esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Case Study
### Superimposed Data Comparison  
As mentioned earlier, the data superimposition method was used to identify the differences of the after-construction results. Comparative analysis was performed on the five regions of the facility as shown in Figure 6 (i.e., location 1, 2, 3, 4, and 5) with respect to whether the curvatures of the panels in each region were accurately maintained. Figure 6 shows the three-dimensional comparison of the facility mass. Red-colored areas indicate that the panels in the areas were overall produced and fabricated as designed. On the contrary, the areas in white color include the panels of which shapes and curvatures were more or less different from the as-designed properties.  
Figure 5: Brief process of modeling Haewoojae through laser scanning.  
Figure 6: As-designed and as-built facility mass comparison.  
Table 1 summarizes the differences. Discrepant contents in region 1, 4, 5 took place in the single-curved panels while regions 2 and 3 involved double-curved panels and their contents showed different features from regions 1, 4, and 5. Based on the superimposition comparison, it could be inferred that as the curvature got more complex and bigger, the panel changes during forming and fabrication became bigger. In order to illustrate numerically measuring the difference between the as-built and the as-designed data, spreadsheet application was performed along with one panel in region 2. Figure 7a,b present the measurement results delivering the X, Y, and Z coordinates of a specific point on the as-designed panel (X, Y, Z = 9188, 5788, 7000) and the as-built panel (X, Y, Z = 9188, 5788, 7050).  
The existence of a 50 mm difference of the Z coordinate means that the designated panel at region 2 was constructed 50 mm higher than initially designed, and the panels that were next to the panel also would be twisted from their original coordinates because of this error. This twisting could cause lifting of panels and panel joints, water leakages, and finish materials falling off when fixing the panels to the main or sub-frames.
## 3 Results and Discussion
### X-ray Diffraction (XRD)  
The formation of biosynthesized KKD-ZnO-NPs was confirmed by X-ray diffraction measurements (Figure 6). The diffraction peaks appeared at 20 value of 31.830\({}^{\circ}\), 34,451\({}^{\circ}\), 36.316\({}^{\circ}\), 47.578\({}^{\circ}\), 56.634\({}^{\circ}\), 62.945\({}^{\circ}\), and 68.023\({}^{\circ}\) corresponding to (100), (002), (101), (102), (110), (103), and (112) crystal planes, respectively. The most intense peak at 36.316\({}^{\circ}\) belongs to the (101) orientation, and its position reflects the hexagonal wurtzite structure in the sample [46].  
The Scherrer equation was used to calculate the average crystallite diameter of KKD-ZnO-NPs by taking the crystallite sizes of the three most intense peaks, namely (100), (002), and (101) in accordance with literature and JCPDS data [47; 48; 49], as shown in Table 1.
# Internalization of Phospholipid-Coated Gold Nanoparticles
## 2 Materials and Methods
### Phospholipid-Coated Gold Nanoparticle Preparation  
Phospholipid-coated gold nanoparticles (PCAuNPs) were prepared by a \"three step\" seed growth mediated method described previously [51; 52]. The first two steps of this method included the preparation of two separate solutions; the growth solution and the seed solution. The PCAuNPs were then prepared by mixing these solutions with a weak reducing agent.  
The growth solution was prepared in a 25 mL round bottom flask. First, potassium gold (III) chloride (KAuCl\({}_{4}\)) was dissolved in water to a final concentration of 0.48 mM. This was stirred in the 25 mL round bottom flask for five minutes. Next, 0.114 g of 1-stearoyl-2-oleoyl-sn-glycero-3-phospho-(1\({}^{\prime}\)-rac-glycerol) (sodium salt) (SOPG) phospholipid, was added to the flask. Last, a nitro-benzoxadiazol conjugated SOPG lipid (NBD-SOPG), 1-oleoyl-2-[12-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino]dodecanoyl]-sn-glycero-3-[phospho-rac-(1-glycerol)] (ammonium salt), was added to the flask at a concentration of 8.8% (_w/w_). The solution was then left to stir for an additional thirty minutes.  
The seed solution was prepared in a separate 50 mL round bottom flask. First, KAuCl\({}_{4}\) was dissolved in water to a final concentration of 0.64 mM. This was stirred in the flask for five minutes. Second, 10 mL of a 3.8 mM solution of trisodium citrate (Na\({}_{3}\)C\({}_{6}\)H\({}_{5}\)O\({}_{7}\)) in water was added to the flask and stirred for an additional ten minutes. Last, 3 mL of a 2.9 mM solution of sodium borohydride (NaBH\({}_{4}\)) in water was added to the flask. The solution was then stirred for five minutes to produce gold \"seeds\" between 2-3 nanometers (nm) in size.  
In the final step, 5 mL of growth solution was first stirred in a separate 25 mL round bottom flask. Second, 0.3 mL of the seed solution was added to the flask and stirred for an additional ten minutes. Last, 0.25 mL of a 9.0 mM solution of ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)) in water was added to the flask to produce phospholipid-coated gold nanoparticles (PCAuNPs) of approximately 20 nm in size. To ensure monodispersity, the PCAuNPs were stirred for one hour prior to being placed in a 4 \({}^{\circ}\)C fridge. The PCAuNPs were maintained in the fridge at 4 \({}^{\circ}\)C prior to experimental use.
## 5 Simulation and Results
### Results Sections
The overall prediction accuracy of the federated model is 98.85%, which is much better as compared to the individual training algorithm and the misprediction rate is also very low, which is only 1.15%. The performance of the proposed model can be stated using some other statistical-based parameters, which can be measured as given in the Section 4 and presented in Table 6.  
Table 6 shows that the WFL is achieving the highest classification accuracy (CA), Sensitivity, Specificity, F1-Score, NPV, and minimum misclassification rate (MCR), False Positive Rate (FPR), and False Negative Rate (FNR). Table 7 compares the prediction performance with the existing approaches used in the literature, where many schemes are adopted and proposed based on machine learning and data packet analysis. The most common performance evaluation parameter is classification accuracy, and Table 7 clearly shows our proposed system's remarkable achievement. Additionally, the proposed scheme can support multiple organizations in distributed mechanism, and a lightweight data transfer is required to share the locally trained weights to a federated server, which can ultimately enhance the capability of the system.
# Modulation Decoding Based on K-Means Algorithm for Bit-Patterned Media Recording
## 3 Proposed Modulation Decoding Scheme
### Modulation Decoding Schemes Based on K-Means Algorithm  
For modulation decoding, the received sequence \(\ell\) must be decoded to a codeword among all codewords by the decoding algorithm. Since this process is similar to the clustering of the K-means algorithm which assigns similar instances to the corresponding clusters, we exploit the K-means algorithm for modulation decoding.  
In the proposed modulation scheme, conventional modulation encoding is used in the same way, but the proposed modulation decoding is based on the K-means algorithm instead of Euclidean distance. Algorithm 1 shows the K-means algorithm. The received sequence \(\hat{c}\) is used as the input sequence for the algorithm. Before the algorithm is exploited, setting initialization for the number of clusters and centroids helps converge to the right solution. Since we use the 4/6 modulation code, we can set the number of clusters to 16 (the number of codewords) and centroids to [+1, +1, -1, -1, -1, -1], [-1, -1, +1, +1, +1],..., [+1, +1, +1, +1, +1], [-1, -1, -1, -1, -1] (the codewords). After the algorithms allocate the received sequence to the closest cluster, the decoder determines the decoded data sequence corresponding to the cluster and implements one-to-one demapping.  
For example, when the user data sequence \(\mathbf{a}\) is [1, 1, 0, 1], the modulation encoder encodes the user data sequence to the coded data sequence \(\mathbf{c}^{13}=\) [+1, -1, +1, +1, -1, +1] by using a look-up table and one-to-one mapping. After passing through the BPMR channel, the readback signal is equalized and detected. When the received sequence \(\hat{c}\) is [+3.68, -2.22, +3.01, +2.74, -2.46, +3.61] from the SOVA detector and it is used as the input sequence for the modulation decoder based on the K-means algorithm, the decoder assigns the received sequence to the index 13 in decimal. Then, since the index 13 represents \(\mathbf{c}^{13}\), it is converted to \(\hat{a}=\) [1, 1, 0, 1] by the one-to-one demapping.  
```
Input (received sequence): \(\mathbb{C}=\{\hat{c}_{1},\ \hat{c}_{2},\ \ldots,\ \hat{c}_{n}\}\) (\(n\) is the number of received sequences) The number of clusters: \(k\) Centroid: \(\mathbf{\mu}=\{\mu_{1},\ \mu_{2},\ \ldots,\ \mu_{k}\}\) While (true) For (\(i=1\) to \(n\)) Assign each received sequence \(\hat{c}_{i}\) to the nearest cluster For (\(j=1\) to \(k\)) Recalculate centroids \(\mu_{j}\) for observations assigned to each cluster
```  
**Algorithm 1** K-Means Algorithm
Sensors_**2012**, _12_, 13508-13544; doi:10.3390/s121013508  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Fundamental Lifetime Mechanisms in Routing Protocols for Wireless Sensor Networks: A Survey and Open Issues  
**[PERSON] * and [PERSON]**  
Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM),  
Johr 81310, Malaysia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +60-17-728-7875; Fax: +60-07-556-5044.  
Received: 22 May 2012; in revised form: 13 August 2012 / Accepted: 28 September 2012 /  
Published: 9 October 2012  
**Abstract:** Wireless sensor networks basically consist of low cost sensor nodes which collect data from environment and relay them to a sink, where they will be subsequently processed. Since wireless nodes are severely power-constrained, the major concern is how to conserve the nodes' energy so that network lifetime can be extended significantly. Employing one static sink can rapidly exhaust the energy of sink neighbors. Furthermore, using a non-optimal single path together with a maximum transmission power level may quickly deplete the energy of individual nodes on the route. This all results in unbalanced energy consumption through the sensor field, and hence a negative effect on the network lifetime. In this paper, we present a comprehensive taxonomy of the various mechanisms applied for increasing the network lifetime. These techniques, whether in the routing or cross-layer area, fall within the following types: multi-sink, mobile sink, multi-path, power control and bio-inspired algorithms, depending on the protocol operation. In this taxonomy, special attention has been devoted to the multi-sink, power control and bio-inspired algorithms, which have not yet received much consideration in the literature. Moreover, each class covers a variety of the state-of-the-art protocols, which should provide ideas for potential future works. Finally, we compare these mechanisms and discuss open research issues.  
**Keywords:** wireless sensor networks (WSNs); network lifetime; energy-efficiency; multi-path; multi-sink; mobile sink; power control; bio-inspired protocols
# Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line
## 2 Literature Review
### Coordinated Feeder Bus Transit Systems (CFBT)  
The issue of integrating trunk service and its feeder bus service has been considered in many studies [38; 40; 41; 42]. [PERSON] and [PERSON] [43] conducted the first study in this regard by considering metro rail as a trunk service; however, they did not consider the coordination of feeder bus and mass transit systems. More studies tried to develop this model, but most of them assumed that there are always enough vehicles to respond to the passengers' demand. Therefore, the two issues of relocating vehicles and waiting times of passengers due to possible queues have been neglected in most of the past studies. Also, considering a specific case when the headway of coordinated mass transit is too short was one of the main weaknesses of the past studies. Although these reviewed studies have provided useful results that can minimize passenger or operator costs, there are still limitations in implementing these approaches in urban and suburban areas. The current study has two main innovations that distinguish it from previous studies. The first is considering the benefits of the user and the operator of a short headway transit service simultaneously in a model. Most of the reviewed studies considered increased operator revenues by scheduling vehicles on optimal routes even though individual passengers' travel time and traveler preferences are important variables that can change travel behavior, specifically when it comes to the use of a mass transit system with short headway. The second is considering the relocation of feeder buses between train stations while the headway of trains is short. There is little knowledge about considering the relocation of fleet service despite the fact that in short-headway and high-demand conditions, fleet relocation might be required. Table 1 shows a summary of selected studies related to designing routing algorithms for demand-responsive feeder transit services.
## 4 Discussion
### Potential Applications and Limitations  
This study comprehensively examined various climatic and vegetation factors and employed multiple trend detection and factor attribution methods to analyze WUEc trends in Xinjiang from 2002 to 2022 and identify key drivers. However, several uncertainties remain. Although WUEc is defined as the ratio of GPPc to Etc, this definition neglects additional water-consuming processes relevant to agricultural production, including runoff, surface evaporation, and deep percolation. MODIS-based WUE estimates are generally reliable--validated using flux-tower data with R\({}^{2}\) values ranging from 0.74 to 0.963--but systematic biases of 10-20% remain, particularly in drip-irrigated cotton fields under much [77]. While this study emphasized major climatic (temperature, precipitation, VPD) and vegetation (LAI, EVI) drivers, WUEc is also affected by stomatal conductance, phenology, drought stress, and CO\({}_{2}\) fertilization. Future research should incorporate direct monitoring of hydrological losses and expand flux-tower networks in drylands to enhance model calibration. Additionally, further exploration of factors influencing WUEc is essential to support the sustainable development of agricultural ecosystems in arid regions.
# Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading
## 5 Conclusions  
In previous research, various models have been devised to predict the power output of basic modules. Additionally, several methods have been proposed to enhance energy production efficiency. This study proposes a model that enables accurate power prediction under partial shading conditions using an artificial neural network and improves the power-generation efficiency using bypass diodes. For photovoltaic systems, which are sensitive to temperature and irradiation, five parameters are defined as the functions of temperature and irradiation and are employed to train an artificial neural network to enable accurate power predictions. The proposed model allows for accurate predictions while increasing the power-generation efficiency under partial shade conditions. Using the proposed model, it is possible to adjust the values of various parameters used in the process of manufacturing solar panels and make more efficient solar panels. Additionally, it is possible to predict power production by applying various environmental variables related to the environment where the solar panels will be installed, allowing the determination of an optimal installation location. In this study, the artificial neural network was trained to learn five key parameters observed in SPICE. However, since various variables, including temperature and irradiation, also affect the system, future research can enable more accurate power predictions by incorporating a wider range of variables into the training process.  
Conceptualization, Y.L. and J.L.; methodology, J.L.; software, Y.L.; validation, Y.L. and J.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and J.L.; visualization, Y.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work is funded by a 2023 research Grant from Sangmyung University.  
Not applicable.  
The data present in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Results
### Simulation Results
#### 4.1.2 CoMTUS Discontinuous Effective Aperture  
Figure 5 shows the simulated PSF and lesion images from the same non-aberrating medium and for increasing the effective aperture and gap of the CoMTUS system. The control case (one probe) is used for comparison (Figure 5a,f). It can be seen that the PSF depends on the size of the effective aperture and the gap between the arrays, and its shape is dictated by the position of the arrays. A similar shape can be observed in the speckle of the lesion images (Figure 5g-j), where the texture shows the same trend as the point target images (Figure 5b-e). Note that a more conventional PSF shape, where the best spatial resolution is aligned with the x-axis, could be obtained by defining the global coordinate system of CoMTUS images at the centre of the effective aperture created by the two arrays [10]. The corresponding lateral PSFs extracted from the point target images are shown in Figure 5a, where it is possible to quantify the width of the main lobe and the amplitude of the side lobes. As expected, the central lobe of the PSF reduces in width with the increase in size of the effective aperture, and so does the speckle size. However, while at extended apertures, the width of the main lobe decreases, the amplitude of the side lobes increases with the corresponding gap in the aperture, affecting the contrast, as can be seen in the lesion images (Figure 5g-j).  
Figure 5: Simulated point targets (upper row) and lesion images (lower row) of the control medium obtained by one probe (**a,f**) and CoMTUS (**b–e,g-j**) with different effective apertures (Ef) and gaps between the two arrays (Gap).  
The corresponding computed image quality metrics (LR, contrast and CNR) as a function of the obtained effective aperture are compared with the one probe system in Figure 6. Results show that both the main lobe of the PSF and the LR decrease with a larger effective aperture size. Since an increasing effective aperture represents also a larger gap between the probes, contrast and resolution follow opposite trends. In general, comparing with the one probe system, CoMTUS produces the best lateral resolution in all the cases, but shows degradation in contrast with increasing probe separation. At the minimum separation, CoMTUS produces an improvement in resolution compared to one probe of more than 50% (0.70 mm vs. 1.78 mm) combined with an improvement in contrast and CNR (\(-17.23\) dB vs. \(-16.78\) dB and 0.854 vs. 0.846, respectively). At the maximum effective aperture simulated, resolution is the best with 0.34 mm, while the contrast and CNR drop to a minimum of \(-15.51\) dB and 0.82, respectively.
# Notes on a New Structure of Active Noise Control Systems
## 5 Conclusions  
In this paper, a new approach to the design of active noise control systems was proposed. It resulted in a new control system structure that is a classical control system equipped with an additional discrete-time filter placed in the error signal measurement path to estimate signal values at the input of this path. The estimated signal values were used for the tuning of a corresponding adaptive filter. The presented discussion was illustrated by simulation case studies that showed the effectiveness of the proposed new active noise control system structure. Its application resulted in faster adaptation and greater unwanted noise attenuation after the initial adaptation phase, regardless of the dynamical properties of the active noise control system plant and spectral properties of the noise attenuated. Although the presented discussion was illustrated by simulation case studies in which a feedforward active noise control systems was applied to create local zones of quiet, it is worth emphasizing that the presented idea may also be applied for other adaptive and non-adaptive active noise control systems as well as other predictors and control systems. It should also be mentioned that the presented idea is also valid for active noise control systems, predictors, and control systems working with a random sampling interval [36].  
Conceptualization, J.F.; methodology, J.F. and M.I.M.; software, M.I.M.; validation, J.F. and M.I.M.; formal analysis, J.F.; investigation, J.F. and M.I.M.; resources, J.F. and M.I.M.; data curation, J.F. and M.I.M.; writing--original draft preparation, J.F. and M.I.M.; writing--review and editing, J.F. and M.I.M.; visualization, J.F. and M.I.M.; supervision, J.F. All authors have read and agreed to the published version of the manuscript.  
The research reported in this paper has been supported by State Budget for Science, Poland: Silesian University of Technology grant number BK/275/RAU2/2020.  
The authors declare no conflict of interest.  
Figure 21: Estimated power spectral densities of the discrete-time signals \(d(i)\) and \(e_{0}(i)\) at the error microphone—the classical FxLMS and new FeFxLMS control system structures with \(F_{est}(z^{-1})\) and \(F_{est1}(z^{-1})\).