esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of Pattern Reconfigurable Antenna Based on RF MEMS Switches
## 1 Introduction  
Reconfigurable antennas are antennas that can change their working modes, such as frequency, radiation pattern, polarization, etc., by shifting the states of switches in the antenna elements and arrays, phase shifters, and T/R (transmit/receive) components while keeping the antenna's mechanical structure unchanged. This enables one antenna to achieve the functions of multiple antennas. Compared with fixed beam antennas, reconfigurable antennas can adjust their characteristics according to the actual environmental requirements in real time. Pattern reconfigurable antennas mainly control the radiation structure of the antenna using switches to realize the switching of working modes so that the antenna can complete the pattern reconfiguration in a very short time. Various methods have been proposed to achieve reconfigurability, such as PIN diodes [1], copper strips [2], and others. Among these methods, RF MEMS technology offers some distinctive advantages, such as smaller size, higher quality factors, and higher linearity [3]. Therefore, many reconfigurable antennas based on RF MEMS switches have been reported in the literature [4; 5; 6; 7; 8; 9; 10; 11; 12]. Nevertheless, most of these antennas operate at low frequencies and lack process consistency, as the RF MEMS switches are added to the circuitry after the antenna has been manufactured, rather than being integrated into the same production process as the antenna patch. This leads to poor process consistency and increased complexity and cost. References [13; 14; 15; 16; 17] used devices such as PIN diodes and Varactor diodes to reconfigure the antenna pattern. These antennas have large reconfigurable angles of the pattern but their length and width are generally above 10 mm and therefore cannot adapt to the trend of device miniaturization.  
In this paper, we introduce a new design for a pattern reconfigurable patch antenna that is specifically tailored for Ka-band applications. Our design incorporates RF MEMS switches, which allow for a wider working range and higher throughput compared to other bands. This makes our antenna an ideal candidate for applications such as 5G mobile communication and satellite communication systems, where there is a need for wider spectrum resources to achieve efficient data transmission.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 4 Depth Adjustment Based Target Tracking in UWSNs  
In this section, depth adjustment based tracking scheme is proposed. The relation between Fisher information matrix (FIM) and nodes' depth is employed to determine the optimal depth, and an improved harmony search algorithm is utilized to solve the problem and improve searching speed.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 2 Deformation Analysis Method
### Verification with Model Test
#### 2.3.2 Data Processing  
The strains were obtained by static strain gauges DH3815 and corresponding system DH3815N. The range of strain gauge is \(\pm\)20,000 \(\upmu\varepsilon\), and the highest resolution is 1 \(\upmu\varepsilon\). The pore pressure was continuously monitored by DAQLab 2005. A horizontal displacement sensor was arranged at the top strain gauge to record the horizontal displacement at mudline. The model of horizontal displacement sensor is MS-50, and the accuracy is 1% mm.  
The loading process was applied in six stages by weights, and each level was 8.3 N, 21.05 N, 33.8 N, 46.55 N, 59.3 N, and 72.05 N. The interval between each load level is 1 h. With the measured strains, the bending moment curve can be calculated as follows:  
\[M(z)=\frac{\varepsilon EI}{a} \tag{7}\]  
where \(\varepsilon\): measured strains and \(a\): outer diameter.  
The displacement can be obtained by integrating Equation (7):  
\[y(z)=\frac{1}{EI}\iint M(z)dz^{2} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \
\begin{tabular}{c} **Cohesion \(c\)** \\ **(kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Friction** \\ **Angle \(\varphi\)** \\ **(\(\mathbf{\text{\text{\text{\text{\text{\text
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 6 Integration Mechanism for Fault Prediction and Scheduling Optimization
### Feedback Loop Mechanism  
The interaction between the deep learning model and the SMT solver operates iteratively. Real-time machine state data, denoted as \(S(t)\), capture variables such as temperature (\(T(M_{i},t)\)), vibration (\(V(M_{i},t)\)), operating hours (\(H(M_{i},t)\)), and tool wear (\(W(M_{i},t)\)). These states are continuously monitored and updated to provide a snapshot of the current operational conditions of the machines.  
* Fault Probability Prediction: The deep learning model predicts fault probabilities \(P(M_{i},J_{j},t)\) for each machine \(M_{i}\) based on real-time machine state data \(S(t)\), which include parameters such as temperature, vibration, tool wear, and operating hours. These predictions are periodically updated as machine states evolve during production.
* Schedule Optimization: The predicted fault probabilities \(P(M_{i},J_{j},t)\) are provided to the SMT solver, which optimizes the scheduling of jobs by minimizing the total fault risk while ensuring that all jobs are completed within their deadlines and that machines are utilized efficiently.
* Continuous Update and Real-Time Adaptation: As new machine state data become available (either after job completions or at regular intervals), the deep learning model recalculates \(P(M_{i},J_{j},t)\); these updated predictions trigger the SMT solver to re-optimize the job schedule based on the latest conditions, enabling continuous real-time adaptation of job scheduling and fault risk mitigation.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 3 Constraints of Stiffeners  
### Constraint of Volume  
In order to ensure that the optimization meets practical volume or mass requirements, material volume constraint for stiffeners is defined as  
\[V=\sum_{e=1}^{n}\bar{\rho}(\mathbf{x}_{e})h_{s}A_{e}\leq\overline{V} \tag{13}\]  
where \(V\) denotes the total volume of stiffeners; \(h_{s}\) is the thickness of stiffener; \(A_{e}\) denotes the area of the \(e\)-th element; and \(\overline{V}\) stands for the maximum allowable volume of stiffeners.
###### Abstract  
Ensuring secure data transmission in agrotechnical monitoring systems using unmanned aerial vehicles (UAVs) is critical due to increasing cyber threats, particularly with the advent of quantum computing. This study proposes the integration of Quantum Key Distribution (QKD), based on the BB84 protocol, as a secure key management mechanism to enhance data security in UAV-based geographic information systems (GIS) for monitoring agricultural fields and forest fires. QKD is not an encryption algorithm but a secure key distribution protocol that provides information-theoretic security by leveraging the principles of quantum mechanics. Rather than replacing traditional encryption methods, QKD complements them by ensuring the secure generation and distribution of encryption keys, while AES-128 is employed for efficient data encryption. The QKD framework is optimized for real-time operations through adaptive key generation and energy-efficient hardware, alongside Lempel-Ziv-Welch (LZW) compression to improve the bandwidth efficiency. The simulation results demonstrate that the proposed system achieves secure key generation rates up to 50 Mbps with minimal computational overhead, maintaining reliability even under adverse environmental conditions. This hybrid approach significantly improves data resilience against both quantum and classical cyber-attacks, offering a comprehensive and robust solution for secure agrotechnical data transmission.  
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# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 5 Field Test of the Micro-Phasor Measurement System and Database Update for DMMS  
The phasor of the distribution transformers recorded in the DMMS database is important for three-phase power flow analysis in the distribution systems that determine if the systems are balanced or unbalanced. An excessive phasor inconsistency between DMMS and on-site distribution transformers hinders power flow analysis and impedes the improvement of unbalanced currents in a three-phase system. Consequently, the problem of relatively high unbalanced current cannot be effectively addressed. The phasor attributes of distribution transformers in the DMMS database is frequently inconsistent with on-site measurement. Consequently, three-phase imbalances grow increasingly severe.  
Table 1 presents the phasor identification results of the distribution room coordinate EC00 in Kaohsiung Meishuguan area. The distribution room comprises one three-phase transformer and seven single-phase transformers. The measurement results indicate that the accuracy rate of the phasor is 40%. After the field test, phasor identification of 102 distribution transformers from 15 selected underground distribution rooms in Kaohsiung Meishuguan area were applied. The measurement results were compared with the DMMS to determine the accuracy rate of the transformer phasor. The results suggest that the rate of phase inconsistency between DMMS and on-site identification results was exceptionally high. Furthermore, 73 of the distribution transformers were identified as exhibiting an inconsistent phase to that registered in the DMMS and 29 were marked correctly, yielding an accuracy rate of 28.43%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Voltage Angle**} & \multicolumn{2}{c}{**Phasor**} & \multirow{2}{*}{**Error**} \\ \cline{2-2} \cline{4-6}  & **Reference** & **On-Site** & **DMMS** & & **Measurement** \\ \hline \multirow{3}{*}{T01} & 68.76 & 69.03 & R & T & 0.27 \\ \cline{2-6}  & 132.12 & 131.46 & T & R & 0.66 \\ \cline{2-6}  & 288.29 & 286.7 & S & S & 1.59 \\ \hline T02 & 290.74 & 289.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 2 Calculation of 2D Rayleigh Wave Phase and Z/H-Ratio Kernel
### Sensitivity Calculation and Analysis
Figure 3 shows the reduced 1D kernels at periods of 10 s, 20 s, 30 s, and 40 s for the phase (Figure 3a) and Z/H ratio (Figure 3b). The results showed that their sensitivity kernels were different but complementary. The Rayleigh wave phase was more sensitive to the deeper structure than the Z/H ratio at the same period, while the Z/H ratio had a strong sensitivity to the shallow structure (e.g., the sedimentary layers). Moreover, the phase-sensitivity kernel was much broader than the Z/H-ratio kernel for the same period, which meant the phase information could be used to solve the smoothed velocity (background model), while the Z/H ratio could deal with the lateral heterogeneity (e.g., velocity anomaly or undulated interface) better. The effect of the velocity anomaly on the Z/H ratio was obvious and easily reachable from Figure 2. Here, we designed another experiment to demonstrate the effect of undulated interface on the Z/H-ratio kernels as shown in Figure 4. We chose a typical structure containing interface variation, which followed a co-sinusoidal function with a maximum amplitude of 5.0 km. The results showed that the Z/H-ratio sensitivity was significantly affected by the undulated interface, and the maximum difference between the undulated interface and the flat interface was about 50%. The experiments described above suggested that the Z/H-ratio kernel was much more sensitive to the model with velocity anomalies and interface variations. This meant that the Z/H ratio could help us to constrain a shallow crust with a complex lateral heterogeneity in which the surface-wave phase cannot be resolved, as well as to improve the accuracy of the inversion. Figure 3: The laterally summed 1D sensitivity kernels to S-wave velocity in depth for (**a**) phase velocity and (**b**) Z/H ratio at four different periods: 10 s (blue line), 20 s (red line), 30 s (green line), and 40 s (black line).
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 2 Multiwavelength Laser Attenuation Characteristics Test in Water-Filled Karst Caves
### Definition of Laser Attenuation Coefficient  
Here, we presume a collimated laser with a wavelength of \(\lambda\) and radiation flux of \(I(\lambda)\) enters into water perpendicular to the glass. During its transmission in the water simulator, if the transmission distance is d\(l\) and the radiation flux loss caused by water scattering and absorption is d\(l(\lambda)\), then the water attenuation coefficient is defined as:  
\[C(\lambda)=-\frac{\text{d}I(\lambda)}{I(\lambda)\text{d}l} \tag{1}\]  
Thus, the radiation flux of transmission distance \(l\) of the laser in the water can be denoted as:  
\[I(\lambda,I)=I(\lambda,0)\exp[-\int\limits_{0}^{l}C(\lambda)\text{d}l] \tag{2}\]  
Presuming the attenuation coefficient remains constant during the transmission, Equation (2) can be modified as:  
\[I(\lambda,I)=I(\lambda,0)\exp[-C(\lambda)\text{d}l]=I(\lambda,0)\exp[-l/l_{0}( \lambda)] \tag{3}\]  
in which \(l_{0}(\lambda)\) is also named the attenuation length.  
In this test system, the laser first entered perpendicularly into and through the dry analog transparent box, and the laser intensity \(I_{1}\) and \(I_{2}\) detected initially and after the attenuation by the laser power meter were recorded. The splitting ratio of the beam splitting prism was set to \(K_{1}\), and the attenuation rate of the glass before and after passing the transparent box was \(K_{2}\). Under the condition of ensuring the vertical incidence of the laser, assuming that the transmittance between the glass and the air interface remains unchanged, the laser intensity after passing through the transparent box is:  
\[I_{2}=I_{1}K_{1}K_{2}{}^{2}T_{1}{}^{4} \tag{4}\]  
When the laser path remained constant, we filled the transparent box with water, and used the laser power meter to measure the light intensity at the initial stage and after attenuation, which were recorded as \(I_{1^{\prime}}\) and \(I_{2^{\prime}}\), respectively. It was assumed that the transmittance of the laser from the air to the transparent box glass was \(T_{1}\), the transmittance of laser entering the water perpendicularly from the glass was \(T_{2}\), the average attenuation coefficient of the laser in the simulated water in thetransparent box was \(\gamma\), and the length of the analog box was \(l\). The light intensity after passing through the box was:  
\[I_{2}^{\prime}=I_{1}^{\prime}K_{1}K_{2}{}^{2}T_{1}{}^{2}T_{2}{}^{2}e^{-\gamma l} \tag{5}\]  
According to the Fresnel formula, under the condition of perpendicular entry of the laser, \(T=\frac{4n_{1}n_{2}}{(n_{1}+n_{2})^{2}}\), commonly the glass refraction index \(n_{1}=1.4985\), and the water refraction index \(n_{2}=1.3228\). It was assumed that the laser was attenuated to 1/1000 after passing through the dry, transparent analog box. By combining Equations (4) and (5), the average attenuation coefficient was:  
\[\gamma=-\frac{1}{l}\ln\left(\frac{I_{2}^{\prime}/I_{1}^{\prime}}{I_{2}/I_{1}} \times 0.927388\right) \tag{6}\]
## 4 Results
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Development Set**} & \multicolumn{3}{c}{**Evaluation Set**} & \multicolumn{3}{c}{**Reported**} \\  & **EXE** & **ESM** & **ETM** & **EXE** & **ESM** & **ETM** & **EXE** & **ESM** \\ \hline DAIL & PLM & 82.9 (3) & 70.0 (6) & 71.5 (5) & 82.2 (3) & 66.1 (4) & 68.1 (5) & 86.2 (2) & 66.5 (4) \\ DIN & PLM & 81.7 (5) & 60.1 (7) & 64.7 (7) & 81.6 (4) & 60.7 (6) & 64.8 (6) & 85.3 (3) & 60.0 (5) \\ C3 & PLM & 79.8 (7) & 46.9 (8) & 59.8 (8) & 79.5 (5) & 43.9 (7) & 58.5 (7) & 82.3 (4) & - \\ Super & PLM & 86.1 (1) & 72.1 (5) & 75.1 (2) & 85.3 (1) & 65.5 (5) & 70.4 (2) & 87.0 (1) & - \\ \hline R+N & FLM & 82.8 (4) & 80.5 (1) & 74.9 (3) & 78.4 (6) & 70.9 (2) & 70.4 (2) & 79.9 (5) & 72.0 (2) \\ G+P & FLM & 80.1 (6) & 77.1 (3) & 72.3 (4) & - & - & - & 77.6 (6) & 74.0 (1) \\ R+P & FLM & 76.7 (8) & 75.2 (4) & 69.3 (6) & 77.9 (7) & 69.3 (3) & 69.6 (4) & 75.5 (7) & 70.9 (3) \\ CodeS & FLM & 83.5 (2) & 79.4 (2) & 76.6 (1) & 83.0 (2) & 73.7 (1) & 73.2 (1) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance on the Spider dataset in %. Column-wise rankings are indicated in parentheses. The Evaluation Set columns display the results from the model outputs reproduced by us, while the Reported columns show the results on the evaluation set as reported in the respective literature and the leaderboard for those models. likely stems from their lack of optimization for certain SQL features that ESM does not assess (Section 3), causing the incorrect handling of those (e.g., generating random conditions for J0 IN would not have any impact on ESM). These findings highlight the need for more robust evaluation metrics, such as ETM, to facilitate further improvements in Text-to-SQL.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 7 Assessment of the Available Architectures
### Open Research Issues  
Smart Meters are designed to stay in place several years with minimal maintenance. On the other hand, the growth of smart-grid services in the low-voltage part of the network and of smart-home services will reveal new needs. We identify the following research issues: scalability of the communications infrastructure, security, privacy, and interoperability.  
Although PLC is a mature technology, its ability to support uncoordinated frequent transmission by several meters is yet to be proved. The interference pattern in CENELEC bands A and C is also evolving over time as newer devices are plugged into the home electrical system [61]. Similarly, both the LPWAN and the cellular-based IoT communications technologies are at their infancy and current studies have mostly focused on coverage issues. Frequent transmissions by hundreds of meters will likely push the capacity to the limit.  
Cyber-physical security of SMs represents a growing concern of governments and operators, especially for their ability to cut the power supply from remote [62] or to take part in Automated Demand-Response. It is still an open issue how to deploy robust security controls capable of quickly detecting compromised devices and take countermeasures.  
Frequent metering paved the way for high-accuracy Non-Intrusive Load Monitoring. Therefore, the privacy of metering data has become a concern for final customers. So far, the issue of privacy was dealt with at the regulatory level by obliging the DSOs and the retailers to comply with procedures ensuring that data are protected and not made available to third parties without consent. However, the expected growth of third-party services will make it more difficult to prevent data theft or misuse. Therefore, at the European level, there is thriving research on how to provide the customer with higher control of how his/her data are used.  
Finally, if the SM is intended to be integrated into a network comprising a wide range of devices, it is necessary that they expose standard interfaces and data models [16; 63]. At the moment, there are multiple possible choices including Device Language Specification/COmpanion Specification for Energy Metering (DLMS/COSEM) [64], Smart Metering Information and Telecommunication Protocol (SMITP) [27], or Zigbee's Smart Energy Profile [65]. In case the market fails in choosing a single interface, an IHD-GW could implement the functions of protocol translations in order to maximize interoperability.
## 5 Discussion
The SWIR1 band generally indicates the reflection features of main granite components such as quartz and plagioclase, while the absorption is shown in the SWIR2 band mainly due to epidote and muscovite. The granite absorption in the TIR bands was captured by PC2 of the raw ASTER (TIR2) band and PC5 of the stretched Landsat-8 (TIR1) band during the mapping of the Kotah Dome in Malakand region. Absorption due to amphiboles, chlorites, epidote, tourmaline, and muscovite in the SWIR2 band was reported by PC5 of the stretched Landsat-8 and the stretched Sentinel-2 data due to OH/metal-OH [64] generally and Mg-OH combinations, particularly in case of muscovite [4]. Nagarparkar granite was delineated by PC2 of the raw ASTER component-bearing TIR band and PC5 of the stretched Sentinel-2 band representing high reflection for amphibole, quartz, and plagioclase in NIR and SWIR1 bands and relatively lesser reflection in the SWIR2 band due to limonite and muscovite. The importance of the fusion of FPCS components-bearing datasets containing TIR, SWIR, and/or SWIR + TIR bands has been highlighted. One Sentinel-2 and two Landsat-8 components in the case of high-vegetation Shewa Shahbazghari granite; one ASTER with two Sentinel-2 components for mild-vegetation Malakand granite and in the case of non vegetated Nagarparkar one component from the ASTER, Landsat-8, and Sentinel-2 data each were used to map granite. In all cases, fundamental low emissivity was observed in the TIR bands (ASTER TIR2 or Landsat-8 TIR1), and the absorption of the SWIR bands (Sentinel-2 or Landsat-8) by granite accessory minerals bands played a role. Due to the high spatial resolution and absorption of the SWIR2 band by accessory minerals, Sentinel-2-bearing components were useful to fuse selective components from ASTER and/or Landsat-8 datasets containing TIR bands. FPCS can be further used by selecting a limited number of bands from the high-resolution hyperspectral data for mapping dimension stones. The proposed methodology can also be used for data annotation [65] in supervised techniques for lithological mapping. Mineralogy is also used to predict geotechnical properties [38; 66; 67]; therefore, SWIR2 bands/components identifying accessory minerals could play an important role in modelling geotechnical parameters via mineralogy through RS. The proposed mapping strategy can be used as a cheap source for mineral resource modelling combined with shallow seismic geophysics to model the deposit features in three-dimensions.