esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Conductors Analyzed and the Experimental Setup
### High-Temperature Low-Sag (HTLS) Conductors  
Due to their much higher ampacity, HTLS conductors are used to replace conventional conductors in congested areas. The increased ampacity at nearly the same cross-sectional area is due to higher temperature operation and low conductor sag due to the exceptional properties of the core [29]. An HTLS conductor with a non-conductive hybrid core and aluminum trapezoidal wires (TW) helically wound around the core is analyzed in this section. It is known as an aluminum conductor composite core (ACCC/TW). This conductor can operate up to 180 \({}^{\circ}\)C [30] using 1350-O annealed aluminum wires [31].  
Figure 2: Aluminum alloy strand configuration (1/6/12/18/24) of the Aster 570 AAAC conductor.  
Figure 4 shows the cross-section of the Dhaka ACCC/TW conductor studied in this section.
###### Abstract  
To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The loading paths (internal pressure, axial feeds, and coefficient of friction) were randomly sampled using the Latin hypercube random sampling method. The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. The maximum average absolute value error and mean square error of the proposed model are less than 0.2, which improves the accuracy by 20.4% compared to the unoptimised PSO-BP neural network algorithm. The maximum error between simulated and predicted results is within 4%. The model allows effective prediction of the hydroforming effect of aluminium alloy variable diameter tubes and improves the computational rate and model accuracy of the model. The same process parameters are experimentally verified, the minimum wall thickness of the formed part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum expansion height is 5.11 mm. The maximum thinning and the maximum thickening rate comply with the standard of hydroforming, and the tube has good formability without obvious defects.  
variable diameter tubes; hydroforming; machine learning; GA-PSO-BP algorithm Article
## 2 Materials and Methods
### SAR Data Description  
A total of 29 high-resolution X-band SAR images were used in this study, including 24 TerraSAR-X and 5 TanDEM-X acquisitions. These images were obtained from August 2016 to September 2018, covering the entire Honam High-Speed Railway corridor. All images were acquired in HH polarization mode and exhibited varying baseline distances, temporal intervals, and Doppler centroid values.  
Table 1 summarizes the detailed specifications of the SAR dataset, including the satellite type, acquisition dates, polarization, and baseline parameters. The image set was carefully selected to ensure consistent coverage and high coherence for the PS-InSAR analysis in this geotechnically sensitive corridor.  
The combination of TerraSAR-X and TanDEM-X datasets ensures a sufficient data density and coverage to identify millimeter-level ground displacement across various structural elements of the railway, including embankments, bridges, tunnel entrances, and station platforms. These datasets form the core input for the interferometric processing described in the following section.  
We exploited the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique [15; 16; 19] with TERRA SAR radar images to study earth fill movements over a period of 3 years. A total of 61 Sentinel-1 images from descending orbit 8 were selected. The images were acquired in IW mode and cover the Paris city area over approximately 2.5 years, from 9 August 2016, to 29 September 2018. Only three bursts from the second swath cover the city and its surroundings, including all sites monitored using topography.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Protocol Description
#### 3.2.1 RTS Stage  
The transmission starts from the RTS stage which is triggered by the first RTS packet sent by an arbitrary node in the cluster when the cluster is idle. Different from the conventional handshaking protocols, each node in the cluster can send out an RTS packet in this stage when it has data to transmit. It means that multiple RTS packets can be sent in the RTS stage. To reduce the collision of the RTS packets, the sending time of the following RTS packets are uniformly distributed in the time interval of \([T^{\prime\mathrm{ts}}-D_{i}^{\prime\mathrm{ts}},T_{0}]\), where \(T^{\prime\mathrm{ts}}\) denote the time stamp of the last received RTS packet, \(D_{i}^{\prime\mathrm{ts}}\) denotes the propagation delay of this RTS packet to the source node \(s_{i}\), and \(T_{0}\) is a constant used to control the length of the time interval, which is larger than the maximum propagation delay in the cluster. Each source node is allowed to send an RTS packet once in a transmission cycle to avoid a long RTS stage. When a node that sends an RTS does not receive the corresponding CTS, it waits for another transmission cycle instead of resending the RTS packet. The RTS stage ends after a fixed delay when the last RTS packet in the cluster is sent out.
## 3 Results  
### Effects Spatial Drivers on in situ Vegetation Characteristics  
The direct effects of drivers behind spatial variation, i.e., flood duration, wave forcing, competition, and Creek influence on in situ measured vegetation properties show that flood duration and wave forcing affected all vegetation characteristics, and their effects were strongest on chlorophyll a+b and caroten content (Figure 1). The level of significance is indicated by p, the r\({}^{2}\)-adj is the adjusted r\({}^{2}\), as reported by the regression functions in R, and RMSE reports the root mean square error.  
Competition is strongly correlated with soil moisture content and LAI. Nearness to creeks was only significantly correlated with chlorophyll a+b and caroten content, but this correlation explained over 40% of the variation in both cases.  
LAI seems to increase at a higher flood duration. However, a low flood duration only occurs at higher elevation, therefore this value is likely codependent on competition, which only occurs at higher elevations. The highest LAI values do not occur at high flood durations. The overall effect of flood duration on the leaf level is relatively small.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Month & 06, from satellite image \\ Day & 05, from satellite image \\ Solar zenith angle (deg) & 28.91, from satellite image \\ Solar azimuth angle (deg) & 171.91, rom satellite image \\ Sensor zenith angle (deg) & 12.79, from satellite image \\ Sensor azimuth angle (deg) & 281.32, from satellite image \\ Atmospheric profile & Mid latitude summer/winter, here summer \\ Aerosol profile & Maritime \\ Target altitude & Sea level \\ Sensor altitude & Satellite level \\ Spectral conditions & RapidEye gain, band 1-5 \\ Ground reflectance & Homogeneous surface \\ Directional effects & No directional effects \\ Input ground reflectance & Mean spectral value \\ Atmospheric correction mode & Atmospheric correction with Lambertian assumption \\ Atmospheric correction target & 0, Reflectance \\ \hline \hline \end{tabular}
\end{table}
Table 2: 6S atmospheric correction model parameters used for the RapidEye atmospheric correction.
## 4 Experiment Results and Analysis
### Properties of Hardened Concrete  
#### 4.2.1 Compressive Strength  
Figure 3 shows the results of compressive strength experiment with respect to the cement replacement ratio of the carbon-capturing concrete composed of blast-furnace slag exposed to high concentration CO\({}_{2}\). The S90 Ca20 Na20 variable with the lowest cement replacement ratio relative to the weight of blast-furnace slag showed the lowest compressive strength of the range from 1 to 90 days of exposure among all the variables. This seems to be due to the differences in the strength expression by the low cement replacement ratio.  
The variables with 20%, 30%, and 40% cement replacement ratios showed compressive strengths of around 20 MPa until 90 days of exposure. The increase in the strength with respect to the progress of the exposure period was generally insignificant. The S60 Ca20 Na20 variable with 40% cement replacement ratio showed 25 MPa or higher compressive strengths at 90 days of exposure. This is due to the differences in strength depending on the higher cement replacement ratio compared to other variables.  
Figure 4 shows the results of compressive strength experiment with respect to the cement replacement ratio of the carbon-capturing concrete composed of blast-furnace slag exposed to  
Figure 3: Compressive strength experiment results of high concentration CO\({}_{2}\) exposure conditions.  
Figure 2: Slump and air content test results.  
the high-purity air. In the experiment results at 1 day of exposure, every variable showed a compressive strength of 20 MPa. Subsequently, until 28 days of exposure, the compressive strength was approximately 25 MPa in the S80 Ca20 Na20 and S70 Ca20 Na20 variables with cement replacement ratios of 20% and 30%, respectively. However, the S60 Ca20 Na20 and S90 Ca20 Na20 variables showed an insignificant increase in strength.  
At 56 days of exposure, the strength improved by approximately 15% compared to 28 days of exposure. At 90 days of exposure, the strength increase over the exposure period compared to 56 days of exposure was generally insignificant. The S70 Ca20 Na20 variable with 30% cement replacement ratio relative to the weight of blast-furnace slag showed a decrease of strength at 90 days of exposure. This seems to be due to poor compaction, which is a factor of the specimen fabrication process.  
The increase of the compressive strength of the carbon-capturing concrete with respect to the cement replacement ratio and exposure conditions over the exposure period was generally insignificant. The variable with the high-purity air condition showed a greater increase of the compressive strength than the variable of exposure in high concentration CO\({}_{2}\) condition. This is considered to be due to the difference in the relative strength expression speed with the variable of high-purity air exposure generated by contraction due to the surface carbon absorption of the specimen exposed to high concentration CO\({}_{2}\).
## 4 Results
### Deformation Results
#### 4.1.3 The Validation of Results  
To quantitatively verify the accuracy of the surface deformation monitoring using Sentinel-1 SAR images and SBAS-InSAR, a cross-validation of differences between descending and ascending SAR image-derived results were performed. This study used the deformation results in the vertical direction for analysis because the ground deformation of the descending and ascending images was inconsistent with the line of sight. The two sets of SAR images had overlapped part of the time series, from June 2018 to November 2018 (six months); thus the differences during the overlapped timing sequence were used to test these two result sets. Figure 7 shows the differences between the two sets of results calculated using the ArcGIS 10.4 raster calculator function.  
Using SAR images, the data statistics of different deformation results (descending and ascending) were calculated. In order to cross-validate the results, the difference between the two data sets was taken. The results of the difference statistics are shown in Table 3, with different intervals for data statistics, including the number of distributions and the cumulative percentage. As shown in Figure 8, the distribution statistics of the verification results and the fitted normal curves were obtained. When the difference was 0, it was proven that the two sets of data are completely consistent. The results of the verification were mainly distributed in the range of \(-5\) mm to \(5\) mm, wherein the data in the range of \(-5\) mm to \(0\) mm accounted for \(42.70\%\), and the data range from \(0\) mm to \(5\) mm accounted for \(28.10\%\). The average difference, maximum (MAX), and minimum (MIN) totaled \(-2.5\), \(81.23\), and \(-83.04\) mm, respectively. According to Figure 7, the deviation of the Erhai region from the mountain was slightly larger, with an absolute value between \(20-40\) mm. Due to the slope instability and the coverage of surface vegetation, the SBAS-InSAR method was prone to errors in the vicinity of the mountains. However, most of the validation results in the study area were less than \(10\) mm, including the distribution of southern urban agglomerations and most rural areas, indicating that the two sets of data were well tested, and the ground deformation rate calculations were reliable.  
Figure 7: Distribution of Difference Value.
###### Abstract  
An L-shaped dual-band multiple-input multiple-output (MIMO) rectangular dielectric resonator antenna (RDRA) for long term evolution (LTE) applications is proposed. The presented antenna can transmit and receive information independently using fundamental TE\({}_{111}\) and higher order TE\({}_{121}\) modes of the DRA. TE\({}_{111}\) degenerate mode covers LTE band 2 (1.85-1.99 GHz), 3 (1.71-1.88 GHz), and 9 (1.7499-1.7849 GHz) at \(f_{r}\) = 1.8 GHz whereas TE\({}_{121}\) covers LTE band 7 (2.5-2.69 GHz) at \(f_{r}\) = 2.6 GHz, respectively. An efficient design method has been used to reduce mutual coupling between ports by changing the effective permittivity values of DRA by introducing a cylindrical air-gap at an optimal position in the dielectric resonator. This air-gap along with matching strips at the corners of the dielectric resonator keeps the isolation at a value more than 17 dB at both the bands. The diversity performance has also been evaluated by calculating the envelope correlation coefficient, diversity gain, and mean effective gain of the proposed design. MIMO performance has been evaluated by measuring the throughput of the proposed MIMO antenna. Experimental results successfully validate the presented design methodology in this work.  
dielectric resonator antenna; MIMO; LTE; mutual coupling +
Footnote †: journal: Sensors
## 3 Result and Discussion
### Percentage Yield and Chemical Composition of C. operculatus Essential Oil
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No** & **Compounds** & **RT (min)** & **SI** & **RIa** & **RIb** & **Content** \\ \hline
1 & \(\alpha\)**-Pinene** & 6.70 & 97 & 932 & 932 & **5.6** \\ \hline
2 & \(\beta\)-Pinene & 8.11 & 96 & 975 & 974 & 0.4 \\ \hline
3 & \(\beta\)-Myrcene & 8.58 & 96 & 990 & 988 & 1.1 \\ \hline
4 & **Limonene** & 10.02 & 90 & 1028 & 1024 & **2.7** \\ \hline
5 & (**Z**)-\(\beta\)**-ocimene** & 10.38 & 97 & 1036 & 1032 & **30.4** \\ \hline
6 & (**E**)-\(\beta\)**-ocimene** & 10.92 & 97 & 1050 & 1044 & **14.0** \\ \hline
7 & Terpinolene & 12.41 & 94 & 1087 & 1086 & 1.1 \\ \hline
8 & Linalool & 12.98 & 94 & 1100 & 1095 & 0.3 \\ \hline
9 & **Allo-ocimene** & 14.44 & 96 & 1134 & 1128 & **31.6** \\ \hline
10 & Veratrole & 14.63 & 82 & 1138 & 1141 & 0.2 \\ \hline
11 & Neo-allo-ocimene & 14.81 & 97 & 1142 & 1140 & 1.4 \\ \hline
12 & \(cis\)-Chrysanthenyl & 20.03 & 84 & 1260 & 1261 & 0.1 \\ \hline
13 & \(\alpha\)-Copaene & 25.09 & 88 & 1377 & 1374 & 0.1 \\ \hline
14 & Geranyl acetate & 25.43 & 94 & 1385 & 1379 & 0.2 \\ \hline
15 & \(\beta\)**-caryophyllene** & 26.96 & 97 & 1421 & 1422 & **4.6** \\ \hline
16 & \(\beta\)-Copaene & 27.33 & 89 & 1430 & 1430 & 0.1 \\ \hline
17 & \(\gamma\)-Elemene & 27.52 & 89 & 1435 & 1434 & 0.1 \\ \hline
18 & Aromadendrene & 27.73 & 93 & 1440 & 1439 & 0.2 \\ \hline
19 & \(\alpha\)-Humulene & 28.34 & 97 & 1455 & 1452 & 1.0 \\ \hline
20 & \(\gamma\)-Muurolene & 29.28 & 92 & 1478 & 1478 & 0.5 \\ \hline
21 & Germacrene-D & 29.47 & 92 & 1482 & 1480 & 0.3 \\ \hline
22 & \(\beta\)-Selinene & 29.68 & 93 & 1487 & 1489 & 0.2 \\ \hline
23 & Viridififorene & 30.04 & 92 & 1496 & 1496 & 0.4 \\ \hline
24 & \(\alpha\)-Bulnesene & 30.54 & 84 & 1509 & 1509 & 0.1 \\ \hline
25 & \(\delta\)-Amorphene & 30.80 & 90 & 1515 & 1511 & 0.2 \\ \hline
26 & \(\delta\)-Cadinene & 31.18 & 94 & 1525 & 1522 & 0.4 \\ \hline
27 & (\(E\))-Nerolidol & 32.74 & 95 & 1565 & 1561 & 0.3 \\ \hline
28 & Caryophyllene oxide & 33.51 & 91 & 1584 & 1582 & 0.4 \\ \hline
29 & Eremoligenol & 35.14 & 87 & 1627 & 1629 & 0.2 \\ \hline
30 & \(\alpha\)-Cadinol & 36.23 & 81 & 1657 & 1652 & 0.1 \\ \hline  & Monoterpene hydrocarbons & & & & 88.3 \\ \hline  & Sesquiterpene hydrocarbons & & & & 8.3 \\ \hline  & Oxygenated sesquiterpenenes & & & & 0.6 \\ \hline  & Oxygenated monoterpenes & & & & 0.9 \\ \hline  & Non-terpenes & & & & 0.2 \\ \hline  & Total & & & & **98.3** \\ \hline \hline \end{tabular} RT: Retention time; SI: similarity index; RIa: Retention indices relative to C\({}_{7}\)–C\({}_{40}\)\(n\)-alkanes calculated on Equity-5 capillary column; RIb: Literature retention indices [13; 14; 21].  
\end{table} 
Table 1: Chemical constituents of the essential oil from _C. operculatus_ leaves.
# Methacrylate Polymer Monoliths for Separation Applications
## 3 Applications of Methacrylate Polymer Monoliths in Separation Science
### Biological Separations and DNA Purification
Other less frequently reported compounds have included angiotensin receptor antagonists and microcystins [135]. Various chromatographic modes have been used, such ion exchange, HILIC, Hydrophobic Interaction Chromatography HIC, reverse phase, size exclusion, enrichment and pre-concentration, and bio-affinity, using capillary columns and also microfluidic platforms. One of the earliest reports of the use of methacrylate monoliths for the separation of biomolecules can be traced to a 1992 contribution by [PERSON] and [PERSON] [3], which used continuous polymer rodsmodified with diol and amine moieties to separate protein mixtures via ion exchange. They suggested that the work may improve the potential of such polymer rods for use as stationary phases in the future. Since that contribution, a large body of work including reports, reviews and books, by many groups has been produced discussing the separation of proteins and other biomolecules using methacrylate stationary phases, in both ion exchange [44; 130; 136; 138] and reverse phase modes [85; 130]. This section will be divided into subsections dealing first with the separation of proteins, peptides and amino acids, then discussing DNA separation and enrichment, including the separation of plasmids, viruses and other biomolecules. A more recent contribution discussed the role of CIM(tm) monolithic supports for the separation of large biomolecules [140]. Section 3.2.1 discusses the amplification and pre-concentration of DNA, plasmids viruses and peptides using methacrylate monoliths as the support material and Section 3.2.2 presents the application of methacrylate monoliths in the chromatographic separation of proteins, peptides and amino acids, involving reverse phase, size exclusion, immuno-affinity and HIC.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 5 Conclusions  
In this paper, different DL-based approaches for signal detection in MIMO communications systems have been presented. An efficient alternative to processing the input data by structuring different labeling strategies to the Tx signals has been introduced. We evaluated the impact of these labeling strategies on the system performance. The of BER curves showed that the proposed schemes perform close to the optimal ML detection criterion with a simple NN architecture assuming perfect CSI. The proposed DL-based detectors presented distances in BER curves of almost 0.5 dB when applying the conventional OH encoding as a labeling method. Nonetheless, the proposed DL signal detection schemes showed lower complexity compared to the optimal ML criterion. In our proposal, the complexity was reduced to \(M\cdot N^{t}\) for the case of OHA, and \(\log_{2}M\cdot N_{t}\) when applying the DSE strategy. The classification performance metrics in terms of F1-score above 0.9 also indicated that the designed DL schemes are promising for the detection of 4-QAM modulated signals. Among the DL-based evaluated schemes, the OH encoder showed the best BER performance. However, it requires more resources as the dimensionality and computational complexity increase. The proposed OHA and the DSE schemes showed a BER performance very close to that of the OH scheme with the advantage of reduced complexity when the number of Tx antennas increases. These approaches are convenient for systems with hardware or signal processing constraints, mainly in massive MIMO designs. Future research improvements include testing higher-order \(M\)-QAM modulation schemes and hyperparameter tuning to optimize system resources. Furthermore, different channel estimation strategies will be considered.  
**Author Contributions:** Conceptualization, R.F.I.-H., F.R.C.-S. and C.A.G.; methodology, R.F.I.-H. and F.R.C.-S.; software, R.F.I.-H.; validation, F.R.C.-S., C.A.G., J.A.-E., L.P.-L, V.I.R.-A. and J.A.D.-P.-F.; Writing--original draft, R.F.I.-H. and F.R.C.-S.; writing--review and editing, F.R.C.-S. and C.A.G.; visualization, J.A.-E., V.I.R.-A., L.P.-L. and J.A.D.-P.-F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Universidad Panamericana, Guadalajara, Mexico and the Secretary of Science, Humanities, Technology, and Innovation of Mexico (SECIIHT).  
Figure 5: Complexity in flops for the developed DL-detectors. The labeling strategies acronyms are ML: maximum likelihood, OH: one-hot encoding, OHA: one-hot encoding per antenna, SE: symbol encoding.
## 4 Conclusions  
This experimental study used machine learning models to predict temperature rises during bone drilling. Tests were conducted on bovine femur bone with different drilling settings to develop predictive algorithms. The study examined how various drilling parameters affected the temperature increases throughout the process, leading to several significant findings.  
* The RSM method found that the best operating conditions were achieved with a tool diameter of 2 mm, a feed rate of 40 mm/min, and a spindle speed of 1000 rpm.
* This study found that the RFR model outperformed the SVM regression model and RSM, as evidenced by the lower errors in the performance metrics when comparing the three methods.
* The precision in predicting the bone drilling temperatures using the RSM method is 79%, while it is 97.3% with RFR and 25.7% with SVR models.
* The machine learning RFR model enhanced the drilling efficiency and reduced the risk of bone thermal damage by accurately predicting the temperature rises.