esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 3 Results
### Intraoperative Solution
#### 3.3.2 Spine Surgery
Two experienced surgeons placed 120 pedicle screws simulated by Kirschner wires, including eighty under AR guidance, resulting in more satisfactory and efficient AR-guided placements than radiographically guided placements. Furthermore, manual alignment of the hologram was judged less performing than automatic alignment, while the accuracy of the two alignment methods was broadly similar. Some of the studies regarding the use of AR for spinal procedures also involve feasibility studies for AR guidance of injections in the lumbar facial joint. For example, in the work of [PERSON] et al. [75], Microsoft HoloLens was used as an AR-based guidance system to perform lumbar facet joint injections on a phantom with two integrated vertebrae. The phantom was equipped with three virtual ribbon markers, which were used as templates for the manual alignment of the hologram. Their study provided convincing evidence to encourage the injections of lumbar facet joint using AR; nonetheless, some drawbacks were present. In fact, their study focused only on the target location, no phantom tracking system was present, and also used a larger needle than typically used in clinical practice. In a retrospective study, [PERSON] et al. [35] investigated a novel three-dimensional MR-based image-guided intraoperative surgical navigation system (MITINS) to support pedicle screw implantation in elderly patients. The authors reported for the first time the use of MR in surgery related to lumbar fracture using the MITINS system, consisting of an electromagnetic transmitter, traKTAR, and sensors. The MITINS planned the 3D position using intraoperative three-point registration and assisted the orthopedic surgeon in inserting the screws into the 3D printed lumbar model. No screws resulted to be outside the pedicle, and there was no need for radiography for additional position information.
## 2 Micro-Actuator Fabrication
### Principle of Two-Photon Polymerization
oil immersion objective lens, resulting in a precise structure with a high resolution. However, the maximum height of a fabricated feature is limited by the working distance of an objective lens. To overcome this limitation, dip-in direct laser writing (dip-in DLW) has been developed [41]. In the galvano mode, scanning is performed in the x and y directions using a focused 2D galvanometer scanner, and the focus position is changed in the z-direction using a 1D piezoelectric stage. This method is superior to other methods in that it is capable of high-throughput processing and it is possible to fabricate a 3D structure with a high height by moving the piezoelectric stage in the z-direction. 3D models can be generated using computer-aided design (CAD) programs such as SolidWorks, AutoCAD, 3 DS Max, and Fusion360, which can be converted into the Standard Tessellation Language (STL) file format which stores information about the surface of the 3D model in the form of triangulated sections [20]. To fabricate a structure with nanoscale features, more precise position information defining a surface is required, and this can be obtained by increasing the number of triangulated sections. After a 3D structure is divided into a sequence of 2D horizontal cross-sections in the depth direction and continuous processing is performed for each divided layer, a nanoscale 3D structure with a high resolution can be fabricated. Photocurable resins have been developed with a wide variety of materials including popular UV lithographic photoresists (e.g., SU-8), various hybrid sol-gel materials (e.g., ORMOCER(r) [40], ORMOCOMP(r) [42], and SZ2080 [43; 44]), and biomaterials [45]. A commercial material such as SU-8 is inferior in that it is difficult to modify the material's properties as desired. Therefore, to use a commercial material for specific purposes requires additional functionalization. Research has been conducted to develop new candidate materials for an effective TPP process. For example, components such as nanomaterials (e.g., nanoparticles and nanowires) have been applied as active components to induce customized additional functions in recent years [46; 47; 48; 49]. For the efficient fabrication and motion control of an actuator, the appropriate modification and synthesis of photocurable resin is an important issue.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 2 Materials and Methods
### Design and Implementation of System Hardware
#### 2.2.3 Wireless Communication Module  
Wireless communication on the sea surface is affected by complex marine electromagnetic noise interference, resulting in the attenuation and distortion of radio frequency signals during transmission [23; 24]. LoRa technology adopts a unique Chirp modulation scheme and spread spectrum communication technology, which establishes the advantages of strong anti-interference ability, high network capacity, and flexible deployment in LoRa communication. The LoRa chip also has low power consumption and an LBT (Listen Before Talk) mechanism [25], which helps in the low-power and stable communication design of LB-PIBS. In this paper, we select SX1268 as the communication chip of LR-MPIBS, and set its transmission power to 22 dBm and communication frequency to 433 MHz.  
LoRa RF signals suffer from transmission and reflection losses during hardware circuit transmission. The reflected signal is prone to mixing with the original signal, resulting in signal distortion. The reasonable design of RF transmission-line impedance can reduce transmission loss [26; 27]. The transmission-line impedance-matching value is simulated through Sf9000. Its relationship with the communication performance is shown in Table 1.  
Figure 3: Falling-water-detection module circuit.  
When the transmission line width is 6.5 mil, the simulated impedance value is 66.91 \(\Omega\) and the communication distance is just 28 m. With increasing line width, the impedance value decreases, and the communication distance is greatly improved. The communication performance is optimal when the RF transmission line width is adjusted to 21.71 mils, with an impedance value of 50 \(\Omega\). Its communication success rate can reach 95% within a distance of 3.5 km in open areas.  
Antenna selection is another key problem in the design of the wireless communication module. The performance of the antenna directly affects the signal gain, directionality, anti-jamming, and stability of the working band [28]. We promote the screening research of the antennas for the PIT and the SMBS. A large suction cup, an omnidirectional copper pillar antenna with a gain of 8 dBi, was installed as the communication antenna for SMBT in the open area at a high altitude on the ship. The antenna selection for the PIT took into account the small size of the equipment, waterproof sealing, and electromagnetic interference. A built-in PCB patch antenna with a gain of 5 dBi was chosen.
# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 2 Related Work
To develop the proposed method, the authors carried out the preprocessing consisting of noise removal by applying the filter, input normalization, and data augmentation steps and achieved an accuracy of 91.93%. Ensemble methods are developed to enhance classification performance. Ensemble methods exploit the diversity in individual models to obtain their higher accuracy. Recently, multiclass skin cancer classification techniques have been developed in the literature using ensemble approaches. [PERSON] et al. [18] proposed how an ensemble of CNNs models can be developed for enhancement of skin cancer classification accuracy and developed an ensemble model for three classes of skin cancer and achieved an accuracy of 84.2%, 84.8%, 82.8%, and 81.4% for the models of GoogleNet, AlexNet, ResNet, and VGGNet, respectively. The authors enhanced the accuracy of 83.8% with the ensemble model of GoogleNet, AlexNet, and VGGNet. In [20], [PERSON] and [PERSON] developed different ensemble methods using CNNs. To develop the proposed technique, the authors performed the preprocessing on ISIC2017 and ISIC2018 datasets using different preprocessing methods and got an accuracy of 93.8%. In [49] [PERSON] et al. carried out skin lesion classification using ensemble of deep learners and developed an ensemble by aggregating the decision of ResNet50 and Inception V3 models to carry out the classification of seven skin cancer classes with an accuracy of 89.9%. In [19], [PERSON] et al. developed the ensemble of VGG16 and GoogleNet architectures using the ISIC 2018 dataset. To develop the proposed ensemble methods, the authors carried out the data augmentation and colour normalization on the dataset. The proposed method achieved an accuracy of 80.1%. [50] [PERSON] et al. developed a multiclass skin cancer classification approach using a weighted averaging ensemble of deep learning approaches using ResNeXt, SeResNeXt, ResNet, Xception, and DenseNet as individual models to develop the ensemble for the classification of seven classes of skin cancer with an accuracy of 81.8%. Previous work for skin cancer classification based on dermoscopy images not only lacks the generality but also has lower accuracy for multiclass classification [11; 19; 32]. In this paper, we propose a multiclass skin cancer classification using diverse types of learners with various properties to capture the morphological, structural, and textural variations present in the skin cancer images for better classification. The proposed ensemble models perform better than both the individual deep learning models and deep learning-based ensemble models proposed in the literature for multiclass skin cancer classification.
## 2 The Proposed RDHEI Method
### Room Reservation and Image Encryption
#### 2.1.3 The Bit Plane Reordering
\[Total\ EC=\sum_{i=1}^{Length(BPs)}\sum_{k=1}^{Length(HBs)}HomogeneousBlocks[i][k].size^{2}- sizeOf(ReferencePixels)\times 8, \tag{2}\]  
where \(BPs\) corresponds to the list of the embeddable bit planes and \(HBs\) is the list of the homogeneous blocks for each bit plane. The function \(sizeOf(ReferencePixels)\) returns the total number of bytes representing the reference pixels used by the predictor. It is worthy to note that the two first rows (i.e., from the top), the two first columns (i.e., from the left), and the last column of an image are considered reference pixels when using the GAP predictor. The reference pixels maintain their original values throughout all the processes, except in the encryption process. The total auxiliary information (in bits) is calculated according to Equation (3). \[ \begin{split} Auxiliary\ Information=8+18\times length(BPs)+10+ sizeOf(compressed(labelMap))\\ +\sum_{i=1}^{length(BPS)}(16+sizeOf(compressed(binaryStr[ i])))\\ +\sum_{i=1}^{length(BPs)}(16+sizeOf(compressed(\ concal_{k=1}^{ length(HBs)}(HomogeneousBlocks[i][k].value)))).\end{split}  \tag{3}\]  
The net payload (in bits) available for data embedding by the data hider is calculated using Equation (4). \[Net\ Payload=Total\ EC-Auxiliary\ Information \tag{4}\]  
Consequently, the ER is computed using Equation (5). \[ER=\frac{Net\ Payload}{n\times m} \tag{5}\]  
where \(n\times m\) is the image size. The ER is a fundamental metric, measured in bits per pixel (bpp), which quantifies the average number of bits that can be concealed within each pixel of an encrypted image. A higher ER signifies the ability to hide more data within the image. Figure 9: Overview of the quadtree decomposition and the bit plane reordering steps. Figure 10 shows the bit-planes of the reordered mapped prediction errors for two images, Lena and Jetplane, before and after adding their corresponding auxiliary information. The figure shows the conversion of binary values \(\{0,1\}\) obtained from the mapped prediction error bit planes into \(\{0,255\}\) to represent the results in a more appropriate manner. In addition, the binary values representing the auxiliary information are transformed from \(\{0,1\}\) to \(\{0,128\}\) in order to differentiate between the different components of each bit plane.
## 3 Results and Discussion
### Effects of NTP Treatment on Extracellular Reactive Species Generation  
The contents of NO and H\({}_{2}\)O\({}_{2}\) in the culture medium were measured immediately after NTP treatment. As mediators of many physiological activities, the contents of NO and H\({}_{2}\)O\({}_{2}\) in the medium increased in a dose-dependent manner with the increase in plasma treatment time (Figure 2A,B). H\({}_{2}\)O\({}_{2}\) concentrations in each treatment group after 4 h incubation were lower than that of 0 h incubation after NTP treatment (Figure 2B). Therefore, it suggested that extracellular ROS might enter the cell and play a role in promoting cell viability.  
Previous studies have confirmed that NTP enhances the formation of neovascularization in burn wounds by regulating the endothelial NO synthase signaling pathway [41]. The promoting role of NO airflow generated by plasma in wound healing was also demonstrated [42]. In addition, cancer cells were more sensitive to plasma-induced RONS (especially nitric oxide (NO) and nitrogen dioxide (NO\({}_{2}^{-}\)) radicals) compared with normal cells, and the apoptotic response induced by plasma mainly occurred in cancer cell [43]. For normal cells, ROS produced by low-dose NTP in the culture medium promotes fibroblast proliferation while high doses inhibited it [18]. In addition, high levels of ROS produced by Erianin inhibited the proliferation and induced apoptosis of HaCaT cells [44]. In our experiment, with the increase in plasma treatment time, the concentration of ROS in the culture medium increased in a time-dependent manner and decreased significantly after 4 h of incubation, and the cell viability reached its peak after 15 s of treatment. Therefore,  
Figure 1: Cell viability of HaCaT cells. (**A**) HaCaT cell viability after NTP treatment for 24 h. (**B**) Effect of NTP on the viability of HaCaT cells pre-incubated with NAC. (**C**) Effect of PI3K inhibitor LY294002 on cell viability. C 1: cells given neither NAC nor NTP treatment; C 2: cells treated with NAC in the absence of NTP treatment. Data represent the mean \(\pm\) SD of three independent experiments. \({}^{*}\)\(p<0.05\), \({}^{***}\)\(p<0.001\), \({}^{***}\)\(p<0.0001\) with ANOVA compared with the control.  
we believed that ROS produced by low-dose NTP might enter the cells from the outside, which promoted the vitality of HaCaT cells, while ROS produced by high-dose NTP may inhibit this effect because excessive ROS reduces cell viability.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 4 Experimental Settings
### Evaluation Metrics  
We employ dataset-specific protocols and metrics to ensure rigorous and comparable evaluations of GAF-Net.  
For iLIDS-VID [5], we use the ten-split protocol, where each split randomly divides the dataset into two groups of 150 individuals for training and testing. We report the average performance across all splits.  
We primarily use Cumulative Matching Characteristic (CMC) curves, reporting results from rank 1 (R-1) to rank 20 (R-20), with an emphasis on accuracies at R-1, R-5, and R-20.  
For the MARS dataset [6], we follow the standard train/test split. In addition to CMC, we incorporate the mean Average Precision (mAP) metric. The mAP is particularly relevant for multi-camera setup of MARS, providing a holistic measure of retrieval quality.  
This combination of metrics offers a comprehensive view of our model's performance across different datasets and scenarios, facilitating fair comparisons with other state-of-the-art methodologies in person Re-identification.
## 6 Conclusions  
In this paper, we explored the secure transmission in cognitive wiretap networks with FD receiver. To investigate the benefits of FD operation at receiver of the considered CRNs, the MRC-ZFB/MRT-SC-ZFB with FD operation and MRC/MRT-SC of HD operation schemes were proposed, respectively. Also, the new closed-form and simple asymptotic expressions for the SOP and ST of our considered networks with MRC-ZFB/MRT-SC-ZFB and MRC/MRT-SC schemes were derived, respectively. Different transmission parameters, such as the SNR, \(Q\) and the number of \(N_{R}\) and \(N_{B}\) are designed to verify the impact on system secrecy performance. The closed-form expressions for the exact and asymptotic SOP concisely characterized the secrecy diversity gain and the secrecy coding gain. Additionally, analytical results and numerical simulations demonstrated that beamforming with AN is an important tool for ensuring secure transmission facing with a malicious eavesdropper in CRNs. Finally, it is also found that the MRC-ZFB/MRT-SC-ZFB scheme outperforms MRC/MRT-SC scheme, which verifies the advantages of the schemes conceived in this paper. Our results provided a unified model to analyse the SOP performance and the ST of the FD receiver cognitive wiretap networks over Rayleigh fading channels and could be easily extended to the scenarios with multiple PUs being faced with multiple eavesdroppers, which is also one of the parts in our further works.  
[PERSON] and [PERSON] conceived the model; [PERSON] performed the simulation results and wrote the paper; and [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] provided some suggestions and revised the paper. All authors have read and agree to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant (No. 61801496, No. 61771487 and No. 61801497), the National Postdoctoral Program for Innovative Talents of China (No. BX20180374), the National University of Defense Technology Research Project (No. ZK17-03-25), the Defense Science Foundations of China (No. 2019-JCJCJ-J221), and the Joint Foundation Project of Zunyi Science and Technology Bureau and Zunyi Medical University of Guizhou Provincial (No. E-282).  
The authors declare no conflict of interest.  
Figure 8: Throughput of the two schemes vs different with SNR = 20 dB, \(Q\) = 20 dB, and different values \(N_{R}\) and \(N_{B}\).
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 3 Synthesis Methods and Their Influence on the Structure and Properties of HEA Alloys
### Influence of the Synthesis Method on the Structure and Properties of HEA Alloys
#### 3.2.2 Influence on the Mechanical Properties of Different HEA Alloys  
The synthesis method used for high-entropy alloys (HEAs) plays a crucial role in determining their mechanical properties. Different fabrication techniques such as casting, mechanical alloying, powder metallurgy, and various additive manufacturing methods influence the microstructure, phase composition, and overall mechanical behavior of HEAs. For instance, traditional casting methods often result in dendritic microstructures with interdendritic segregation, which can impact the hardness, ductility, and tensile strength of the alloy [28]. On the other hand, methods like mechanical alloying combined with spark plasma sintering (SPS) can produce fine, homogeneous microstructures with enhanced mechanical properties due to the severe plastic deformation and rapid consolidation process.  
Additive manufacturing (AM) techniques, such as selective laser melting (SLM) and laser powder bed fusion (LPBF), offer the advantage of rapid cooling rates, which can refine the microstructure and improve properties like strength and hardness. These methods can also introduce unique microstructural features such as fine BCC grains within an FCC matrix, as observed in some studies [87]. The choice of synthesis method can significantly influence the phase stability, grain size, and distribution of precipitates within the alloy, thereby affecting its mechanical performance. Consequently, understanding the relationship between synthesis techniques and the resulting mechanical properties is essential for optimizing HEAs for various engineering applications.  
Table 3 presents a brief review of some studies focused on the influence of different synthesis methods on the mechanical properties of HEA alloys.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 5 Results
### Results
#### 5.2.3 Evaluating Rewritings  
In this experimental protocol we focused more on the effectiveness of the rewritings, when the incremental processing approach was used to refresh previously generated results. We also used the queries defined in previous experiment for each rewriting rule. Firstly, we evaluated the non-rewritten set \(Q\) by running the query evaluation process for it. We started by including only one query in \(Q\), and gradually increasing the query number to reach \(n=5\) queries. In this scenario, each included query in \(Q\) was executed for each micro-batch individually, and we report the average execution time. Secondly, we evaluated the execution of the rewritten set \(Q^{\prime}\) by starting again with one query (\(n=1\)) and gradually increasing the query number to \(n=5\). In this scenario, each included query in \(Q^{\prime}\) is executed for each micro-batch, and the average execution time is reported.  
Figures 11 and 12 illustrate the results for this series of experiments. As shown, when \(Q\) was executed without rewritting, the execution cost increased as the number of participating queries increased as well. Moreover, we observed that even with the more queries participating in the rewritten set \(Q^{\prime}\), the execution cost remained the same. The variation in the execution time between the rewriting rules arose due to the nature of the queries included in the non-rewritten set Q and depends on the types of shared common attributes.  
The evaluation of \(CGMRR\) and \(GMRR\) demonstrates that the reduction of evaluation cost was significant while the number of queries included in the query set increased. This is because the grouping construction and grouping reduction steps were performed once by applying the \(n\) operations, whereas in the case of the non-rewritten set, the grouping construction and grouping reduction steps were executed as many times as the number of queries included. The effectiveness of the \(CGORR\) and the \(BRR\) rewritings was significant, as the number of included queries increased. The rewritten set \(Q^{\prime}\) consisted of five queries and each one used the answer of the base query as its measure. To investigate the effectiveness of the rewriting rule, we ran the experiments for the sets \(Q\) and \(Q^{\prime}\), starting initially with one query and adding each time another one, till we reached a total number of five queries to be executed each time. We noticed that as more queries participated in the rewritten \(Q^{\prime}\), a significant improvement in the performance resulted due the exploitation of the intermediate answers of each additional query.  
Figure 10: Evaluation of CGMRR and CGRR when the SQL execution model is used over an structured dataset.