esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, these poisoning defense strategies inherently conflict with privacy-preserving FL, which seeks to maintain the indistinguishability of private gradients. To simultaneously address both poisoning attacks and privacy protection in FL, we propose a novel BFL framework specifically designed for secure medical data exchange within smart healthcare ecosystems. Our contributions are as follows:  
* A robust global model is maintained through rigorous cosine similarity analysis to filter out harmful gradients, effectively mitigating the risks of data poisoning attacks. This approach enhances model integrity by systematically identifying and removing manipulated parameter updates during distributed training processes. * We propose an actively secure MPC framework that maintains data confidentiality throughout FL processes, effectively addressing the inherent tension between data privacy preservation and robust defense against poisoning attacks. * We establish a hierarchical framework to mitigate poisoning attacks and protect the data privacy of devices while using blockchain, in order to facilitate transparent processes and the enforcement of regulations. This innovative framework facilitates the formation of an autonomously coordinated learning alliance, eliminating the need for a central coordinating authority during model training processes. * Through conducting rigorous evaluations across two widely-accepted benchmark datasets, our proposed framework exhibits superior performance compared to existing cutting-edge methodologies, in terms of operational stability and computational effectiveness. The remainder of this paper is organized as follows: Section 2 introduces foundational concepts. Section 3 surveys the existing literature. Section 4 describes the proposed framework and its objectives. Section 5 elaborates on the developed approach. Sections 6 and 7 examine security evaluation and efficiency assessment, respectively. Finally, Section 8 summarizes the key findings and conclusions of the study.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 4 Discussion
### Scale Difference between Remote Sensing Data and In Situ Data  
For model validation and comparison, FLUXNET GPP was used in this paper. From the scatter plots in Figure 4, scatters below the 1:1 line can be observed for DBF, SAV, GRA, and CRO sites, which indicates underestimations of both the direct and diffuse radiation (DDA) method and total APAR-based method. This may be a scale difference between the in situ data and remote sensing data. On the one hand, the in situ GPP data observed by FLUXNET was influenced by multiple factors, such as climate conditions (temperature, moisture, winds, etc.) and atmosphere conditions (clouds, aerosols, particles, etc.). On the other hand, the remote sensing data used in this paper included FAPAR and clumping index products, which represent the pixel information with a range of 500 m \(\times\) 500 m, while the in situ GPP data represent a single point. Furthermore, the vegetation types at FLUXNET sites were not actually totally homogenous, and the corresponding remote sensing pixels may not have represented the real value of the actual vegetation types. Therefore, the inconsistency in the in situ data and remote sensing data introduced uncertainties into model performance and validation results.
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
## 4 Results
### Data Collection  
In this study, experimental data were collected from a rice field located in Southern China (113.636888 N, 23.240441 E). Data collection was conducted when the weeds and crops were in their early tillering stages. The flight height was set to 6 m above the ground and the forward lap and side lap for imaging were set to 50% and 60%, respectively. A total of 1092 samples were collected and each sample was a \(1000\times 1000\) pixel RGB UAV image, where each image corresponds to a \(30\) cm \(\times\) 30 cm field area. Each pixel in each image was classified as one of three classes: rice, weed, and other. To perform network training and validation, the images were manually labeled at the pixel level, as shown in Figure 7. In our dataset, 892 samples were used for training and the remaining 200 images were used for network validation.
## 4 Results and Discussion
### The Influence of Hash Functions on the Parallel Performance
Figure 3: Execution time for parallel medium-size rainbow tables generation for SHA-256, SHA-512, MD5 and NTLM (NTLMv2) cryptographic hash functions across an increasing number of computing nodes. The lines for SHA-256, MD5 and NTLM overlap due to the relatively small table dimensions. of parallel computing in reducing computational overhead. Among the hash functions, SHA-512 has the highest execution time (1119 s for medium table and 11,229 s for large table) with a single node, followed by SHA-256 (730 s for medium table and 8888 s for large table), MD5 (704 s for medium table and 7134 s for large table) and NTLMv2 (722 s for medium table and 7148 s for large table). However, as the number of nodes increases, the execution time difference between the hash functions decreases, with all four functions converging to similar execution times beyond 10 nodes. The cryptographic hash functions, SHA-256, MD5 and NTLMv2, have similar execution times in Figure 3, and they remain similar as the number of nodes increases and show no significant difference. As seen in Figure 4, this difference becomes more significant as the table dimensions increase, but there is still no significant difference between MD5 and NTLMv2 because NTLMv2 uses MD4 internally, which is similar to MD5 with very minor changes. As a result, the lines for NTLMv2 and MD5 overlap in the figures. The second part of the experiment focuses on the speedup gained from the proposed implementation. The findings from this part of the experiment are shown in Tables 1 and 2, which illustrate the speedup gains achieved by parallelizing rainbow table generation for the four previously mentioned cryptographic hash functions. In an ideal case, the speedup would be represented as a straight line, indicating that with \(p\) processes, we can achieve \(p\) times faster generation process. In our case, we did not achieve a perfectly linear speedup, but it is remarkably close to this ideal speedup, a scenario known as near-linear speedup. The tables demonstrate that the speedup achieved for all hash functions closely follows the ideal linear trend, indicating efficient utilization of computational resources in the parallel environment. This consistent performance across hash functions highlights the scalability and effectiveness of the parallel implementation, even for computationally intensive functions like SHA-512. We can also report that the efficiency is good as well and remains within the range of 95-99%. These results indicate that the parallel computing approach using MPI achieves near-linear speedup, regardless of the computational complexity of the hash function and the table dimensions.
## 4 Result and Discussion
### Beneficial Effect of Combining Similar Sensors  
This subsection investigates the benefit of coupling all the sensors of a similar type. Figure 7 illustrate the filtered results for a single experiment for 25 sets of VL53L0X, 25 sets of VL53L1X, and 25 sets of HC-SR04 in the absence of excessive ambient light with the targeted object exactly 29 cm away, respectively. In these figures, the vertical axes represent the distance of the jack's upper jaw from the device, as measured by the sensors. The horizontal axes indicate the number of measurement cycles. In every Figure, 25 different colors show the measurements of the 25 sensors of each type.  
Analysis of Figure 9 shows the fluctuation of acquired data by various distance measuring technologies. VL53L0X has higher data fluctuation and sensitivity to the environmental situation than the other sensor types. VL53L1X shows more stable results. For example, although data acquisition by VL53L0X shows 90 mm of data fluctuation, VL53L1X shows less than 40 mm. Figure 9 also shows that HC-SR04 has the most stable data report among the selected distance measuring sensors. In fact, HC-SR04 has less than 6 mm of data fluctuation. The standard deviation of a single VL53L0X, VL53L1X, and HC-SR04 sensor from Figure 9 was on average about 7.3, 5.1, and 0.9, respectively.  
Figure 9: Filtered output of distance sensors for an experiment: (**a**) results of VL53L0X, (**b**) results of VL53L1X, and (**c**) results of HC-SR04.  
The results of all 25 sensors for each cycle have been averaged to illustrate the benefit of the sensor combination. Figure 10 illustrates the results of this development. The orange line is the measurement carried out by a steel measurement pattern.  
Analysis of Figure 10 shows the benefit of using the averaged results of 25 sensors for measuring a distance instead of using the results of a single sensor. The fluctuation of VL53L0X data decreased to 7 mm, which is 12 times less fluctuation. The fluctuation of VL53L1X and HC-SR04 was reduced to approximately 4 mm and 1 mm, respectively. The standard deviation of 25 fused VL53L0X, 25 fused VL53L1X, and 25 fused HC-SR04 sensors was 1.3, 1.1, and 0.2, respectively. The combing sensors significantly improved noise-canceling and graph smoothing, as well. Each sensor has inherent noises different from the other similar sensors. By averaging the results of multiple sensors, these noises either get smaller or are canceled altogether. The randomness of these noises has been investigated through a few experimental tests (citing conference papers). Further analyses of Table 3 and Figure 10 show the potential bias associated with the incorrect or lack of calibration of individual sensors. In fact, the offset of the VL53L0X sensor from the steel measurement pattern measurements is about two centimeters. Additional calibration of this sensor is needed for sensitive measurements.
# Antenna Design for Microwave and Millimeter Wave Applications: Latest Advances and Prospects
## 11 Inverted F Type Antenna Design for the Cattle Activity and Estrus Detection Sensor Module  
[PERSON] [10] designed and optimized an inverted F type antenna (IFA) for ZigBee communication of a sensor board. The designed antenna can replace the chip antenna on a radio frequency (RF) module that is not performing well enough for the ZigBee communication. The sensor board detects cattle behavior and identifies the breeding (estrus) period and transmits the data to the main station by the RF module and IFA antenna. The proposed and optimized transmitting/receiving (TRx) IFA antenna of the ZigBee communication module had a return loss of \(-\)19 dB and a gain of 1.6 dBi at 2.45 GHz. The size was about 2.5 \(\times\) 0.5 cm in width and vertical length, and the height was 0.55 cm. The strengths of signals with the chip antenna and the IFA antenna have been measured and compared. There was about a 20 dB enhancement with the IFA antenna compared to the chip antenna. The antenna was designed and applied to the RF transmission and reception (TRx) module. This antenna and sensor module can be applied to livestock in general as well as cattle.  
[PERSON] et al. [11] proposed a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity was extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula was derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 were derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 were then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. The proposed IDW interpolation had the lowest average root-mean-square error (RMSE) of \(\
abla\)M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando were obtained using the standard atmospheric condition of \(\
abla\)M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface were illustrated.
## 5 Experiments
### Experiment Procedure  
To test and verify that our approach for depth measurement is effective, we acquire the images and follow the method steps to carry out experiments. Figure 6 illustrates the procedure for our approach. There are original images, the depth image of objects and some significant interim result images, etc., in Figure 6.  
Figure 5: (**a**) is the schematic diagram of the process acquiring the two images; (**b**) is the real scene which was used to capture the image \(A\) and \(B\).  
Figure 6: The experimental procedure. (**a**) The original image \(A\) was acquired at the place where the object distance is \(u\); (**b**) the original image \(B\) was acquired at the place where the object distance is \(u+d\); (**c**) and (**d**) are the segment results of the image (**a**) and (**b**), respectively. In order to make the sub-regions clear, we label the different sub-regions with different colors; (**e**) SIFT feature points detected and matched. The true-color image \(A\) and \(B\) are converted to grayscale; then, detect the SIFT feature points in them, and match the points. The locations with a pink star “**” are the locations of SIFT feature points; (**f**) matching the objects by taking advantage of the SIFT feature points and the segment results. Then we get the convex hull of the set of SIFT feature points of each object to be measured by Graham scan. Lastly, the pair of straight line segments are chosen by the algorithm described in Section 4.3. They are labeled by drawing a sign “\(\circ\)” on it; (**g**) the depths of objects are expressed by the gray-scale. The gray value of pixel is lower, the value of depth is lower, too; (**h**) this image shows the spatial relationship of the objects. Namely, there are the different distances between camera and the different measured objects. It is convenient for us to compare with image (**g**).  
From Figure 6f, we can see that a whole image of measured objects is not necessary to measure the depth of object, because the depth can be computed by the lengths of a pair of straight line segments. And the pair of straight line segments can usually be obtained by a part of the image of the object. Consequently, our method is robust to occlusion or partial loss of image of object.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 3 Results
### Surface Morphology  
When deposited in the form of thin film, PHA samples were transparent to visible light at the macroscopic scale. To isolate homogenous portions of material for the tip indentation AFM test, the surface morphology of the PHA samples was observed.  
The PHA morphology was investigated by tapping mode AFM, once deposited by spin-coating on a glass substrate having an arithmetic mean height Sa and a root mean squared height Sq equal to 16.7 nm and 22.8 nm, respectively. Note that 50 \(\times\) 50 (\(\upmu\)m) and 2 \(\times\) 2 (\(\upmu\)m) height and demodulation images are reported in Figure 1.  
Figure 1: Images of surface morphology acquired by AFM in tapping mode for PHA thin films. (**Top**): height (left) and demodulation (right) 50 \(\times\) 50 (\(\upmu\)m) images; (**bottom**): height (left) and demodulation (right) 2 \(\times\) 2 (\(\upmu\)m) images.  
The 50 \(\times\) 50 (\(\upmu\)m) height image is coupled with a line profile (Figure 2) to highlight the typical roughness showed by the PHA samples.  
Choosing a reference plane, whose area is A, and defining Z(x,y) as the distance between the points and the reference plane, roughness parameters are defined as follows:  
\[\mathrm{S_{a}}=\frac{1}{\mathrm{A}}\iint|Z(\mathrm{x,y})|\mathrm{dx}\;\mathrm{ dy} \tag{1}\]  
\[\mathrm{S_{q}}=\sqrt{\frac{1}{\mathrm{A}}\iint Z^{2}(\mathrm{x,y})\mathrm{dx} \;\mathrm{dy}} \tag{2}\]  
where \(\mathrm{S_{a}}\) and \(\mathrm{S_{q}}\) are the arithmetic mean height and root mean squared height, respectively. For the surface morphology shown in Figure 2, the surface roughness parameters, specifically the arithmetic mean height \(\mathrm{S_{a}}\) and root mean squared height \(\mathrm{S_{q}}\), were calculated. \(\mathrm{S_{a}}\) and \(\mathrm{S_{q}}\) resulted to be equal to 51.4 nm and 66.2 nm, respectively.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Machine Learning Techniques
#### 5.3.1 Feature Scoring  
In this first step, a default model was designed for each ML technique to find the features of the dataset that were the most important in the classification process.  
For the RF technique, a model was trained using 10 estimators and the entropy criterion. The importance of the dataset features was estimated using the _feature_importances_ attribute provided by the classifier. This calculation was based on the mean and standard deviation of the impurity reduction that occurred within each tree.  
In Figure 10, the results show the relative importance values assigned to each feature, with a total sum equal to one. There was a significant impact on the distance between nodes and in the parameters resulting from DEN2 DE (_total_balance_ and _abs_flux_), which corresponded to the gateway node load after energy balancing and the total flow of resources exchanged in the distribution process, respectively. However, this method introduced a bias towards features with high cardinality, or in other words, those with a large number of unique values. This bias occurred because such features created deepersplit nodes in the trees, as there were more options for dividing the dataset. Therefore, these types of features were more likely to receive higher importance scores [47].  
Taking this into account, the permutation importance method was used for a comparative analysis. In this second method, the feature values were permuted randomly one at a time, and the classifier's performance was evaluated at each iteration, so a feature that significantly decreased the model's accuracy when its values were permuted scored higher in importance. In this case, as shown in Figure 11, the parameters resulting from DEN2 DE (_total_balance_ and _abs_flux_) still showed high importance scores. However, now, the lengths of the node labels and the link capacity also exhibited high importance scores.  
For the SVM technique, a default model was trained with an Radial Basis Function (RBF) kernel, suitable for non-linearly separable datasets. Consequently, the importance of  
Figure 11: Feature importance scoring for the RF using the _permutation_importance_ method.  
Figure 10: Feature importance scoring for the RF using the _feature_importance_ attribute.  
the dataset's features was evaluated through the _support_vectors_ and _dual_coef_ attributes provided by the class. As depicted in Figure 12, the results indicated that the lengths of the source and destination labels had a significant influence on the dataset. As for the RF, the permutation method was applied, obtaining similar results. In Figure 13, the lengths of the labels, the link capacity, and the total energy flow (_abs_flux_) were highlighted in the classification.  
Figure 12: Feature importance scoring for the SVM using the _support_vectors_ and _dual_coef_ attributes.  
Figure 13: Feature importance scoring for the SVM using the _permutation_importance_ method.
## 3 Results and Discussion
### \(\alpha\)-Glucosidase Inhibitory Activity  
The \(\alpha\)-glucosidase inhibition activity, according to Table 3, was evaluated as a measure of hypoglycemia, with lower values indicating stronger blood glucose-lowering ability. All samples from the four regions exhibited weaker activity compared to the positive control acarbose, but the samples of W3 (\(l\) = 7.99 mg/mL) demonstrated the highest inhibitory activity of all samples, followed by W1 with a value of 11.86 mg/mL. Samples of W2 showed moderate activity with a value of 12.38 mg/mL, while W4 exhibited the weakest inhibitory activity.  
Figure 6: DPPH scavenging ability of samples from four regions.  
Figure 7: Antioxidant activities of samples from four regions in ABTS and FRAP assay.  
Previous research demonstrated the potential benefits of rutin (CP5) in managing hypertension, hyperlipidemia, and hyperglycemia [32]. Both naringin (CP10) and hesperidin (CP11) were found to reduce blood glucose levels by inhibiting stress responses induced by glucose [33]. Moreover, hesperidin (CP11) has the ability to mitigate oxidative stress through the enhancement of kinase activity, thereby contributing to its hypoglycemic effects [34]. These three compounds were detected in all samples, providing a comprehensive explanation for the observed \(\alpha\)-glucosidase inhibitory activity. The \(\alpha\)-glucosidasealae inhibitory activity cannot be solely attributed to rutin, naringin, and hesperidin, as it may also be related to other compounds. For instance, studies indicated that quercetin exhibits significant inhibitory effects on \(\alpha\)-amylase and \(\alpha\)-glucosidase, making it an ideal compound for targeting diabetes management [35; 36]. Based on the obtained chemical composition, and without considering synergistic effects, it was observed that the compound quercitrin (CP16) detected in samples of W3 was not found in other samples. Therefore, the presence of quercitrin in samples of W3 may serve as a crucial indicator of its superior inhibitory activity compared to other samples. Additional research is warranted to explore the interactions between various compounds and the potential synergistic effects that may arise from these interactions.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs
### Developments on FO-SPR-MIP-Based Sensors  
#### 6.3.1 Tnt  
The explosive detection in aqueous media is quite essential for human health, safety and environmental monitoring reasons. Trinitrotoluene (TNT) is one of the common explosives which are important to detect. [PERSON] et al. reported a TNT detection scheme based on the combined approach of FO-SPR and the molecular imprinting technique [152]. SPR was realized by the coating of 60 nm thick gold film over the core of the fiber by a sputtering method. Further, MIP was coated over the Au coated region by spin coating to ensure its uniformity. MIP was prepared using TNT as template, MAA as functional monomer, divinylbenzene (DVB) as cross-linker and AIBN as initiator  
Figure 8: Generic experimental arrangement for fiber optic SPR sensor utilizing MIP as analyte recognition layer.  
The sensing method showed the detection limit of 5.1 \(\times\) 10\({}^{-5}\) M while the sensor has the sensitivity of 2.7 \(\times\) 10\({}^{4}\) nm/M. The sensitivity of the TNT sensor was improved by incorporating the LSPR phenomenon instead of SPR [153]. LSPR was implemented using five branched Au nanostars which were synthesized by a conventional seed growth method [154]. Au nanostars were dispersed in MIP medium and coated over the core of the fiber. The method of synthesis of the MIP layer was the same as discussed in previous study [152]. After characterization of the sensing probe, the method showed better values of sensitivity and LOD as 8.4 \(\times\) 10\({}^{4}\) nm/M and 2.4 \(\times\) 10\({}^{-6}\) M, respectively.