esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 G-R for HSRs
### Planning of the Handover Zone  
The model of the handover zone is established as shown in Figure 4. The width of the rail is \(w_{1}\) with the value of 1435 m (the international standard gauge), and the width of the HST is \(w_{2}\) with the value of 3328 m. The overlapping area of station\({}_{i}\), station\({}_{(i+1)}\), and the range within \(w_{2}/2\) on both sides of the central axis of the rail is the handover zone of the wireless network. The effective height of the station\({}_{i}\) is  
\[h~{}=~{}h_{1}+h_{2}-h_{3} \tag{1}\]  
Figure 3: Interfaces between elements in 5G-R for HSRs.  
Figure 2: Architecture of networks in 5G for railway (5G-R) for high-speed railways (HSRs).  
where, \(h_{1}\), \(h_{2}\), and \(h_{3}\) respectively denote the height of the station, the vertical distance between the station foundation plane and the rail plane, and the vertical distance between the geometric center of the train's window and the rail plane. The station spacing \(d_{1}\) is the distance between station\({}_{i}\) and station\({}_{(i+1)}\) as indicated in Equation (2):  
\[d_{1}\ =\ 2\times(\sqrt{d_{2}^{2}-h^{2}-d_{3}^{2}}-d_{4}) \tag{2}\]  
where \(d_{2}\), \(d_{3}\) and \(d_{4}\), severally, represent the coverage radius of the station, the horizontal distance between gNodeB and rail, and the length of unidirectional overlapped coverage area. Suppose the point \(o\) is the location of the UE at any time and the point \(o^{\prime}\) is the handover location of two stations, then the distance that the HST travels with the handover hysteresis is  
\[d\ =\ d_{5}-d_{4} \tag{3}\]  
Assuming the process of the handover takes \(\Delta t\), the planning distance of the handover zone is shown as Equation (4). As the moving speed of the train is known and the coverage capacity of cells is limited, the handover zone can be reduced by decreasing the delay of the handover, that is, the spacing between gNodeBs can be increased and the construction cost of 5G-R can be reduced. With the continuous raising of the speed of HST, the reduction of the delay of the handover can ensure that UE can complete the whole handover process within the given handover zone.  
\[D_{\rm{han}}\ =\ 2\times d_{4}>2\times(v\times\Delta t) \tag{4}\]  
As shown in Figure 5, the process of handover consists of three phases: measurement, judgment, and execution. The measurement configuration message is the radio resource control (RRC) information sent by gNodeB to UE, including objects, identities, and types. Physical layer filtering and RRC layer filtering can reduce the jitter amplitude of measured values. The measurement is reported according to the interval cycle of measurement configuration parameters. When the \(\rm{Xn/NG}\) signaling interaction is triggered when the rules of the handover are met, the Scell's resources occupied by UE are released after the handover is completed.  
Figure 4: Model of the handover zone.
## 3 Results and Analysis
### Bond-Slip Curves
As shown in Figure 8, the rising trend of curves becomes flatter with the increase in \(V_{f}\) before the peak point. The bond-slip curve reaches the peak of the curve at point C. Then, the concrete surface starts to produce obvious splitting cracks. The temperature has a significant effect at this stage. The ultimate bond strength of SFRC specimens decreases significantly as the temperature rises. The bond-slip curve near the peak point flattens out with the increase in temperature at the same \(V_{f}\). The incorporation of steel fibers greatly improves the ductility of the specimen at high temperature. (4) Stress drop phase (CD)  
When the ultimate bond strength is reached, the concrete in contact with the rebar rib is crushed and broken. The bond stress is provided by mechanical bite and frictional resistance. The contact surface between the rebar rib and the surrounding concrete gradually smooths out. The roughness of the contact surface decreases as the slip increases, which causes a reduction in mechanical bite force. As a result, after the peak point, the bond stress between rebar and concrete gradually deteriorates with the increase in slippage. The bridging effect of the steel fibers diminishes with the increase in slippage. Cracks in the concrete around the rebar gradually become deeper and wider and rapidly extend to the boundaries of the concrete. (5) Residual pull-out phase (after the D point)  
At this stage, the bond-slip curve tends to flatten out. The specimen fails as the rebar is pulled out of the concrete. Because frictional resistance, aggregate bite and steel fiber remain between rebar and concrete, the bond-slip curves of SFRC specimens appear as longer horizontal segments. This indicates that the residual bond stress is maintained at high temperatures.
## 2 Results and Discussion  
### General Grape and Wine Parameters  
Tables 1 and 2 highlight the data at the two ripeness levels analyzed both in 2018 and 2019, and data which might have had a great impact on wine quality and aroma as well. The results obtained for the date of harvest, when the physico-chemical parameters of the musts (Brix, pH, total acidity, tartaric acid, and malic acid) were measured, and for the end of alcoholic fermentation (alcohol, pH, and total acidity), pointed to different trends for the wines made from Verdejo and the white crosses. As can be seen in Table 1 for the musts harvested in 2018, the highest Brix values were provided by the crosses MC180, MS30, MS82, and MV67, while the MV11 cross, along with Verdejo grape, showed the lowest values, differences that would be partly related with the intrinsic characteristics of each grape variety [17], as all the cultivars were planted in the same experimental field with the same climatic conditions. As regards total acidity, MS30 and MC69 were the most acidic musts due to their \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & **Verdejo** & **MC180** & **MC69** & **MS30** & **MS33** & **MS82** & **MT103** & **MV11** & **MV67** & **MV7** & \(p\)**-Value** \\ \hline
**HARVEST** & & & & & & & & & & & \\
**DATE** & 27 Aug* & 29 Aug & 29 Aug & 03 Sep & 28 Aug & 28 Aug & 29 Aug & 03 Sep & 30 Aug & 29 Aug & \\
**MUST** & & & & & & & & & & & \\
**Brix** & 21.73 d & 22.45 a & 22.30 b & 22.51 a & 22.24 b & 22.51 a & 22.10 c & 20.91 e & 22.54 a & 22.23 bc & *** \\
**Total acidity** & 3.25 h & 3.86 g & 5.95 b & 6.14 a & 3.25 h & 4.57 e & 5.14 c & 3.92 g & 4.96 d & 4.35 f & *** \\
**pH** & 3.73 a & 3.71 ab & 3.46 d & 3.38 e & 3.66 b & 3.35 e & 3.37 e & 3.73 a & 3.55 c & 3.53 c & *** \\
**Tartaric acid** & 4.21 h & 4.08 i & 4.75 g & 6.21 a & 5.06 e & 6.12 b & 5.83 c & 4.21 h & 5.34 d & 4.94 f & *** \\
**Malic acid** & 2.30 e & 2.86 d & 3.77 a & 3.12 b & 2.11 g & 2.21 f & 1.91 h & 3.05 b & 2.94 c & 2.08 f & *** \\
**WINE** & & & & & & & & & & & & \\
**Alcohol \%** & 12.91 h & 13.46 g & 13.68 d & 13.62 e & 13.76 a & 13.71 b & 13.65 d & 12.73 i & 13.75 xb & 13.57 f & *** \\
**pH** & 3.25 c & 3.29 b & 3.53 a & 3.29 b & 3.15 e & 3.15 e & 3.15 e & 3.15 e & 3.24 d & 3.33 b & 3.18 e & *** \\
**Total acidity** & 7.03 e & 7.63 c & 7.03 e & 6.86 f & 6.72 g & 7.93 a & 7.21 d & 6.64 h & 6.52 i & 7.85 b & *** \\ \hline \end{tabular}  
* The relative standard derivation is less than 0.05 (RSD \(<\) 0.05) in all the analyses. Total acidity, tartaric acid and malic acid are measured in g L\({}^{-1}\).* Aug: August: Sep: September.  
\end{table} 
Table 1: Analytical parameters in must at harvest and in wine at the end of alcoholic fermentation in 2018 season.
## 2 Study Site and Methodology
### Eddy Covariance Flux and Ancillary Measurements  
Net ecosystem exchange (NEE) rates of carbon dioxide (\(F_{\mathrm{c}}\), mg m\({}^{-2}\) s\({}^{-1}\)) in Yenicaga peatland were estimated using an eddy covariance (EC) system consisting of an open-path CO\({}_{2}\)/H\({}_{2}\)O gas analyzer (LI-7500, Licor Inc., Lincoln, NB, USA), a 3-D sonic anemometer/thermometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA), a data logger (CR3000, Campbell Scientific Inc.), and a 3-m tower on which EC flux sensors were mounted. The distance between the LI-7500 and CSAT3 sensors was 0.15 m, with CSAT3 oriented towards the prevailing wind direction (an azimuth angle of 30\({}^{\circ}\) from true north) and LI-7500 vertically rotated 15\({}^{\circ}\) towards the footprint.  
Eddy fluxes and associated signals were recorded at 10 Hz, block averaged over one hour (h) and corrected for the effects of fluctuations in air density on CO\({}_{2}\)/H\({}_{2}\)O fluxes (\(F_{\mathrm{c\_wpl}}\) and LE\({}_{\mathrm{wpl}}\) with WPL correction) through the online flux computation. EC data were collected swapping two 2-GB Compact Flash cards at 14-to-18-day intervals. The net radiation (\(R_{\mathrm{n}}\)), downwelling and upwelling longwave (4 to 50 \(\mu\)m) and shortwave radiation (0.2 to 4 \(\mu\)m) (\(R_{\mathrm{l\_dn}}\), \(R_{\mathrm{l\_up}}\), \(R_{\mathrm{s\_dn}}\), and \(R_{\mathrm{s\_up}}\), W m\({}^{-2}\)) were measured using Kipp & Zonen CNR-4 net radiometers (Kipp & Zonen USA Inc., Bohemia, NY, USA). Air temperature (\(T_{\mathrm{a}}\), \({}^{\circ}\)C), and relative humidity (RH, %) were sampled using HMP45C probe (Vaisala, Finland). Precipitation (PPT, mm), evapotranspiration (ET, mm), soil water content (SWC, %), and mean, maximum and minimum soil temperature (ST, ST\({}_{\mathrm{max}}\), and ST\({}_{\mathrm{min}}\), \({}^{\circ}\)C) were measured on a hourly basis using ET107 weather monitoring station (Campbell Scientific Inc.). Photosynthetically active radiation (PAR) was estimated from net shortwave radiation (\(R_{\mathrm{s\_n}}\)) using a conversion factor of PAR: \(R_{\mathrm{s\_n}}\) = 0.5 [7-9]. The values of CO\({}_{2}\) fluxes in unit of mg m\({}^{-2}\) s\({}^{-1}\) were also converted to unit of kg C ha\({}^{-1}\)day\({}^{-1}\), based on the following conversion ratios of mg:kg = 10\({}^{6}\); s:day = 86,400; m\({}^{2}\):ha = 10,000; and CO\({}_{2}\):C = 44/12.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 3 Super-Resolution Imaging for Passive Radar
### RELAX-Based Passive Radar Imaging  
RELAX, which relaxes the hypothesis of the additive noise and system errors, is an effective spectrum estimation algorithm. By adopting an iterative procedure, the spatial frequencies of the scatterers can be estimated by the nonlinear least square (NLS) method. When the RELAX algorithm is employed, Equation (16) should be recast as:  
\[\left\{\mathbf{h}^{\prime},\mathbf{\omega}^{\prime}\right\}=\operatorname*{arg\,min}_{ \mathbf{h},\mathbf{\omega}}\left\|\mathbf{s}-\mathbf{Q}\mathbf{h}\right\|_{2}^{2}, \tag{17}\]  
where \(\mathbf{h}^{\prime}\)_and_\(\mathbf{\omega}^{\prime}\) are the estimates of \(\mathbf{h}\)_and_\(\mathbf{\omega}\), respectively; and \(\left\|\cdot\right\|_{2}\) denotes the \(\ell_{2}\) norm. Let  
\[\mathbf{s}_{k}=\mathbf{s}-\sum_{i=0,j\
eq k}^{K-1}h^{\prime}\left(i\right)\mathbf{f}\left( \omega^{\prime}{}_{i}\right), \tag{18}\]  
where \(\left\{h^{\prime}(i),\omega^{\prime}{}_{i}\right\}\left(i=0,1,...,K-1,i\
eq k\right)\) are assumed to be given. Then, the minimization of Equation (17) is equivalent to  
\[\left\{h^{\prime}(k),\omega^{\prime}_{k}\right\}=\operatorname*{arg\,min}_{h \left(k\right),\omega_{k}}\left\|\mathbf{s}_{k}-h\left(k\right)\mathbf{f}\left(\omega _{k}\right)\right\|_{2}^{2}. \tag{19}\]  
The NLS estimates of \(h^{\prime}(k)\) and \(\omega^{\prime}_{k}\) are given by [16], respectively:  
\[h^{\prime}(k)=\frac{\mathbf{f}^{H}\left(\omega^{\prime}{}_{k}\right)\mathbf{s}_{k}}{M}, \tag{20}\]  
\[\omega^{\prime}{}_{k}=\operatorname*{arg\,max}_{\omega_{k}}\left\|\mathbf{f}^{H} \left(\omega_{k}\right)\mathbf{s}_{k}\right\|_{2}^{2}. \tag{21}\]  
It should be noted that \(\omega^{\prime}_{k}\) is the dominant peak position of \(\left\|f^{H}\left(\omega_{k}\right)\mathbf{s}_{k}\right\|_{2}^{2}\), which can be efficiently obtained by fast Fourier transform (FFT) for the data sequence \(\mathbf{s}_{k}\) padded with zeros. According to [22], the locally optimal solution of \(\left\{h^{\prime}(i),\omega^{\prime}{}_{i}\right\}\) obtained by the NLS estimate is globally optimal. That is, we can obtain two independent sets \(\left\{\mathbf{h}^{\prime},\mathbf{\omega}^{\prime}\right\}\) estimated from the two external illuminators. Subsequently, a passive ISAR image can then be attained with these two estimated sets \(\left\{\mathbf{h}^{\prime},\mathbf{\omega}^{\prime}\right\}\).
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods
### An Overview of the Proposed Framework  
The proposed method is shown in Figure 2. Since the background was used as a reference temperature, different background processing methods have their own advantages and disadvantages. Therefore, our architecture consists of two branches that achieve complementary effects. The processing of these two branches is early fusion and late fusion [35] according to the background, with each branch calculating a temperature level score (TLS). For convenience, we use symbols to represent TLS\({}_{\text{ef}}\) (Temperature Level Score of the Early Fusion Branch) and TLS\({}_{\text{lf}}\) (Temperature Level Score of the Late Fusion Branch), and then use MLP to identify the final water stress classes according to these scores. On TLS\({}_{\text{ef}}\) and TLS\({}_{\text{lf}}\) ground truth scores, well-watered is 1.0 point, slight stress is 0.5 points, and mild stress is 0.0 points. In addition, in the late fusion branch, the foreground is separated from the background, so RGB images are used to segment the thermal image.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Water Stress Situation** & **Number of Data** \\ \hline Well-watered & 136 \\ Slight stress & 137 \\ Mild stress & 130 \\ Total & 403 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset for our experiments.  
Figure 1: RGB (**upper** row) and thermal (**lower** row) images in a rice field.
## 1 Introduction
### Contribution of the Study  
The main contributions of this paper are summarized below:  
* The paper investigated the time taken to detect an attack using ML approaches and proposed a novel approach that applied correlation analysis to reduce the training and testing time of these algorithms. The new approach was evaluated on various ML models, including RF, DT, NB, MLP, ANN, LR, and GB. The findings indicated that correlation analysis was significantly beneficial in the feature engineering process, primarily in identifying the most relevant features in the MQTT dataset.
* The paper investigated how to categorize and prioritize attacks based on their frequency using the MQTSet reduced dataset. In particular, the article identifies DoS, BF, and Malformed attacks as the most frequent attacks and selected them for further simulation.
* A comprehensive evaluation of state-of-the-art ML-based methodologies for anomaly-based intrusion detection systems in the IoT was conducted. Notably, when applying ML algorithms such as RF, DT, NB, MLP, ANN, LR, GB, and DNN to datasets without irrelevant attributes, significant reductions in training time were achieved. The evaluation was based on metrics such as precision, recall, F1 score, training time, and testing time, which are indicative of prediction accuracy.  
This study discusses the challenges and future direction of ML-based anomaly detection in the IoT domain. The rest of the article is organized as follows: In Section 2, the research efforts on IoT attacks and AD are described using a related critical review. In Section 3, by using the dataset, different types of sensors and anomalies are attacked. Section 4 discusses the proposed approach and methodology using a Pearson correlation analysis. Section 5 discusses an experimental setup using ML models, significant findings, and a conclusion.
# Crosslinked Bifunctional Particles for the Removal of Bilirubin in Hyperbilirubin Cases
###### Abstract  
This work describes the development of styrene-divinylbenzene (St-DVB) particles with polyethylene glycol methacrylate (PEGMA) and/or glycidyl methacrylate (GMA) brushes for the removal of bilirubin from blood in haemodialyzed patients. Bovine serum albumin (BSA) was immobilized onto the particles using ethyl lactate as a biocompatible solvent, which allowed the immobilization of up to 2 mg BSA/g of particles. The presence of albumin on the particles increased their capacity for bilirubin removal from phosphate-buffered saline (PBS) by 43% compared to particles without albumin. The particles were tested in plasma, finding that St-DVB-GMA-PEGMA particles that had been wetted in ethyl lactate with BSA reduced the concentration of bilirubin in plasma by 53% in less than 30 min. This effect was not observed in particles without BSA. Therefore, the presence of albumin on the particles enabled quick and selective removal of bilirubin from plasma. Overall, the study highlights the potential use of St-DVB particles with PEGMA and/or GMA brushes for bilirubin removal in haemodialyzed patients. The immobilization of albumin onto the particles using ethyl lactate increased their capacity for bilirubin removal and enabled quick and selective removal from plasma.  
crosslinking; suspension polymerization; BSA immobilization; bilirubin removal; wetting effect +
Footnote †: journal: materials  
Article
# Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool
## 4 Experiments
### Effectiveness Analysis of the Proposed Strategy Pool and Self-adaptive Mechanism  
In experiment 1, we wanted to probe the following two problems:  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Function** & \multicolumn{2}{l}{**ABC-Rand**} & \multicolumn{2}{l}{**ABC-Pbest-1**} & \multicolumn{2}{l}{**ABC-Pbest-2**} & \multicol  
* Problem 1: Is it necessary to assemble the five different strategies? * Problem 2: Is the self-adaptive mechanism required and are the results affected when the self-adaptive selection mechanism is replaced by a random selection mechanism? To solve problem 1, each single strategy was embedded into the original ABC, to make a result comparison between each strategy and SAABC-CS. As for problem 2, we tested two different strategy selections. One was the random strategy selection mechanism, and the other was the self-adaptive selection mechanism. The ABC algorithms including various search methods mentioned below examined the efficacy of the strategy pool and the self-adaptive mechanism. * ABC-rand: the original ABC with rand strategy;
* ABC-pbest-1: the original ABC with pbest-1 strategy;
* ABC-pbest-2: the original ABC with pbest-2 strategy;
* ABC-current-to-pbest: the original ABC with current-to-pbest strategy;
* ABC-pbest-to-rand: the original ABC with pbest-to-rand strategy;
* SAABC-CS: ABC with self-adaptive selection mechanism in the strategy pool;
* RABC-CS: ABC with random selection mechanism in the strategy pool. The fundamental settings for the seven algorithms listed above were as follows: the SN, D, limit, MaxFEs, and running times were set to 100, 30, 100, 5000-D, and 30, respectively. Table 1 displays the outcomes of the ABC using the RABC-CS and SAABC-CS single search techniques on the fundamental 22 functions. SAABC-CS in Table 1 outperformed ABC-rand, ABC-pbest-1, ABC-pbest-2, ABC-current-to-pbest, and ABC-pbest-to-rand on 14, 16, 15, 14, and 13 of the 22 test functions, respectively. This demonstrated that the combination of five techniques increased the test function accuracy. SAABC-CS outperformed RABC-CS, which chooses methods at random, on 14 functions, while being comparable for 8 of them. In this test suite, the self-adaptive selection mechanism performed better than the random selection method. To determine the contribution of each strategy, we counted the use times of each strategy during the whole processes for two multimodal functions (\(f14\), \(f17\)) and three unimodal functions (\(f2\), \(f3\), \(f7\)). The outcomes are displayed in Figure 6. As shown in the figure, the strategies with the highest frequency for f2, f3, f7, f14, and f17 were \"pbest-1\", \"pbest-to-rand\", \"current-to-pbest\", \"pbest-2\", and \"rand\", respectively.
## 3 Results and Discussion
### Process Parameter Interaction and PCB Yield Optimization  
The interactions between pairs of independent variables are illustrated in the 3D response surface plots (Figure 2). As shown in Figure 2a,b, the yield of PCB increased as the extraction temperature increased from 50 to 70 \({}^{\circ}\)C, but decreased between 70 \({}^{\circ}\)C and 90 \({}^{\circ}\)C. A decline in the PCB yield beyond a certain temperature threshold has been reported in previous studies [12]. While the extraction time showed a slight increase and decrease as it was extended from 8-24 h, there was a significant interaction between the extraction time and temperature, as indicated in Table 3. Longer extraction times at higher temperatures resulted in a considerable reduction in yield. The C-PC to solvent ratio, which had the most significant effect on the extraction yield, increased as the yield increased, as shown in Figure 2b,c. However, from an economic perspective for industrial applications, an excessive increase in solvent use should be avoided [31].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Run** & **A: Temperature** & **B: Time** & **C: C-PC to Solvent Ratio** & **Yield** \\  & **(\({}^{\circ}\)C)** & **(h)** & **(\(w\)/\(v\))** & **(\%)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Central composite design (CCD) runs and corresponding PCB yields.  
Figure 2: Three-dimensional surface graphs of extraction temperature (factor A, \({}^{\circ}\)C), extraction time (factor B, h), C-PC to solvent ratio (factor C, \(w\)/\(v\)), and PCB yield (%); **(a)** factor A and factor B; **(b)** factor A and factor C; **(c)** factor B and factor C. Range of factor C (20–100, \(w\)/\(v\)) indicates range of 1:20–1:100 (\(w\)/\(v\)).  
Sixteen solutions with a desirability of 0.8 or higher were generated by setting each parameter within the specified range (extraction temperature, 50-90 \({}^{\circ}\)C; extraction time, 8-24 h; and C-PC to solvent ratio, 1:20-1:100, _w/v_) and conducting a numerical optimization analysis to maximize the PCB yield. Among these, the solution closest to a desirability of 1.0 had a desirability of 0.905, with a predicted yield of 29.18%. Considering the minimum unit of the extraction equipment parameters, the first decimal place was rounded off for the validation experiment. The actual PCB yield of 29.67 \(\pm\) 1.33% was found to have no significant difference from the theoretical value (Table 4). These optimized extraction conditions with ethanol require a higher extraction temperature than conventional extraction with methanol, and the extraction time can be reduced by more than 1 h [10].