esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Radiation Pattern  
The radiation pattern analysis of the designed patch antenna at frequencies of 6 GHz, 7.5 GHz, 9.5 GHz, and 11 GHz (Figures 11 and 12) provides insights into its performance characteristics, including directivity, gain, and overall efficiency. At 6 GHz, the radiation pattern exhibits a broad main lobe, suggesting effective energy distribution. Sidelobes are minimal, indicating good directivity and reduced unwanted radiation, which enhances the antenna's performance for applications in this frequency range. At 7.5 GHz, the main lobe remains pronounced, with a slight shift in direction compared to the 6 GHz pattern. The overall gain appears to increase, reflecting improved efficiency in energy radiation, which is beneficial for communication applications. At 9.5 GHz, the radiation pattern becomes more concentrated, with a narrower main lobe indicating enhanced directivity. This change likely results from the optimal operation of the slots in the ground plane, which affects the current distribution and improves performance. At 11 GHz, the pattern shows a well-defined main lobe, indicating strong performance at the antenna's center frequency.  
Figure 10: (**a**) Peak gain of the antenna and (**b**) total efficiency.  
Further minimization of sidelobes suggests excellent radiation efficiency and directivity, confirming that the antenna is effectively tuned for this frequency. Overall, the analysis of the simulated radiation patterns from 6 GHz to 11 GHz demonstrates that the antenna maintains good performance across the tested frequencies. The presence of the L-slot and circular slot significantly enhances the radiation characteristics, contributing to improved directivity and overall gain. The patterns indicate that the antenna is well-suited for dual-band operation, showcasing effective energy radiation and minimal sidelobe interference,  
Figure 11: Two-dimensional radiation pattern at different frequencies.  
Figure 12: Three-dimensional gain plot at different frequencies.  
confirming its applicability for various wireless communication applications. The theta and phi of the 2D and the 3D are obtained at (\(0\leq\theta\leq\pi\)), and phi ranges from (\(0\leq\varphi\leq 2\pi\)).
## 2 Acceleration Based Moving Force Identification Theory
### New Acceleration-Based MFI Algorithm
The error function for the previous time step, \(j-1\) can be written as:  
\[f_{j-1}(\{\widetilde{X}\}_{j-1})=\min\Bigl{[}([Q]\{\widetilde{X}\}_{j-1}-\{d \}_{j-1},[W]([Q]\{\widetilde{X}\}_{j-1}-\{d\}_{j-1}))+(\{r\}_{j-1},[B]\{r\}_{j -1})+f_{j}(\{\widetilde{X}\}_{j})\Bigr{]} \tag{22}\]This function is a minimum when the partial derivative of force with respect to \(r_{j-1}\) is zero:  
\[\frac{\partial f_{j-1}(\{\widetilde{X}\}_{j-1})}{\partial\{r_{j-1}\}}=0 \tag{23}\]  
The first derivative of force is calculated by:  
\[\{r\}_{j-1}=\Big{[}2[B]+2[T]^{T}[Q]^{T}[W][Q][T]\Big{]}^{-1}\{2[T]^{T}[Q]^{T}[W ]\{d\}_{j}-2[T]^{T}[Q]^{T}[W][Q]\{\widetilde{M}\}_{j-1}\{\widetilde{X}\}_{j-1}\} \tag{24}\]  
Substituting \(r_{j-1}\) into Equation (22) and re-arranging, the error function can be found at any time step \(n\), and the error function can be written in the following format:  
\[f_{n}(\{\widetilde{X}\}_{n})=(\{\widetilde{X}\}_{j-1},[R_{n}]\{\widetilde{X} \}_{j-1})+(\{\widetilde{X}\}_{j-1},\{S\}_{n})+q_{n} \tag{25}\]  
The first derivative of the force vector can then be calculated by:  
\[r_{n-1}=-\Big{[}2[B]+2[T]^{T}[R]_{n}[T]\Big{]}^{-1}\{[T]^{T}\{S\}_{n}+2[T]^{T}[ R]_{n}\{\widetilde{M}\}_{j-1}\{\widetilde{X}\}_{j-1}\} \tag{26}\]  
Before calculating the force vector, a backward sweep is used to calculate \(R_{i}\) and \(S_{i}\), starting at \(i=n\). Then, the force vector is calculated in a forward sweep, which uses Equation (26) to find all \(n\) components. Further details are given in Appendix A. Like previous MFI algorithms, an L curve is used to find the optimal regularization parameter in this paper. This reformulation of the underlying MFI equations allows for the first time the new A-MFI algorithm to be directly applied to acceleration data, which is damage sensitive.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 3 Methodology  
In this Section, the methodology employed is described in a modular fashion. These modules include data acquisition, ROM identification, load reduction, and secure transmission.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### Estimation of Optimization Criteria
#### 2.2.1 Estimation of Power Consumption
Then, the switching activity \(SA_{r}\) of memory element \(r\) is described by the following formula:  
\[SA_{r}=\sum_{i=1}^{M}\sum_{j=1}^{M}P\big{(}a_{i}\to a_{j}\big{)}\times \Big{(}V^{\prime}(c_{i})\bigoplus V^{\prime}\big{(}c_{j}\big{)}\Big{)}, \tag{3}\]  
where \(P(a_{i}\to a_{j})\)--probability of transition from state \(a_{i}\) to state \(a_{j}\) (\(a_{i}\), \(a_{j}\in A\)); and \(\bigoplus\)--logic operator \"exclusive or\" (XOR). The probability of transition \(P(a_{i}\to a_{j})\) can be calculated using the following equation:  
\[P\big{(}a_{i}\to a_{j}\big{)}=P(a_{i})\times P\big{(}X\big{(}a_{i},a_{j} \big{)}\big{)}, \tag{4}\]  
where \(P(a_{i})\)--probability that the \(a_{i}\) is the current state of the FSM; and \(P(X(a_{i}\), \(a_{j}))\)--probability that the input vector is equal to \(X(a_{i}\), \(a_{j})\), which causes a transition from state \(a_{i}\) to state \(a_{j}\). Let \(V^{b}(X)\) represent the value of the \(b\)-th variable of input vector \(X\). The probability \(P(X(a_{i}\), \(a_{j}))\) that input vector of the FSM is identical to \(X(a_{i}\), \(a_{j})\) is described by the equation  
\[P\big{(}X\big{(}a_{i},a_{j}\big{)}\big{)}=\prod_{b=1}^{L}P\Big{(}V^{b}\big{(} X\big{(}a_{i},a_{j}\big{)}\big{)}=d\Big{)}, \tag{5}\]  
where \(d\in\) [\"1\", \"0\", \"-\"]; and \(P(x_{b}=d)\)--the probability that input variable \(x_{b}\) from input vector \(X(a_{i}\), \(a_{j})\) is identical to \(d\). In our method, we assume that probabilities of both 0 and 1 on any FSM input are the same, thus \(P(x_{b}=0)=P(x_{b}=1)=\frac{1}{2}\) and \(P(x_{b}=\"-\")=1\). Notice that we do not consider the correlations between the values on individual inputs. Next, from the following system of equations, we can determine the probability \(P(a_{i})\) that a current state of FSM is \(a_{i}\), \(i=<\)1, \(M>\):  
\[P(a_{i})=\sum_{j=1}^{M}P\big{(}a_{j}\big{)}\times P\big{(}X\big{(}a_{j},a_{i} \big{)}\big{)},i=\langle 1,M\rangle. \tag{6}\]  
When no transitions between states \(a_{j}\) and \(a_{i}\) exist, it can be assumed that \(P(X(a_{j}\), \(a_{i}))=0\). Consequently, when transitions from the state \(a_{j}\) to state \(a_{i}\) exist, the value \(P(X(a_{j}\), \(a_{i}))\) is defined as a sum of the probabilities for every input vector, which causes a transition from state \(a_{i}\) to state \(a_{i}\). The Formula (6) denotes the linear system of \(M\) equations in \(M\) variables \(P(a_{1})\),..., \(P(a_{M})\). The system is linearly dependent, and the number of its solutions is infinite. However, we can notice that the machine is always in one of its internal states, and Formula (7) is correct:  
\[\sum_{i=1}^{M}P(a_{i})=1. \tag{7}\]  
One of the equations in (6) should be substituted by Equation (7) to solve the system of Equation (6). The power estimation algorithm was fully described in [35].
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 4 Results
### Single Robot Results
#### 4.1.2 Experimental Results  
A real-world experiment was conducted to validate the simulated results. A rectangular workspace, with approximate dimensions of 2.6 m \(\times\) 4 m, was constructed and three inner obstacles were placed inside it. The obstacles consist of two rectangular boxes and a T-shaped barrier. The workspace is visible in Figure 8. To explore the workspace, an AmigoBot robot is chosen due to its small size, versatility, and its ROS integration through the RosAria package. The AmigoBot is connected to an ODROID computer to run ROS locally. Additionally, an RPLIDAR A1 and the odometer data provided by the robot are used for SLAM that is provided by the gmapping ROS package. The robot manages to fully explore the workspace. The resulting PD is 7.33 m and the ET 124.2 s. The full path can be observed in Figure 9.
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### Smart Healthcare IoT  
The healthcare industry's major goal is to deliver better healthcare to everyone, everywhere in the globe, at any time. This should be executed in a way that is more patient-friendly and cost-effective. Therefore, health monitoring devices need to be applied to increase the efficiency of patients' health care. Today's medical world faces two challenges in patient monitoring: first, the requirement for healthcare providers and caretakers to be present at the patient's bedside, and second, the patient's confinement to bed and connection to huge apparatus [40]. These requirements must be met to provide flexible and friendly patient care, and as bioinstrumentation and telecommunications technologies advance, it is becoming more feasible to design a home-based vital sign monitoring system that can gather, display, record, and transmit physiological data from a human body to any location.
## 2 Review Methodology  
This study employed the guideline proposed by PRISMA to select relevant articles for the study. PRISMA provides comprehensive guidelines for reporting systematic review and meta-analysis studies using an evidence-based minimum set of items [23]. This review considered publications that focused on ECG interpretation and diagnosis, mobile applications or frameworks for ECG interpretation, as well as studies on ES and DSS for ECG interpretation and diagnosis. These publications were retrieved through a careful literature search using reputable high-impact research databases such as IEEE, PubMed, ScienceDirect, Springer, Wiley and ACM. The following query set was used to retrieve the relevant publications:  
(\"mobile application\" OR \"framework\") AND (\"ECG\" OR \"ECG tracing\" OR \"electro-cardiogram\") AND (\"expert system\" OR \"decision support system\"). This query set retrieved a total record of 8381 publications, which were passed to the screening phase of the model. By following PRISMA guidelines, the titles and abstracts of the total articles retrieved were thoroughly screened for relevance. The eligibility criteria were based on the relevance of the articles using the major keywords employed in the search query. After careful consideration of the inclusion and exclusion criteria, a total of 85 studies were considered for review. Figure 1 shows the flow chart of the study selection based on PRISMA guidelines. The included studies in the review covered over a decade that spanned thirteen years period from 2010 to 2022. More specifically, we focused on publications written in the English language. Table 1 summarizes the studies considered in this review. Interestingly, research on ES and DSS for ECG interpretation and diagnosis has witnessed tremendous growth over the past few years. Researchers have investigated and explored the possible use of mobile application development to reduce the stress in ECG data acquisition and diagnosis [24; 25; 26; 27]. Cloud-based models [4; 14], ontology framework [28; 29], wearable sensors [10; 30] and a XML rule-based repository [31], as well as Internet-of-Things (IoT) [4], among others, have been considered to aid in the diagnosis of heart diseases. Recent research efforts have been directed towards developing solutions for securing ECG data based on biometric authentication [6; 32]. There have been a significant number of studies on arrhythmia detection and diagnosis [17; 33; 34; 35]. The subsequent sections detail the efficacy of research efforts in ECG interpretation and diagnosis. Figure 2 shows the percentage distribution of the publications considered in this review. This distribution is to ensure that recent studies are critically examined while, at the same time, the research trends in the domain of ES and DSS for ECG interpretation and diagnosis are explored. Figure 1: Study selection flow chart based on the PRISMA model.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 5 Dimensionality Reduction Techniques
### Independent Component Analysis (ICA)
#### 5.3.2 Whitening  
A data value is referred to as white when its constituents become uncorrelated, and their alterations are equal to one. The purpose of whitening or sphering is to transform the data so that their covariance matrix becomes an identity matrix [74]. The eigenvalue decomposition (EVD) of the covariance matrix is a popular method for the implementation of whitening. Whitening eliminates redundancy, and the features to be estimated are also reduced. Therefore, the memory space requirement is reduced accordingly.
## 5 System Simulation and Results Analysis  
### Mathematical Analysis of Model Establishment  
According to the law of energy conservation, the relationships between the input resistance Rb-in and the output resistance Rb in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) of the buck circuit can be obtained as follows:  
\[\mathrm{R_{b}}=\left\{\begin{array}{ll}\mathrm{D^{2}R_{b-in}}&\mathrm{CCM}\\ \frac{\mathrm{D^{1}R_{b-in}^{2}T_{b}^{2}-4D^{2}L_{b}R_{b-in}T_{b}}+4L_{b}^{2}} {\mathrm{D^{1}R_{b-in}-T_{b}^{2}}}&\mathrm{DCM}\end{array}\right. \tag{19}\]  
According to the law of energy conservation, when the input voltage is AC, the relationship between the AC equivalent impedance Ri of the buck circuit and the output impedance Rb in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) can be derived as follows:  
\[\mathrm{R_{i}}=\left\{\begin{array}{ll}\frac{8}{\pi^{2}\mathrm{D^{2}}} \mathrm{R_{b}}&\mathrm{CCM}\\ \frac{4\left(\mathrm{D^{2}R_{b}T_{b}+4L_{b}+\sqrt{R_{b}T_{b}D^{2}\left(R_{b}T_ {b}D^{2}+8L_{b}\right)}}\right)}{\pi^{2}\mathrm{D^{2}T_{b}}}&\mathrm{DCM}\end{array}\right. \tag{20}\]  
A theoretical analysis of the two aforementioned equations reveals that in Continuous Conduction Mode (CCM), the relationship between the equivalent resistance and the output resistance solely depends on the square of the duty cycle D\({}^{2}\). Therefore, tracking the optimal efficiency point can be achieved by adjusting duty cycle D. Conversely, in Discontinuous Conduction Mode (DCM), factors influencing efficiency include inductance, time constants, and duty cycle interaction. By adjusting these parameters, tracking the optimal efficiency point can be achieved.
## 4 Grape Pomace Extracts and Their Bioactives as Ingredients in Sun Oils for Skin Care
### Grape Pomace--Circular Economy  
Grape Pomace (GP) is a by-product of winemaking and it is divided into two categories, the seedless pomace (pulp, skin, and stem) and the grapeseed (GS). GP is rich in bioactive compounds (BACs), such as unsaturated fatty acids, vitamins, and natural antioxidants, primarily in the form of FC (phenolic acids, flavonoids, tannins, carotenoids, etc.). GS contains the majority of the overall extractable PC, with the norm being phenolic acids, flavonoids, and procyanidins. In fact, studies have shown that these compounds have anti-aging, anti-cancer, antimicrobial, etc., properties, but their antioxidant capacity, which can be contributed to the PC's ability to neutralize cellular oxidative stress, deserves to be highlighted [34; 35]. Also, these antioxidants reinforce the photoprotection and as a result, play an important role in photoaging and the prevention of UV-induced erythema [34].  
Furthermore, GP and GS obtain oils that mostly consist of unsaturated fatty acids (linoleic acid, oleic and palmitic acid, etc.), with their embclient properties being the reason why they are so broadly used in the cosmetic industry. GS oil is being used in the cosmetic industry and more specifically in skin hydration products, moisturizing cremes, sunscreens, etc., because of its soothing and moisturizing effects [34]. It is also important to note that the vitamin E contained in GS and GP oils contributes to the high concentrations of tocopherols and tocotrienols, which are effective in anti-aging [34; 35; 36].  
Grape stems are products produced by the viticulture industry and if treated as waste, there is a risk of pollution, contamination, costly disposal issues, and economic losses. Their reuse is mainly for animal feed and fertilizers. However, grape stems are distinguished from other food by-products because of their high content of high-value compounds, such as bioactive lipids, phenolics, and other natural antioxidants. The bioactive compounds contained in the by-products are available in large quantities, at a fairly low cost, making their utilization economically viable and at the same time environmentally friendly. Recent studies focus mainly on their antioxidant activity, due to their rich content in several phenolic bioactives with free radical-scavenging antioxidant capacity and potent anti-inflammatory benefits (Figure 2) [10]. Consequently, the recycling of bioactive compounds that are beneficial to human health has started to attract interest over synthetics in dietary supplements and medicines and is increasingly active in cosmetics [9; 11; 37; 10].
# Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation
## 4 Strategies for Overcoming Challenges
### Improved Battery Technology  
The limitations of battery technology are one of the main obstacles to the widespread use of electric vehicles (EVs). The present battery design for EVs has a poor energy density, which impacts the vehicle's driving range [58]. To improve EV efficiency, a variety of battery technologies and combinations have been created over time. Users see electric vehicles as a real alternative to internal combustion engine vehicles because of the development of better, more affordable, and higher-capacity batteries, which will increase vehicle autonomy.  
Since batteries are vital to EVs, more manufacturers (such as LG, Panasonic, Samsung, Sony, and Bosch) are investing in creating better, more affordable batteries.  
The battery bundle is the costliest part of any EV. For instance, the Nissan LEAF's lithium-ion batteries originally accounted for one-third of the total cost of the car. However, it is anticipated that this cost will gradually decrease; as of the end of 2014, the battery pack cost around $500 per kWh (half the price in 2009); now, the price per kWh is $200, and it is anticipated to drop to approximately $100 in 2025. The fact that Tesla Motors is creating a \"Mega factory\" to lower manufacturing costs and enhance battery output is another piece of data supporting the trend towards lower battery costs [59].  
The price of EVs would naturally decrease because of decreasing battery costs, making them more competitive with other types of cars.  
Figure 6 depicts the battery capacity of various EVs from 1983, when the Audi Duo was first sold, when it had an 8-kWh battery, through 2022, when Tesla claimed it would sell a Tesla Roadster with a 200-kWh battery. The GMC Hummer EV Pickup Edition 1 has the largest battery capacity at 212 kWh [60].  
The following section discusses three issues with suitable proposals relating to EV battery technology, including battery nature, battery price, and electric vehicle chargers.  
Figure 6: Battery capacity development from 1980–2025 [61].
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 1 Introduction
[23] designed a hybrid architecture with a control unit that configures the processor as a pipeline structure when the FFT point is less than or equal to 512 and as an iterative structure that can support maximum 4096-point FFT operations in case of a number of points greater than 512. A complete performance comparison of different HW structures is very difficult due to many specific parameter choices, such as the number of samples of the input sequence, the specific HW implementation, the approach for computation units, the recourse to FPGA or DSP, whether floating or fixed point, and whether the architecture of the HW structure as pipeline, sequential, etc. In this paper, a novel approach is presented for the DFT computation. Our method exploits the finite word-length precision of the input sequence by splitting input sequence samples into bit slices and applying the DFT to each bit slice. The computation is thereby simplified because only complex sums are required instead of complex multiples. Each bit slice can be carried out in a time-sequential approach with a low-cost system. With this architecture, the output values of the evaluated transform accumulate and update the partial results until the last sample of the sequence is acquired. The conceived architecture is a low-latency solution, which is very useful in many demanding technology systems like in 5G communication systems for very low-latency applications, like in \"smart roads\" applications, smart electric utility grids, and industrial automation systems. Furthermore, in general, it can be usefully adopted in real-time applications where latency is a crucial aspect limiting the system performance. In this work, a low-power hardware solution is synthesized, which is based on FPGA device. The rest of the paper is organized as follows. In Section 2, a summary of DFT/FFT characteristics is indicated. Section 3 deals with the definition of the binary DFT, while in Section 4 the designed architecture is indicated. Section 5 presents the hardware implementation of the FPGA-based solution for the BFT computation in an orthogonal frequency-division multiplexing (OFDM) system, underlining its specificity and usefulness for 5G telecommunications systems. Section 8 completes the paper.
## 2 Materials and Methods
### Hs-SPME-GC-MS Analysis  
HS-SPME-GC-MS was used to separate and analyze the VOCs of the four fermentation groups. The sample, diluted two-fold and mixed with 2 g of sodium chloride, was placed in a headspace vial with 2-octanol (200 ug\(\cdot\)mL\({}^{-1}\)) as the internal standard. The prepared sample was incubated at 55 \({}^{\circ}\)C for 15 min, followed by extraction and adsorption using a 50 \(\upmu\)m/30 \(\upmu\)m CAR/DVB/PDMS SPME fiber for 30 min. The fiber was then immediately inserted into the GC-MS injector for desorption at 250 \({}^{\circ}\)C for 0.5 min. The analysis system consisted of a Thermo Trace1310 gas chromatograph ((Thermo Fisher Scientific, Waltham, MA, USA) with a Thermo ISQ LT mass spectrometer and a TG-WAXA column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). The injector temperature was set to 250 \({}^{\circ}\)C, and the GC temperature program was as follows: initial temperature of 40 \({}^{\circ}\)C, ramped at 3 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 180 \({}^{\circ}\)C, then at 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 230 \({}^{\circ}\)C, held for 15 min. High-purity helium (99.999%) served as the carrier gas at a flow rate of 1.2 mL\(\cdot\)min\({}^{-1}\). Mass spectrometry conditions were as follows: ion source temperature at 260 \({}^{\circ}\)C, electron ionization energy at 70 eV, and monitoring of the total ion current in the mass range of 20-450 m\(\cdot\)z\({}^{-1}\). Retention indices (RIs) were determined using C\({}_{6}\)-C\({}_{30}\) n-alkane standards. Volatile organic compounds were identified by MS, and by comparing the Kovats retention indices (RI) with the NIST database and literature reports. Quantification of VOCs was based on the internal standard (2-octanol, 200 ug\(\cdot\)mL\({}^{-1}\)) and the linear relationship between compound peak area and concentration.