esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Performance Evaluation Metrics and Experimental Settings  
#### 4.1.1 Performance Evaluation Metrics  
The specific environment of the blockchain determines that the requirements of the subject-sensitive hashing algorithm specifically for blockchains are different from other subject-sensitive hashing algorithms, as analyzed in Section 2.1. In the experiments, we will evaluate our method and the comparison methods from the following two points:  
1. For operations that do not change the content of the HRRS images, such as image format conversion and copyright watermark embedding, keep them as robust as possible. The robustness evaluation index is defined as: \[R=\frac{Num_{R}}{Num_{Total}}\] (2) where \(Num_{Total}\) represents the total number of images for robustness testing and \(Num_{R}\) represents the number of images whose hash sequence variation is lower than the preset threshold \(T\).
2. Regarding the tamper sensitivity, even pixel-level content changes should be detected as much as possible. The tamper sensitivity index is defined as: \[S=\frac{Num_{S}}{Num_{Total}}\] (3) where \(Num_{S}\) represents the number of tampered HRRS images detected under the threshold \(T\).
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method Used for the Simulation Study  
The LTDN predicted variation, presented in Figure 2, was used as input to simulate the evolution of synthetic surface reflectances for a sample vegetated site as a function of the temporally varying sun-viewing conditions.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 3 Design of the Tag Antenna
### Simulation Results  
Figure 5 shows the simulated magnitudes of the generalized reflection coefficient (1) for the optimized tag antenna (Table 1) operating in five different scenarios in the range of frequency 0.5 GHz-2 GHz: (i) Tag attached to a tube full of blood, (ii) tag attached to an empty tube, (iii) tag removed from the tube but maintaining the initial curvature of the tube, (iv) tag removed from the tube and unfolded, and (v) tag unfolded and attached to a bag full of blood (dimensions length \(\times\) width \(\times\) thickness in mm: \(160\times 130\times 30\)). As it can be observed, a good impedance matching is obtained in the desired band when the tag is attached to a tube full of blood, thus indicating that the design process described previously has been successful. The resonant frequency and 3 dB bandwidth are, respectively, \(f0=867\) MHz and \(\Delta f=75\) MHz (covering the range 830 MHz-905 MHz). The return loss (\(RL\)) at the resonant frequency is \(RL=11.5\) dB. However, the resonant frequency is shifted upwards when the tag is attached to an empty tube. In this case, a return loss of \(RL=9.8\) dB is obtained at \(f0=1.2\) GHz. This resonant frequency is far from the range allocated to the RFID system. This property of the proposed tag is due to the capacitive design employed, which makes the tag to be very sensitive to the high real relative permittivity value of the blood. This behavior may be of great interest for traceability of blood tubes. In fact, using the described capacitive loading effect, the tags will only be detected when the tubes are full of blood, with a drastic decrease in reading range as the blood tubes are emptied. This can reduce the amount of data collected in a specific traceability application, when the information of empty tubes is not considered relevant. When the tag is removed from the tube but keeps its initial curvature, the resonant frequency shifts to a higher value. The return loss is \(RL=13.4\) dB at \(f0=1.3\) GHz. This demonstrates that the PET material of the tube has also some influence on the operation of the tag. This last resonant frequency moves to an even higher value (\(f0=1.32\) GHz) when the tag is removed from the tube and unfolded (Figure 5). This confirms that  
Figure 4: Simulated input impedance \(Za\) of the designed tag antenna (Figure 2b) for different values of \(W1\). (**a**) real part of \(Za\); (**b**) imaginary part of \(Za\). The other dimensions are defined in Table 1.  
a curvature radius of the tube does not have very strong effect in the resonant frequency when the tag operates in air. Finally, when the tag is unfolded and attached to a bag full of blood, the antenna cannot match the complex impedance of the chip (\(Z_{c}=16-j\ 156\ \Omega\)). This is because the environment becomes too capacitive due to the presence of the blood, and the tag cannot provide enough inductive component to compensate for it.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques  
A major limitation of sensor nodes is their limited battery capacity. Several energy-efficient schemes have been introduced in previous research to solve the power consumption problem of sensor nodes. An alternative method that has been utilized to address the problem of limited node lifespan is the use of energy-harvesting techniques. Numerous techniques have been developed to allow sensor nodes to harvest different types of energy, such as solar, wireless power transfer (WPT), mechanical vibration, kinetic, and wind energy, from different environments [110] (Figure 2). Compared with traditional sensor nodes, rechargeable sensor nodes can work continuously for a long lifetime. Ambient energy can be converted to electrical energy and used directly to supply the sensor nodes or can be stored and used later on. In agricultural applications, energy harvesting can be used to prolong the lifetime of sensor nodes. Energy-harvesting techniques for previous research works employed in precision agriculture are shown in Table 7. The said table lists the categories of energy-harvesting techniques, types of wireless protocols, and output energy/power of energy-harvesting techniques. In addition, the form of agricultural applications, and limitations of each article are also highlighted.  
Energy-harvesting mechanisms can be implemented with batteries for the sensor nodes. For example, a sensor node using solar energy to charge its batteries can work extensively during daytime when adequate sunlight is available. Meanwhile, the sensor node can use its batteries through power reduction techniques, such as sleep mode (i.e., duty cycle), at night when sunlight is unavailable to save energy. In addition, the sensor node can enter restricted sleep periods (i.e., low duty cycle) and reduce transmission power when its batteries possess low residual energy [111]. Furthermore, adopting a maximum power point tracking system is a reliable battery charging technique for long-term operation. The charge-discharge cycle of the battery is minimized [112].
## 4 Experiments and Results
### Benchmark Schemes for Comparison
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methodology Used** & **Advantages** & **Limitation** & **Network Performance/Changing Ratio (\%)** & **Application Type** \\ \hline
**D-FL+RL** (Similar to [22]) & \begin{tabular}{c} \\ fuzzy membership \\ ranges \\ \end{tabular} & \begin{tabular}{c} \\ \end{tabular} & \
\begin{tabular}{c} Single fuzzy rule set may \\ not optimally scale to very \\ large densities \\ \end{tabular} &
\begin{tabular}{c} • Up to 7-10\% reduced Delay \\ Maintains Coverage \\ while reducing Redundancy \\ \end{tabular} \\
**RL-Parameter Tailoring (RL-PT)** & \begin{tabular}{c} \\ \end{tabular} & \begin{tabular}{c} Downamally prioritizes \\ relevant metrics (mobile- \\ giveue length, etc.) \\ Flexible weighting offers \\ \end{tabular} & \begin{tabular}{c} • Rapid parameter flips can \\ destabilize policy \\ \end{tabular} & \
\begin{tabular}{c} • Superior to static approaches \\ \end{tabular} &
\begin{tabular}{c} Mixed traffic (video \& voice) \& \\ slightly higher Delay \\ spikes in image topologies \\ \end{tabular} \\
**Neuro-Fuzzy RL (NFRL)** & \begin{tabular}{c} \\ fuzzy sets \\ \end{tabular} & \
\begin{tabular}{c} Complex to train (risk of \\ overfitting) \\ \end{tabular} &
\begin{tabular}{c} Coverage up to 95-97\%, \\ Possible 10-15\% Overhead fluctuations during \\ training \\ \end{tabular} \\
**Proposed Method A** (Fuzzy + RL w/ Dynamic Membership) & \begin{tabular}{c} Single fuzzy profile but \\ adaptive membership \\ boundaries \\ \end{tabular} & \
\begin{tabular}{c} • Less fine-grained in extremely heterogeneous \\ networks \\ \end{tabular} &
\begin{tabular}{c} • Up to 12\% reduction in \\ Overhead vs. single- \\ profile baselines \\ \end{tabular} \\
**Proposed Method B** (Multi-Profile Fuzzy + RL) & \begin{tabular}{c} Multiple fuzzy profiles \\ (energy-aware, velocity- \\ used, etc.) \\ \end{tabular} & \
\begin{tabular}{c} • Slight Overhead in deciding which profile to activate \\ vote \\ \end{tabular} &
\begin{tabular}{c} • 10-15\% reduction in energy use for high node densities \\ \end{tabular} \\
**Profile switching, provides specialized adaptation** & \begin{tabular}{c} • Profile switching, provides specialized adaptation \\ \end{tabular} & \
\begin{tabular}{c} More complex membership \\ ship/rule maintenance \\ \end{tabular} &
\begin{tabular}{c} • Step Overhead vs. single- \\ profile baselines \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of advanced broadcasting schemes.
## 2 Materials and Methods  
The proposed CTS method consists of two parts: coarse shadow detection based on DEM and fine shadow detection based on the MC3 index. Firstly, we perform image enhancement [41] with linear and logarithmic stretching on the ortho-corrected RGB image to extend the low gray range of the image to better separate the shadow from the background. Then, the Fractal Net Evolution Approach (FNEA) based on SLIC is performed on the enhanced image to generate multi-scale superpixels for the subsequent shadow detection. DEM-based coarse shadow detection uses DEM data as input to obtain a rough shadow mask; MC3-based fine shadow detection utilizes the RGBNIR four-band pixel values of the image for the calculation of the MC3 shadow index. Subsequently, We derive the Shadow-pixel Proportion Map (SPM) and the MC3 mean map based on superpixels. These two maps are then fused with specific weights to generate the Joint Shadow probability Map (JSM), which indicates the likelihood of each superpixel belonging to the shadow. Finally, we apply a multi-level Otsu threshold to the JSM to generate the final shadow mask. Figure 1 illustrates the CTS processing flowchart.
## 5 Multi-Objective Reliability Optimization of Suspension Control Arm
### Kriging Surrogate Models of Control Arm  
Surrogate model technology is a method used to approximate the relationship between a set of input variables and output variables by mathematical model. The sampling method has a great influence on the accuracy of the surrogate model. The optimal Latin hypercube design can sample uniform points throughout the design space, so that the fitting of factors and responses is more accurate [36].  
Therefore, the optimal Latin hypercube sampling method was adopted to generate 41 sample points for the six design variables, which were employed to build the Kriging surrogate models of the control arm. The performance indexes were obtained through finite element analysis of the control arm. The accuracy was verified by the determination coefficient \(R^{2}\), expressed as:  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{y})}}{\sum\limits_{i= 1}^{n}{(y_{i}-\overline{y})}} \tag{20}\]  
where \(n\) is the number of sample points used to verify the accuracy of Kriging model; \(\hat{y}_{i}\) is the predicted value of the \(i\)th response; \(y_{i}\) is the simulation value of the \(i\)th response; and \(\overline{y}\) is the average value.  
The closer to 1 the determination coefficient \(R^{2}\) is, the higher the prediction accuracy of the surrogate model is. Ten sample points were selected to verify the accuracy of the Kriging models. The \(R^{2}\) of the fatigue life, mass, first-order natural frequency, longitudinal stiffness, and transverse stiffness of the control arm were 0.9808, 0.9830, 0.9821, 0.9672 and 0.9936, respectively. The Kriging surrogate model accuracy verification results of fatigue life and mass are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Variable Description** & **Initial Value** & **Lower Limit** & **Upper Limit** \\ \hline \(x_{1}/\text{mm}\) & Variation of flanging height & 0 & \(-5\) & 5 \\ \(x_{2}\) & Width scaling at outer point & 1 & 0.9 & 1.1 \\ \(x_{3}\) & Width scaling at front point & 1 & 0.9 & 1.1 \\ \(x_{4}\) & Width scaling at rear point & 1 & 0.9 & 1.1 \\ \(x_{5}\) & Scaling of hole diameter & 1 & 0.9 & 1.1 \\ \(x_{6}/\text{mm}\) & Thickness & 5 & 4 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design variables and value range of control arm.  
Figure 10: Parameterized model of the control arm and its design variables.
## 3 Four-Port MIMO Antenna
### Results and Discussion
#### 3.2.2 Radiation Pattern of MIMO Antenna  
Figure 12 shows the ultra-wideband MIMO antenna's observed and predicted radiation pattern at resonance frequencies of 30 GHz and 38 GHz. It is clear that the antenna provides both frequencies with a broad side radiation pattern in the E-plane and a slightly inclined radiation pattern in the H-plane. The loading of several stubs is what caused the little distortion in the radiation pattern. Both the generated and observed radiation patterns exhibit strong correlations with one another. When testing, a little discrepancy that results from manufacturing flaws or connection loss is found. Because of the results and correlation between measured and simulated outcomes, future 5G devices running at broadband and high gain may be interested in the proposed study. Moreover, low cross-polarization of \(<\)\(-\)10 dB is observed for both frequencies.  
Figure 11: Comparison among S-parameters of MIMO antenna.
## 4 pLEO Time Scale Realizations  
In the capability concept of a pLEOT time scale within AltPNT, measurements taken between ensemble members are leveraged to accurately estimate the state of each participating clock relative to the theoretical reference known as the implicit ensemble mean (IEM). Furthermore, the Kalman filter provides estimates of the member clocks from the ensemble clock on the anchor platform, which can be shared with all participating orbital platforms. While there are various methods to achieve this, the simplest and most effective approach is to periodically transmit the clock state estimates determined by the Kalman filter to these platforms. By doing so, each platform receiving timing signals from its physical clock can utilize these estimates to adjust its corrected clock. As a result, this data-driven correction leads to a more stable timing signal compared to that of a free-running physical clock, enhancing overall performance and reliability.
# Model-Based Design of Aircraft Landing Gear System
## 3 Dynamic Verification Model
### Landing Gear Model
#### 3.2.2 Braking System Control  
To obtain a simulation of the braking system that is closer to the real application, a control logic for the said system has been implemented in the \"Braking system control\" block. As can be seen in Figure 7, three checks are defined. First, a time delay in the braking action with respect to the wheels touching the ground, equal to 5 s, is included. This is done in order to allow the aircraft to achieve vertical stability before actual braking, and because during the very first moments of landing, the aerodynamic drag tends to provide a higher braking contribution.  
The next one causes the interruption of the braking action when the aircraft speed is equal to zero (necessary just to avoid divisions by zero). Finally, a switch monitors the slip coefficient of the wheels and, when it goes below \(-0.3\), the braking action is stopped. This last one simulates the control logic of an antiskid system, which, in real applications, avoids the locking of the wheels, which is a phenomenon that heavily reduces the braking capability of the aircraft and its stability on the runway. By removing the braking action when the slip coefficient goes below a certain value, where the wheel is very likely to lock, such a risk is very much reduced.  
Apart from this kind of control, this subsystem also provides the signal for the actuation force applied on the disc brakes. Since the actuation of the brake is never instantaneous, when the braking action is requested, it is supplied as a linearly increasing signal up to the maximum value defined during the sizing of the system. As can be seen in the diagram,  
Figure 7: Workflow diagram for the braking system control logic.  
whenever the antiskid control is activated, the actuation force is reset again to zero, thus restarting the linear increase of the actuation force.
## 4 Results
### Security Analysis
#### 4.1.5 Distributed Denial of Service (DDoS)  
In this study, FIDO2 is integrated with the decentralized characteristics of the blockchain to ensure the authenticity and legitimacy of identity verification information. Each participant's identity verification data are recorded on the blockchain, preventing attackers from impersonating legitimate users for distributed denial of service (DDoS) attacks. Additionally, the distributed nature of the blockchain aids in distributing network traffic and load, thereby mitigating the attack pressure on a single target. Since identity verification and authorization information are stored on multiple nodes, attackers would need to simultaneously attack multiple nodes to disrupt the system's availability. Thanks to the decentralization of the blockchain, the failure of a single node does not result in the entire system becoming unavailable. Even if certain nodes are under attack or experience failures, other nodes can continue to provide identity verification services, thereby reducing the impact of DDoS attacks on the system.
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 6 Conclusions  
This paper proposes a mobility-assisted localization scheme with time synchronization-free feature (MALS-TSF) for 3D large-scale UWSNs. Compared to previous schemes, in this scheme, we considered the drift state of sensor nodes underwater and how to make anchor nodes deployed in the monitoring area. The localization scheme consists of two phases: In the first phase, mobile anchor nodes are distributed in the monitoring area, thus reducing part of the monitoring cost. Then, we address a time-synchronization-free localization scheme to obtain the coordinates of the unknown sensor nodes. In the second phase, we use the method of two-way TOA to locate the remaining ordinary sensor nodes. Therefore, the localization scheme does not require time synchronization. The simulation results illustrate that the localization scheme proposed in this paper has superiority over others in the localization ratio. The future work is to further consider the localization of edge nodes and the energy consumption of nodes.  
Figure 14: The location of the sensor node projected to the 2D plane.  
Figure 13: The localization ratio versus beacon interval.  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON], and [PERSON] contributed reagents/materials/analysis tools; and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript  
**Funding:** This work was supported in part by the National Natural Science Foundation of China and the Civil Aviation Administration of China under Grant U1733110, in part by the Fundamental Research Funds for the Central Universities, under Grant 2672018 ZYGX2018J018; in part by the Major Science and Technology Projects in Science & Technology Department of Sichuan Province, under Grant 2018 GZDZX0023; and Scientific and Technological Innovation and Entrepreneurship Talents and Seeding Engineering Projects in Science & Technology Department of Sichuan Province, under Grant 2020 JDRC0007.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### US Findings in First Referrals  
US results were positive in 91 (82%) patients with a final confirmed diagnosis of GCA, negative in 175 (99.4%) patients without a confirmed diagnosis of GCA, and dubious in one patient without GCA. The sensitivity of US was 81.98% (95% CI 73.55-88.63%), and the specificity 99.43% (95% CI 96.88-99.99%).  
The diagnosis of GCA was not supported by a positive US in 20 (18%) patients; of these, 4 patients underwent TAB (2 with histologic findings confirming GCA), one patient  
Figure 2: Clinical presentation, symptoms overlap, and corresponding ultrasound findings in patients with GCA. Panel A: overlap of presenting symptoms in patients referred to the fast-track clinic and receiving a final diagnosis of GCA. Panel B: number of patients with a positive ultrasound according to the different types of clinical presentation.  
refused to receive TAB, and 5 patients had confirmed LV-GCA at subsequent PET-CT. The 11 (9.9%) patients with a clinical diagnosis all had very typical clinical presentation, laboratory findings, and response to treatment, with a diagnosis of GCA that was also confirmed at follow-up visits.  
US findings were found at the level of the TA in 85 patients and AX in 18 patients. The mean number of sites with halos at the level of the TA was 2 \(\pm\) 1.6 (minimum 0; maximum 6). Bilateral halos were found in 46 (55%) patients with TA involvement and 4 (22%) with AX involvement. Seventy-one patients (77%) had cranial involvement only; 5 (5%) had isolated extra-cranial involvement, and 15 (16%) had both TA and AX halo signs. Two patients had isolated carotid involvement and one patient occipital arteries involvement. Facial arteries were involved in 2 patients but never as isolated.
## 2 Methodology
### Two-Stage Feature Selection and Ensemble of Classifiers  
The ensembles of descriptors were evaluated through the application of a two-stage feature selection process. The aim is to reduce the dimensionality of the feature space and identify the most suitable descriptors within each subset [62; 93; 94]. Initially, our approach involves ranking the feature sets through the Relieff algorithm [95] and then applying a threshold to decrease the number of potential matches. The threshold considered the 100 best-ranked descriptors in each ensemble [13]. Therefore, the second phase intended to identify the optimal matches within each narrowed subset, which is accomplished through wrapper selection and the exploration of various metaheuristics, namely: genetic algorithm (GA) inspired by genetic evolution, particle swarm optimization (PSO) inspired by particle swarm behavior, and binary gray wolf optimization (bGWO) inspired by the hunting strategy of gray wolves [96]. We used the K-nearest neighbors algorithm to assess the effectiveness of each of these feature selection methods. This proposal allowed the investigation of possible patterns resulting from the combination of techniques in multiple H&E datasets [61; 62; 63]. Due to the stochastic characteristic of the wrapper selection algorithms, the final performance of a combination was determined from the arithmetic average of 10 different runs of the selection step. Also, the most selected features were defined by their frequencies in the ten runs [97].