esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management Scheme for an EV Smart
## 4 EMS for a Smart Charger
### Reactive Power Support for Voltage Regulation  
The smart charger also serves to regulate the voltage at the PCC bus with its reactive power action. In this study, voltage sag and swell are induced by the consumption and production of reactive power, respectively. The DC-link capacitor is sufficient for reactive power operation, and the EV battery energy is not utilized. Thus, the presence of EVs at the charger is not a concern. This feature enables the stationary off-board smart charger to be capable of providing reactive support at all times. Reactive power is controlled by shifting the current phase angle to provide inductive or capacitive action. The voltages are computed in RMS, and the balanced sag or swell factor \(\zeta\) can be calculated for each phase as:  
\[\zeta=\frac{V_{pcc}}{V_{pcc}^{0}} \tag{34}\]  
where \(V_{pcc}\) is the operational RMS PCC voltage measured at all instances and \(V_{pcc}^{0}\) is the RMS PCC voltage in normal conditions. In systems with a low impedance between the adjacent buses as assumed in this study, the voltage on adjacent buses is almost equal during normal conditions, i.e., \(V_{pcc}^{0}\simeq V_{g}\). Therefore,  
\[\zeta=\frac{V_{pcc}}{V_{g}} \tag{35}\]  
Rearranging the reactive power transfer equation and using Equation (35), we can derive the reactive power as a function of sag or swell factor \(\zeta\) given by:  
\[Q_{sc}^{*}=\frac{V_{g}^{2}}{X_{g}}(1-\zeta\cos\delta) \tag{36}\]  
and when the power angle \(\delta<20^{\circ}\), Equation (36) reduces to the following:  
\[Q_{sc}^{*}=\frac{V_{g}^{2}}{X_{g}}(1-\zeta) \tag{37}\]  
The \(Q_{sc}^{*}\) reference calculated by the EMS is forwarded to the inverter controller as \(Q_{(inv)}^{*}\). The negative sign of \(Q\) means inductive operation, and the charger absorbs the reactive power, decreasing the voltage and supporting swell suppression. Positive \(Q\) implies capacitive action, and the charger supplies the reactive power to improve voltage sag. The maximum reactive power capability of the charger is calculated by using the following equation.  
\[Q_{sc(max)}=\sqrt{S_{n}^{2}-(P_{sc}^{*})^{2}} \tag{38}\]  
The full reactive power support can be provided by the charger when \(P_{sc}^{*}=0\). While the EVs are absorbing maximum power \(P_{sc}=S_{n}\), reactive support cannot be provided. Therefore, it is a recommended practice to increase the charger kVA rating by 10% to 20% above the maximum active power to keep the charger capable of reactive power support at all times [1]. In summary,\[\mathcal{Q}_{(inv)}^{*}=\begin{cases}\frac{V_{g}^{2}}{X_{g}}(1-\zeta)\,&\forall\quad \zeta\
eq 0\quad\wedge\quad Q_{sc}^{*}<Q_{sc(max)}\\ \sqrt{S_{n}^{2}-(P_{sc}^{*})^{2}}\,&\forall\quad\zeta\
eq 0\quad\wedge\quad Q_{sc}^{* }\geq Q_{sc(max)}\\ S_{n}\,&\forall\quad\zeta\
eq 0\quad\wedge\quad P_{sc}^{*}=0\\ 0\,&\forall\quad\zeta=1\end{cases} \tag{39}\]
## 3 Design and Electromagnetic Field Analysis of IPM Synchronous Motor  
### 3.1. Stator Analysis Model Specification  
A motor was designed using JMAG, an electromagnetic field analysis program, and its performance was verified through no-load and load simulations in this study. The design of the IPM motor was performed to satisfy the desired characteristics by solving the magnetic phenomenon based on Maxwell's equation [18].  
The stator model specifications of the IPM motor used in this study are listed in Table 1. A total of 36 stator slots and a rated DC voltage of 67.882 V were considered in the main specification. The analysis was performed by equally inputting all currents according to the speed. The motor-rated maximum current value was 80 A. A cross-sectional diagram of the 36 slots is shown in Table 1. The outer diameter of the interior permanent magnet motor stator was 81 mm. The counter-electromotive force waveform in the stator was designed in a distribution area for a sinusoidal wave to be formed. Moreover, it was designed not to exceed 45% at 43.69% by considering the insulation thickness because the fill factor for winding was reported to be below 45%. In addition, a winding method of winding six 0.4 mm coils for 10 turns was applied to reduce the current density.  
Typically, interference between the permanent magnet and the slot in conventional commercial motors is present. This interference deteriorates the magnetic flux and strength of the magnet and provides a space for flux to flow between the magnets. Therefore, the design was focused on removing the space of flux between magnets and creating the flow of magnetic flux sound by opening the surface of the permanent magnet in the slot direction.  
Through JMAG, we performed a simulation to measure the motor output according to the inverter vector control of the IPMSM. The combination of the \(q\)-axis and \(d\)-axis current has a point that can produce maximum torque with a minimum stator current and is referred to as the maximum torque per ampere (MTPA), which uses MTPA control to control the motor with this combination of \(q\)-axis and \(d\)-axis current.
## 6 Energy Detection Algorithm
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Quantity** \\ \hline Modulation of PU signal & OFDM \\ Type of OFDM (constellation) & QPSK, 16 QAM, 64 QAM, 256 QAM \\ Type of channel noise & AWGN \\ Quantity \(N\) of samples (FFT size) & 128, 256, 512, 1024 \\ SNR range at SU position (dB) & \(-\)25–10 \\ The range of detection and false alarm probabilities & 0–1 \\ Quantity of Monte Carlo iterations per simulation & 10,000 \\ Noise variance \(\sigma_{n_{i}}^{2}\) in the case of DT (\(\rho=1.00\)) & 1.00 \\ Noise variance \(\sigma_{n_{i}}^{2}\) in the case of NU and DT (\(\rho>1.00,\rho^{\prime}>1.00\)) & 1.01 \\ NU factor \(\rho\) & 1.00, 1.02, 1.03, 1.05 \\ DT factor \(p^{\prime}\) & 1.00, 1.01, 1.03, 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters used in simulations. The decision process is performed according to the relations (7) and (8) and presented in lines 20-26 of Algorithm 1. For every scenario analysed, the comparison of the threshold is performed using the corresponding received signal energy: _test_stat_1_ and _test_stat_2_. If the received signal energy is equal to or larger than the threshold, hypothesis \(H_{0}\) is confirmed and PU is present as indicated in relation (1). If the received signal energy is lower than the threshold, hypothesis \(H_{1}\) is confirmed (according to relation (1)) and PU is absent. In lines 27-30 of the algorithm, determining the probability of PU signal detection (\(P_{d_{i}}\)) is performed through Monte Carlo simulations in order to obtain the most realistic results for all cases analysed. The false alarm probability (\(P_{fa_{i}}\)) passes through a set of values in the range 0-1. For each false alarm probability (\(P_{fa_{i}}\)), the detection probability (\(P_{d_{i}}\)) is calculated in order to be in the range 0-1. Analyses are performed for the different values of DT (\(\rho^{\prime}\)) and NU (\(\rho\)) factors, which differ among the specific simulation cases.
## 2 Materials and Methods  
### Data Sources and Search Strategy  
A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [20], and the protocol was pre-registered in the PROSPERO database (CRD42024619808). A comprehensive literature search was carried out from 27 November 2024, to 23 December 2024, across different database, including MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WOS), IEEE Xplore, and ProQuest. Specific search strategies were tailored to each database using combinations of key terms and Boolean operators. For example, the MEDLINE (PubMed) search included the following terms: (\"_artificial intelligence_\" [Title/Abstract] OR \"_machine learning_\" [Title/Abstract] OR \"_deep learning_\" [Title/Abstract] OR \"_convolutional neural networks_\" [Title/Abstract]) AND (\"_bone age_\" [Title/Abstract] OR \"_bone age assessment_\" [Title/Abstract] OR \"_skeletal maturity_\" [Title/Abstract]) AND (\"_radiology_\" [Title/Abstract] OR \"_medical imaging_\" [Title/Abstract] OR \"_pediatric imaging_\" [Title/Abstract]). Equivalent strategies were applied across the other databases to ensure comprehensive coverage of the relevant literature.  
The literature search was independently conducted by two researchers ([PERSON] and [PERSON]). Articles were initially screened based on title and abstract, with potentially relevant studies undergoing a full-text review to determine eligibility. In the case of any discrepancies between researchers, an independent and blinded researcher ([PERSON]) was consulted to reach a final consensus. Table S1 provides a detailed outline of the search strategy used across all databases.
## 1 Introduction  
Structural carbohydrates, also referred to as crude fiber, are sugars including cellulose, hemicellulose, lignin, and pectin substances [1]. They have a significant impact on the amounts of nutrients in forage and, consequently, on livestock productivity [2]. Crude fiber content of the biomass varies across the harvests of the same growing season, and it depends mainly on the plant species, its cultivar, and age [3], as well as on fertilizer treatment, or the volume of atmospheric precipitation [4; 5]. [PERSON] and [PERSON] [1] report that minimum crude fiber content in dry matter is 10-15%. For maintaining proper digestibility and high energy value of nutrients, it should not be higher than 28-30%.  
There are three fiber fractions: neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL). NDF contains cellulose, hemicellulose, and lignin, ADF contains cellulose and lignin without tannins, and ADL contains lignin, cutin, and ash. Increased NDF content in the forage reduces its intake [6; 7; 8; 9; 10], while the amount of the ADF fraction is negatively correlated with its digestibility [10]; its low content increases net energy concentration [11].  
An important species of fodder grasses is _Festulolium branuii_, a cross between _Festuca pratensis_ and _Lolium multiform_. This grass produces higher yields of better quality than the species from which it originates [12]. In addition, it is drought resistant and grows very well when planted on its own in arable fields [13].  
The purpose of this study was to assess the proportions of crude fiber fractions in _Festulolium branuii_ treated with slurry on its own and combined with soil conditioners or mineral fertilizers.  
When conducting the studies, the following research hypotheses were adopted:  
1. Slurry supplemented with soil conditioners as well as slurry supplemented with mineral NPK fertilizers will have a more beneficial effect on the content of raw fiber and its fractions in _Festulolium braunii_ than slurry applied on its own.
2. Slurry supplemented with soil conditioners will have a more positive effect on the content of fiber fractions in _Festulolium braunii_ than slurry used in combination with NPK mineral fertilizers.
## 2 Materials and Methods
 \tag{1}\]  
Horizontal displacement of a point \(P\) between time (\(t\)) and (\(t+\Delta t\)) is calculated as follows:  
\[d_{i}=\sqrt{\Delta n^{2}+\Delta e^{2}}\ \ \ (i\ =\ 1,\ 2,\ 3,\ \ldots,\ n) \tag{2}\]  
The errors were determined by subtracting each estimated horizontal displacement from the real displacement values for all the 3 h, 6 h, 8 h, 12 h, and 24 h observation times. \[\varepsilon_{i}=d_{i}-d\ (i\ =\ 1,\ 2,\ 3,\ \ldots,\ n) \tag{3}\]  
where \(d\) is the real displacement value, \(d_{i}\) is the estimated value, and \(\varepsilon_{i}\) is the error for the session \(i\). The Root Mean Square Error is determined by Equation (4). \[RMSE_{d}=\sqrt{\frac{\varepsilon_{1}^{2}+\varepsilon_{2}^{2}+\ldots\varepsilon _{n}^{2}}{n}} \tag{4}\]  
The values of di in Equation (2) were computed after processing the data with the GAMIT/GLOBK software. The value of d given in Equation (3) represents the known displacement values from Table 3, while di indicates the displacement values calculated using Equation (2) after the GAMIT/GLOBK processing. Since the displacement accuracy produced by the Zeiss BRT 006 basis-reduktionstachymeter is given as 0.06% or 0.03 mm per 5 cm, the artificially generated displacement values were considered as known values in this study. The differences between these known displacement values and the displacement values obtained from GNSS data are used as error values. Figure 5: (**a**) Blue triangles illustrate IGS stations used for GAMIT/GLOBK processing, and the red circle shows the location of the UZEL test site (**b**) Yildz Technical University Civil Engineering Faculty and the UZEL test site on the roof of the faculty. Calculations given above were performed based on the coordinate values determined from observations collected over a 24 h period. Additionally, processes were conducted to obtain coordinates from observations of 3, 6, 8, and 12 h durations derived from the same dataset. Since there was only a single observation for the 24 h period for each increment of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm, a RMSE could not be calculated for these datasets. However, RMSEs were computed for all other observations derived from the division of the 24 h data, and these values are subsequently shared in the Section 3.
# New Light on Phtyoremediation: The Use of Luminescent Solar Concentrators
## 1 Introduction
The evaluation of the responses to plant stress and all those measures that can have significant positive effects on the efficiency of phytoremediation, particularly root-microorganism interactions, is often crucial to the success of the technology. Proper management of greenhouse conditions (light, tempera ture, humidity, and irrigation) can considerably improve phytoremediation feasibility tests' efficiency and speed. In semi-controlled and protected conditions, some obstacles to plant growth can be studied and overcome, such as the reduced biological activity of plants due to seasonality and the bioavailability dynamics of pollutants. In particular, aim of this study was to provide the information necessary for evaluating the possibility of coupling Luminescent Solar Concentrators with phytoremediation in remediation procedures. Indeed, as the wave-guiding material is semi-transparent and wavelength selective, LSCs could also find promising applications in greenhouse roof panels: by selecting only the light that plants do not use for photosynthesis [25], it should be possible to produce electricity without penalizing plants growth, or possibly even increase agricultural productivity. For this purpose, phytoextraction tests were carried out with three plant species (_Brassica juncea, Helianthus annuus_ and _Lupinus albus_) to assess the effect of LSC panels on plant growth and the absorption capacity of contaminants by plants on a soil polluted by arsenic and lead. Experiments were carried out by comparing plants grown under LSC panels versus plants grown under polycarbonate panels. LSC panels doped with Lumogen Red F305 (BASF) as luminescent dye were used to be evaluated as optical filter and not as a PV device for electricity production. The parameters examined were those essential for a phytoremediation feasibility test:  
* possibility of plant growth in contaminated soil;
* biomass production;
* uptake of contaminants by plants;
* total removal of pollutants by plants. This is a highly innovative perspective since, to the best of our knowledge, there are no consolidated studies and results on the use of LSC in the remediation of contaminated sites using phytoremediation technologies, when, due to soil contamination, plants should grow under significant stress conditions.
# Malware Detection Using Memory Analysis Data in Big Data Environment
## 2 Related Works
In addition to these models, the performances of SVM and RF classifiers were also evaluated. The proposed method achieved 97% accuracy in binary classification. [PERSON] et al. [38] proposed an ensemble classification-based methodology for malware detection. The first stage is fully connected and is handled by CNN, while the final classification is handled by meta-learner. The performance of 13 different machine learning systems were compared in the study. Experimental studies were carried out on the Classification of Malware with PE headers (ClaMP) dataset. The best performance was obtained by using the 5 dense and CNN in the first stage and the ExtraTrees classifier in the last stage. [PERSON] et al. [39] proposed an ensemble-learning method for malware detection. In the proposed method, the base stage consists of a fully connected and 1D CNN network, while the end stage consists of a machine learning algorithm. The authors compared the performance of 15 different machine learning methods as a meta-learner. Experimental studies were carried out on the Windows Portable Executable (PE) malware dataset. As seen in related works, studies focus on extracting memory dumps and detecting malware using them. In the studies, malware classification was made using various machine learning and deep learning algorithms. However, these classifications were made with one or more specified algorithms. In this study, nine different machine learning and deep learning methods were applied for malware detection on memory dumps. In this way, the classification performance of many methods was compared. Unlike the related studies, a balanced memory analysis dataset was used. In addition, the studies were carried out on the big data platform. As a result of the study, very high accuracy rates were determined. Table 1 provides summaries of studies focusing on malware detection in the literature by best or recommended models, datasets, samples, and accuracy rates. Accordingly, it is seen in the literature that various machine learning and deep learning approaches are used in malware detection studies. Different datasets are used to test the proposed methods in the studies. In studies focusing on malware detection with memory forensics, it is seen that, generally, malware and benign samples collected from repository are used. In our study, the CIC-Malmem-2022 dataset based on memory analysis features was used. In this study, a higher accuracy rate was obtained with the balanced CIC-Malmem-2022 dataset compared to other studies.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Thermal Analysis of the Belt  
The belt is a long and thin structure, and because of its approximately rectangular cross section, the analytical method is applicable to perform its heat transfer analysis. The temperature distribution of the belt can be considered uninformed because it has a thickness of as low as 0.05 m but a thermal conductivity of as high as 0.8 W/(m-K). The experiments described in Section 3 demonstrate that the two sides of the belt have similar temperatures, as shown in Figure 3a,b. The temperature distribution obtained using a simple numerical simulation also supports this assumption, as shown in Figure 3c.  
Hence, the temperature distribution inside the belt can be replaced by a single value \(T_{b}\). All the values in matrix \(\mathbf{T_{b}}\) thus become \(T_{b}\). The heat dissipation on the belt surfaces can be easily calculated [31], thereby yielding function \(k(\mathbf{T_{b}},\dots\) ). The thermal balance of the belt can be expressed as  
\[\sum_{n=1}^{N}{(1-\xi_{n})P_{fn}+P_{h}}=A_{b}h_{b}(T_{b}-T_{a}) \tag{6}\]  
where \(A_{b}\) is the total belt surface area exposed to the environment, \(h_{b}\) is the heat transfer coefficient of the belt surfaces, and \(T_{a}\) is the constant ambient temperature in this static analysis.  
In Equation (6), \(h_{b}\) varies with the local air speed. The relationship between \(h_{b}\) and the local air velocity \(V_{b}\) can be represented through a fourth-order polynomial created by the curve-fitting method:  
\[h_{b}=k_{1}V_{b}^{4}+k_{2}V_{b}^{3}+k_{3}V_{b}^{2}+k_{4}V_{b}+k_{5} \tag{7}\]  
where \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), and \(k_{5}\) denote five coefficients whose values related to the material properties of the belt. In this study, \(h_{b}\) is measured from a small wind tunnel device, and the coefficients for the belt are as follows: \(k_{1}=-6.93\times 10^{-5}\), \(k_{2}=0.00714\), \(k_{3}=0.252\), \(k_{4}=4.3\) and \(k_{5}=1.7\).  
This model focuses on the static condition, meaning that the belt speed is constant under each scenario. Equation (7) is used to calculate the corresponding \(h_{b}\) under the designed belt speed, which is then substituted into Equation (6) for the model calculation.
# Using the Guided Fireworks Algorithm for Local Backlight Dimming
## 1 Introduction
As an important branch of computational intelligence (CI), the EC algorithm has a strong global search ability and good performance in solving complex optimization problems [13]. EC algorithms such as Genetic Algorithm (GA) [14], Artificial Bee Colony (ABC) [15], Particle Swarm Optimization (PSO) [16], and Ant Colony Optimization (ACO) [17] have been successfully applied to different optimization areas. The EC algorithm applied in our work [12] is Shuffled Frog Leaping Algorithm (SFLA) [18]. Based on the original SFLA, we proposed the improved algorithm named Improved Shuffled Frog Leaping Algorithm (ISFLA). It was proven that our method could achieve a higher image quality with the same or lower power consumption, compared with traditional parameter-based local dimming methods. On the basis of our previous work [12], this paper continues to take local dimming as an optimization problem, and focuses on improving the displayed image quality. There are two important elements needed to achieve higher image quality: the evaluation of the image quality, and the search ability of the algorithm. On the one hand, when searching using EC algorithms, the solutions are selected based on the image quality evaluation results. Therefore, with an accurate evaluation method,  
Figure 1: A local dimming system. LED: light emitting diode; LC: liquid crystal. the solutions that have high performance can be selected accurately. On the other hand, the solutions obtained by EC algorithms are usually near-optimal solutions, which means that the solutions' quality can be further improved by improving the search ability of the algorithms. Therefore, this paper uses a structural similarity (SSIM) index [19] instead of PSNR to better evaluate image quality, and proposes a new local dimming algorithm. Our proposed algorithm is based on the Fireworks Algorithm (FWA) [20], which is an EC algorithm proposed in recent years. To improve the search ability of FWA, we introduced a guiding strategy into the original FWA to give an improved algorithm: the Guided FWA (GFWA). Compared with the ISFLA, the search ability of the GFWA is further improved. The rest of this paper is organized as follows: Section 2 introduces our new model of the local backlight dimming problem; Section 3 analyses the FWA and proposes the improved algorithm, the GFWA; Section 4 shows the experimental results; and in Section 5, we conclude our work.