esa-sceva/satcom-chunk-collection huggingface dataset:
# The Synergy of Renewable Energy and Desalination:
## 5 Energy Efficient Operation of Centrifugal Pumping System in Desalination
A neural network was created to forecast wind speed 24 h in advance in order to estimate the number of operating units; it showed a correlation of R = 0.64 and an RMSE of 1.54 m/s against actual information. In order to maximize productivity or minimize unscheduled shutdowns during modular operation, two scheduling strategies, i.e., high- and low-output scheduling, were established. When compared to unplanned operation, the high- and low-output scheduling decreased the number of startup/shutdown cycles by 37.5 percent and 75 percent, respectively, improving specific energy consumption by 1.9 percent and 2.3 percent.
###### Abstract  
Drought and climate change have decreased water availability for agriculture, especially in the desert of southwestern USA. Efficiency enhancements in irrigation management aimed at conserving water are key to adjust to limits in water supply, improve profitability and sustainability of alfalfa production in arid and semiarid areas. This study intended to conduct a field-scale analysis to develop yield and ET estimation tools for the effective use of irrigation water in a desert alfalfa production system. Extensive data collection and trials were carried out over three years in nine fully irrigated commercial alfalfa fields in the low deserts of California. The seasonal crop water consumption measured using the residual of energy balance method varied from 1381 mm to 1596 mm across the experimental sites and crop seasons. Variable seasonal dry mater (DM) yields ranged from 23.01 Mg ha\({}^{-1}\) to 29.90 Mg ha\({}^{-1}\). The results indicated that the first five cuttings each year were the most productive cuttings with a mean DM value ranged between 3.29 (cut 1) and 4.21 (cut 4) Mg ha\({}^{-1}\) but declined in later cuttings. An average annual water productivity (WP) value of 17.0 kg ha\({}^{-1}\) mm\({}^{-1}\) was determined across the sites varying from 15.5 to 18.9 kg ha\({}^{-1}\) mm\({}^{-1}\). The findings suggested that one may lose up to 1.44 Mg ha\({}^{-1}\) alfalfa yields with moderate summer deficit irrigation strategies, using 40% less water applied than full irrigation practices over the summer period of July-September. A more severe summer water deficit, with no irrigation event over the summer period of July-September may result in a potential water savings of 0.234-0.246 (ha-m) ha\({}^{-1}\) and 19-21% seasonal yield losses in the desert environment. This study describes the seasonal yield pattern, the crop water use-production function, and the crop coefficient values for various harvest cycles over the crop season. These tools may assist farmers to quantify water savings and estimate yield losses for more accurate and effective irrigation management strategies to meet water conservation objectives and for the resiliency of alfalfa production in the desert region.  
alfalfa hay; Colorado River Basin; desert; drought; irrigation water management; deficit irrigation strategies; _Meliago sativa_ L.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 2 Related Work
### Signal Classification and Speech Processing  
CVNNs have also been proven effective in signal classification tasks, particularly when dealing with complex-domain inputs such as electromagnetic, optical, sonar, and radio frequency signals [32]. The inherent phase and amplitude components of these signals make CVNNs more suitable than real-valued networks (RVNNs). For instance, a complex wavelet neural network (CWNN) was used in sonar echo classification and solar irradiation forecasting, achieving superior performance by leveraging wavelet activation functions [33]. Further, [34] introduced sparse Fourier feature extraction prior to CWNN classification, outperforming traditional Fourier transform methods. Fully CWNN-based models also achieved enhanced accuracy in solar forecasting by effectively learning correlations between real and imaginary components [35].  
In speech applications, CVNNs are leveraged to process acoustic signals by capturing both phase and magnitude. [PERSON] et al. [36] developed a sound localization system using FFT and CVNNs, while [37] demonstrated that complex-valued U-Net outperformsconventional models in speech enhancement. [PERSON] et al. [38] introduced a hybrid acoustic model incorporating complex-valued layers with a batch amplitude mean normalization strategy, which improved training stability and robustness under noisy conditions. However, [PERSON] et al. [39] reported that a direct, unoptimized extension of KVNNs into the complex domain yielded no significant performance gains, highlighting the need for task-specific CVNN design. Additionally, CVNNs have been utilized in voice-based [PERSON] disease detection. Methods such as Mc-FCRBF [40], feature selection with mRMR [41], and feature weighting with KMC [42] have collectively improved classification accuracy to as high as 99.52%.
###### Abstract  
(1) Background: Cushing's disease (CD) is a serious endocrine disorder caused by an adrenocorticropic hormone (ACTH)-secreting pituitary neuroendocrine tumor (PitNET) that stimulates the adrenal glands to overproduce cortisol. Chronic exposure to excess cortisol has detrimental effects on health, including increased stroke rates, diabetes, obesity, cognitive impairment, anxiety, depression, and death. The first-line treatment for CD is pituitary surgery. Current surgical remission rates reported in only 56% of patients depending on several criteria. The lack of specificity, poor tolerability, and low efficacy of the subsequent second-line medical therapies make CD a medical therapeutic challenge. One major limitation that hinders the development of specific medical therapies is the lack of relevant human model systems that recapitulate the cellular composition of PitNET microenvironment. (2) Methods: human pituitary tumor tissue was harvested during transspheroidal surgery from CD patients to generate organoids (hPITOs). (3) Results: hPITOs generated from corticotroph, lactotroph, gonadotroph, and somatostatin thumors exhibited morphological diversity among the gonadal lines between individual patients and amongst subtypes. The similarity in cell lineages between the organoid line and the patient's tumor was validated by comparing the neuropathology report to the expression pattern of PitNET specific markers, using spectral flow cytometry and exome sequencing. A high-throughput drug screen demonstrated patient-specific drug responses of hPITOs amongst each tumor subtype. Generation of induced pluripotent stem cells (iPSCs) from a CD patient carrying germline mutation CDH23 exhibited dysregulated cell lineage commitment. (4) Conclusions: The human pituitary neuroendocrine tumor organoids represent a novel approach in how we model complex pathologies in CD patients, which will enable effective personalized medicine for these patients.  
orgnoids; neuroendocrine tumors; induced pluripotent stem cells; _CDH23_
## 2 Proposed Antenna Design  
A full parametric study is conducted on all variables to acquire the dimensions reported in Table 1. The proposed antenna with allocated parameters is illustrated in Figure 1. The antenna was built on a double-sided FR-4 substrate with a dielectric constant of 4.4 and a loss tangent of 0.02. The antenna's overall dimensions are 30 \(\times\) 30 \(\times\) 1.6 mm\({}^{3}\). The right side of the bowtie is linked to the feed line, while the other portion of the bow tie is grounded using a shorting via to enhance the resonances since without the shortingpin; only two resonances are developed. Both bowtie-shaped resonators are located at the top layer as shown in Figure 1a where each side of the bowtie has a gradually increasing meander line arms. The arm's length, width, and spacing have been optimized to produce several resonances with an acceptable bandwidth for the utilized services and several notches with an acceptable rejection level to filter out other services. Figure 1b shows the modified ground plane that has been adjusted to sweep over the UWB scope. Figure 1c displays a side view of the board to indicate the shorting pin placement.  
To reach the proposed design, it first passes through mainly two steps as shown in Figure 2a. First, a meander bowtie antenna is considered on a full ground plane (ANT I) which produces three frequency bands that require to be enhanced as depicted in Figure 2b. Then in (ANT II), a partial ground plane is utilized for better antenna characteristics that result in three operating bands in addition to other bands that required impedance matching. To optimize the matching impedance, the meandered arm's length, width, and spacing are considered as shown in (ANT III), which produces the proposed seven resonating bands with optimized rejection bands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline WS & 30 & LS & 30 \\ LG & 11 & W1 & 1.5 \\ W2 & 2.5 & W3 & 2.8 \\ W4 & 0.6 & W5 & 0.4 \\ W6 & 0.5 & W7 & 1 \\ W8 & 13.2 & W9 & 15.3 \\ L1 & 18.75 & L2 & 13.25 \\ L3 & 9.25 & L4 & 1.75 \\ L5 & 4.25 & L6 & 6.75 \\ L7 & 0.25 & L8 & 1.25 \\ L9 & 5.25 & L10 & 23.25 \\ \hline \end{tabular}
\end{table}
Table 1: Suggested Antenna Lengths in mm.  
Figure 1: The suggested antenna; (a) front view, (b) back view, and (c) side view.
# Contrast Enhancement for Topographic Imaging in Confocal Laser Scanning Microscopy
###### Abstract  
The influence of the axial pinhole position in a confocal microscope in terms of the contrast of the image is analyzed. The pinhole displacement method is introduced which allows to increase the contrast for topographic imaging. To demonstrate this approach, the simulated data of a confocal setup as well as experimental data is shown. The simulated data is verified experimentally by a custom stage scanning reflective microscopy setup using a semiconductor test target with low contrast structures of sizes between 200 nm and 500 nm. With the introduced technique, we are able to achieve a contrast enhancement of up to 80% without loosing diffraction limited resolution. We do not add additional components to the setup, thus our concept is applicable for all types of confocal microscopes. Furthermore, we show the application of the contrast enhancement in imaging integrated circuits.  
microscopy; confocal microscopy; contrast enhancement 1
## 1 Introduction
However, these works consider only either single-parameter attacks or multiple attacks of equal magnitude. With the integration of machine learning in SG technologies, refs. [29] developed a parameter error detection technique that uses both machine learning based multivariate linear regression models and the Innovation-based SE [30]. However, the work proposed in [29] deals with detection of the parameter FDI attack only and leaves parameter FDI correction modeling for future work. Correction is a critical step in ensuring the SE produces reliable results for future measurement sets. In the authors' previous work [31], a parameter FDI correction model is presented. However, the limitations of [31] are twofold: (1) the assumption of the availability of a measurement set at the FDI location, i.e., both real power flow and reactive power flow measurements are available for parameter correction. (2) the method is deterministic, i.e., the residual is zero, which means inaccuracy in the measurement set will lead to an inaccurate correction of parameters. These limitations naturally inspired the authors to develop a new, overdetermined correction model that doesn't assume all of the real measurements necessary are always available as well as accurate. In order to do this, an additional measurement set is generated, called Synthetic Measurements (SM) in this paper, to increase the redundancy level and enable a new SE towards parameter correction. The linear regression prediction used in [29] was used to generate Synthetic Measurements (SM) in a similar way as in [32]. Therefore, the contributions of this work are threefold:1. Creating synthetic measurements based on weights obtained from machine learning linear regression prediction;
2. Developing an overdetermined physics-based model for parameter FDI correction;
3. Incorporating synthetic measurements in the parameter FDI correction model. The remainder of this paper is organized as follows. Section 2 provides theoretical background on state estimation with synthetic measurements, the machine learning (ML) model for measurement prediction and the correction model for unbalanced parameter FDI attacks. Section 3 presents the parameter FDI correction physics-based model. Test results of a case study are shown in Section 4. Finally, Section 5 presents conclusions and remarks of this work.
## 2 Results
### Selectivity Studies
#### 2.3.2 Binary Competitive Assays
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Polymer**} & \multirow{2}{*}{**Binding 1**} & \multicolumn{4}{c}{**MIP**} & \multicolumn{4}{c}{**NIP**} \\ \cline{3-10}  & & **1** & **17** & **18** & **19** & **1** & **17** & **18** & **19** \\ \hline \multirow{5}{*}{E7\({}_{1\text{MIPCHCl3}}\)} & Reference (\%) 2 & \(51.5\pm 3.4\) & & & & \(18.0\pm 3.6\) & & & \\  & Normalised 3 & 1.09 & 0.19 & & & 1.19 & 0.41 & & \\  & 1.05 & & & 0.38 & & 0.77 & & 0.38 & \\  & 0.94 & & & 1.73 & 0.66 & & & 2.50 \\  & SI 4 & 1.00 & 0.18 & 0.37 & 1.85 & 1.00 & 0.34 & 0.49 & 3.80 \\ \hline \multirow{5}{*}{E7\({}_{2\text{MIPCHCl3}}\)} & Reference (\%) & \(82.9\pm 2.2\) & & & & \(37.2\pm 3.1\) & & & \\  & Normalised & 0.93 & 0.15 & & & 1.11 & 0.16 & & \\  & 0.86 & & & 0.22 & & 0.88 & & 0.02 & \\  & SI & 1.00 & 0.16 & 0.26 & 1.23 & 1.00 & 0.14 & 0.02 & 2.11 \\ \hline \multirow{5}{*}{T7\({}_{1\text{MIPCHCl3}}\)} & Reference (\%) & \(43.3\pm 3.4\) & & & & \(11.9\pm 1.2\) & & & \\  & Normalised & 1.12 & 0.18 & & & 1.90 & 0.56 & & \\  & 1.24 & & 0.20 & & 1.64 & & 0.56 & & \\  & 0.74 & & & 1.29 & 1.15 & & & 3.15 \\  & SI & 1.00 & 0.16 & 0.16 & 1.74 & 1.00 & 0.30 & 0.34 & 2.74 \\ \hline \multirow{5}{*}{T7\({}_{2\text{MIPCHCl3}}\)} & Reference (\%) & \(76.4\pm 3.4\) & & & & \(24.3\pm 3.6\) & & & \\  & Normalised & 0.89 & 0.11 & & & 1.11 & 0.10 & & \\  & 0.89 & & 0.20 & & 1.38 & & 0.26 & & \\  & 0.78 & & & 1.04 & 0.92 & & & 2.28 \\  & SI & 1.00 & 0.12 & 0.23 & 1.32 & 1.00 & 0.09 & 0.19 & 2.47 \\ \hline \multirow{5}{*}{E16\({}_{\text{MIPCHCl3}}\)} & Reference (\%) & \(26.3\pm 2.5\) & & & & \(8.7\pm 2.0\) & & & \\  & Normalised & 1.01 & 0.07 & & & 0.72 & 0.37 & & \\  & 0.33 & & & 0.15 & & 0.35 & & 0.20 & \\  & 0.63 & & & 1.14 & 0.17 & & & 1.54 \\  & SI & 1.00 & 0.07 & 0.46 & 1.81 & 1.00 & 0.51 & 0.59 & 8.85 \\ \hline \multirow{5}{*}{T16\({}_{\text{MIPCHCl3}}\)} & Reference (\%) & \(53.0\pm 1.4\) & & & & \(4.5\pm 1.3\) & & & \\  & Normalised & 0.71 & 0.13 & & & 0.89 & 0.60 & & \\  & 0.33 & & & 0.20 & & 0.63 & & 0.66 & \\  & 0.42 & & & 0.57 & 0.34 & & & 1.18 \\  & SI & 1.00 & 0.18 & 0.61 & 1.34 & 1.00 & 0.68 & 1.05 & 3.50 \\ \hline \hline \end{tabular}  
* Rebinding studies were conducted by incubating 20 mg of polymer in 1 mL of 0.8 mM solution of analyte in CHCl3 for 60 min. All binding tests were performed in triplicates. 2 Binding of **1** as sole analyte. 3 Binding of binary analytes with respect to reference. 4 SI = selectivity index = normalised analyte binding/normalised **1** binding.  
\end{table} 
Table 3: Binary competition results for **1**-MIPs against cocaine (**17**), morphine (**18**) and ephedrine (**19**).
## 5 Basic Experiments
### Pca  
As the second approach, we applied dimensionality reduction with PCA as described in Section 4.2. We used the training set \(\mathbb{D}_{1}\) to learn the PCA basis and evaluate the approach with the test set \(\mathbb{D}_{2}\). Table 4 shows the evaluation results for two different PCA-based reduction strategies: The left columns (GRO) refer to the reduction of individual chroma vectors as done by [PERSON] and [PERSON] (2012), and the right columns (PCA) refer to the proposed reduction of entire shingles. The rows correspond to the considered dimensionalities 40, 60, and 80. Let us take the case of \(K=40\) as an example. In the first approach (GRO), each chroma vector was reduced to two dimensions, leading to the dimensionality of \(K=2\times 20=40\). This strategy resulted in an MAP value of 0.832. In the shingle-based reduction (PCA), an entire 240-dimensional feature sequence was reduced altogether to a 40-dimensional vector. This approach led to an MAP value of 0.964. In general, our experiments showed that a shingle-based reduction leads to better retrieval results. This is not surprising, because this approach can exploit temporal redundancies for dimensionality reduction. In the following, we aim to reduce the dimensionality to a degree that would not have been possible with the first approach (GRO). Columns 2-4 of Table 5 show the evaluation results obtained with our shingle-based approach for much lower dimensionalities. The retrieval quality consistently increases with an increase  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Brute-Force Approach** & **Data Set** & **P@1** & **P\({}_{R}\)** & **MAP** \\ \hline \multirow{2}{*}{Shingles (\(K=240\))} & \(\mathbb{D}_{1}\) & 0.993 & 0.936 & 0.966 \\  & \(\mathbb{D}_{2}\) & 0.996 & 0.941 & 0.972 \\ \hline \multirow{2}{*}{SDTW (CENS, \(1\,\mathrm{Hz}\))} & \(\mathbb{D}_{1}\) & 0.991 & 0.947 & 0.971 \\  & \(\mathbb{D}_{2}\) & 0.994 & 0.947 & 0.975 \\ \hline \multirow{2}{*}{SDTW (Chroma, \(10\,\mathrm{Hz}\))} & \(\mathbb{D}_{1}\) & 0.995 & 0.966 & 0.980 \\  & \(\mathbb{D}_{2}\) & 0.999 & 0.978 & 0.989 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Retrieval results for various brute-force approaches: The shingle-based brute-force approach (\(K=240\)), an alignment-based brute-force approach using chroma energy distribution normalized statistics (CENS) features (\(1\,\mathrm{Hz}\)), and an alignment-based brute-force approach using chroma features (\(10\,\mathrm{Hz}\)) without CENS post-processing.  
of dimensionality from an MAP value of 0.580 for \(K=3\) to 0.952 for \(K=30\). Let us consider the dimensionalities of 6 and 12 as exemplary cases. For \(K=6\) the P@1 value is 0.857, i.e., for 472 of the 3300 queries, the top-ranked document was not relevant. For \(K=12\) (P@1: 0.957), this is the case for only 142 queries.
# Experimental Study on Mechanical Behavior of Shear Connectors of Square Concrete Filled Steel Tube
## Specimen Design and Test Scheme  
### Specimen Design  
A total of 14 square concrete-filled steel tubes (CFSTs) with identical outer diameter of 200 mm have been designed and tested, as shown in Figure 4. The section size is a 1/3 scale model for CFST columns in high-rise buildings. In low-rise buildings, such as two or three stories buildings, the specimens can be considered as a full-scale model. At the bottom 30 mm of the specimens, it was not filled with concrete. This is to ensure that during the test, the push-out is smooth. The design parameters of the tests include the wall thickness of steel tube: \(t\) = 4, 6, 8, 14 mm (width to thickness ratio: \(B/t\) = 50.0, 33.3, 25.0, 14.3); thickness of steel plate: \(c\) = 4, 8, 12 mm; length of steel plate: \(l\) = 80, 120, 150 mm; number of steel plate layer: single-layer (Figure 4a), double-layer (Figure 4b); welding condition of steel plate: double-sided welding (Figure 4c), single-sided welding (Figure 4d), and the interlayer spacing: \(s\) = 100, 150, 200 mm; types of shear connectors: steel plate, steel bar (Figure 4e); and strength of concrete: C30, C40. The details of all the specimens are shown in Table 1. Taking 'P8-14-150-120' to explain the name of the specimens. 'P8' refers to the thickness of the steel plate \(c\) = 8 mm, '14' refers to the thickness of the steel tube \(t\) = 14 mm, '150' refers to the interlayer spacing of the steel plate \(s\) = 150 mm, and '120' refers to the length of the steel plate \(l\) = 120 mm.  
Figure 4: Details and dimensions of specimens. (**a**) Single-layer; (**b**) Double-layer; (**c**) Double-sided welding; (**d**) Single-sided welding; (**e**) Steel bar.
# Effects of Intrinsic Factors on the Stability of Silver Nanowire-Based Flexible Electrodes
###### Abstract  
Silver nanowire (AgNW)-based electrodes are susceptible to unstable behavior under external light illumination, humidity, or heat treatment due to their material and structural limitations. To clarify the correlations between the stability of AgNW-based electrodes and their intrinsic factors, AgNW electrodes with PET (polyethylene terephthalate) substrates and AgNW electrodes with PVA (polyvinyl alcohol) substrates were prepared using vacuum filtration and substrate transfer methods. The effects of the intrinsic parameters of AgNW electrodes, including the bonding strength between AgNPsNWs and flexible substrates, AgNW surface densities, and AgNW network connections on the stability of AgNW-based electrodes were investigated. It was found that the stability of AgNW-based electrodes is strongly dependent on the encapsulation of flexible substrates, the surface density of AgNPs, and the curing strength of AgNW network junctions. PVA-AgNW electrodes have much higher stability than PET-AgNWs because PVA substrates can encapsulate AgNPs and protect AgNPs from external damage. Modifying the surface of AgNW electrodes with graphene oxide (GO) can also significantly improve the thermal and light stability of AgNW electrodes because the covering of GO can strengthen the underlying AgNW connecting junctions.  
silver nanowire-based electrodes (AgNW-based electrodes); flexible substrate; substrate encapsulating; AgNW connecting junctions; thermal stability; light stability +
Footnote †: journal: Applied Sciences
## 3 Results
### Monitoring Results
#### 3.1.2 Crack Measure Points  
Apart from four general measurement points, the results of two crack measuring points are shown in Figure 9. The crack is shown in Figure 2b. In site, the crack was vertical so that two FBG sensors were horizontal with an upper one and a lower one in height. The strain of crack No. 1 seemed just like the general measuring points. However, the strain of crack No. 2 reached 330 \(\mu\epsilon\) which was the maximum of all FBGs. In contrast to other measuring points, during the time from 100 h to 123 h, the strain of crack No. 2 had a sharp increase of about 130 \(\mu\epsilon\) at 0.483 MPa compared to 0.42 MPa. As the pressure inside the PCCV increased, the compression strain in the circumferential direction decreased (see Figure 10). During this period, the prestress loss happened. In the actual engineering, the prestress was conducted to the concrete by the grouting in the prestress duct. However, the prestress was not  
Figure 8: Comparison between the strain of MP 1 and measured temperature. **(a)** MP 1; **(b)** Temperature of each measure point.  
uniform so the crack started to open at the low prestress site. As shown in Figure 9, the strain of crack No. 2 was larger than for crack No. 1 at 0.42 MPa. This meant that the prestress loss at crack No. 2 was larger than that at crack No. 1. Now that the cracks had opened, this meant the prestress was overcome by the inner pressure. Without the prestress constraint, crack No. 2 had larger strain variation due to the prestress loss. It was possible that the strain of crack No. 2 continued to increase. As a result, the PCCV was not tight enough and the leakage might happen there due to increasing inner pressure. Therefore, crack No. 2 should be paid more attention.  
Figure 10: The results of vertical direction strain and circumferential direction strain.  
Figure 9: The strain of two crack measuring points. (**a**) Crack No. 1; (**b**) Crack No. 2.