esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Nanomaterials in RFID Wireless Sensor Components
###### Abstract  
Radio Frequency Identification (RFID) technology, capable of wirelessly processing large amounts of information, is gaining attention with the advancement of IoT technology. RFID systems can be utilized as Wireless Sensor Network (WSN) technology by introducing sensing materials responsive to external environmental stimuli. To achieve effective information communication and sensing capabilities, various types of nanomaterials are being used as various components of RFID sensors. This paper provides an overview of the RFID sensor system and the nanomaterials used in their composition. Polymers that can achieve flexibility are attracting more attention as user-friendly substrates as demand for them increases in the wearable market. Additionally, advancements in inkjet printing technology, allowing cost-effective and simple production of components, introduce nils and manufacturing methods utilizing various nanomaterials such as metal nanoparticles, carbon materials, and composites. Furthermore, nanomaterials utilized as sensing materials enable the detection of various external environments with high sensitivity and a wide detection range. Consequently, RFID sensor systems that achieve wireless detection in a variety of environments are actively utilized in many applications. By analyzing the current research progress and problems faced in RFID sensor technology, this paper suggests future research directions for its development as a next-generation wireless sensor system.  
wireless communication; Radio Frequency Identification (RFID); nanomaterials; sensor systems +
Footnote †: journal: Applied
## 3 Analysis and Design of the Proposed System
### Main Goals of the System  
The objective of the proposed system is to exploit the capabilities of AR to facilitate, support, and optimize production and assembly tasks in an industrial workshop. With this objective, the next sections describe the development and testing of an application based on the Microsoft HoloLens smart glasses that is able to guide the operators and facilitate their training tasks in a visual and practical way.  
The developed application allows for the visualization of industrial 3D models in which the assembly steps to be carried out in each part of the process are highlighted by means of animations. The application seeks to reduce assembly times and to visually illustrate the contents of the assembly manuals, which in most shipyards are still only available for the operators on paper. Thus, the developed system is expected to optimize the processes and reduce the risks associated with errors during assembly.  
Specifically, the developed application was designed having in mind the following main goals:  
* It should allow for scaling and moving the displayed 3D models with the aim of facilitating their visualization, thus enabling placing them where the user considers appropriate. In addition, when moving an object, an automatic scanning of the environment will be carried out to avoid the model being placed in an intersection with real objects, as this would cause an uncomfortable effect when visualizing it.
* The assembly sequence should be visualized step-by-step through animations and contextual text instruction that provide the necessary technical details.
* It should be possible to visualize the documentation associated with every relevant part, especially its blueprints and physical measures.
* The developed application should implement a shared experience system that allows multiple AR devices to interact with the same virtual part at the same time.
* Ease of use: The application interface should be as simple and intuitive as possible to avoid misunderstandings during the training process. Animations and interactions should also be reinforced through the reproduction of sounds that guide each action (e.g., start and end of the animations, selections in the menus).
## 4 Performance Evaluation and Discussion
### Analysis of Reliability and Risks  
A reliability and risk analysis based on epistemic uncertainty was performed on the model [17; 18; 19; 20]. Uncertainty modelling was performed using Monte Carlo simulations to model the uncertainty of payment failure by randomly sampling 5000 data from the statistics of payment failure reasons to obtain a sample set of random payment failure reasons. Furthermore, the uncertainty was propagated through the model by introducing randomness into the model, such as using Gaussian noise or random initialization parameters in the LSTM layer or other layers. By introducing randomness or noise, the robustness and stability of the model under different payment failure reasons could be tested and thus the reliability of the model evaluated. The results of the risk analysis are shown in Table 9.  
Based on the given probability distribution and risk metrics, a risk index was calculated for each cause of non-payment. The risk index is derived by multiplying the probability by the risk metric and reflects the extent to which each cause contributes to the overall risk. The higher the risk index, the greater the contribution of that cause to the overall risk.  
According to the model output results, the risk measure for each cause can be calculated, and then the corresponding decision strategy can be developed based on the risk measure, such as developing a different payment failure handling process or adjusting the payment security policy.  
Figure 13: Confusion matrix for the final LSTM model.
## 4 Discussion
### Canada Goose Abundance Hypothesis
The absence of clear patterns across abundance levels highlights the need for more granular data and supports the growing consensus in recent literature that the species' role in HPAI dynamics is still not well understood. While Canada geese may not currently be major contributors to HPAI spread, their widespread distribution and behavioral tendencies--particularly their habituation to human environments--warrant continued surveillance and inclusion in future risk assessments [13; 18]. Our findings therefore support the continued use and reinforcement of existing biosecurity protocols, since Canada goose abundance was not associated with elevated H5N1 risk. Had a positive association been observed, targeted interventions--such as visual deterrents (e.g., predator-mimicking balloons)--might have been warranted [31]. While reinforcing existing biosecurity protocols remains essential for reducing H5N1 risk, emerging technologies--such as artificial intelligence (AI), robotics, and sensor networks--offer promising solutions to overcome current limitations, particularly the lack of robust, accessible data. AI-enabled platforms can enhance rapid detection, modeling, and response to HPAI outbreaks without relying on centralized or restricted datasets. For instance, the Biothreats Emergence, Analysis, and Communications Network (BEACON), launched in April 2025, integrates AI with expert human oversight to collect, validate, and disseminate outbreak intelligence [32]. Its ability to generate GIS-compatible, location-tagged data presents a valuable resource for enhancing ecological modeling efforts like ours. Platforms like BEACON also hold significant promise because, in theory, they can collaborate with government agencies to leverage restricted datasets in generating public-facing outbreak intelligence--without compromising the confidentiality of the underlying data. In addition, smart chemical sensors coated with HPAI-specific antibodies--when paired with AI classifiers--enable real-time detection of viral particles. These systems are particularly beneficial on farms housing mixing vessel mammals, where early identification is critical [33]. By facilitating immediate quarantine and targeted biosecurity responses, such technologies have the potential to significantly reduce viral spread and associated economic losses. Large poultry producing companies investing in AI is a cost-effective strategy since it will reduce the number of livestock lost due to the efficient rapid detection capabilities AI technology can provide. AI implementation would be costly for smaller farms, but stakeholders could apply for USDA grants and technical assistance that award funding for innovative production [34]. Stakeholders should also collaborate to request more government funding for the implementation of novel technologies on farms. The justification would be that the improved rapid detection capabilities of AI will result in fewer livestock lost, therefore reducing the need for indemnity payments from the government agency USDA-APHIS.
[16] proposed a power allocation algorithm for a discrete Fourier transform (DFT) beamforming matrix to maximize the ergodic secrecy rate. A deep neural network (DNN)-based secure precoding scheme is proposed in [29] to jointly design the precoderand AN signal when the channel estimation is imperfect and the channels are spatially correlated. Because the secrecy rate cannot be arbitrary values in real systems, the authors in [22; 28] minimize the secrecy outage probability when a specific target secrecy rate is chosen. However, maximizing the secrecy rate does not imply that the capacity of the eavesdropper is sufficiently small. Due to the application of error correcting code (ECC) in the communication systems, an eavesdropper can successfully decode the information when the minimum SINR requirement is satisfied. To reduce the risk of eavesdropping, we consider maximizing the BLER of the eavesdropper by minimizing the SINR of the eavesdropper. QoS-based transmit beamforming has proven to be a viable and versatile approach [30]. The QoS is measured by the average SINR. Two design formulations are proposed in [30] for AN-aided secret transmit beamforming, namely a total power minimization formulation and a user's SINR maximization formulation. The signal-to-noise ratio (SNR) outage probability criterion is proposed in [31]. In this paper, based on the error correction capability of ECC, a beamforming design scheme is proposed to minimize the SINR of an eavesdropper and maintain the minimum SINR requirement of the legitimate user. The performance of the proposed scheme is verified through simulations using Polar code [32]. Location-based beamforming can be implemented with low-cost at an analog beamforming module. Analog circuits can significantly improve the power efficiency of the device compared to digital circuits [33]. Due to the non-linear characteristics of power amplifiers, it is not suggested to adjust the amplitude of the signals for beamforming use at the radio frequency (RF) module [34]. The location-based beamformer only shifts the phase of the signal without changing its modulus, which can be considered as a phase-adjusted DFT beamformer. DFT codebook can be embedded on field-programmable analog arrays [33] with reduced power consumption. We note that when the system operates at mmWave band with an extremely large-scale antenna array (ELAA), near-field propagation needs to be considered [35]. Due to the spherical wavefront of near-field radiation, the DFT type beamforming is no longer applicable [36; 37]. Secure beamforming with ELAA can be considered for our future work. The rest of this paper is organized as follows. Section 2 introduces the system model, where the SINR expressions of the legitimate user and the eavesdropper are derived. In Section 3, the approximate PDFs of the SINRs are derived, and then the expectations of the SINRs and the BERs are deduced. In Section 4, the beamforming design scheme is proposed to minimize the SINR of the eavesdropper. The simulation results are presented in Section 5. Section 6 concludes this paper.
## 2 Materials and Methods
### Evaluation  
The zero-normalized cross correlation (ZNCC) similarity metric, Mean Absolute Error (MAE), Structural Similarity Index Measure (SSIM), as well as the Dice coefficient for bone class were computed to quantitatively measure the quality of the synthesized pseudo-CT volumes compared with the ground-truth CT volumes and thereby checking if there is an overlap of tissues, following Equations (1)-(4):  
\[\text{ZNCC}=\frac{1}{N}\sum_{x,y,z}\frac{\left(\text{pCT}(x,y,z)-\mu_{\text{ PCT}}\right)\left(\text{CT}(x,y,z)-\mu_{\text{CT}}\right)}{\sigma_{\text{pCT}} \sigma_{\text{CT}}} \tag{1}\]  
\[\text{MAE}=\frac{1}{N}\sum_{x,y,z}\lvert\text{pCT}(x,y,z)-\text{CT}(x,y,z)\rvert \tag{2}\]  
\[\text{SSIM}(\text{pCT},\text{CT})=\frac{\left(2\mu_{\text{pCT}}\mu_{\text{CT} }+c_{1}\right)\left(2\sigma_{\text{pCT},\text{CT}}+c_{2}\right)}{\left(\mu_{ \text{pCT}}^{2}+\mu_{\text{CT}}^{2}+c_{1}\right)\left(\sigma_{\text{pCT}}^{2 }+\sigma_{\text{CT}}^{2}+c_{2}\right)} \tag{3}\]  
where \(x\), \(y\), and \(z\) are the three-dimensional image dimensions; \(N\) is the total amount of voxels; pCT (\(x\),\(y\),\(z\)) and CT(\(x\),\(y\)) are the pseudo-CT and ground-truth CT voxel values for a given (\(x\),\(y\),\(z\)) position, respectively; \(\mu_{\text{pCT}}\) and \(\mu_{\text{CT}}\) are the mean HU pseudo-CT and ground-truth CT images, respectively; \(\sigma_{\text{pCT}}\) and \(\sigma_{\text{CT}}\) are the standard deviation for the pseudo-CT and ground-truth CT images, respectively, and \(\sigma_{\text{pCT},\text{CT}}\) is the joint standard deviation; and \(c_{1}=\left(k_{1}L\right)^{2}\), \(c_{2}=\left(k_{2}L\right)^{2}\) are two variables to stabilize the division with weak denominator depending on \(L\)= dynamic range of pixel values (typically \(2^{\text{gtbis per pixel}}-1\)), \(k_{1}=0.01\) and \(k_{2}=0.03\) by default.  
The distance range of ZNCC is the interval \((-1,1)\) (1 for perfect direct correlation, \(-1\) for perfect inverse correlation, and 0 for non-correlation).  
\[DICE_{bone}=\frac{2\left|M_{\text{pCT}}\cap M_{\text{CT}}\right|}{\left|M_{ \text{pCT}}\right|+\left|M_{\text{CT}}\right|} \tag{4}\]  
where \(M_{\text{pCT}}\) and \(M_{\text{CT}}\) are the mask segmentations obtained by thresholding Hounsfield Units values for bone tissues in the pseudo-CT and ground-truth CT, respectively.  
Mean \(\pm\) standard deviation (SD), as well as the 95% confidence interval (CI) for ZNCC, MAE, SSIM, and Dice were computed.  
Voxel-by-voxel analyses were performed to determine differences in synthesized pseudo-CT compared to the ground truth CT. Voxel-by-voxel correlation plots, Bland-Altman plots, bias, and variability Pearson correlation coefficients were calculated for comparisons. Statistical significance was considered when the \(p\) value was lower than 0.01.
## 2 Sentinel-1 SAR Data and the Ancillary Datasets
### Ancillary Datasets  
#### 2.2.1 Inundation Maps Derived from Landsat-8 Data  
Landsat-8 satellite collects visible and shortwave images (30 m spatial resolution). NIR wavelength reflects less solar radiation than the red wavelength over water bodies [1; 3], and surface water maps can be derived from the NDVI maps (water pixels and non-water pixels correspond to negative and positive values of NDVI, respectively) [24; 25]. Other indices have been used to detect water, but the NDVI is effective when properly corrected from the atmospheric contamination. In this study, official and reliable atmospheric corrected Landsat-8 NDVI images are ordered directly from the U.S. Geological Survey (USGS) website ([[https://espa.cr.usgs.gov/index/](https://espa.cr.usgs.gov/index/)]([https://espa.cr.usgs.gov/index/](https://espa.cr.usgs.gov/index/))). To limit cloud effects, only images with less than 10% of cloud contamination are used. The selected images are further filtered using the Landsat-8 quality assessment to remove pixels that might be affected by instrument artifacts or subject to cloud contamination. Figure 2 (bottom) shows the NDVI from Landsat-8 (d), the resulting surface water map based on negative NDVI values (e), and the quality flag (f), for the same regions and the same day (17 December 2015) as previously presented. Over the Lower Mekong Delta (lower than latitude number 15), there are \(\sim\)250 Landsat-8 images available between January 2015 and January 2016. However, there is only \(\sim\)10% (27 images) with less than 10% cloud contamination. Among the remaining images, only 1/3 was selected for this study since they were observed with a time difference of less than 3 days from a Sentinel-1 image.  
Figure 1: Pre-processing steps for Sentinel-1 Synthetic Aperture Radar (SAR) images.  
Figure 2: Examples of satellite observations from Sentinel-1 (top) and from Landsat-8 (bottom), over the lower part of the Tonle Sap Lake (Cambodia) after the pre-processing steps: (**a**) SAR backscatter coefficient at VH polarization; (**b**) SAR backscatter coefficient at VV polarization; (**c**) SAR incidence angle; (**d**) The Normalized Difference Vegetation Index (NDVI) from Landsat-8; (**e**) Surface water estimated from Landsat-8; and (**f**) Landsat-8 quality flags. The white areas are cloud-covered pixels detected by the Landsat quality flags, and have been removed. Both Sentinel-1 and Landsat-8 images were taken on 17 December 2015.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 3 Calculation of Additional Stresses in Tunnel Undercutting
### Additional Stress Caused by Grouting Pressure  
Suppose the pressure at the tail is \(p\), and its effective range corresponds to the length of a ring of segments, denoted as \(L_{S}\). Consider the center coordinates of the excavation face of the shield machine to be \((0,0,h)\). For the surface of the shield machine, define the coordinates of a microelement as \((-x_{0},R\cos\theta,h-R\sin\theta)\). The grouting pressure \(p\) is then decomposed into horizontal \(p_{h}\) and vertical \(p_{v}\) components [26]. By integrating over the length range of the shield machine, the following results are obtained:  
\[\begin{split}&\sigma_{p_{h}}=-\int\limits_{-L-L_{g}}^{-L}\int \limits_{0}^{2\pi}\frac{p(y-R\cos\theta)}{8\pi(1-v)}\Bigg{[}\frac{1-2v}{R_{ 7}^{3}}-\frac{1-2v}{R_{8}^{5}}-\frac{3(3-4v)(z+h-R\sin\theta)^{2}}{R_{8}^{5} }-\frac{3(z-h+R\sin\theta)^{2}}{R_{7}^{5}}+\frac{6(h-R\sin\theta)}{R_{8}^{5}} \\ &(h-R\sin\theta+(1-2v)(z+h-R\sin\theta)+\ \frac{5z(z+h-R\sin\theta)^{2}}{R_{8}^{2}} )\Bigg{]}R\cos\theta d\theta dx_{0}\end{split} \tag{22}\]  
\[\begin{split}&\sigma_{p_{v}}=-\int\limits_{-L-L_{g}}^{-L}\int \limits_{0}^{2\pi}\frac{-p}{8\pi(1-v)}\Bigg{\{}-\frac{3(z-h+R\sin\theta)^{3}} {R_{7}^{5}}+\frac{(1-2v)(z-h+R\sin\theta)}{R_{8}^{3}}-\frac{(1-2v)(z-h+R\sin \theta)}{R_{7}^{3}}-\\ &\frac{1}{R_{8}^{5}}[3z(3-4v)(z+h-R\sin\theta)^{2}+3(h-R\sin \theta)(z+h-R\sin\theta)\left(\begin{array}{c}5z-h+R\sin\theta\\ \end{array}\right)\Big{]}-\\ &\frac{30(h-R\sin\theta)z(z+h-R\sin\theta)^{3}}{R_{8}^{2}} \Bigg{)}\Bigg{\}}R\sin\theta d\theta dx_{0}\end{split} \tag{23}\]  
Among them:  
\[\begin{split}& R_{7}=\sqrt{\left(x-x_{0}\right)^{2}+\left(y-R \cos\theta\right)^{2}+\left(z-h+R\sin\theta\right)^{2}}\\ & R_{8}=\sqrt{\left(x-x_{0}\right)^{2}+\left(y-R\cos\theta\right)^{2} +\left(z+h-R\sin\theta\right)^{2}}\end{split}\]  
where: \(L_{g}\) is the width of a ring of pipe sheets; \(p\) is the synchronized grouting pressure; \(\sigma_{p_{h}}\), \(\sigma_{p_{v}}\) is the extra stress brought on by the grouting pressure's horizontal and vertical components.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 2 System of Remote-Controlled Robotic Arms
### Communication Protocol Between Leader and Follower  
The communication protocol uses the QUARC software's Stream modules, employing TCP/IP for reliable data transmission. Data packets are sent every 0.02 s, with typical transmission delays below 10 ms. Zero matrices are used to initialize the data buffer, ensuring correct data alignment and handling. This allows the QArm to accurately synchronize with the Geomagic Touch, maintaining real-time coordination and operational efficiency.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks
### The Fuzzy Logic Identifier to Distinguish Useful Disturbance
#### 2.3.1 Set the Rules for Disturbance Identification  
Equation (9) designs an index \(Dir\). By comparing its value with zero, this index represents the disturbance direction theoretically. However, if \(Dir\) is too close to zero, the mis-judgement could be made due to the inevitable errors in potential transformer (PT) and current transformer (CT), as well as in the computation process. Therefore, a safety margin is imposed to enhance the accuracy of the disturbance direction identification. The practical criterion is formulated as Equation (10).  
\[\left\{\begin{array}{ll}Dir<-\varepsilon,&\text{upstream disturbance}\\ -\varepsilon\leq Dir\leq\varepsilon,&\text{ignored}\\ Dir>\varepsilon,&\text{downstream disturbance}\end{array}\right. \tag{10}\]  
where \(\varepsilon\) is a small positive value predefined to establish the safety margin. In this paper, it is experimentally set as 0.01. Equation (10) ensures that those unclear disturbance conditions with \(Dir\) laying in a small vicinity of zero, will be ignored. Such conditions could be simultaneous disturbances on both grid and load side, or those with slight voltage and current fluctuations.  
Concerning the identification of the effective non-trivial disturbances, the relative percentages of the value changes are employed. To take consideration the operation states before and after the disturbance, the original values compromise to adopt the averages of those before and after the disturbance.  
It is obvious that these two scalar positive parameters range from 0 to 2, which can be used for evaluating the severity of the disturbance.  
\[\left\{\begin{array}{ll}\Delta\hat{U}=\frac{\left|\Delta\hat{U}\right|}{ \text{average}\left(\left|\hat{U}_{1}\right|\left|\hat{U}_{2}\right|\right)} =\frac{2\left|\hat{U}_{2}-\hat{U}_{1}\right|}{\left|\hat{U}_{1}\right|+\left| \hat{U}_{2}\right|}\\ \Delta\hat{I}=\frac{\left|\Delta\hat{I}\right|}{\text{average}\left(\left| \hat{U}_{1},\hat{U}_{2}\right|\right)}=\frac{2\left|\hat{U}_{2}-\hat{U}_{1} \right|}{\left|\hat{U}_{1}\right|+\left|\hat{U}_{2}\right|}\end{array}\right., \tag{11}\]  
where the function \"average(\(x_{1}x_{2}\))\" represents the mathematical average operation of the variables \(x_{1}\) and \(x_{2}\).
## 3 System Implementation  
The system is designed in 40 nm CMOS technology. Figure 5 shows the full system design that includes active self-interference canceler, LNTA, mixer, and TIA.
## 3 Numerical Simulation in Typical Application Scenarios
To enhance the stability of the demodulated vibration signal, this paper proposes a dual-channel signal enhancement method based on orthogonal demodulation. To investigate the influence of wavelength variation on speckle characteristics, we conducted numerical simulations on randomly generated rough surfaces and statistically analyzed the coupling efficiency differences between wavelengths in the 1551-1570 nm band relative to the 1550 nm reference wavelength. This study employed a Monte Carlo approach with 1000 independent trials to calculate the root mean square (RMS) values of the coupling efficiency variations, as shown in Figure 6.  
The RMS value of coupling efficiency variation quantifies the speckle pattern differences for the same rough target at different wavelengths. A higher RMS value indicates greater speckle pattern disparity. This characteristic enables the selection of two wavelengths with appropriate differentiation to ensure complementary interference signal strengths--when one channel's interference signal is weak, the other channel can maintain high signal intensity. As shown in Figure 6, the RMS variation in coupling efficiency \(\eta_{RMS}(\Delta\lambda)\) exhibits a monotonic increase with wavelength separation \(\Delta\lambda\), saturating at 0.27 when \(\Delta\lambda\geq 14\) nm. This saturation threshold indicates the decorrelation condition where speckle pattern similarity asymptotically approaches zero, establishing 14 nm as the minimum wavelength separation for statistically independent speckle realizations.