esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Materials and Methods
### In Silico Analyses of Type II TA Systems in Pathogenic E. coli Strains  
To determine the distribution of type II TA systems in aEPEC, tEPEC, ExPEC, and hybrid EPEC/ExPEC strains, in silico analyses were conducted on 53 bacterial genomes deposited in the NCBI database, including our prototype strain BA1250. The bacterial genomes used are described in Table S3. For the selection of hybrid strains, bacteria were chosen based on the presence of _eae_ and _bfp_ genes as EPEC genetic markers and the presence of _fyuaA_, in conjunction with at least two other genes among _vat_, _cltuA_, and _yfcV_, as the definition of UPEC ([PERSON] and [PERSON], 2015). In addition, the genes defined by [PERSON] et al. (2016) to determine the intrinsic virulence of ExPEC (_papA_ and/or _papC_, _afa/dra_, _ituA_, and _kpsM_) were employed.  
Sequences of genes used to identify hybrid strains (_eae_, _vat_, _fyuaA_, _cltuA_, _yfcV_, _incD_, _iutA_, _kpsM_, _papA_, _papC_, _sfaB_, _sfaC_, _sfaD_, _sfaE_, _sfaE_, _sfaC_, _sfaD_, _sfaE_, _sfaC_, _sfaD_, _afaE_, _bfpA_, _bfpB_, _bfpC_, _bfpE_, _bfpC_, _bfpC_, _bfpC_, _bfpH_, _bfpH_, _bfpH_, _bfpK_, and _bfpL_) and to detect the five type II TA systems (_ccdB-ccdA_, _mazF-mazE_, _pasT-pasI_, _vhaV-prLF_, and _yoBe-yefM_) were  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Primer Sequence** & **Product** \\ \hline _ccdB_ & F: GCCGCGTTTCCCTTGTTAT & 215 pb \\ \hline _R_: CAAAATTTCGTTCCACGGC & \\ \hline _ccdA_ & F: GAGTTAGCTCAATCGCGCCTG & 158 pb \\ \hline _R_: CATCCGGTTTTCATCAGCCAA & \\ \hline _yhaV_ & F: AAAAGGTTAATGGTTGGGGCG & 214 pb \\ \hline _prLF_ & F: GGACAAAACAATATCCGC & 250 pb \\ \hline _mazF_ & F: TGTTGTCCTCAGGTCCA & 197 pb \\ \hline _mazE_ & F: TGTTGTCCTAGCTCCGTTA & 161 pb \\ \hline _mazE_ & F: AAAGCGTTGGGGAATTCAC & 171 pb \\ \hline _yoeB_ & F: CTGGCTCTAGGAATATGGGGA & 171 pb \\ \hline _yefM_ & F: AAAATCCCGGACCAGA & 174 pb \\ \hline _pasT_ & F: GTTTACAACCCCAACCAGT & 188 pb \\ \hline _pasI_ & F: GGCGACGTTGAAGAGCTAT & 161 pb \\ \hline _kcaT_ & F: GTTGCCTGGTTGATTAGTCG & 165 pb \\ \hline _rpoA_ & F: CGGCACAATCGATCCGAAG & 173 pb \\ \hline \end{tabular}
\end{table}
Table 2: Primer sequences of the toxin–antitoxin system genes and their products.  
further analyzed. The analysis to identify similar TA nucleotide sequences and virulence genes in our genomic database was performed using BlastN. Identification of homologous genes was considered for hits with more than 80% identity and 80% coverage. Data were presented as a heatmap and a hierarchical cluster, carried out in the R environment. To construct the phylogenetic tree of the analyzed genomes, kSNP3.0 was utilized [50].
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 2 LPWAN and LoRa
### Regional Parameters  
LoRa Alliance recommends using a specific media access control method to prevent saturation of the network capacity. For instance, the European government regulates the duty cycle of LoRa devices. It limits the fraction of time during which a device occupies a wireless channel in a day in an attempt to decrease the probability of a packet collision. Most existing research evaluating LoRa MAC performance is based on the duty-cycle scheme.  
Unlike the European government, the Korean government permits a choice between the duty-cycle limitation or Listen-Before-Talk Adaptive Frequency Agility (LBT AFA) transmission management. The LoRaWAN specification for the KR 920-923 MHz unlicensed band in the LoRa Alliance [12] requires that only LBT be used for the maximum size of data transmission. A node with data to send in LBT first detects whether a radio channel is being used by another node (listening) before transmitting data (talking). When in use, it jumps to another channel and starts listening again. Korean regulations require that the node continues sensing a channel for more than 5 ms during the listening step, and a sensing threshold of \(-65\) dBm has been defined [13]. In addition, the transmission must be completed within 4 s. The sending node remains idle for more than 50 ms after a successful transmission. We refer interested readers to [12; 13] for more details. For instance, they define the number of channels, transmission power, maximum payload size, etc.
## 3 Results  
### Adsorption-Desorption  
The Freundlich adsorption coefficient (\(K_{ads}\)) and slope (\(1/n_{ads}\)) along with the corresponding coefficients of determination (\(R^{2}\)) are given in Table 2. The results from the experiment adequately fit into the Freundlich isotherm (\(R^{2}>0.96\)) for the range of herbicide concentrations from 0.1 to 10 mg L\({}^{-1}\) in all the soil types under both aerobic and anaerobic soil conditions. The \(1/n_{ads}\) values ranged from 0.69 to 0.91 among the treatments. The \(K_{ads}\) of the metolachlor in the soils kept under aerobic and anaerobic conditions ranged from 1.23 to 5.85 L \(\mathrm{Kg^{-1}}\) and 1.04 to 4.54 L \(\mathrm{Kg^{-1}}\), respectively. The anaerobic soil environment had significantly lower (\(p<0.05\)) \(K_{ads}\) values consistently across the studied soil types. Furthermore, the \(K_{ads}\) was the lowest in the Catlin soils and the highest for Drummer soils under aerobic and anaerobic conditions, respectively.  
The desorption isotherms for metolachlor in all three soils and two environmental conditions also fit well with the Freundlich equation (\(R^{2}>0.80\)). The calculated Freundlich coefficients for desorption (\(K_{des}\)) of metolachlor are presented in Table 3. The \(K_{des}\) values of metolachlor were lower (\(p<0.05\)) in the anaerobic soils when compared with the aerobic soil treatments. Among the three soils tested, the \(K_{des}\) values of metolachlor were highest for the Drummer soil irrespective of the redox conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & \multicolumn{2}{c}{\(K_{ads}\)} & \multicolumn{2}{c}{\(1/n_{ads}\)} & \multicolumn{2}{c}{\(R^{2}\)} \\ \cline{2-7}  & **Aer** & **An** & **Aer** & **An** & **Aer** & **An** \\ \hline Catlin & 1.23 \({}^{\mathrm{e}t}\) (\(\pm 0.04\)) \({}^{\star}\) & 1.04 \({}^{\mathrm{f}}\) (\(\pm 0.02\)) & 0.69 (\(\pm 0.03\)) & 0.91 (\(\pm 0.02\)) & 1.00 & 0.96 \\ [PERSON] & 3.50 \({}^{\mathrm{c}}\) (\(\pm 0.05\)) & 2.16 \({}^{\mathrm{d}}\) (\(\pm 0.03\)) & 0.82 (\(\pm 0.03\)) & 0.89 (\(\pm 0.01\)) & 1.00 & 0.99 \\ [PERSON] & 5.85 \({}^{\mathrm{a}}\) (\(\pm 0.08\)) & 4.54 \({}^{\mathrm{b}}\) (\(\pm 0.07\)) & 0.78 (\(\pm 0.03\)) & 0.80 (\(\pm 0.02\)) & 1.00 & 1.00 \\ \hline \hline \end{tabular} \(K_{ads}\) = Freundlich adsorption coefficient, \(1/n_{ads}\) = adsorption isotherm slope, \(R^{2}\) = goodness of fit for the Freundlich model. \({}^{\mathrm{f}}\) Mean values followed by the same letter superscripts are not significantly different (\(p<0.05\)). \({}^{\star}\) Values in the parentheses are the 95% confidence intervals.  
\end{table}
Table 2: Adsorption (Freundlich model) of \({}^{14}\)C-metolachlor in different soil types under aerobic (Aer) and anaerobic (An) environmental conditions.
# Prediction of Post-Intubation Tachycardia Using Machine-Learning Models
## 4 Discussion  
Of the five feature sets, hand-crafted features generally provided the best performance.  
The eight-dimensional hand-crafted features include sex, HR_mean, remifentanil_CE_max, remifentanil_CP_max, remifentanil_VOL_max, remifentanil_VOL_mean, BIS_min, and MV_max. Each of the features was carefully chosen by intensive examination of the datasets, and we found that there have been studies that support the choices. For example, it was discovered that female patients were more likely to have arrhythmia than male patients, which supports the variable \"sex\" [24; 25]. In [26], the heart rate feature was utilized to predict tachycardia, which supports the variable \"HR_mean.\" The four variables related to remifentanil are related to the fact that it may decrease heart rates [27; 28] and that post-intubation tachycardia occurs more frequently in small dose patients than large dose patients [29]. The BIS values are known to be strongly related to prognoses in patients [30], which are related to the variable \"BIS_min.\" It has been discovered that respiration features, which are related to the variable 'MV_max', contribute to prediction of tachycardia [31].  
One can argue that using all 153-dimensional features \(\mathbf{f_{input}}\) must be better than other feature sets. More features might help improve performance, but they often fail when we do not balance model complexity and inherent data complexity. The total number of datasets for each experiment is close to 102, and the dimension of all features is 153. Such imbalance between them causes the machine learning models to overfit to a given training data; they will exhibit almost perfect performance only for the training data as shown in Figure 5. Note that we performed a grid search to find optimal parameter settings for every feature set. For example, the ANN model has an L2 penalty of 0.1, and the C value of SVM is 0.1 for \(\mathbf{f_{input}}\). The LR model appears not to be overfit, which may be related to the fact that the complexity of the LR model was successfully balanced by regularization. On the other hand, SVM and ANN were overfit, meaning that they were poorly generalized. Nonetheless, in terms of precision, the SVM with hand-crafted features outperformed the LR. In terms of recall, the ANN achieved the same performance as the LR. This implies that different models can be chosen for different purposes or requirements. In general, recall for tachycardia is the most important, so the LR with hand-crafted features will be optimal.  
Figure 4: Receiver operating characteristic curves using Logistic regression.  
This study is limited by its small dataset. Although the original number of collected datasets was 1931, we had only 257 patients data after filtering. As machine learning models learn from the dataset, we believe that performance will improve if more data are gathered. We will continue collecting more data to create better models.
## 1 Introduction  
Pavement surface layer must have the ability to be a wearing layer and have good performance during its service life. The increase of traffic congestion has caused damage in pavement surface layer so that it cannot reach the expected service life. The repetition of traffic loads resulting from traffic density causes the accumulation of permanent deformation in the asphalt concrete mixture and decreases its service life [1, 2]. One way to solve the problem is by using additives in the asphalt mixture. One of the additives is plastic waste, which contains polymer and found as plastomeric in the nature [3, 4]. Several studies have suggested that PET plastic waste as an added material can improve the asphalt mixture performances [5, 6, 7, 8, 9].  
In Indonesia, particularly in the island of Buton, the province of Southeast Sulawesi, natural deposit of asphalt or rock asphalt, namely Asbution (Natural Asphal Buton) can be found with abundant quantity. Asbution is a naturally categorized as hydrocarbon material [10, 11, 12, 13]. Asbution bitumen content varies from 10% to 40%, and the rest is a mineral. The Asbution deposit is quite large, around 600 million tons [14]. Moreover, the Asbution deposit is estimated to be equivalent to 24 million petroleum asphalt [15, 16, 17]. It has been established in some previous studies that the combination of PET plastic waste and Asbutton can increase the asphalt mixture's stiffness, particularly the Marshall characteristics. They can improve several essential aspects of the asphalt mixture [18, 19, 20].  
However, the optimum proportion of Asbutton and PET plastic waste for the asphalt mixture remains unclear. In general, experimental method was undertaken to evaluate a factor's effect in one experiment, associated with several variations and several experiments. In research terms, this is called a single factor experiment. The experimental method's weakness is that the conclusions obtained are only related to the experimental factors and are limited to 1 to 2 variables. Meanwhile, in reality, the quality of a product under study is influenced not only by one factor but also by several factors such as the level of modified Asbutton and the level of plastic waste. The proportions of these ingredients have interactions with one another, which significantly affect the quality of the Asphalt concrete-wearing course (AC-WC) mixture produced [21, 22].  
The method of quantifying the optimum proportion of PET plastic waste and the modified Asbutton in the asphalt mixture of AC-WC is still insufficient. Therefore, this study aims to investigate that optimum proportion of the modified Asbutton and PET plastic waste by using statistical techniques of Response Surface Methodology (RSM). This statistical method can take the contribution of two or more factors in an experiment into account and estimate the interactions and relationships between the experimental factors [23, 24, 25, 26].
## 4 Results and Discussion
### Construction of Standard Summer Maize EVI Time Series Curve in the Study Area
#### 4.2.3 Validation of Standard Summer Maize EVI Time Series Curve in the Study Area  
Ten counties were randomly selected in the study area to validate the standard summer maize EVI time series curve. According to the method in Section 3.1, the planting area of summer maize in 10 validated regions was mapped by Landsat-7 ETM+ images. The maize distribution region extracted by Landsat was used to mask the MODIS EVI time images, and the actual MODIS EVI time series values of summer maize in 10 validated areas were obtained by calculation. The actual MODIS EVI time series values of summer maize were compared with the standard EVI time series values of summer maize by Equation (15). The results showed that the correction coefficient between the actual summer maize MODIS EVI time series curve and the standard summer maize EVI time series curve was all greater than 0.9 with \(p<0.01\) (Table 4). It indicated that the standard curve simulated by Equation (15) was consistent with the actual curve.
## 5 Proposed Adaptive Beaconing System
### Density Estimation  
In this section, we explain the method used by the proposed protocol to determine the network density level (dense or not dense). First, we derive an expression to estimate the density threshold (\(DT\)). IEEE 802.11p operates in the 5.9 GHz frequency band, with 7 channels of 10 MHz each. One of these channels is a control channel (CCH) and the other six are service channels (SCHs). The CCH is reserved for safety related messages, service announcements, and context-aware information, while SCHs are reserved for comfort and driving efficiency messages. Beacons are exchanged over the control channel (CCH) [9; 10]. The channel time in IEEE 802.11p is divided into synchronization periods of 100 ms each, composed of equal-length alternating 50 ms control channel interval (CCHI) and 50 ms service channel interval (SCHI). There is a 2 ms guard interval (GI) at the start of each one of the 50 ms time intervals to account for the timing inaccuracies of the communicating devices and the radio switching delay [9; 10; 60].  
The beacon load (\(BL\)) is the maximum portion of the CCHI that can be used by beaconing, and the beacon rate (\(BR\)) is the number of packets sent per second. Consider a frequency of 10 Hz and a CCHI of 50 ms. Also, consider a maximum beacon load of 60% [21] and an approximate beacon transmission time of 1 ms [61]. Over a 1000 ms interval, the density threshold (\(DT\)) can be estimated as follows:  
\[DT=(1000/(CCHI+SCHI))*(CCHI-GI)*BL*(1/BR) \tag{7}\]  
Using the following values (discussed above) \(CCHI\) = 50 ms, \(SCHI\) = 50 ms, \(GI\) = 2 ms, \(BL\) = 0.6, and \(BR\) = 10, in Equation (7) results in a \(DT\) of 28 vehicles. Every vehicle determines the number of direct neighbors, \(ND\), and compares it to \(DT\). We consider the network to be dense if \(ND>DT\) since in that case the channel is saturated. Otherwise, the network is not dense. The density is estimated by each vehicle utilizing the received beacons from neighbors within the transmission range.  
If the network is determined to be dense, the protocol uses Set 3's parameters as inputs to its fuzzy logic system. Otherwise, the protocol determines the type of environment to select the suitable set of parameters that it needs to use in its fuzzy logic system as is explained in the next subsection.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 6 Conclusions  
In this paper, deep learning technology is introduced into the field of MIMO radar imaging, and a new MIMO radar imaging method suitable for non-orthogonal waveforms is proposed. This method only needs to transmit the common non-orthogonal signal waveforms, and the echo is post-processed through the training deep network. It can effectively suppress the coupling interference of different waveforms and efficiently obtain a high-resolution image of the target. The experimental results verify that, by training the proposed imaging network on the simulation data set, a good imaging effect can be obtained on the point scattering target data and electromagnetic calculation data. In the follow-up, we will continue to study the organic combination method of existing imaging theory and deep networks, build a more advanced echo transformation module, improve the quality of target echo feature extraction, and further improve the recovery effect of the imaging network on weak scattering points. Moreover, near-field microwave-based tomography, which is closely related to radar imaging, has wide application prospects, such as medical diagnosis or industrial testing. Thus, it would also be valuable to expand our proposal to the field of near-field microwave-based tomography.  
Methodology, H.L.; resources, Q.Z.; writing--review and editing, H.L.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61531015.  
Not applicable.  
The datasets generated during the current study are not publicly available but are available from the first author on reasonable request.  
Thanks to [PERSON] for the many valuable suggestions.  
The authors declare no conflict of interest.
## 4 Results
### Scanning Electron Microscopy, Coupled with Energy-Dispersive Spectroscopy
The EDS revealed a good correlation between the elemental composition and the crystalline phases, as highlighted by the XRD analysis. SEM, coupled with the EDS results regarding the M sides of the investigated samples, revealed the samples' surface characteristics following ceramic veneering at different firing temperatures. In the case of the as-3-D-printed sample (of Co-Cr-W dental alloy), the surface exhibited a rough texture, while the chemical composition provided by the EDS analysis was close to the alloy's nominal composition, although possessing a slightly higher Si content. The other M samples also exhibited varying degrees of roughness, which is in accordance with the AFM results. Moving on to the feldspathic ceramic-veneered samples, the opaqueer layering (2_P1 and 2_P2), and the differently applied firing parameters certainly affected the surface quality and the chemical composition of the samples. The 2_P2_M sample fired at 940 \({}^{\circ}\)C exhibited a higher volume fraction of the ceramic products on the metal's surface than the 2_P1_M sample, which was fired at a lower temperature (930 \({}^{\circ}\)C). This observation is also confirmed by the elemental analysis provided in Table 8, which, in the case of the 2_P2_M sample, revealed a much higher Si content: 12.6 wt %, instead of 4 wt %, and 3 wt % in the case of the 2_P1_M sample and the as-3D-printed Co-Cr-W alloy, respectively. Similarly, the 4_P1_M and 4_P2_M samples, which received a multi-layering ceramic coating using different firing conditions, exhibited surface roughness and chemical composition differences. The 4_P2_M sample, which was veneered at higher temperatures, was characterized by a higher volume fraction of ceramic microstructural constituents, as shown in the above SEM images, also exhibiting a larger Si content compared to the 4_P1_M sample, which was fired at lower temperatures. In particular, the 4_P1_M sample contained almost half the content of Si in comparison with the 4_P2_M samples. Based on these microstructural observations, combined with the elemental analysis results, it can be concluded that the firing process parameters in both types of feldspathic ceramic coatings (opaque layered and multi-layered) can also have a significant effect on the surface characteristics and chemical composition of the M sides of the studied samples.
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
###### Abstract  
This article presents a quad-element MIMO antenna designed for multiband operation. The prototype of the design is fabricated and utilizes a vector network analyzer (VNA-AV3672D) to measure the S-parameters. The proposed antenna is capable of operating across three broad frequency bands: 3-15.5 GHz, encompassing the C band (4-8 GHz), X band (8-12.4 GHz), and a significant portion of the Ku band (12.4-15.5 GHz). Additionally, it covers two mm-wave bands, specifically 26.4-34.3 GHz and 36.1-48.9 GHz, which corresponds to 86% of the Ka-band (27-40 GHz). To enhance its performance, the design incorporates a partial ground plane and a top patch featuring a dual-sided reverse 3-stage stair and a straight stick symmetrically placed at the bottom. The introduction of a defected ground structure (DGS) on the ground plane serves to provide a wideband response. The DGS on the ground plane plays a crucial role in improving the electromagnetic interaction between the grounding surface and the top patch, contributing to the wideband characteristics of the antenna. The dimensions of the proposed MIMO antenna are 31.7 mm \(\times\) 31.7 mm \(\times\) 1.6 mm. Furthermore, the article delves into the assessment of various performance metrics related to antenna diversity, such as ECC, DG, TARC, MEG, CCL, and channel capacity, with corresponding values of 0.11, 8.87 dB, \(-\)6.6 dB, \(\pm\)3 dB, 0.32 bits/sec/Hz, and 18.44 bits/sec/Hz, respectively. Additionally, the equivalent circuit analysis of the MIMO system is explored in the article. It's worth noting that the measured results exhibit a strong level of agreement with the simulated results, indicating the reliability of the proposed design. The MIMO antenna's ability to exhibit multiband response, good diversity performance, and consistent channel capacity across various frequency bands renders it highly suitable for integration into multi-band wireless devices. The developed MIMO system should be applicable on n77/n78/n79 5G NR (3.3-5 GHz); WLAN (4.9-5.725 GHz); Wi-Fi (5.15-5.85 GHz); LTE5537.5 (5.15-5.925 GHz); WiMAX (5.25-5.85 GHz); WLAN (5.725-5.875 GHz); long-distance radio telecommunication (4-8 GHz; C-band); satellite, radar, space communications and terrestrial broadband (8-12 GHz; X-band); and various satellite communications (27-40 GHz; Ka-band).  
chapter 1