esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### QCA Gates and Clock Sates  
A QCA cell consists of four quantum dots and is located at each corner of a square. Two electrons repel each other by Coulomb repulsion and exist in two quantum dots located diagonally among the four quantum dots. There are two such cases, and each polarization is expressed as \(P=+1\) or \(P=-1\), and in binary operation, they correspond to \"1\" and \"0\", respectively [38; 39]. Logical operations in the QCA environment are based on a majority vote function in which the result value is the value of two or more of the three inputs and are implemented through a majority gate in the QCA environment. Figure 1a,d show a majority gate and a rotated majority gate with three inputs A, B, and C, respectively. If the polarization of one of these inputs is fixed to \(P=+1\) or \(P=-1\), it is used as a 2-input AND and OR logic gate, respectively, as shown in Figure 1b,c. Another representative logical operation is an Inverter. Figure 1e is a robust NOT gate that makes the output signal stronger, and Figure 1f is a simple NOT gate that can be easily implemented in a small space [40; 41].  
A QCA cell has four clock states, and the circuit operates by repeating changes from clock0 to clock3 [19; 39]. The switch state refers to a state in which the barrier between quantum dots gradually increases as the potential energy of electrons becomes stronger. In the hold state, the potential energy of electrons becomes high enough to prevent them from crossing the barrier between quantum dots. At this time, each cell has a clear polarization and the strongest potential energy. In the release state, electrons gradually lose potential energy, and the barrier between quantum dots gradually decreases. In the relaxed state, the potential energy of the electrons is lowered so that they can freely pass through the barrier between quantum dots, and the cell does not have any polarization. Figure 2 shows a graph of the barrier height and time relationship between quantum dots according to the four states of the QCA cell.  
Figure 1: QCA logic gates: (**a**) 3-input majority gate; (**b**) AND gate; (**c**) OR gate; (**d**) rotated 3-input majority gate; (**e**) robust NOT gate; (**f**) simple NOT gate.
# A Smart Rehabilitation Glove Based on Shape-Memory Alloys for Stroke Recovery
## 2 Design
### Online Control in Mobile App  
As shown in Figure 2, the online real-time monitoring function of the rehabilitation glove provides an efficient and low-cost health assessment solution for patients with hand dysfunction and their doctors. Among them, Figure 2a presents the perspective of the patient-side interface, and Figure 2b shows the perspective of the doctor-side interface. The mobile application employs a Flutter-based three-tier architecture:  
Frontend: Patient interface with mode sliders (low/med/high) and training dashboards; doctor interface for multi-patient real-time monitoring.  
Communication: BLE 5.0 enables 128 kbps encrypted transmission (AES-128, implemented by Nordic Semiconductor, Oslo, Norway), with an end-to-end latency of 120 ms.  
Cloud: Firebase stores anonymized data (SHA-256 encryption, provided by Google LLC, Mountain View, CA, USA) for remote telemedicine syncing.  
For elderly users, the UI features high-contrast color blocks with <2 s anomaly alert response (ISO 13482 compliant [32]). To further address potential accessibility challenges, the system integrates two additional components: (1) a physical tactile button module for direct activation of the rehabilitation glove, enabling operation without a smartphone;  
Figure 1: A schematic diagram of the smart rehabilitation glove. (**a**) The structural components of the rehabilitation glove. (**b**) Details of the amplifier. (**c**) The recovery process of a dysfunctional hand, including (I) the natural state, (II) the intermediate state, and (III) the fully extended state.  
(2) a caregiver-assist mode that supports real-time monitoring and remote management by family members. These multi-faceted measures collectively enhance technological accessibility for elderly users, aligning with best practices in gerontechnology design and ensuring seamless integration of rehabilitation therapy into daily life.  
A data transmission workstation is used to collect signals from patients when using the application. Sensors embedded in the rehabilitation gloves gather various physiological signals that are subsequently processed by the chip, converted into electrical signals, and transmitted to the application via a wireless-transfer module [33]. The data collected on the mobile is constantly uploaded to the cloud server for storage through the network. Meanwhile, the application and cloud server are responsible for the generation, transmission, and storage of the data. Overall, the use of an SMA-based rehabilitation glove in conjunction with an online monitoring application offers several advantages. Firstly, this type of glove is user-friendly to wear, due to the compact and lightweight design of the product. Patients are allowed to select a proper mode to complete their daily training based on their specific condition via the mobile application, which is especially convenient for elderly patients. Moreover, doctors are capable of remotely tracking the patient's recovery progress and receiving timely feedback, which greatly reduces healthcare costs.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 6 Discussion  
The agricultural sector produces massive data that can enhance the sector as a whole and anticipate market needs. This data is either produced by captors in the crops/tanks or collected from commercial activity. In addition, through big data technologies, advanced data analytics platforms are designed to facilitate the process of extracting valuable information from stored data for farmers and researchers [36]. For instance, weather forecasting can be performed based on the collected data from APIs since it is considered a key factor for fish farming. Moreover, it is very interesting for fish farmers to continuously measure tank temperature and Ph, which can be easily retrieved from data. In addition, big data technologies offer huge opportunities for farmers to adopt better fish management by analyzing different stages of fish farming, such as fingering, nutrition, and production time [37].  
Another critical point is to analyze the market information to obtain profit from fish production. It has to be noted that many parameters can constitute market data, such as input cost, price trends, farming cost, demand and supply, and transportation cost. Furthermore, this data may be accessible to other public and private parties [38].  
Given these points, the proposed fish farming data lake architecture is designed to initiate the adoption of a data-driven strategy based on the results of analyzing huge data existing on the data lake. This architecture is designed to collect data existing in different farms with different formats and types. Then, it is directly saved into the RZ without applying any transformation to it, with the aim of forming a solid historical repository of the received data. Following that is the TZ, where lean transformations are applied to make data ready for further analysis. Finally, the AZ is made up of a dedicated layer with the required data for each team.
## 5 Results and Discussion
### VSWT Hybrid WDPS Performance Under Normal Conditions  
In this case, we considered both the ramp and random response (normal conditions) as real-world scenarios for hybrid WDPSs. A gradually increased ramp response applied as a wind speed to the VSWT reflects the increase in wind speed during a windy storm or windy season. However, random signals are applied to the VSWT to reflect wind speed fluctuations. The detailed effect of each response is discussed one by one below.
# Vibration Compensation of the Frequency-Scanning-Interferometry-Based Absolute Ranging System
## 5 Conclusions  
The FSI-based absolute ranging system is very sensitive even to a tiny target's movement. To improve the ranging accuracy in practical applications, the vibration influence on the FSI-based absolute ranging system is profoundly analyzed. Then, from the point of view of system instrumentation and practicability, we propose to use a tunable laser to generate a triangular-wave frequency-modulated signal, and then to process the beat signals of two adjacent scanning periods to produce a signal which is immune to vibration. The proposed method was evaluated by the experiments in the vibration environment and natural environment, and the experimental results showed that the distance spectrum of the beat signal was offset and broadened when the target vibrated. Moreover, even tiny vibration displacements were magnified causing the error in the distance value. When the target was subjected to a sinusoidal vibration with the amplitude of 10 \(\upmu\)m at the frequency of 1 Hz, the standard deviation before and after the vibration compensation was 775 and 12 \(\upmu\)m, respectively. Besides, in a natural environment, after the vibration compensation, the standard deviation of 11 \(\upmu\)m was obtained, which further proved the effectiveness of the proposed method. To summarize, when the laser scanning bandwidth was 1 nm, and the scanning frequency was 100 Hz, the vibration compensation effect was better for the target with a lower vibration velocity and at the vibration frequency lower than 6 Hz.  
Methodology and Writing the Paper, Y.-T.L.; Analysis and Investigation, F.-M.Z.; Experiments, Y.-T.L., X.-H.Q. and F.-M.Z.; Results Optimization, H.P. and C.-Z.S.  
This research was funded by National Key Research and Development Program of China (2018 YFF0212702) and Natural National Science Foundation of China--NSFC (51675380, 51775379).  
The authors declare no conflict of interest.
## 3 Results
### Statistical Validation of Prediction Performance  
To further validate the predictive accuracy and generalization capability of the best-performing CNN1-1 model, a series of statistical validation analyses were conducted.  
A scatter plot of the predicted versus measured _chlorophyll a \(+\) b_ content was generated, and a linear regression line was fitted to visualize their relationship, as shown in Figure 8.  
The R\({}^{2}\) was 0.925, indicating a strong correlation and high predictive accuracy. The regression equation was  
\[Predicted=0.939\times Measured+1.090 \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cross-**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} \\  & **Validation** & & & & \\ \hline \multirow{2}{*}{SVR} & None & 0.131 & 54.862 & 7.407 & 6.084 \\  & 10-Fold & 0.148 & 70.792 & 8.302 & 6.860 \\ \multirow{2}{*}{RFR} & None & 0.879 & 7.619 & 2.760 & 2.162 \\  & 10-Fold & 0.794 & 15.260 & 3.781 & 2.838 \\ \multirow{2}{*}{GBR} & None & 0.871 & 8.140 & 2.853 & 2.117 \\  & 10-Fold & 0.813 & 14.170 & 3.663 & 2.729 \\ \multirow{2}{*}{PLSR} & None & 0.771 & 14.475 & 3.805 & 2.692 \\  & 10-Fold & 0.769 & 17.387 & 4.100 & 3.063 \\ \hline \hline \end{tabular} Note: Results are rounded to three decimal places, and numbers in bold highlight the models with relatively better results.  
\end{table}
Table 8: _Cont._To assess the statistical reliability of the predictions, two one-sample \(t\)-tests were performed. First, the slope was tested against the ideal value of 1. The result (\(t=-1.427\), \(p=0.161\)) showed no significant deviation, indicating that the predicted values are proportional to the measured chlorophyll content. Second, the intercept (bias) was tested against 0, yielding \(t=-0.152\), \(p=0.880\), which suggests no significant systematic bias between the predicted and actual values.  
The SEP was calculated to be 2.199 \(\mathrm{\mu g/cm^{2}}\), reflecting the typical deviation of predicted values from the reference measurements. To evaluate potential overfitting, an \(F\)-test was conducted to compare the variance of SEP with that of the training error. The result (\(F=0.454\), \(p=0.993\)) showed no significant difference, providing statistical evidence that the model generalizes well and is not overfitted to the training data.  
Collectively, these validation results demonstrate that the CNN1-1 model delivers robust predictive performance with high accuracy, minimal bias, and strong generalization ability.
## 3 Simulations and Validations
The separation rate of signals will be larger than 80% when SNR \(>\) 10 dB and more than 10 characteristic parameters are selected. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & **Localization Result of** & **Localization Result of** & **Localization Result of** \\  & \(P_{1}\) **(in Meters)** & \(P_{2}\) **(in Meters)** & \(P_{3}\) **(in Meters)** \\ \hline
50 & (2.06, 6.54) & (2.93, 6.07) & (1.04, 5.95) \\
25 & (2.13, 6.67) & (2.85, 6.12) & (1.07, 6.09) \\
10 & (2.19, 6.73) & (2.79, 6.24) & (0.87, 6.16) \\
5 & (2.27, 6.84) & (2.67, 6.29) & (1.25, 6.19) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Location results of simulated PDs under different signal to noise ratios (SNRs). Figure 4: Fisher-like class separability measure of the simulated signals. (a): Separability measures between signals radiated by \(P_{1}\) and \(P_{2}\). (b): Separability measures between signals radiated by \(P_{1}\) and \(P_{3}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & **\multirow{2}{*}{**\(Q=5\)**} & **\multirow{2}{*}{**\(Q=8\)**} & **\multirow{2}{*}{**\(Q=10\)**} & **\multirow{2}{*}{**\(Q=12\)**} & **\multirow{2}{*}{**\(Q=15\)**} & **\multirow{2}{*}{**\(Q=20\)**} \\ \hline
50 & 92\% & 94\% & 95\% & 95\% & 95  
Furthermore, the separation rate of the algorithm based on selected bispectra will be insensitive with varying number of characteristic parameters when more than 10 characteristic parameters are selected. Therefore, it is easy to determine how many characteristic parameters to choose. Traditional methods for separating multiple PD sources are mainly based on correlation of observed time-domain signals and their power spectra. Therefore, noises of unknown correlation relationships cannot be efficiently suppressed from signals, and it is difficult to separate the signals radiated by different sources. The multiple simulated PD sources were compared with the separation results obtained by the proposed method. However, when PD sources in a substation are close to each other, they cannot be identified by the localization results. The exact location of PD sources cannot be obtained by the algorithms based on time delays if less than four antennas are applied for acquiring signals. In these cases, the problem of separating the UHF signals radiating from different PD sources can be successfully resolved by the proposed algorithm.
## 1 Introduction
This research represents a notable contribution to both technical and domain-specific knowledge, detailed as follows:  
* While many articles have investigated the DIR system, this work stands out as the first to introduce the concept of creating an ensemble of retrievals to achieve high mean average precision. It also provides a framework for developing such ensembles, pushing the boundaries of DIR research. This approach overcomes the limitations of single-perspective DIR systems by combining multiple retrieval methods that consider various characteristics of both query and data images. This ensemble approach leads to more accurate and efficient DIR. This approach assigns higher weights to retrievals with a proven track record of effectiveness, ensuring that the most relevant results are prioritized. * The design of both weak retrieval and strong retrieval, in this study, is innovative and draws inspiration from various fields, including DIR, image retrieval, and information retrieval. These novel retrieval methods contribute to the advancement of the field. The weak and strong retrievals are categorized based on their ability to handle extensive document datasets. Weak retrievals are computationally efficient and suitable for initial retrieval stages, while strong retrievals offer higher precision for more refined retrieval tasks. * A particularly distinctive aspect of this research is its application of DIR to the field of education. This pioneering effort bridges the gap between printed textbooks and digital information, offering a fresh perspective on knowledge dissemination in the educational domain. * Another contribution of this paper is the introduction of two ensemble DIR methods: \"Vote-Based DIR\" and \"The Strong Decision-Based DIR\". It also demonstrated that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content. The remaining sections of the manuscript are structured as follows: Section 2 provides an overview of the background problem and reviews pertinent literature in the field. Section 3 discusses the research methodology employed in this study, elucidating the approach taken for investigation. Section 4 presents the research findings, offering insightsand results gleaned from the study. Section 5 engages in an in-depth discussion of the findings, exploring their implications for future research and addressing the current study's limitations. Finally, Section 6 concludes the paper.
## Materials and Methods
### Research Methods
#### 2.3.2 Indicators Used in the Verification of PT-JPL Model Results  
For data validation, we selected the statistics of deviation Bias, RMSE (root mean square error) and intercepted to validate the results of our simulations [19; 31]. The deviation Bias responds to the degree of deviation from the average of the two sets of data. It is calculated by the following equation [32]:  
\[\textit{Bias}=\frac{1}{n}\sum_{i=1}^{n}\left(\theta_{i}^{sim}-\theta_{i}^{obs}\right) \tag{21}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left[\theta_{i}^{obs}-\theta_{i}^{sim} \right]^{2}} \tag{22}\]  
In these equations, \(n\) is the number of samples, \(\theta_{i}^{sim}\) is the simulated value of the model, \(\theta_{i}^{obs}\) is the measured value, \(\textit{Bias}<0\) is the cold bias, and \(\textit{Bias}>0\) is the warm bias [33].  
We use the Pearson correlation coefficient \(R\) to indicate the degree of correlation between the PT-JPL simulation results and the MODIS ET, as well as the multi-year scale water balance ET, with values between \(-1\) and \(1\). When \(R=1\), it means that the trends in the two types of data are exactly the same [34]. The calculation equation is as follows:  
\[R=\frac{\sum(O_{i}-\overline{O})\left(M_{i}-\overline{M}\right)}{\sqrt{\sum( O_{i}-\overline{O})^{2}\sum(M_{i}-\overline{M})^{2}}} \tag{23}\]  
In this equation, \(O_{i}\) represents the measured ET data, \(\overline{O}\) is the mean value of the measured ET data, \(M_{i}\) is the ET of the regionally averaged PT-JPL simulation results, and \(\overline{M}\) is the simulation results' mean value. Larger values for \(R\) indicate that the method is able to simulate its trends [35]. For the integration of long-term sequence data, we also use the 2D Smoother APP of Origin2024b software, specifically using the linear fitting function in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{k_{1}}\) & \(\mathbf{k_{2}}\) & **beta (\(\mathbf{\beta}\))** \\ \hline forest & 0.57 & 0.81 & 1.28 \\ shrub & 0.56 & 0.91 & 1.17 \\ cropland & 0.59 & 0.84 & 1.43 \\ grassland & 0.59 & 0.8 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values.  
the Trendline part of this APP, which directly helped us to obtain the linear fitting equation and R\({}^{2}\) of long-term sequence data.
## 1 Introduction
First, a RBFNN approach is employed, in order to address the unknown disturbances. Second, the disturbances in the position and pose control input are suppressed with the modified neural network-based methodology. Third, the stability of the proposed control approach is proven through Lyapunov stability analysis. Finally, the RBFNN-based nonlinear controller is verified by real-time outdoor experiments. The main three contributions of this paper are as follows:  
* The design of a novel elastomer landing gear is achieved. It guarantees safe landing on a convex surface with diameter smaller than the quadrotor body length through a conventional vertical landing strategy. * A neural network-based backstepping technique is adopted to meet the desired vertical landing requirement, and soft landing is achieved using the proposed controller with near-zero landing speed. The stability of the landing control system is proved through the Lyapunov approach and the backstepping technique. * The proposed platform is challenged in a real flight outdoor scene, which validates the controller's effectiveness and robustness. This paper is organized as follows. Section 2 introduces the modeling of the UAV and soft elastomer landing gear. In Section 3, we introduce the landing control algorithm and stability analysis. The simulation of the quadrotor helicopter landing is discussed in Section 4. The prototype of the UAV and real-world outdoor setup used to assess the control method are demonstrated in Section 5. Finally, the conclusion and future work are discussed in Section 6.
###### Abstract  
The knowledge of soil water content (SWC) dynamics in the upper soil layer is important for several hydrological processes. Due to the difficulty of assessing the spatial and temporal SWC dynamics in the field, some model-based approaches have been proposed during the last decade. The main objective of this work was to assess the performance of two approaches to estimate SWC in the upper soil layer under field conditions: the physically-based thermal inertia and the Hydrus model. Their validity was firstly assessed under controlled laboratory conditions. Thermal inertia was firstly validated in laboratory conditions using the transient line heat source (TLHS) method. Then, it was applied in situ to analyze the dynamics of soil thermal properties under two extreme conditions of soil-water status (well-watered and air-dry), using proximity remote-sensed data. The model performance was assessed using sensor-based measurements of soil water content acquired through frequency (FDR) and time domain reflectometry (TDR). During the laboratory experiment, the Root Mean Square Error (_RMSE_) was 0.02 m\({}^{3}\) m\({}^{-3}\) for the Hydrus model and 0.05 m\({}^{3}\) m\({}^{-3}\) for the TLHS model approach. On the other hand, during the in situ experiment, the temporal variability of SWCs simulated by the Hydrus model and the corresponding values measured by the TDR method evidenced good agreement (_RMSE_ ranging between 0.01 and 0.005 m\({}^{3}\) m\({}^{-3}\)). Similarly, the average of the SWCs derived from the thermal diffusion model was fairly close to those estimated by Hydrus (spatially averaged _RMSE_ ranging between 0.03 and 0.02 m\({}^{3}\) m\({}^{-3}\)).  
soil water content; soil thermal inertia; Hydrus numerical model; sparse vegetation +
Footnote †: journal: Applied Sciences