected by walls. Position 5 is more accurate than position 1 because it is above and relatively close to the receiving end, resulting in a slightly higher recognition rate.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Achieving Real-Time Intrusion Detection  
The system supports data transfer between the ESP32 sensor and the cloud, leveraging many design decisions to ensure real-time operation with minimal latency. The real-time capability of the SFSS is achieved by a mix of lightweight, optimized models and efficient communication protocols.  
* Optimized Machine Learning Model: The YOLOv8 model is intended for real-time object identification in particular. Its design enables high-speed image processing with minimum computing cost, especially when working on resource-constrained devices like the ESP32. The model's quick inference time--typically under 30 milliseconds per frame--ensures that object recognition occurs virtually instantly upon data reaching the cloud server.
* Efficient Data Transmission Protocols: To minimize transmission delays, the system leverages the MQTT (Message Queuing Telemetry Transport) protocol, which is optimized for low-latency, lightweight data transport. The MQTT is particularly suitable for IoT applications [28; 29; 30; 31], providing a speedy and reliable connection between the ESP32 sensors and the cloud, thereby ensuring that data are delivered and analyzed in near real time.
* Data Compression and Prioritization: The system leverages image compression techniques to minimize the size of the data transported from the ESP32 to the cloud, further lowering transmission time. Additionally, only crucial information--specifically frames indicating possible intrusions--is transferred to the cloud for processing, limiting the volume of data that require real-time management.
* Cloud Processing Power: The cloud infrastructure deployed in this system is designed to grow automatically based on the incoming data load, eliminating processing bottlenecks. The elastic nature of the cloud means that the model can accommodate enormous data volumes without them affecting its real-time performance.
* Latency Management: The whole round-trip time, from data collection to alert production, is closely managed to keep it within real-time restrictions. Empirical testing suggests that the whole delay--from data acquisition at the sensor to warning delivery--typically runs from 100 to 200 milliseconds, which is well within acceptable limits for real-time monitoring systems.  
Figure 21: Alert messages sent to owner.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 4 Summary of Findings and Conclusions  
Short-term aging occurs during mixing in the plant, transportation to the site, and laying and compaction of asphalt mixtures. Aging an asphalt mixture increases its susceptibility to fatigue cracking, leading to reduced durability under traffic. As a general practice, loose asphalt mixtures are aged at 135 \({}^{\circ}\)C for 4 h to simulate short-term aging during construction. However, dissimilarities in the short-term aging simulation protocol have been reported by various researchers (ranging from 2 to 4 h at 135 \({}^{\circ}\)C and so on); thus, it is essential to validate the short-term aging protocol for different conditions, including the one in the Middle East. This study evaluated the short-term aging protocol that is suitable for asphalt mixtures used in asphalt roads in the State of Qatar.  
Three loose asphalt mixtures that are often used in the State of Qatar were evaluated in this investigation. Mixtures 1 and 3 were collected from construction sites, while mixture 2 was obtained from the plant. Virgin aggregates and binders were also collected to reproduce the mixtures in the laboratory. Laboratory-produced mixtures were conditioned at 135 \({}^{\circ}\)C for different lengths of time. The mechanical properties of laboratory-produced samples were compared to samples produced at the site. The comparison of dynamic modulus master curves of the field and plant mixtures with their counterpart lab mixtures indicated that the short-term aging protocol lies between 3 and 4 h at 135 \({}^{\circ}\)C. Furthermore, the binder test results (viscosity, true PG, and MSCR) confirmed that aging asphalt mixtures at 135 \({}^{\circ}\)C for 4 h would produce asphalt mixtures with comparable binder properties to asphalt mixtures produced at the site. In summary, the proper short-term aging protocol for asphalt mixtures often used in pavement construction in Qatar would involve heating asphalt mixtures at 135 \({}^{\circ}\)C for 4 h before laboratory compaction. An appropriate short-term aging protocol simulates the plant operation of asphalt mixtures in Qatar and implementation of the protocol prior to compacting the specimen would provide a more accurate evaluation of pavement characterization and performance.  
Conceptualization, O.S. and E.K.; Investigation, D.K.P., O.S. and M.O.; Writing, reviewing, editing, and improving manuscript, O.S., D.K.P., E.K. and M.O.; Formal Analysis, D.K.P., O.S. and M.O.; Funding acquisition, O.S. and E.K.  
This paper was made possible by the NPRP grant (NPRP 6-773-2-320) from the Qatar National Research Fund (a member of the Qatar Foundation). The findings herein reflect this work and are solely the responsibility of the authors.  
The authors declare no conflict of interests.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
###### Abstract  
Numerous studies have developed extensions to the IFC schema to meet the needs of specialized domains or represent nascent technologies, and in turn have expanded the scope of interoperability for BIM data exchanges. However, these studies used varying approaches for IFC extensions and validation, making it difficult to identify research gaps and agree on legitimate extension protocols. This study collected 64 studies of IFC schema extensions spanning over two decades, from 2001 to 2022. The analysis first focused on categorizing these cases with respect to their target domains and sectors, their purpose and extension approaches, as well as their methods for implementation and validation. Timeline analyses were also conducted to track the temporal trends over the specified period. The results revealed that architectural cases have recently shifted from process to product representations due to new technology adoptions, while infrastructure cases, initially centered on major sector elements, have transitioned towards operation and maintenance processes. The findings also showed the need for a more holistic and organized approach for extensions, as current ad hoc developments were limited to products and processes only applicable for specific sectors.  
Keywords:industry foundation classes; IFC schema extension; building information modeling; IFC literature review +
Footnote †: journal: Applied Sciences