esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Macroalgae contain the typical polysaccharides, such as mannan, ulvan, carrageenan, agar, laminarin, alginate and fucoidan, which are not found in lignocellulosic terrestrial biomass [5]. Like terrestrial plant biomass, macroalgae generally do not possess high contents of starch and lipids, except for green algae (1-4% for starch and 0-6% for lipids). Their lack of lignin makes them less rigid and contributes to the flexibility of their cell wall. Carbohydrate compounds are abundant in macroalgae. Their contents in green, red and brown algae vary between 25% and 50%, 30% and 60%, and 30% and 50% of their dry weight (dw), respectively [10]. Their composition evidently differs between species. While cellulose is a structural polysaccharide (exists in the cell wall) common to all macroalgae, each group has other typical structural and storage polysaccharides [11]. As the structural polysaccharides, green macroalgae features ulvan [12], while red algae contain either carrageenan (up to 75% dw) or agar (up to 52% dw) [13]. When it comes to brown algae, their major polysaccharide is alginic acid (i.e., alginate). As storage polysaccharides, green and red algae contain starch (floridan starch in the case of red algae) while brown algae have laminarin (\(\beta\)-1,3-glucans), which accounts for up to 35% dw. It is relevant to note that the composition of macroalgae of the same species can also differ. Local conditions at the collection site, such as light, salinity, nutrients, temperature, pollution and water motion, can considerably influence the metabolite levels and bioactive compound contents. The biological status of the algae (e.g., life cycle, development stage and thallus structure) can also have an impact on their biochemical composition [14].
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Parameters and Basic Structure of 5G PHY Layer
#### 2.2.1 5G Numerology over Frequency Bands  
In the realm of NR FR1, the maximum bandwidth is 100 MHz, whereas in the millimeter wave range FR2, it extends up to 400 MHz; see Table 1. Specific Sub-Carrier Spacing (SCS), such as 15 and 30 kHz, are exclusive to the sub 6 GHz range, while 120, 480, and 960 kHz spacing is solely applicable to the millimeter wave range FR2. However, a spacing of 60 kHz can be utilized in both the sub 6 GHz FR1 and the millimeter wave range FR2. The configuration of specific parameters is determined by the network operator. Overview tables of individual channels are provided in references [54; 55].
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.6 Thresholding the Heatmap of the CAM  
The aim of this algorithm is to isolate the region of interest from the background. Thus, given a heatmap image \(I\), fixed thresholding groups the pixels of the heatmap into sets \(H_{0}\) and \(H_{1}\), where \(H_{0}\) contains all the elements less than or equal to a particular threshold \(T\), and \(H_{1}\) contains all the elements greater than the threshold, as shown in Equation (11):  
\[(i,j)\in\begin{cases}H_{0},&\text{if }I(i,j)\leq T.\\ H_{1},&\text{if }I(i,j)>T.\end{cases} \tag{11}\]  
However, this thresholding cannot adequately extract the ROI from the heatmap, for example when confronted with pixels that contain varying levels of interference, or ROIs that have lower values than the fixed threshold.  
Therefore, to efficiently separate the ROIs from interference, we first create an \(n\times m\) sliding window that moves through the entire 2D heatmap image. The sliding window contains reference cells, guard cells and the cell under test. We then determine an adaptive threshold value \(T(m,n)\) for each sliding window position based on the average intensity of the reference cells, and then we compare this value with the magnitude of the cell under test (CUT) \(x(i,j)\). If the magnitude of the CUT is greater than the threshold, we declare it a region of interest. We test all the cells of the heatmap until we get a clearer picture of all ROIs. We use the guard cells to create a buffer zone around the CUT. By doing this, we avoid the ROIs from leaking into the reference cells, which could affect the value of the threshold. We estimate the interference levels from the reference cells or neighboring cells of the \(j\)th sliding window using Equation (12):  
\[Z=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{12}\]  
If \(Z\) is the average intensity value of the reference cells, the adaptive threshold value of the \(j\)th sliding window is calculated using Equation (13):  
\[T_{i,j}=\frac{\alpha}{N}\sum_{i=1}^{N}x_{i} \tag{13}\]  
where  
\(T_{i,j}\) is the adaptive threshold for each sliding window position;  
\(\alpha\) is the threshold factor;  
\(N\) is the number of samples in the sliding window;  
\(x_{i,j}\) is the reference cell.  
Algorithm 1 describes the implementation of the proposed adaptive threshold method. It takes a 2D heatmap and the number of reference cells and guard cells as inputs, and it returns a 2D heatmap of the ROIs. Figure 8 shows a graphical description of the algorithm.
## 3 Speech Recognition Results
### Evaluation Metrics  
As evaluation metrics, we used character error rate (CER), word error rate (WER), and space-normalized WER (sWER), which are described below. All metrics were measured using the Score Lite toolkit [37], which is a tool for scoring and evaluating the output of speech recognition systems. This toolkit compares the hypothesis text (HYP) output by the speech recognizer and the reference text (REF). Here, the hypothesis text and the reference text are composed of three units: (1) characters (syllables in Korean), (2) words (word-phrases in Korean), and (3) space-normalized words. Figure 2 provides examples of calculations for metrics, and Appendix B shows their pseudocode.  
The CER was measured by converting character or sub-word units predicted from the end-to-end model into character units, as shown in Figure 2b. Here, the CER was calculated using both character units and spaces. The WER was measured after restoring the sub-word units predicted from the end-to-end model to the original word units, as shown in Figure 2c. This is an evaluation metric commonly used in speech recognition and may present incorrect performance depending on whether or not spaces are used in Korean, as described in Appendix B.  
Finally, we propose sWER as a new evaluation metric. In Korean, space rules are flexible; inconsistent spacing is frequently seen in spontaneous speech transcriptions, like in KsSponSpeech. However, this causes a problem in the evaluation of speech recognition because correct results are classified as errors due to this spacing variation. Thus, we used sWER, which gives a more valid word error rate by excluding the effects of inconsistent spaces. This metric was measured from space-normalized texts, which was performed only on the hypothesis text, based on spaces in the reference text, as shown in Figure 2d. The detailed algorithm is described in Appendix B. In all evaluation processes, words with the same definition but different forms such as \"2\" and \"two\", were still regarded as incorrect.  
Figure 2: Examples of units and metrics: (**a**) the human transcribed reference text (REF) and the hypothesis text (HYP) obtained from recognizer, (**b**) character units and character error rate (CER), (**c**) word-phrase units and word error rate (WER), and (**d**) space-normalized word-phrase units and space-normalized word error rate (sWER). In the evaluation results (_Eval_), \(C\), \(S\), \(I\), and \(D\) denote the correct, substituted, inserted, and deleted words, respectively. A yellow box denotes space-normalized words, underscore denotes whitespace, bold text denotes incorrect words, and asterisk denotes inserted words.
# Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
###### Abstract  
This paper investigates the impacts of element grouping on the covert communication performance of an active reconfigurable intelligent surface (ARIS)-aided uplink non-orthogonal multiple access (NOMA) system. Through element grouping, each element of the ARIS works in either the reflecting mode to reflect the information signal or the jamming mode to generate a jamming signal. Optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming jointly is necessary to maximize the covert communication rate. To tackle the unsolvable covert communication rate maximization problem, we decouple the original problem into three sub-problems of optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming, respectively. To tackle the mixed-integer non-linear programming for the element grouping, we introduce the arithmetic- and geometric-mean-based penalty term and apply the Dinkelbach transform to reformulate the optimization problem. Next, we propose an alternating optimization algorithm to optimize the system parameters. The numerical results demonstrate the effectiveness of the element grouping in improving the covert communication rate. However, the element grouping scheme achieves a lower covert communication rate performance compared to the scheme without element grouping, indicating that using element grouping for covert communications in the ARIS-aided uplink NOMA system is not the preferred option.  
c 2024 13, 2882. [[https://doi.org/10.3390/electronics13142882](https://doi.org/10.3390/electronics13142882)]([https://doi.org/10.3390/electronics13142882](https://doi.org/10.3390/electronics13142882))
## 2 Related Works
### Typical 2-to-1 Multiplexer  
A multiplexer is a circuit that outputs only one signal among several input signals based on a selected input value. A multiplexer is used as a key element in various circuits. A D-latch, which is a key circuit of a RAM cell that can generate a loop section, can be designed using a multiplexer. A D-latch can be designed simply by deleting one input cell of the multiplexer and extending and connecting the wiring of the output cell to that location. Therefore, the multiplexer is a key circuit in the design of RAM cells. There are two types of conventionally proposed 2-to-1 multiplexers: a majority gate-based design using three majority gates and one inverter, and a design based on interactions between electrons inside the cell. Figure 3 shows a multiplexer based on a majority vote gate among the previously proposed QCA 2-to-1 multiplexers. Figure 3a,b shows circuits proposed by [PERSON] et al., and [PERSON] et al., respectively, and both are designed based on a 3-input majority vote gate and are designed for stable signal transmission rather than circuit size [37; 38]. Figure 3c,d shows circuits proposed by [PERSON] et al., and [PERSON] et al. The area of the circuits is greatly reduced using a combination of the rotated 3-input majority gate in Figure 2d and the general 3-input majority gate [39; 40]. [PERSON] et al., also used the rotated gate based on a multilayer structure [41], while [PERSON] et al. proposed a cell-interaction-based multiplexer with minimal area but latency in 2023 [42].  
Figure 1: Two polarization states of the QCA basic cell: (**a**) P = \(+1\) (binary 1), P = \(-1\) (binary 0); (**b**) QCA cell-based wiring.  
Figure 2: QCA basic gates: (**a**) 3-input majority gate; (**b**) AND gate; (**c**) OR gate; (**d**) rotated 3-input majority gate.
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 5 Discussions
### Reliability, Stability and Robustness  
Table 4 presents some other clustering performance measurements that help assess the reliability, stability and robustness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{_d 1_} & \multicolumn{2}{c}{**\% Changes**} & \multicolumn{2}{c}{**Side / Mean of**} & \multicolumn{1}{c}{**Side / Mean of**} \\  & & **in WCSS 2** & **Kernel Kmeans++ w/RP 3** & **Vanilla Kernel Kmeans 4** \\ \hline \multirow{3}{*}{**MNIST**} & 100 & \(-1.18\)\% & \(1.83\)\% & \\  & 200 & \(-1.47\)\% & \(1.81\)\% & \(1.6\)\% \\  & 500 & \(-1.40\)\% & \(1.70\)\% & \\ \hline \multirow{3}{*}{**ImageNet**} & 100 & \(-2.14\)\% & \(4.78\)\% & \\  & 200 & \(-2.84\)\% & \(5.01\)\% & \(4.32\)\% \\  & 500 & \(-2.59\)\% & \(3.99\)\% & \\ \hline \multirow{3}{*}{**20 Newsgroup**} & 100 & \(0.41\)\% & \(0.53\)\% & \\  & 200 & \(0.04\)\% & \(0.69\)\% & \(0.5\)\% \\  & 500 & \(-0.21\)\% & \(0.75\)\% & \\ \hline \multirow{3}{*}{**Reuters**} & 200 & \(0.16\)\% & \(0.40\)\% & \\  & 300 & \(0.20\)\% & \(0.38\)\% & \(0.23\)\% \\  & 400 & \(0.27\)\% & \(0.81\)\% & \\ \hline \multirow{3}{*}{**GLI-85**} & 50 & \(0.60\)\% & \(1.55\)\% & \\  & 100 & \(0.11\)\% & \(1.53\)\% & \(1.41\)\% \\  & 200 & \(1.12\)\% & \(1.48\)\% & \\ \hline \multirow{3}{*}{**SMK-187**} & 50 & \(-0.02\)\% & \(2.13\)\% & \\  & 100 & \(0.37\)\% & \(1.96\)\% & \(1.35\)\% \\ \cline{1-1}  & 200 & \(-0.67\)\% & \(2.16\)\% & \\ \hline \hline \end{tabular}  
* The projected dimensionality _d._ 2 Percentage change in result WCSS by the proposed kernel k-means++ with random projection, compared to the vanilla kernel k-means. 3 Fluctuation of the result WCSS by the proposed kernel k-means++ with random projection (kernel k-means++ w/RP). 4 Fluctuation of the result WCSS by the vanilla kernel k-means.  
\end{table}
Table 4: Other clustering accuracy measurements of our experiments.
## 2 Results and Discussion
### Volatile Compounds in CRMP  
In total, 35 aroma compounds were evaluated in CRMP (Figure 1 and Table 2). These compounds belong to different chemical families, including aldehydes, ketones, esters, acids, sulfur compounds, aromatic compounds and oximes. Note that alkanes, pollutants and unidentified chemicals were not listed. Table 2 shows the aroma compounds and their relative contents (AR values) determined in CRMP on the day 45 of storage. 2,3-Butanedione, 2,3-pentanedione, 3-hydroxy-2-butanone (acetoin), butyric acid and methoxyphenyl oxime were not observed in DAC, and acetoin was not detected in the cheese made from _S. thermophilus_ TM11.  
Figure 1: Comparison of typical total ion currency (TIC) profile of the volatiles from the direct-acidified cheese (**A**: DAC) and the cultured cheeses with _S. thermophilus_ (**B**: TM11 and **C**: SP1.1). Peak numbers, 1–36, were marked. Alkanes, heterocyclic compounds excluding indene, pollutants (such as naphthalene, 32.34 min) and the un-identified chemicals were not listed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Peak** & **Retention** & & & **AR** & \\
**number *** & **time, min** & & **DAC** & **TM11** & **SP1.1** \\ \hline
1 & 4.84 & Acetone & 5.80 \(\pm\) 1.46 a & 7.24 \(\pm\) 3.57 a & 2.30 \(\pm\) 0.94 b  \\
2 & 5.17 & Thiourea & 0.41 \(\pm\) 0.35 a & 0.39 \(\pm\) 0.47 a & 0.59 \(\pm\) 0.27 a  \\
3 & 5.26 & Carbon disulfide & 0.79 \(\pm\) 0.83 a & 1.29 \(\pm\) 0.77 a  & 1.67 \(\pm\) 0.85 a  \\
4 & 5.95 & 2,3-Butanedione & - & 1.02 \(\pm\) 0.41 a  & 0.84 \(\pm\) 0.29 a  \\
5 & 6.22 & 2-Butanone & 0.56 \(\pm\) 0.68 a  & 0.42 \(\pm\) 0.57 a  & 0.62 \(\pm\) 0.81 a  \\
6 & 6.58 & Ethyl acetate & 1.11 \(\pm\) 0.89 a  & 1.66 \(\pm\) 1.41 a  & 0.54 \(\pm\) 0.33 a  \\
7 & 7.85 & Benzene & 0.03 \(\pm\) 0.02 a  & 0.40 \(\pm\) 0.03 a  & 0.08 \(\pm\) 0.04 a  & 0.08 \(\pm\) 0.04 a  \\
8 & 9.15 & 2,3-Pentanedione & - & 3.58 \(\pm\) 1.83 a  & 2.34 \(\pm\) 1.06 a  \\
9 & 9.99 & 3-Hydroxy-2-butanone & - & 0.36 \(\pm\) 0.22 a  & 1.17 \(\pm\) 1.22 a  & 2.03 \(\pm\) 1.14 a  & 2.50 \(\pm\) 2.19 a  & 10.45 \(\pm\) 0.99 a  & 2.78 \(\pm\) 0.99 a  & 3.12 \(\pm\) 1.24 a  & 2.59 \(\pm\) 1.25 a [FOOTNOTE:a]Footnote a: Peak numbers were in conformity with the numbers marked in Figure 1; *\({}^{\rm ac}\) Values (mean
## 1 Introduction
[PERSON] and others put forward a damage-assessment method, which can identify crops and weeds in the greenhouse with high accuracy [20]. In industry, it is mainly used to detect surface defects of workpieces, and through machine vision technology, it can identify small defects on the surface of workpieces [21] and diagnose faults in rotating parts [22]. The classification technique based on deep learning can improve the accuracy of classification in theory, but the data balance of different types of data sets will affect the performance of the network and make the network training effect unstable [23]. Traditional classification methods assume that the data is balanced. However, when the number of classes is uneven, the classification model tends to be biased towards the larger number of classes, which leads to a decrease in the recognition accuracy for the smaller number of classes [24]. To verify the effect of data balance and network structure on the accuracy of soybean integrity recognition, AlexNet, VGG, ResNet, and DenseNet networks were used to carry out the experiments in this paper to achieve the classification requirements of qualified soybeans. The classification accuracy was improved by data augmentation and referencing different loss functions, and the data were balanced to obtain a better classification effect.
## 3 Tracking Loops Error Characterization
### Allan Deviation Phase Noise  
The Allan deviation phase noise is caused by the drift of the receiver oscillator, which is determined by the oscillator's material and craft. The phase noise induced by the frequency drift can be expressed as follows [20]:  
\[\sigma_{A}^{2}=\frac{1}{2\pi}\int_{0}^{\infty}S_{\phi}\left(\omega\right)\left| 1-H\left(\omega\right)\right|^{2}d\omega \tag{3}\]  
where \(S_{\phi}\left(\omega\right)\) is the single-sided oscillator phase noise spectrum density. \(\left|1-H\left(\omega\right)\right|^{2}=\frac{\omega^{2n}}{\omega_{L}^{2n}+ \omega^{2n}}\) is the parameter which depends on loop order. In the equation, \(\omega_{L}\) can be computed by using \(\omega_{L,1}=4B_{PLL}\), \(\omega_{L,2}=1.885B_{PLL}\), \(\omega_{L,3}=1.2B_{PLL}\) for first-order, second-order, and third-order PLLs, respectively.  
In Equation (3), \(S_{\phi}\left(\omega\right)\) can be described as follows:  
\[S_{\phi}\left(\omega\right)=\left(2\pi L_{c}\right)^{2}\cdot\frac{S_{y}\left( \omega\right)}{\omega^{2}} \tag{4}\]  
where \(L_{c}\) is the carrier frequency. \(S_{y}\left(\omega\right)\) is the clock's power spectrum, which can be expressed as follows. The flicker phase and white phase noise is omitted due to their weak contribution on the oscillator error:  
\[S_{y}\left(\omega\right)=\frac{2\pi^{2}h_{-2}}{\omega^{2}}+\frac{\pi h_{-1}}{ \omega}+\frac{h_{0}}{2} \tag{5}\]Combining Equation (3), Equation (4) and Equation (5), the Allan deviation phase noise is derived for a second-order PLL:  
\[\begin{split}\sigma_{A}^{2}&=2\pi L_{c}^{2}\int_{0}^{ \infty}\left[\frac{2\pi^{2}h_{{}_{-2}}}{\omega^{4}}+\frac{\pi h_{{}_{-1}}}{ \omega^{3}}+\frac{h_{0}}{2\omega^{2}}\right]\frac{\omega^{4}}{\omega_{L}^{4}+ \omega^{4}}\,d\omega\\ &=2\pi^{2}\ L_{c}^{2}\left[\frac{\pi^{2}h_{{}_{-2}}}{\sqrt{2} \omega_{L}^{3}}+\frac{\pi h_{{}_{-1}}}{4\omega_{L}^{2}}+\frac{h_{0}}{4\sqrt{2 }\omega_{L}}\right]\qquad\quad\left(rad\,\right)\\ &=2\times 180^{2}\cdot L_{c}^{2}\left[\frac{\pi^{2}h_{{}_{-2}}}{\sqrt{2} \omega_{L}^{3}}+\frac{\pi h_{{}_{-1}}}{4\omega_{L}^{2}}+\frac{h_{0}}{4\sqrt{2 }\omega_{L}}\right]\qquad\quad\left({}^{\circ}\right)\end{split} \tag{6}\]  
In this equation, the clock parameters \(h_{{}_{-2}}\), \(h_{{}_{-1}}\), and \(h_{0}\) listed in Table 1 represent the frequency stability of a certain oscillator.
## 5 MIP Applications for the Extraction of Antibiotics in Milk
### Quinolones
Furthermore, the developed PMME protocol was compared with classic C18 SPE protocol. The chromatograms demonstrated no matrix interference for the PMME protocol in contrast with the C18 SPE protocol, proving once more the selectivity of the developed method [68]. A DSPE-HPLC-UV method was developed for the determination of OFL, NOR and ENR. SiO\({}_{2}\) nanoparticles (1 g) and 0.5 mL of KH-570 were dispersed in ethanol, the mixture was stirred 25 \({}^{\circ}\)C for 24 h and the SiO\({}_{2}\)-KH-570 particles were dried under vacuum. OFL (0.1 mmol), MAA (0.8 mmol), and SiO\({}_{2}\)-KH-570 particles (0.5 g) were dispersed in acetonitrile (20 mL) and the mixture was placed in an ice bath and stirred for 8 h. EGDMA (4 mmol), AIBN (0.12 mmol) and dibutyl isophthalate (0.28 mmol) were added and polymerization was carried out under nitrogen stream and stirring at 60 \({}^{\circ}\)C for 24 h. The developed MIPs (100 mg) were treated with 40% HF aqueous solution, vortexed for 15 min, kept for 2 h, centrifuged, washed with methanol and dried under vacuum. Hollow OFL-MIPs (20 mg) were dispersed in pretreated spiked milk extract (10 mL) and the mixture was shaken for 15 min and centrifuged. The analytes were eluted, the eluate was evaporated and the dry residue was reconstituted with mobile phase. In comparison with the respective NIP, MIP adsorption capacity for OFL was higher, with Q\({}_{\text{max}}\) values of 133 and 274 umol/g, while binding specificity study between OFL, sulfamethazine (SMZ), ibuprofen and Sudan I showed MIP selectivity over OFL and imprinting factor was 2.6 for OFL [69]. A MSPE-HPLC-UV method was developed for the extraction and determination of OFL, CIP and LOM. SupelMIP-fluoroquinolones SPE (Sigma-Aldrich, St. Louis, MO, USA) sorbent material (20 mg) and Fe\({}_{3}\)O\({}_{4}\) magnetite nanoparticles (20 mg) were mixed with methanol (1 mL) and the mixture was strongly vortexed for 1 min. The MMIPs were collected by means of an external magnet, the supernatant was discarded, ultra-pure water (2 mL) was added to the MIPs, the mixture was vortexed for 1 min and the supernatant was discarded. Pretreated spiked milk extract (2 mL) was added and the mixture was vortexed for 1 min. The MMIPs were collected with a magnet, washed with ultra-pure water (3 mL), acetonitrile (1 mL) and 15% acetonitrile in ultra-pure water (1 mL). The analytes were eluted with 7% ammonia in methanol (1 mL) and vortexing for 2 min, the MMIPs were removed by means of a magnet, the eluate was evaporated under nitrogen stream at 35 \({}^{\circ}\)C and the dry residue was reconstituted with mobile phase. The developed MSPE protocol was compared with a MISPE protocol and showed similar or even increased clean-up efficiency and higher preconcentration capability for the examined quinolones [70]. All the reported applications are summarized in Table 3.