esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Privacy Attacks in FL
### Training Samples and Labels Inference
The attack recovers a private training image up to a resolution of 256*256 and a batch size of 256, which surpasses the previous state of the art. The proposed method addresses three major challenges in existing methods: model stability, the feasibility of recovering data from large batch size, and fidelity with high resolution. GRNN consists of a GAN model for generating fake training data and an FCN for generating the corresponding label. A fake gradient is generated given the shared model, and the two generators are optimized by approximating this fake gradient to the true gradient. The extensive experiments conducted by the authors show that their work outperforms DLG in terms of the addressed challenges. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & **Assumption** & \multirow{2}{*}{**Goal**} & \multirow{2}{*}{**Exploit**} \\  & & **Adversary** & & \\ \hline
[41] & 2017 & Client & Active & Class representative inference & Influencing the learning process. \\
[39] & 2018 & Server & Active & Class representative inference & Influencing the learning process. \\
[37] & 2019 & Client & Active/Passive & Membership inference & Vulnerabilities of the SGD algorithm. \\
[38] & 2022 & Client/Server & Passive & Membership inference & Prediction confidence series. \\
[43] & 2018 & Client & Passive & Properties inference & Global model updates. \\
[44] & 2018 & Client & Passive & Global Properties inference & Shared gradients. \\
[4] & 2019 & Server & Passive & Training data inference & Shared gradients. \\
[5] & 2020 & Server & Passive & Training data inference & Shared gradients and their signs. \\
[6] & 2020 & Server & Passive & Training data inference & Shared Gradients and Cosine similarity. \\
[46] & 2021 & Server & Passive & batch data recovery & Gradient inversion. \\
[9] & 2021 & Server & Passive & Large batch data recovery & Shared aggregated gradients. \\
[7] & 2022 & Server & Passive & Training image recovery & Shared gradients. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Privacy inference attacks against FL.
## 4 Conclusions  
Aluminum composites reinforced with BiSn alloy particles can be developed by microwave-assisted PM followed by the hot extrusion process. The microstructural, mechanical, and thermal performances of Al-BiSn composites were investigated. The FE-SEM microstructures reveal a uniform distribution of BiSn particles in the aluminum matrix. Tensile results showed a 58% increase in the strength, from 117 \(\pm\) 3 MPa for pure Al to 185 \(\pm\) 3 MPa for Al-(15 wt.% BiSn) composite. The enhanced mechanical properties are ascribed to the homogeneously distributed BiSn particles and their strengthening effects. The CTE values for the Al-BiSn composites decreased with increasing BiSn content. Good mechanical and thermal properties of Al-BiSn composites make them attractive for aerospace, automobile, defense, and electrical industries. From the synthesis perspective, HMWS has a wide range of potential advantages for developing Al-based nanocomposites because of its rapid heating capability.  
[PERSON] and [PERSON] wrote the main part of the manuscript and developed the planning of the experiment. [PERSON], [PERSON], and [PERSON] carried out the preparation of different composite materials and tested thermal properties. [PERSON], [PERSON], and [PERSON] performed characterization of the microstructure and mechanical studies of the samples. [PERSON] and [PERSON] supervised the materials preparation process and commented on the manuscript draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors greatly acknowledge the technical support of the Center for Advanced Materials (CAM), Qatar University, 2713, Doha, Qatar. The authors would also like to thank the Central laboratory Unit (CLU), Qatar University, for SEM analysis.  
The authors declare no conflict of interest.  
Figure 10: The variation of coefficient of thermal expansion (CTE) of extruded Al-BiSn composites as a function of BiSn content.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
[26] investigates multiuser MISO networks with simultaneous information and energy users under incomplete channel state information by optimizing the information beam and energy so that the weighted sum of the total harvested power is maximized. Further, [27] studied the power minimization problem under power splitting architecture and proposed two algorithms, optimal and suboptimal, with the suboptimal algorithm having lower complexity, and the results showed that the power splitting architecture can save the power loss of the system in a better way compared to the time splitting architecture. However, in the information and energy co-transmission communication system, the path loss determines the level of energy harvesting at the user end. To reduce the path loss, the energy-harvesting efficiency can be significantly improved by combining the RIS technology and multi-antenna technology using the dual beam-forming gain of the base station (BS) and RIS.
###### Abstract  
Post-prostatectomy incontinence (PPI) is a major complication that can significantly decrease quality of life. Approximately 20% of patients experience consistent PPI as long as 1 year after radical prostatectomy (RP). This study develops a preoperative predictive model and compares its diagnostic performance with conventional tools. **Methods**: A total of 166 prostate cancer patients who underwent magnetic resonance imaging (MRI) and RP were evaluated. According to the date of the RP, patients were divided into a development cohort (n = 109) and a test cohort (n = 57). Patients were classified as PPI early-recovery or consistent on the basis of pad usage for incontinence at 3 months after RP. Uni- and multi-variable logistic regression analyses were performed to identify associates of PPI early recovery. Four well-known machine learning algorithms (k-nearest neighbor, decision tree, support-vector machine (SVM), and random forest) and a logistic regression model were used to build prediction models for recovery from PPI using preoperative clinical and imaging data. The performances of the prediction models were assessed internally and externally using sensitivity, specificity, accuracy, and area-under-the-curve values and estimated probabilities and the actual proportion of cases of recovery from PPI within 3 months were compared using a chi-squared test. **Results**: Clinical and imaging findings revealed that age (70.1 years old for the PPI early-recovery group vs. 72.8 years old for the PPI consistent group), membranous urethral length (MUL; 15.7 mm for the PPI early-recovery group vs. 13.9 mm for the PPI consistent group), and obturator internal muscle (18.2 mm for the PPI early-recovery group vs. 17.5 mm for the PPI consistent group) were significantly different between the PPI early-recovery and consistent groups (all _p_-values < 0.05). Multivariate analysis confirmed that age (odds ratio = 1.07, 95% confidence interval = 1.02-1.14, _p_-value = 0.007) and MUL (odds ratio = 0.87, 95% confidence interval = 0.80-0.95, _p_-value = 0.002) were significant independent factors for early recovery. The prediction model using machine learning algorithms showed superior diagnostic performance compared with conventional logistic regression (AUC = 0.59 \(\pm\) 0.07), especially SVM (AUC = 0.65 \(\pm\) 0.07). Moreover, all models showed good calibration between the estimated probability and actual observed proportion of cases of recovery from PPI within 3 months. **Conclusions**: Preoperative clinical data and anatomic features on preoperative MRI can be used to predict early recovery from PPI after RP, and machine learning algorithms provide greater diagnostic accuracy compared with conventional statistical approaches.  
prostate cancer; machine learning; incontinence; prediction
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 3 Results and Discussion
### Carbon Dots with Up-Conversion Luminescence as a pH Nanosensor  
It follows from the obtained results that the dependences of the integrated intensity for UCL of CDs' aqueous solutions on the pH have linear sections: CD-COOH in the pH range from 2 to 9, CD-poly in the pH range from 5 to 9 (Figure 8a). This means that the studied CDs with UCL can serve as promising nanoagents for use as nanosensors of pH in liquid media, including at the cellular level.  
Approximations of the linear regions for the dependences of the UCL integrated intensity of aqueous solutions of CDs on pH (Figure 8a) can be used as calibration curves for the determination of the pH of the CDs' aqueous environment. Using them, the pH of the environment can be measured from the UCL spectra of CD-poly with an accuracy of 0.13, and from the U  
As can be seen from the presented results, the pH values of egg whites obtained by two independent methods are in good agreement. The error of the determination of pH for the CD-poly samples is smaller than for the CD-COOH samples, what was already observed for CDs' aqueous suspensions. In addition, the slope of the calibrations' straight line for CD-poly is greater in modulus than for CD-COOH (Figure 6), which also affects the accuracy of determining the pH of the medium. Thus, the functionalization of the CDs' surface with carboxyl groups did not give a benefit in pH measurement. However, it is known that it is the carboxylation of the surface of carbon nanoparticles that provides them with a high degree of biocompatibility, which is extremely important when using CDs in nanomedicine.  
It should be noted that the accuracy of pH measurement according to the UCL spectra of CDs in aqueous solution is higher than that in chicken egg white. This is explained by the fact that the calibration dependences of the UCL intensity on pH obtained for an aqueous solution of CDs were used to determine the pH in the egg white. Nevertheless, the accuracy of determining the pH of both water and biological tissue from the UCL spectra of the nanosensor based on CDs (Table 1) satisfies the needs of biomedicine.  
Thus, the pH nanosensor on the basis on CDs' up-conversion luminescence intensity was successfully approached for the biological substances. Due to the fact that the anti-Stokes luminescence does not overlap with the autofluorescence of the biological medium, a good accuracy of pH determination was achieved. All pH nanosensors based on Stokes luminescence would be hindered by the biological autofluorescence, fully avoided for UCL-based probes.
## 4 Validation Analysis Using a Real Engineering Case  
Finally, a case study was performed on the III601 UMC in the ZCM, owned by the Huaibei Mining Holdings Co., Ltd, Huaibei, Anhui, China. The distribution characteristics of the roof bending moment and the specific values of the roof limiting span at different extraction stages were analyzed. Field measurements were performed for validation to determine the working resistance of hydraulic supports deployed in the coalface at different extraction stages. The measurement results demonstrate the accuracy of the proposed mechanical model and the analysis process.
## 2 Calibration Status of Landsat 8 Operational Land Imager (OLI)
### Landsat-8 Geometric Calibration
#### 2.5.1 Geometric Calibration Parameters  
Table 4 summarizes the geometric calibration parameters that are maintained on-orbit for the OLI. The table identifies the calibration operation that is used to update each parameter, and briefly describes the purpose and frequency of each calibration. These parameters can be adjusted to improve internal image geometry (focal plane alignment), band registration accuracy (band alignment), and absolute geolocation accuracy (sensor alignment).  
Prior to launch, it was expected that all parameters would require some fine-tuning during commissioning but that only the OLI sensor to spacecraft attitude control system (ACS) alignment would be likely to require subsequent, possibly seasonal, adjustment during normal operations. This has been confirmed by events with only two minor calibration parameter adjustments (in July 2013 and February 2014), both to the OLI-to-ACS alignment, having been applied since the end of commissioning. This stable calibration has made it possible to achieve absolute geolocation accuracy of 18 m (CE90) for the duration of the mission to date [10].
## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.3 Reconstruction-Based Algorithms  
Reconstruction-based methods operate under the presumption that normal data can be integrated into a lower-dimensional domain where normal samples and anomalies are represented in various ways [57]. An **Autoencoder (AE)** is a feed-forward neural network that includes an encoder and a decoder structure [58]. The objective is to train the network to capture the important parts of the input data and learn a lower-dimensional representation of the higher-dimensional data. The **Variational Autoencoder (VAE)** is a type of AE that includes an encoder network and a decoder network. The encoder network maps the input data to a low-dimensional latent space, while the decoder network maps the latent space back to the original data space. In this method, the VAE is trained on normal videos. The trained model is then used to reconstruct the input video, and the reconstruction error is calculated. Anomalies are detected by thresholding the reconstruction error. Any frame with a reconstruction error above a certain threshold is considered anomalous. The **Convolutional Autoencoder (CAE)** is also a type of AE consisting of convolution, deconvolution, pooling, and unpooling layers. The first two layer types may be found in the encoding step, whereas the others may be found in the decoding stage [59]. The **Variational Autoencoder (VAE)** is another type of AE that incorporates convolution, deconvolution, pooling, and unpooling layers. The first two layer types are used in the encoding step, while the others are used in the decoding stage [59]. Reconstruction-based methods are a variation of adversarial generative methods. **Generative-Adversarial-Network (GAN)**-based networks consist of two neural networks: a Generator (G) and a Discriminator (D) [58]. The generator network creates new examples in the target domain by mapping examples from the source domain to the target domain. The discriminator network then tries to distinguish between examples created by the
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 2 Materials and Methods
### Methodology for Burn-Area and Severity Mapping
Four classes are established as parameters of the algorithm, corresponding to each of the degrees of severity: low (insignificant to slight damage), low-to-moderate (moderately damaged), moderate to high (very damaged), and high severity (destroyed). Again, employing an automatic clustering algorithm avoids the use of fixed thresholds. From both the burned-area map and the severity map obtained through the previous method, a pixel-by-pixel calculation of the areas can be performed. To obtain a more accurate measurement of the surface affected for each area, the surface is measured by its average slope extracted through a digital terrain elevation model (DEM). Figure 3 shows the DEM processing scheme to obtain the slope at each point that will be used as a weighting coefficient for each pixel. Primary elevation data were obtained from the Shuttle Radar Topography Mission (SRTM), which flew aboard the space shuttle Endeavor from 11 February to 22 February 2000. This information was provided by the National Aeronautics and Space Administr  
Figure 2: Scheme of the methodology to obtain the maps of burned area and fire severity. tion (NASA) and the National Geospatial-Intelligence Agency (NGA) who participated in an international project to acquire radar data [53]. SRTM 1 Arc-Second Global elevation data (DOI number: /10.5066/F7 PR7 TFT) provide global coverage of gap-filled data with a resolution of 1 arc second (30 m). Moreover, this high-resolution global data set is distributed as an open source. The process (Figure 3) includes the reprojection of the area using the geographic information provided by the PlanetScope images as metadata. The slope of each point is then calculated and must finally be resampled to 3 m at the points of interest.
# Mechanical Characterisation and Shrinkage of Thermoactivated Recycled Cement Concrete
## 2 Experimental Programme
### Test Methods  
The produced concretes were tested in the fresh state, for slump and fresh density, and in the hardened state, for dry density, compressive strength, splitting tensile strength, ultrasonic pulse velocity, modulus of elasticity and shrinkage. The slump and fresh density were determined according to EN 12350-2 [32] and EN 12350-6 [33], respectively. After demoulding, all produced specimens, except those for drying shrinkage, were kept in an environment of over 95% RH until the testing age (28 days). The dry density was determined from two 100 mm cubic specimens per composition, according to EN 12390-7 [34]. The compressive strength and ultrasonic pulse velocity were tested in three 150 mm cubic specimens per composition and testing age, according to EN 12390-3 [35] and EN 12504-4 [36], respectively.  
The ultrasonic pulse velocity (UPV) was determined by direct transmission, using a _PUNDIT_ portable ultrasonic non-destructive digital indicating tester. The time taken by a pulse to travel through the concrete was determined with an accuracy up to 0.1 \(\upmu\)s, using 54 kHz transducers positioned in the middle of each opposing concrete cast face. Finally, UPV is the ratio between the length traveled by the pulse (150 mm) and the measured time.  
The splitting tensile strength and the modulus of elasticity were performed on three \(\upmu\)150 \(\times\) 300 mm cylinders each per composition, according to EN12390-6 [37] and LNEC E 397 [38], respectively. For the modulus of elasticity, two linear variable displacement transducers of 25 mm stroke were applied, operating over an initial gauge length of 150 mm. The test involved, at least, 8 cycles of loading at a rate of about 0.5 \(\pm\) 0.01 MPa/s, and unloading, with an applied stress varying between 1 MPa and 1/3 of the estimated compressive strength, until the difference between the average strain for consecutive cycles was lower than 10%.  
The drying shrinkage was determined from two 350 \(\times\) 100 \(\times\) 100 mm prisms per studied composition, according to LNEC E 398 [39]. Two steel pins were glued to each specimen, 200 mm apart, and the drying shrinkage was monitored over time by means of a _DEMEC_ mechanical strain gauge with a precision of 1 \(\upmu\)m and a gauge length of 5 mm. After demoulding, the specimens were kept in a controlled chamber with 20 \(\pm\) 2 \({}^{\circ}\)C and 50 \(\pm\) 5% RH, over the measurement period, which lasted up to 90 days.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
###### Abstract  
The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6 LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6 LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6 LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.  
Internet of Things (IoT); precision agriculture; wireless sensor networks (WSNs); IPv6 routing protocol; low-power and lossy networks (RPL) +
Footnote †: journal: Information  
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# Active Tunable Elastic Metasurface for Abnormal Flexural Wave Transmission
## 3 Numerical Results and Discussion
### Transforming Guided Wave  
In addition to the functions described above, the metasurface can also transform circular waves into plane waves. The transformation of the guided wave was investigated using a focused metasurface model (80 mm, 0) with a displacement in the -z direction applied at coordinates (\(x\),\(y\)) = (\(-\)80 mm, 0) to excite the circular wave. Figure 10 shows the out-of-plane amplitude plot (\(z\)-direction) of a circular wave converted to a plane wave at operating frequencies of (a) \(f\) = 6 kHz and (b) 7 kHz. From Figure 10, it can be seen that the circular wave formed by the point source excitation as the incident applied on the left side can be successfully transformed into a plane wave when propagating through the transmitted field, completing the waveform transformation as designed. Numerical simulation results demonstrate that the metasurface not only achieves flexural wave focusing but also transforms circular waves into plane waves.