esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Studies
### Brief History and Location of the Structures Taken under Investigation
#### 4.1.2 San Cono Bridge  
San Cono bridge spans the Bianco river located in the municipality of Buccino, in southern Italy (Figure 3a,b). As reported by the inscription on the bridge, the construction of San Cono bridge can be dated to the Augustan age (Figure 3c).  
Originally, the bridge had a pronounced donkey-back profile with two shoulders and a steep slope at the ends and a pylon with a triangular rostrum [20]. Now, the current shape of the bridge is incorporated into a new bridge, which in 1872 levelled the road and widened the site (taking it from 3.20 m to 6.45 m), covering it, so as to leave only the original arches visible, below the new ones. In this way, the intervention represented an exceptional example of respect for the ancient monument. As for the bridge architecture, it has two spans of unequal light, for a total length of 40 m. Part of the ancient arches can still be seen below the nineteenth-century one, which changes its profile. The central round arch has a light of 17.3 m and at the base there are five projecting brackets with three others at a higher altitude to complete the support of the rib; the minor arc has a light of 5.9 m with three shelves.  
The original vestments of the typani were in square work; today they are inserted in the new 19 th-century vestments, with an upper parquet that modifies the original donkey back profile [20] (Figure 3d).  
Figure 3: San Cono bridge: location (**a**,**b**) and panoramic images of masonry bridge (**c**,**d**).
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis  
This section presents the simulation setup and evaluation results of the proposed EWS-16T cell, implemented using a 90 nm CMOS technology. All simulations were performed with an operating voltage of 1.0 V and an ambient temperature of 27 \({}^{\circ}\)C. Identical simulation conditions were applied to all evaluated cells to ensure fairness in comparison. The cell is composed of 16 transistors, and the transistor sizing is carefully optimized for enhanced write performance and power efficiency. Transistors P1-P6 and N1, N2, N4, and N5 are set to the minimum width to achieve a compact cell area. Pulldown transistors N3 and N6, which are connected to internal nodes, are sized to 1.8 times the width of their respective access transistors. Access transistors N7 and N8 are sized to 1.08 times the minimum width. Furthermore, the PMOS pull-up transistors P7 and P8, which are connected to the storage nodes, are set to 1.7 times the width of the access transistors. This sizing helps reduce the write access time and improve the charging path, thereby enhancing overall write performance.  
This study presents experimental results for the read delay, write delay, WWTV, hold power, read static noise margin (RSNM), and hold static noise margin (HSNM) of the proposed EWS-16T cell. The results are compared with those of six previously reported radiation-hardened SRAM cells: QCCS [8], SCCS [8], SAW16T [9], HP16T [10], RHSCC16T [11], and S8P8N [12]. The transistor sizes of the comparison cells were adopted from the values reported in the corresponding references. For designs without explicitly specified dimensions, reasonable estimates were applied to ensure fair and consistent experimental conditions. Figures 6-11 present the bar graphs in the order of QCCS, SCCS, SAW16T, HP16T, RHSCC16T, S8P8N, and EWS16T from left to right.  
Figure 6: Write access time comparison for different SRAM cells.  
Figure 8: RAT comparison for different SRAM cells.  
Figure 7: WWTV comparison for different SRAM cells.  
Figure 10: HSNM comparison for different SRAM cells.  
Figure 9: RSNM comparison for different SRAM cells.
## 3 The Principle of CEEMDAN-WP and RDT
### The CEEMDAN-WP Model  
The combined CEEMDAN-WP model has comprehensive advantages over both the individual CEEMDAN and WP methods. Its specific steps may be presented as follows.  
1. CEEMDAN is employed for decomposition to obtain a series of IMFs.
2. Because of the existence of background noise, some IMF components are noise dominated. Reconstruct signals after removing the components which are noise dominated.
3. A three-level WP is used to decompose the signal obtained in Step 2.
4. Determine the classic wavelet basis.
5. Select proper thresholds and quantify the decomposed coefficients.
6. Reconstruct the signal and export.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 4 Conclusions  
In this paper, we proposed a multiple-attention LSTM (MA-LSTM)-based TEC prediction model, and applied it to the TEC prediction of eight locations--Beijing, Guangzhou, Harbin, Tibet, Yushu, Ziyang, Hangzhou and Heze in China. We compared our model in this paper with LSTM, GRU and Att-BiGRU. The experimental results show that the MA-LSTM model in this paper is obviously superior to the comparison models. We also discussed the prediction performance of our model in different month. Results show that our MA-LSTM model has the highest prediction performance in July, August and September, with the R-square reaching above 0.99. In March, April and May, the prediction  
Figure 11: Comparison of the predictive performance of a (**a**) magnetic quiet day and a (**b**) magnetic storm day.  
Figure 10: Histograms of absolute error distribution during (**a**) magnetic quiet periods and (**b**) magnetic storm periods.  
performance is slightly poor, but even at the worst time, the R-square still reaches 0.965. We further discussed the prediction effect during a magnetic quiet period and a magnetic storm period. The results show that the prediction effect of a magnetic quiet period is slightly better than that of magnetic storm.  
The author will study the grid prediction of TEC at multiple time points in the future.  
Writing--review and editing, H.L.; writing--original draft and methodology, D.L.; validation and resources, J.Y.; data curation, G.Y.; investigation, C.C.; software, Y.W.; project administration, W.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Self funded scientific research and development program of Langfang Science and Technology Bureau, China (2022011046).  
Not applicable.  
Publicly available datasets were analyzed in this study. Ionospheric TEC data can be found here: [[https://pan.baidu.com/s/lyojZd9](https://pan.baidu.com/s/lyojZd9) MKWc_COcbK5 Xk_XA]([https://pan.baidu.com/s/lyojZd9](https://pan.baidu.com/s/lyojZd9) MKWc_COcbK5 Xk_XA), accessed on 27 August 2022.  
Thanks to the TEC data provided by the European orbital determination Center (CODE), Thank [PERSON] of the Chinese Academy of Sciences for his help and suggestions.  
The authors declare no conflict of interest.
## 3 Simulated Phased Array with P-CRLH Phase Shifters
### Main Beam at 30\({}^{\circ}\) Off-Broadside  
Using Equation (3), \(\Delta\varnothing=-90^{\circ}\) for main beam location at \(\theta_{0}=30^{\circ}\). This adjacent element phase shift of \(-90^{\circ}\) is achieved with the P-CRLH phase shifter in Section 2.2. Now, the four patch antenna elements in the array are fed with unit cell and cascaded unit cell P-CRLH phase shifters having excitation phases of \(-92.3^{\circ}\), \(-184^{\circ}\), \(-275.5^{\circ}\) and \(-367.4^{\circ}\) (see Figure 7). The far-field radiation pattern at 5.5 GHz is shown in Figure 12. Again, for comparison, the array elements are directly excited with computed phases (\(-90^{\circ}\), \(-180^{\circ}\), \(-270^{\circ}\), \(-360^{\circ}\)) using Equation (3), and the directly excited radiation pattern is also shown in Figure 12. For both the directly and P-CRLH-excited radiation patterns, the location of the main beam is at 28\({}^{\circ}\) with a peak gain of 11.5 dB. The difference in the first sidelobe level is 0.77 dB.  
Figure 11: Simulated main beam at off-broadside \(\theta_{0}=15^{\circ}\) with directly excited and P-CRLH-excited phases.  
Figure 12: Simulated main beam at off-broadside \(\theta_{0}=30^{\circ}\) with directly excited and P-CRLH-excited phases.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background
#### 2.1.7 Access Control Based Blockchain  
Another line of research looked at how to handle the access control and the privacy of EHR sharing on the blockchain when third-party organizations or individuals other than hospitals' or patients' attempts to obtain patient data, patient privacy may be compromised.  
Recently, fine-grained access control of encrypted data can be achieved using many methods such as attribute-based encryption (ABE) and proxy re-encryption (PRE).
* Proxy re-Encryption scheme: a cryptographic-based technique that re-encrypts ciphertext by using a semi-trusted proxy server with a user public key into ciphertext encrypted with another user's public key [6]. A proxy re-encryption scheme ensures access is denied after a specific time frame. In comparison to existing schemes, the proposed protocol ensures fine-grained access control, flexible client revocation, and lower storage and encryption time costs [6]. As shown in Figure 2, [PERSON] sends a message via a semi-trusted proxy server to [PERSON] without the need to share the private key of [PERSON] with any entity while ensuring the message's privacy.
## 2 Spatially Fixed Virtual Sensing Algorithms  
Spatially fixed virtual sensing algorithms are used to obtain estimates of the error signals at a number of spatially fixed virtual locations using the error signals from the remotely located physical error sensors, the control signal and knowledge of the system. These virtual sensing algorithms are then combined with an active noise control algorithm to generate zones of quiet at the fixed virtual locations. A number of spatially fixed virtual sensing algorithms have been developed in the past including _the virtual microphone arrangement_[4], _the remote microphone technique_[5], _the forward difference prediction technique_[6], _the adaptive LMS virtual microphone technique_[7], _the Kalman filtering virtual sensing method_[8] and _the stochastically optimal total diffuse field virtual sensing technique_[9]. A discussion of these algorithms is provided as follows.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 2 Preload and Rigidity of Linear Guide  
### Preload Amount  
Applications of linear guides in the positioning mechanisms often require a proper preload to increase the rigidity of the load-carrying capacity and to eliminate internal clearance between assembled components. The preload is commonly generated by the interference between balls and raceways, which is achieved by loading oversized balls in ball grooves. To prevent any damage to the linear guides, the preload is also limited to values of a percent of the dynamic load capacity, depending on the design and manufacturing in the factory. To facilitate the selection of a linear guide, manufacturers usually define the preload at different levels by fine-tuning the ball diameter, such as light preload (Z0 = 0.0-0.02 C), medium preload (Z1 = 0.05-0.07 C), and heavy preload (Z2 = 0.10-0.12 C), where C is the dynamic rated load of the slide rail [38].  
In this study, the linear guide was designed with four rows of rolling balls in a back-to-back contact configuration (DB configuration), as shown in Figure 1. Details of the specifications are presented in Table 1. Three different preload amounts were generated by inserting the oversized rolling balls into the ball grooves, which induced interface interference in the range of 0-2 \(\upmu\)m for light preloads (Z0), 4-7 \(\upmu\)m for medium preloads (Z1), and 8-10 \(\upmu\)m for heavy preloads (Z2).
# The Application Status and Trends of Machine Vision in Tea Production
## 3 Intelligent Tea Picking  
With the gradual upgrading and development of the global tea industry, tea production, as a labor-intensive industry, is constrained by the scarcity of labor. The development of mechanized tea picking has started to accelerate. Since Japan developed the world's first mobile tea picking machine in 1960, research on mechanized tea picking has become a hot topic and has gradually matured [30]. However, due to the different eating habits and cultural backgrounds of different countries, the mainstream tea-picking research worldwide pursues efficiency and speed, without considering the integrity of buds and leaves. Therefore, traditional mechanical picking and extensive harvesting operations cannot meet the Chinese standards for high-quality tea picking. Famous and high-quality tea relies heavily on manual picking, with a low mechanization rate. The mechanization rate of tea garden management operations in China is less than 10% [31], and the labor force for tea picking operations accounts for over 60% of the total tea garden management labor force. It is the most frequently performed and labor-intensive task in the entire tea production labor force [32]. With the economic development in China, the efficiency and quality requirements for high-quality tea picking are also increasing. As an emerging discipline, machine vision has encouraged more and more scholars to participate in the research of its application in tea picking production.
###### Abstract  
In this study, the technology identification, evaluation, and selection (TIES) method was implemented to explore an optimum design space appropriate for a personal air vehicle (PAV) at the conceptual design stage. A morphological matrix was employed to identify possible alternative configurations and performance targets. The Microsoft Excel add-in IMP, a commercial statistical tool, and a PAV sizing tool developed for this study were used for modelling and simulation. After the screening test, seven design variables having significant impacts on the design were finally chosen, specifically the range, maximum speed, cruise speed, cruise altitude, passengers, takeoff ground roll, and stall speed. Response surface equations (RSEs) were created as a function of the seven design variables. The generated RSEs were then used to perform a Monte Carlo simulation (MCS) to explore a feasible design space. As a result, it was confirmed that all seven design variables can be employed for an optimization process. In addition, k-factor and technology sensitivity analyses were conducted to evaluate applicable technologies quantitatively. Consequently, the selected set includes a flow circulation flap, leading edge blowing, a nanocoating, liquid metal, and an advanced composite material, which are technologies that greatly influenced the target criteria. Furthermore, the target value variations were analyzed as the k factors changed.  
personal air vehicle; PAV; PAV design; aircraft design parameter; design of experiments; k-factor sensitivity analysis +
Footnote †: journal: Applied Sciences
## 4 Materials and Methods
### Tweaked Parameters
First, in wavelet analysis, the selected wavelet type to be used in the decomposition is _db4_, and the selected decomposition level is 5. Second, for higher-order spectra, the selected number of segments for calculating the power spectral density (PSD), _nperse_, is 256, and the number of overlaps between segments, _nooverlap_, is 128. Third, for fractal dimension's Higuchi, the maximum number of intervals, _knax_, is 10, which defines the maximum scaling factor for constructing the needed subsequences. Finally, the performance of the classifiers used, GSVM, CART, LinearSVM, and SVM with a polynomial kernel, is tweaked, and 5-fold cross-validation is applied. First, the GSVM classifier has a C value of 20, an _rbf_ kernel type, a degree of 10, an _auto_ gamma value, a _coef_0 of 0.0, a _tol_ of 0.001, a cache size of 10000, a max iteration of \(-1\), and a decision function of _ovr_. Second, the CART classifier has a max depth of 10, a random state of 1, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Related to** & **Value** \\ \hline svc\_w\_poly\_kernel\_degree & SVM with Polynomial Kernel Classifier & \(10\) \\ svc\_w\_poly\_kernel\_gamma & SVM with Polynomial Kernel Classifier & auto \\ svc\_w\_poly\_kernel\_coef & SVM with Polynomial Kernel Classifier & \(0.0\) \\ svc\_w\_poly\_kernel\_tol & SVM with Polynomial Kernel Classifier & \(0.001\) \\ svc\_w\_poly\_kernel\_cache\_size & SVM with Polynomial Kernel Classifier & \(10000\) \\ svc\_w\_poly\_kernel\_max\_iter & SVM with Polynomial Kernel Classifier & \(-1\) \\ svc\_w\_poly\_kernel\_decision\_fx & SVM with Polynomial Kernel Classifier & ovr \\ gsvm\_c & SVM with RBF Kernel (GSVM) Classifier & 20 \\ gsvm\_kernel\_type & SVM with RBF Kernel (GSVM) Classifier & rbf \\ gsvm\_degree & SVM with RBF Kernel (GSVM) Classifier & 10 \\ gsvm\_gamma & SVM with RBF Kernel (GSVM) Classifier & auto \\ gsvm\_coef & SVM with RBF Kernel (GSVM) Classifier & \(0.0\) \\ gsvm\_tol & SVM with RBF Kernel (GSVM) Classifier & \(0.001\) \\ gsvm\_cache\_size & SVM with RBF Kernel (GSVM) Classifier & \(10000\) \\ gsvm\_max\_iter & SVM with RBF Kernel (GSVM) Classifier & \(-1\) \\ gsvm\_decision\_fx & SVM with RBF Kernel (GSVM) Classifier & ovr \\ fs & Sampling Frequency & 250.0 \\ no\_channels & Number of EEG Channels & 22 \\ no\_subjects & Number of Subjects & 9 \\ no\_classes & Number of Classes & 4 \\ no\_splits & Number of Folds in Cross-Validation & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. and a criterion of _gini_. A _best_ splitter is considered, the minimum sample split is 2, and the minimum sample leaf is 1. Third, LinearSVM has a \(C\) value of 0.1, a scaling interception value of 1, a _hinge_ loss, a maximum iteration of 1000, an _ovr_ multi-classification, a penalty of \(l2\), a random state of 1, and a _tol_ of 0.00001. Lastly, LinearSVM with a polynomial kernel, a \(C\) value of 0.1, a _poly_ kernel type, a kernel degree of 10, an _auto_ gamma, a _coef_0 of 0.0, a tol of 0.001, a cache size of 10000, a maximum iteration of \(-1\), and an _ovr_ decision function.
## 5 Experimental Results and Discussion  
### Datasets Details  
The implementation of the proposed algorithm is done using Matlab. Seventeen data sets from the University of California at Irvine (UCI) machine learning repository [34] has been selected to evaluate the proposed EChOA-SVM approach. Table 1 describes the number of instances, the number of features, and the number of classes of the data set. Seventeen data sets of different sizes and latitudes are selected to observe the performance of the algorithm at different scales.  
All the experimental series are carried out on Matlab R2016a, and the computer is configured as Intel(R) Core (TM) i5-1035G1 CPU @ 1.00 GHz 1.19 GHz, using Microsoft Windows 10 system. Each experiment is run ten times independently to reduce the random influence. In addition, the general parameters are set as follows: the population size is 30, and each run is set to 100 iterations as stopping criteria.  
Figure 2: The flow chart of EChOA-SVM.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Cell Differentiation in Multicellular Bacteria  
C-di-GMP is involved in cell differentiation and multicellular bacteria. Under conditions of nitrogen starvation, the cyanobacterium _Anabena_ forms heterocysts, specialized cells with the function of carrying out oxygen-sensitive nitrogen fixation; the heterocysts supplement nitrogen to the multicellular filaments. This process of heterocyst formation is associated with c-di-GMP because the inactivation of the All2874 protein, which is a GGDEF domain DGC, inhibits this process [44].  
In the case of multicellular bacteria, _Streptomyces coelicolor_ starts its life cycle from the germination of a spore to produce a branched vegetative filament, which eventually forms a network of multinucleate aerial hyphae (mycelium). Overexpression of CdgA, a DGC enzyme, blocks aerial hyphae formation and generates a bald colony phenotype. The c-di-GMP also affects mycelium formation in _S. coelicolor_. Pigmentation and production of the antibiotic actinorhodin are also affected [45].
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 3 Design of System and Structure
### System Architecture and Data Communicating  
As the on-board processing algorithm becomes increasingly complex, the debugging cycle of traditional HDL finite state machines becomes longer, which increases the cost of maintenance. As an alternative, the solution SoPC has been widely used. As shown in Figure 2, the SoPC system has four parts: controlling, computing, arbitrating, and storing.  
For the controlling part, compared with the traditional HDL finite state machine, the Microblaze embedded processor soft core replaces the conventional finite state machine, software programming supersedes hardware programming, and software testing replaces hardware debugging. For a SoPC system, control signals are exchanged through AXI4-Lite, and data is processed through AXI4-Stream and visited by the storage space through the AXI4 [55].  
The computing part realizes InSAR signal processing, containing the major operation units FFT and Cordic. FFT is used to transform data between the time domain and frequency domain, while Cordic is used to calculate the coherence and phase. The Matrix Transposition module is designed to improve the efficiency of accessing range and azimuth data and increase processing bandwidth.  
The arbitration part based on AXI Interconnect consists of CrossBar, which can arbitrate reading/writing data operations between the FIFOs on both sides. The arbitration structure solves the problem caused by the algorithm function and the dual-channel storage structure. In the process of interferogram generation, either chip of DDR3 SDRAM includes both reading and writing operations simultaneously, which cannot be achieved without the arbitration structure. Therefore, CrossBar implements arbitration of reading/writing operations through the Shared-Address Multiple-Data (SAMD) topology to realize equivalent pipeline processing [56].
# Worldwide Coverage Mobile Systems for
## 6 Discussion and Future Trends  
In this section, we summarize the main findings of our investigation on antennas for worldwide coverage satellite communications.  
Traditional satellite antennas are not appropriate for designing the terminals for the future global communications mobile system based on LEO satellite constellations, due to their size factor, i.e., they are not hand/pocket devices.  
Planar antennas have marked the pathway to give a solution to size concerns. Beyond these antennas, SIW technology-based antennas show more appropriate features to be integrated into the future worldwide coverage mobile communications supported by the emerging LEO satellites constellations.  
SIW technology embeds in the substrate different RF devices, including waveguides and slot antennas. It is also useful for feeder, combiner, divider, and lamped elements design.  
Table 24 compares the most significant features of the antennas analysed along with this investigation versus the antenna design proposed by the authors. Some parameters were not provided by designers.  
There are many techniques to design antennas, but few of the mare appropriate to design antennas for terminals to be used for worldwide mobile communications, due to size, weight, and steering capabilities to operate in the Ku-band linking LEO satellites. Conventional antennas, like primary focus, offset, or double focus, are not suitable for personal terminals. Planar antennas are more appropriate than traditional techniques due to the lower weight and smaller size factor, and good performance. SIW technology is providing high-performance antennas and is currently the most promising technology for designing global coverage future mobile terminals.