esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Adaptive Pre-Distortion System
#### 3.1.1 Least Squares Method (LSM)  
LSM estimates nonlinear coefficients by minimizing the squared error between reference signals \(\hat{x_{k}}\) and \(y_{k}\). LSM for the signal after channel equalization is described in Figure 4. In this case, vector \(\hat{\mathbf{b}}\) can be determined as follows:  
\[\hat{\mathbf{b}}=(\hat{\mathbf{X}}^{T}\hat{\mathbf{X}})^{-1}\hat{\mathbf{X}}^{T}\mathbf{y}. \tag{6}\]  
where \(\hat{\mathbf{b}}\), \(\hat{\mathbf{X}}\), and \(\mathbf{y}\) are represented as  
\[\hat{\mathbf{b}}=\left(\begin{array}{c}\hat{b_{0}}\\ \hat{b_{1}}\\ \hat{b_{2}}\end{array}\right), \tag{7}\]  
\[\hat{\mathbf{X}}=\left(\begin{array}{ccc}1&\hat{x_{0}}-I_{DC}&(\hat{x_{0}}-I_{ DC})^{2}\\ \vdots&\vdots&\vdots\\ 1&\widehat{x_{N-1}}-I_{DC}&(\widehat{x_{N-1}}-I_{DC})^{2}\end{array}\right), \tag{8}\]  
\[\mathbf{y}=\left(\begin{array}{c}y_{0}\\ \vdots\\ y_{N-1}\end{array}\right), \tag{9}\]  
By determining \(\hat{\mathbf{b}}\) in this manner, the influence of noise is effectively mitigated.  
Figure 3: Proposed adaptive compensation system.  
Denoting the number of reference signals used as \(N\), this algorithm has a computational complexity of \(O(N)\).
## 2 Experimental Studies
#### Data Acquisition  
The data acquisition part of our prototype was configured as shown in the block diagram below, Figure 3. First, analog electrical signals from the photodetector were  
Figure 2: Experimental setup on the (**left**) is shown on tripod. On the (**right**), the basic layout of the LRF is depicted.  
amplified by the 2-stage voltage amplifier whose circuit diagram is also shown in the Figure 4. The gain of the amplifier was calculated as 420.  
When detecting low power optical signals, amplification of the standard output with band pass filtering was required to provide sufficient gain. To read out the SiPM from the standard output, the photocurrent generated on the detection of photons was first converted to the voltage by 50 \(\Omega\) load resistor and then is amplified by using a voltage amplifier as depicted in Figure 4. LMH6624 is ultra-low noise op-amp with 1.5 GHz gain bandwidth product. The cut off frequency of the amplifier circuit was designed to be at 80 MHz as to be compatible with the pulse width for the maximum gain of approximately 420. The amplified and filtered signal was then fed into the high-speed ADC module (ADS42B49, Texas Instruments). It converted analog voltage to 14 bit parallel discrete digital data at a 100 MHz sampling rate. An FPGA module acquired the digital data and transferred the average of 1000 frames at 1 Hz to the computer via a USB port, continuously. Averaging processes were performed by summing each frame point by point and then divided by the total number of frames. Each frame had 4096 sampled data points with a corresponding data point interval of 10 ns. A signal extraction algorithm was developed in Labview platform, running in the computer. Second averaging was done in the algorithm. The number of averaging frame was selected by the user.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 4 Parametric Study
### Changing the Width of the Ground Slot (c)  
Figure 9 shows the reflection coefficients and axial ratio for the proposed antennas with different slot widths \(c\). The MIMO-CP antenna slot widths vary from 0.5 to 2 mm. It is clear from the figure that this is when the width of the slot decreases or increases from 1.5 mm, affecting impedance matching of the S-parameter, with relatively less effects on the axial ratio. An optimum value of \(c\) is found to be 1.5 mm. Thus, the width of the slot is fixed to \(c=1.5\) mm, to improve impedance matching of the S-parameter and have a better axial ratio.
## 1 Introduction
Deep learning (DL) has been applied in various scenario, and has provided significant developments for these fields [16; 17; 18]. Recently, [PERSON] and [PERSON] proposed an adaptive spectrum sensing (ASS) algorithm [19]; [PERSON] et al. proposed an algorithmic model that fused convolution neural network (CNN) and long short-term memory (LSTM) network [20], which improved the performance of SNR evaluation relative to the traditional algorithm. However, the existing DL-based SNR evaluation methods are at the expense of computation complexity and give less attention to the two-dimensional features of signals. Meanwhile, the lack of open dataset of UAV control signals also increases the difficulty of model training. To sum up, it seems indispensable to build a two-dimensional dataset of UAV control signals, thus containing enough salient features and avoiding too much interference information. In addition, the corresponding model design is also a time-limited task. Motivated by the information mentioned above, this paper proposes an intelligent SNR evaluation method based on a single-way convolution neural network, which generates UAV signal datasets by converting one-dimensional signals into two-dimensional ones, trains neural networks of different structures on them, and finally confirms an evaluation network model that can perform a smaller SNR error on the input signals, effectively enhancing the SNR assessment, allowing the neural network model to pay more attention to signal feature information [21]. Specifically, the algorithm model is firstly presented and is applied in the field of SNR evaluation of UAV frequency hopping signals. In addition, this paper also proposes a more optimised two-way convolution neural network-based intelligent SNR evaluation method based on a classifiable model for better and more extraction of signal features. The main contributions of this paper are concluded as follows:  
(1) A two-dimensional dataset of UAV remote control signal is constructed and expanded. Firstly, a one-dimensional frequency hopping signal is generated based on the UAV frequency hopping communication system, and then the one-dimensional signal data are converted to two-dimensional signal data based on the signal time-domain diagram method. (2) The feature fusion method is considered to combine features of different levels, then the proposed model can further classify as many features as possible and reduce randomness. (3) A two-path convolution neural network is proposed that fuses the features extracted from two neural networks with different structures, and the proposed model can further improve the accuracy of SNR evaluation.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 3 FDIA Detection  
In this section of the paper, the FDIA detection scheme using advances in FFT and SVD are presented. We first illustrate the structure and datum of the suggested technique. Then, the full implementation of the technique is presented with brief presentations to the methods employed. Eventually, the threshold of detection of the suggested technique is discussed.
## 3 Results and Discussion
### Influence of BTH Treatment on the Physicochemical Parameters of Cabernet Gernischt Grapes
#### 3.1.3 Titractable Acidity and pH  
The titratable acidity (TA) of grapes initially increased and then decreased during the ripening process. The highest content of TA was observed at the mid-swelling stage (week 5), after which there was a rapid decline at veraison. The TA content of BTH-treated grapes was significantly lower than that of CK grapes at end-veraison (week 11) and when the grapes were ripe (week 15), with reductions of 1.49 g/L and 0.77 g/L, respectively (Figure 1E). The pH of grapes also increased and decreased during the ripening process. It reached its highest peak at mid-ripening (week 13) and then declined to a level similar to that observed at veraison. The pH of BTH-treated grapes significantly increased by 5.48%, 1.97%, and 3.35% compared to the CK at the stages of end-veraison, mid-ripening, and ripe grapes, respectively (Figure 1F). In summary, BTH treatment reduced TA and increased the pH during grape ripening. The results are consistent with a previous study that treated Muscat Hamburg with 200 mg/L SA [16].  
Cabernet Gernischt grapes contain organic acids, such as tartaric, malic, and citric acids [39]. Citric acid is involved in the synthesis of fatty acid, which is a precursor to suberin polyaliphatic, and ATP-citrate lyase hydrolyzes citric acid in the plastids, producing oxaloacetate and acetyl-CoA [40]. Fatty acid synthesis is limited to plastids due to the inability of acetyl-CoA to traverse mitochondrial membranes [41]. Therefore, citric acid synthesized in the mitochondria can be transported to the cytosol and subsequently to the plastids [42]. Studies have shown that BTH facilitates the deposition of suberin polyaliphatic in wounds [40]. BTH treatment of Monastrell grapes at the veraison stage did not result in statistically significant differences in total acidity and tartaric acid content; however, malic acid concentrations were lower in the treated grapes than in control grapes [25]. The mechanism hypothesized in our study is similar to that observed in a previous study where BTH was found to decrease citric acid levels during wound healing in muskmelons [40]. Therefore, it can be hypothesized that BTH mainly affects the citric acid and malic acid content during grape ripening, although this effect is subject to variations based on grape variety.
## 1 Introduction
There are many licensed bands for different radio technologies, but many of them are heavily underutilized. One possible way to optimization is to allow unlicensed users to opportunistically employ licensed frequencies when they are idle. CR manages this and benefits from SDR technology: it is possible to develop context-dependent systems, optimize battery life, design collaborative transmission protocols, and so forth. In this paper we propose, develop and test the concept of low-cost _cognitive mobile BTS_. On the one hand, our proposal provides normal BTS communications, whereas, on the other hand, it provides cognitive sensing features to prevent interferences with coastal BTSs and other vehicles. Figure 1 depicts a possible scenario for our system: a private BTS on board a ship reselects channels to avoid interfering with coastal BTSs. We exploit one of the key properties of SDR, its flexibility, to reconfigure the basic hardware to support the sequence of operational modes (e.g., sensing \(\rightarrow\) transmission). A novel dynamic channel change mechanism handles ongoing communications seamlessly. In addition, as SDR confers the  
Figure 1: System overview. possibility to develop new value-added services, we have included a special functionality for terminal detection in emergency situations. In a few seconds, it allows the BTS to identify all the terminals in its area of influence, and consequently to detect that a person has fallen overboard, for example. This functionality could be added to a healthcare system with a more general purpose, like the one described [11]. It is important to remark that the user terminals in our system are normal commercial devices, and therefore it can be deployed at low cost. In the rest of this paper we review the background (Section 2) and describe the details of its architecture and implementation (Section 3). In Section 4 we validate it with real tests and demonstrate its suitability for the target scenarios. In Section 5 we conclude the paper.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Algae-Derived Antioxidants
#### 4.10.4 Rhodophyceae (Red Algae)  
Red seaweeds represent a large and diverse group of species, predominantly found in marine environments. The biodiversity of red seaweeds is greater than that of green and brown algae. Rhodophyta contain photosynthetic pigments, including chlorophyll a, chyobilins such as R-phycocyanin and R-phycoerythrin, and carotenoids such as \(\beta\)-carotene, lutein, and zeaxanthin. Red algae are a major source of carbohydrates and are widely used in the industrial production of hydrocolloids, such as agar and carrageenan. Other bioactive molecules found in Rhodophyta include essential fatty acids, phycobiliproteins, vitamins, minerals, and various secondary metabolites. The active compounds of red seaweeds are frequently utilized in photoprotective and anti-photoaging products [199].  
Proteins, polyphenols, and polysaccharides derived from red macroalgae help prevent skin aging, regulate transepidermal water loss (TEWL), stimulate sebum production, and increase erythema and melanin production [185].  
Methanol extract from marine red alga _Corallina pilulifera_ has the ability to prevent UV-induced oxidative stress and also the expressions of MMP-2 and MMP-9 in human dermal fibroblast (HDF) cells [180].  
High concentrations of MAAs were found in the red macroalgae _Perplyra umbilicalis_, _Palmaria palmata_, _Gelidium corneum_, and _Osmundea pimnatifida_[181,191]. Helionori(r) by Gelyma and Helioguard365(r) is a sunscreen product containing MAAs from the red seaweed _Porphyra umbilicalis_. Another algae species that possesses anti-melanogenic activity is _Schizymenia dubyi_. Sulfated polysaccharides with antioxidant and antiaging activity were also found in _Chondrus canaliculatus_, _Gracilaropsis leameiformis_, _Porphyna huitanensis_, _Porphyridium_ sp., and _Rhodella reticulata_[191].
## 3 Geological Setting and Database  
### Geological Setting and Data of Hungarian Nuclear Waste Repository  
The research areas comprise granitic rock masses near Bataapati in Hungary and anisotropic siltstones, sandstones, and quartzite in Western Australia.  
The fractured granitic body forms the host rock of the Hungarian National Radioactive Waste Repository (NRWR). Until now, a more than 6 km long tunnel network has been constructed, and all the tunnel faces have been documented [4,48]. From a geological aspect, three main rock types can be distinguished in the carboniferous granite formation: monozgranite, monzonite, and hybrid rocks [3,49]. This granitic body is transected by Cretaceous trachyandesite dykes with NE-SW strike and randomly distributed aplitic veins also [50]. The four repository chambers were excavated in monozgranite with aplitic veins and scarce monzonite enclaves.  
The results of previous field observations and models suggested that the granite formation is hydraulically strongly compartmented, dividing the underground flow system into several blocks of limited hydraulic connection. Based on field observations, it was distinguished as less transmissive blocks bordered by more transmissive zones. The repository for low and medium level nuclear waste disposal is placed in a less transmissive hydraulic compartment [51,52]. Based on measured geometric data (spatial position, length, orientation, and aperture), fracture networks are simulated to study connectivity relations and for computing the fractured porosity and permeability at different scales. The results prove the scale-invariant geometry of the fracture system [4,53]. Geotechnical data from drill core logging and tunnel face documentation show a clear connection between rock mass classification and rock type [26,49,50].  
The database consists of two types of RMR\({}_{89}\) and GSI values: field values, which were estimated directly from slope or tunnel faces, and values back-calculated from other methods (e.g., GSI from RMR\({}_{89}\)). The database covers the ratings from repository chambers and other tunnels with smaller cross-sections (Figure 3).
## 2 Materials and Methods
### Analytical Methods
#### 2.4.10 Statistical Analysis  
The Shapiro-Wilk normality test was used to check the normality of the data, while the homogeneity of variances was tested using the Leven test. Significance was tested using parametric analysis of variance (ANOVA) for variables with normal distribution, and means were separated using Stats-Fisher's LSD test. For the identified variables with non-homogeneous variances and non-normal distribution, the non-parametric Kruskal-Wallis test was used, and the means were separated using the Bonferroni test. Some missing data for M-C sAF in 2020 (TDM, TAC, pH, TP, Tan, and 1-butanol) were replaced by the mean values for each parameter. When only two groups of samples were compared, significance was tested using the parametric Student's \(t\)-test. For the identified variables with non-homogeneous variances and a non-normal distribution, the non-parametric Mann-Whitney test was used. Different letters indicate significant differences at \(p\leq 0.05\). Asterisks indicate the significance level: * \(p\leq 0.05\), ** \(p\leq 0.01\) and *** \(p\leq 0.001\). The unit variation and mean centring were used to create the PCA. The statistical analyses were performed with XLSTAT software version 2023.3.0 [51].
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Effect of Drying Treatments on the Metabolite Profiles of AS Fruit  
Different dehydration treatments significantly impact the metabolic composition of AS fruits [48]. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) is a widely used dimensionality reduction technique that effectively identifies key metabolites responsible for group separation [49; 50]. Volcano plots further facilitate this analysis by visualizing the statistical significance (\(p\)-value) against the magnitude of change (fold change) in metabolites, making them particularly useful for detecting biologically relevant variations [51].  
To evaluate the metabolic differences induced by different drying methods, OPLS-DA models and volcano plots were employed. In OPLS-DA analysis, we used the normalized response values of 60 volatile compounds detected. The result demonstrated distinct metabolic variations among different drying methods. The OPLS-DA score plot revealed two well-separated clusters (R\({}^{2}\)Ycum \(>\) 0.9, Q\({}^{2}\)cum \(>\) 0.9), indicating strong model reliability and predictive accuracy (Figure 4(A1,A2)) [52]. Additionally, the volcano plots (Figure 4(B1,B2)) demonstrated significant differences in metabolite distribution across the five drying methods, further confirming that drying treatments lead to substantial metabolic alterations. These findings underscore the strong discriminative capability of OPLS-DA and highlight the influence of drying methods on the metabolite composition of AS fruits.
# Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
###### Abstract  
The paper applies jellyfish search algorithm (JSA) for reaching the maximum profit of IEEE 30-node and IEEE 118-node transmission power networks considering electrical market and wind turbines (WTs). JSA is compared with the particle swarm optimization (PSO), genetic algorithm (GA), moth swarm algorithm (MSA), salp swarm algorithm (SSA), and water cycle algorithm (WCA) for three study cases. The same and different electric prices for all nodes are, respectively, considered in Case 1 and Case 2, whereas Case 3 considers different prices and the placement of one WT. As a result, JSA can reach higher profit than MSA, SSA, WCA, PSO, and GA by 1.2%, 2.44%, 1.7%, 1.3%, and 1.02% for Cases 1, 2, and 3. Then, JSA is applied for optimizing the placement of from two to four WTs for the first system, and from zero to five wind farms (WF) for the second systems. Comparison of profits from the study cases indicates that the network can reach higher profit if more WTs and WTs are optimally placed. The placement of four WTs can support the two systems to reach higher profit by $130.3 and $34770.4, respectively. The greater profits are equivalent to 2.6% and 97.2% the profit of the base system. On the other hand, the obtained results also reveal the important order of location for installing wind power generators. The important order of nodes is, respectively, Nodes 5, 2, 1, and 10 for the first system, as well as Nodes 29, 31, 71, 45, and 47 for the second system. Thus, it is recommended that renewable energies are very useful in improving profit for transmission power systems, and the solutions of installing renewable energy-based generators should be determined by high performance algorithms, such as JSA.  
optimal power flow; wind turbines; nodal price; jellyfish search algorithm; maximum profit Article
# Table Recognition for Sensitive Data Perception in an IoT Vision Environment
## 2 Related Works  
### Table-Recognition Methods  
An important and primary step in table recognition is table detection. At present, most research on table recognition is aimed at scanned documents. Table features, such as text blocks [11; 12], word spacing [13; 14], column spacing [15], junctions [16], and ruling lines [17] are used to detect tables in scanned document images. All the above methods assume that the text lines and table boundaries are parallel, but they are not parallel in intelligent IoT vision device-captured images due to perspective transformation [18; 19].  
There is a small amount of research on table recognition in intelligent IoT vision device-captured images, all of which is based on the junction detection method [20; 21; 22]. These methods match the corners of table cells to labels which include connectivity information between junctions. However, junction miss and junction error are common in these methods and the performance is unsatisfactory.  
The ruling-line feature is considered in order to achieve table recognition in intelligent IoT vision device-captured images in our method. [PERSON] et al. [23] presented a structure named directional single connected chain (DSCC), which can be used to search diagonal lines in scanned documents. Because ruling lines in intelligent IoT vision device-captured tables, which are subject to perspective transformation, are diagonal, DSCC can be referenced to recognize intelligent IoT vision device-captured tables. However, the DSCC search algorithm that [PERSON] et al. [23] proposed will make too many broken lines, which affects the performance of table-frame detection. Therefore, a new chain-search algorithm, which has more resistance to line breakage, is proposed. The chain searched by our algorithm is named the directional connected chain (DCC), because it is not singly connected, while DSCC is singly connected. Then, the table-recognition method is proposed based on DCC.
###### Abstract  
In the present study, we investigated and compared the effect of microwaves and freeze-drying methods on the dehydration and rehydration kinetics in the phenolic, anthocyanin, aroma profiles, and antioxidant properties of tree tomato fruit (_Solanum betaceum_). The tree tomatoes were dried using microwaves at 350 W, 500 W, and 650 W, and then freeze-dried. The obtained drying curves were processed to find the most suitable mathematical modeling among the different moisture ratio expressions. Total phenolics, total anthocyanins, total flavonoids total carotenoids, vitamin C, Ferric Reducing Antioxidant Power (FRAP), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) were tested. Using High performance Liquid Chromatography (HPLC), phenolic and anthocyanin compound profiles were identified. The aroma profile was analyzed using gas chromatography-MS. The Midilli model, among others, precisely describes the dehydration methodology of all used drying methods with the coefficient of determination R\({}^{2}\) = 0.99. On the other hand, the Weibull model precisely describes the rehydration process of the used drying methods (R\({}^{2}\) = 0.99). Physical changes (color, shrinkage) were also studied. The freeze-dried tree tomatoes had a high number of phenolic compounds with 3.94 \(\pm\) 0.26 mg GAE/g and total carotenoid compounds with 0.48 \(\pm\) 0.04 mg/g. Epicathcoin was the most abundant compound among the tested phenolics, followed by Cathechin. The Pelargonidin-3-glucoside was the most abundant anthocyanin whereas in freeze-dried tree tomatoes, 1.22 \(\pm\) 0.01 mg/g. Fifty-four aroma compounds were detected and quantified. Among others, Eucalyptus was one of the most abundant aroma compounds analyzed in dried tree tomato fruit. Freeze-dried tree tomatoes retained most of the antioxidant and flavor compounds analyzed.  
tree tomatoes; dehydration; mathematical modeling; antioxidant; aroma Article