esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Blockchain-Based Federated Learning
## 5 Challenges and Future Directions
### Efficiency, Performance, Scalability  
When incorporating specific privacy encryption algorithms into FL systems, there is a substantial deceleration in the system's processing speed. This has made the practical application of robust privacy protection mechanisms in FL systems particularly challenging. In the realm of blockchain systems, cryptocurrency platforms such as Bitcoin's blockchain can execute an average of four transactions per second, while Ethereum manages roughly twelve. When compared with VISA's capability to process millions of transactions every second, such a performance is obviously unsatisfactory. Current research focuses on side-chains (also known as off-chains) [139] to enhance blockchain performance, facilitating rapid settlements between parties outside the main chain, with daily consolidations on the primary chain. Emerging blockchain variants have considerably refined their consensus algorithms for mining nodes. Platforms like Algorand [140] and IOTA [141], for instance, offer superior performance compared to the Ethereum and Hyperledger blockchains. Nevertheless, there remains a pressing need to amplify scalability, address extant performance issues, and thereby elevate the integrated system's performance when paired with federated learning systems.  
Additionally, the encryption/decryption processes inherent to blockchains, coupled with the PoW mechanism, substantially hamper the efficiency of model training due to their complexity. For more sophisticated models, encryption and the subsequent transmission of model parameters consume significant time. Furthermore, the storage of large-scale models during iterative processes in blockchains incurs elevated storage costs. Future iterations of BFL systems necessitate further enhancements to their practicality, striving to augment their tangible value in real-world applications.
## 4 Experiments and Analysis
### Experiment Setup  
This paper's experimental configuration employs a Windows 11 OS, a 13 th generation Intel (R) Core (TM) i9-13900K 3.00 GHz CPU, 128 GB RAM, and a pair of NVIDIA GeForce RTX 3090 GPUs. PyTorch 2.5.1 functions as the foundational deep learning framework, in conjunction with CUDA version 12.1, while Python 3.10 is used. For comparative experiments with other algorithms, the backbone used is YOLOv11m, while the ablation experiments use RetinaNet, YOLOv8m, and YOLOv11m as backbones. The improved network does not use pretrained model weights during initialization. The training phase of the model ends when the loss function is smoothed out. The input image's dimensions are set at 640 and the batch size at 64, and the number of epochs is maintained at 300. The starting rate of learning is established at 0.001. Table 2 displays the precise hyperparameters employed in the model's training.  
To prevent model overfitting, we implemented a series of measures. Firstly, we employed data augmentation techniques such as scaling, translation, and Mosaic methods to enhance the validity and diversity of the dataset. Secondly, we applied L2 regularization to  
Figure 6: Description of the IRMT-UAV dataset: (**a**) The distribution of height and width of bounding boxes for drone targets. The dimensions have been normalized, meaning that the height and width of the targets are divided by the height and width of the image, respectively. (**b**) The distribution of the signal-to-noise ratio (SNR) in the dataset. We have compiled the distribution statistics of the SNR for 4697 instances in the dataset, where density represents the probability density.  
suppress excessively large weights in the model, thereby reducing the model's dependency on these weights. Additionally, we adjusted training parameters to address potential instability and oscillation during the training process. Specifically, we set the batch size to 64, utilizing a larger batch size to reduce noise in the training data. We adopted the Stochastic Gradient Descent (SGD) algorithm to optimize the model weights, with an initial learning rate of 0.001, a momentum parameter of 0.9, and a weight decay coefficient of 0.0005. By introducing a regularization term in the loss function, we further mitigated the risk of overfitting.
## 3 Methodology
### Data Preprocessing
#### 3.4.4 Sampling Methods  
The dataset shows an imbalance in terms of injury prediction, with just about 30% of players reporting injuries. If a random train-test split is performed, this can result in an unbalanced test set. As a result, the test set may contain significantly fewer injured players than the entire dataset, which could impair the ML model's capacity to forecast injuries accurately.  
Stratified sampling is used to overcome this problem. This method ensures that the ratio of injured to healthy players in the training and test sets is the same as it was in the original dataset. This strategy ensures that both classes are fairly represented in each subset, resulting in a more useful dataset for training and evaluating models.
## 5 Conclusions  
In summary, we used MS to prepare multifunctional MS/CID/ZrO\({}_{2}\) adsorbents via biomineralization with excellent removal efficiency for different pollutants in water. The aldehyde group of CID reacted with the amino group on the surface of MS and grafted the CID to the surface of MS. Then ZrOCl\({}_{2}\) -8H\({}_{2}\)O was added and ZrO\({}_{2}\) nanoparticles were formed in the original position of the material. CID and ZrO\({}_{2}\) nanoparticles exhibited a synergistic antibacterial effect, and revealed excellent removal effects on _E. coli_ in water. In high-fluoride uranium-containing wastewater with three different uranium concentrations, the removal efficiency of U(VI) and F- by the MS/CID/ZrO\({}_{2}\) composites reached over 99%, and the removal rate of MB by MS/CID/ZrO\({}_{2}\) also reached over 99%. Additionally, the MS/CID/ZrO\({}_{2}\) demonstrated a high bactericidal effect on _E. coli_. The obtained results indicated that both CID and ZrO\({}_{2}\) nanoparticles contributed to the high adsorption performance  
Figure 8: Sustainability analysis of MS/CID/ZrO\({}_{2}\) through a comparison with four other U(VI) adsorbents.  
of the synthesized MS/CID/ZrO\({}_{2}\). It is evident that MS/CID/ZrO\({}_{2}\) performs effectively under various wastewater conditions. Its low cost, simple fabrication process, and high adsorption efficiency highlight its significant potential in the field of water purification.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15094794/s1](https://www.mdpi.com/article/10.3390/app15094794/s1)]([https://www.mdpi.com/article/10.3390/app15094794/s1](https://www.mdpi.com/article/10.3390/app15094794/s1)), Figure S1: (a) Removal efficiency of uranium in high-fluorine uranium-containing wastewater by MS. (b) Removal efficiency of fluorine in high-fluorine uranium-containing wastewater by MS. Figure S2: Removal efficiency of MB by MS.  
Conceptualization, G.W. and L.G.; data curation, W.Y. and X.F.; formal analysis, W.Y., X.F. and W.L.; funding acquisition, G.W. and L.G.; investigation, W.Y. and W.L.; methodology, W.Y. and X.F.; project administration, G.W. and L.G.; software, X.F.; supervision, G.W. and L.G.; validation, W.L.; writing--original draft, W.Y.; writing--review and editing, G.W. and L.G. All authors have read and agreed to the published version of the manuscript.  
The authors thank the financial support of the Taishan Scholars Program of Shandong Province (No. tsqn201909104) and the High-Grade Talents Plan of Qingdao University.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We appreciate [PERSON], who works in the Institute of Advanced Cross-field Science, College of Life Sciences, Qingdao University, for her help in some experiments.  
The authors declare no conflicts of interest.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 3 Proposed Method
### Features Extraction
In paper [19], [PERSON] proposed a compact color distribution feature for illuminant estimation, which is a combination of the number of image colors \(n^{c}\), chromaticity features in Equation (10), and color moments in Equation (9). For an image \(I\), the obtained CDF vector is as follows:  
\[CDF(I)=\Big{[}n^{c},\lambda_{1}r^{a},\lambda_{1}g^{a},\lambda_{1}r^{b},\lambda _{1}g^{b},\lambda_{1}r^{d},\lambda_{1}g^{d},\lambda_{1}r^{m},\lambda_{1}g^{m},\lambda_{2}\varphi_{1},\lambda_{2}\varphi_{2},\cdots,\lambda_{2}\varphi_{81 }\Big{]}, \tag{11}\]  
where the two adjustable factors, \(\lambda_{1}\) and \(\lambda_{2}\), control the influences of different components on the illuminant estimation. **Initial illuminant features (IIF)**. In paper [19], some unitary algorithms, i.e., GW, WP, SoG, GE1, GE2, GGW, PCA, and LSR, are applied to get eight initial illuminant estimates for an image. These initial estimates are integrated into a vector, i.e., IIF, as a metric of illuminant characteristics. For an image \(I\), the IIF vector is expressed as follows:  
\[IIF(I)=[\gamma(e_{i},e_{j})],i=1,2,\cdots,8,j=(i+1),(i+2),\cdots,8, \tag{12}\]  
where \(\gamma(e_{i},e_{j})\) is defined as the Euclidean distance between initial illuminant estimates \(e_{i}\) and \(e_{j}\).
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 2 Materials and Methods
### Digital Model Watermarking  
Traditional watermarking methods are designed to insert fingerprints into geometries of polygons or parametric meshes [22; 23]. Nonetheless, in this work, the target contents are volume models comprised with voxels. They lack connectivity and topological information, and thus conventional watermark embedding methods are not applicable for them. To overcome this problem, we develop a self-organizing mapping (SOM) procedure to encode watermarks for these models. The fundamental concept of this procedure is depicted in Figure 4. At first, the watermark is rasterized in a 2D lattice of nodes. Hence, this lattice forms a binary image of the watermark; some of its nodes are watermarked while others remain intact. Then, an iterative correspondence training process is triggered to create links between the lattice nodes and the ROI voxels. Finally, the watermark is placed inside the ROI via these correspondent relations. The iterative correspondence training and embedding computations are described in the following contexts.
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Top View of Antenna** & **Location of** & **Method for Circular** & **Number of Loop** & **Spacing between** & **3 dB Axial Ratio** \\  & **Configuration** & **Parasitic Loop** & **Polarization** & **Corner Connections** & **Two Loops, \(S\)** & **Bandwidth** \\ \hline \hline \multirow{2}{*}{[7]} & \multirow{2}{*}{inner} & perturbation & \multirow{2}{*}{4} & Fixed & \multirow{2}{*}{3} \\  & & & segments & & (\(S=0.02\lambda_{0}\)) & \\ \hline \hline \multirow{2}{*}{[9]} & \multirow{2}{*}{\(\text{F}_{\text{\tiny{\textbackslash}}}\)} & \multirow{2}{*}{\(\text{\tiny{\textbackslash}}\)} & \multirow{2}{*}{\(-^{1}\)} & quasi-two sources & \(-^{1}\) & \(-^{1}\) & \(12\) \\ \hline \hline \multirow{2}{*}{present} & \multirow{2}{*}{\(\text{F}_{\text{\tiny{\textbackslash}}}\)} & \multirow{2}{*}{outer} & quasi-two sources & \multirow{2}{*}{2} & optimized & \multirow{2}{*}{23} \\  & & & & & (\(S=0.02\lambda_{0}\)) & \\ \hline \hline \multirow{2}{*}{\(\text{F}_{\text{\tiny{\textbackslash}}}\)} & \multirow{2}{*}{\(\text{\tiny{\textbackslash}}\)} & \multirow{2}{*}{inner} & quasi-two sources & \multirow{2}{*}{2} & optimized & \multirow{2}{*}{40} \\  & & & & & (\(0.02\lambda_{0}<0.05\lambda_{0}\)) & \\ \hline \hline \end{tabular} \({}^{1}\) Not applicable.  
\end{table} 
Table 1: Improvement of the present study compared to previous ones. Figure 1: Antenna with an outer parasitic loop that has (1) a branched feedline \(F_{\text{n}}\)–\(B\)–\(F_{2}\)\((n=1\) and \(2)\) and (2) the feedline is vertical to the ground plane. (**a**) Perspective view. (**b**) Top view. (**c**) Side view. After describing the design mechanism, this paper first analyze an antenna whose driven loop has an outer parasitic one. Next, we analyze a parasitic loop inside of a driven one and reveal the effects of parasite locations on radiation characteristics. Lastly, we present experimental work that was performed to validate the simulated results.
## 2 Modelling of PV System's Components
### Modelling of Photovoltaic Panel
proposed a model for photovoltaic system using adaptive artificial neural network, that combines Levenberg-Marquardt algorithm (LM) as learning algorithm and infinite impulse response (IIR) filter to accelerate convergence of the network [26]. SAPV experimental setup at Tahifet, Algeria validated the new proposed model. Solar radiation, ambient temperature and humidity are the simulation's input, and the outputs are voltage and current for photovoltaic generator. From the result, the authors prove that the proposed model is able to simulate different weather condition, and it is possible to generate current used by the load. Later, by using the same experimental setup for PV array area as before in [26], a similar case study with new modelling technique was introduced in [27], using Adaptive Neuro-Fuzzy Interference Scheme (ANFIS). ANFIS is a method that applies learning techniques in neural network and fuzzy inferences system, where the method can exploit both data and knowledge. From global radiation, ambient temperature and clearness index as an input, voltage and current for photovoltaic generator was generated. The result was proven to have reasonable accuracy compared to measured data, and more accurate compared to ANN model. As mentioned before, the real energy yield is always lower than theoretical energy production, due to power derating factor, \(\eta\) such as losses caused by dirt, shading, mismatch factor, mounting condition, manufacture tolerance, cable loss, aging and inverter efficiency. Most models correlate de-rating factor with ambient temperature. Most studies calculate temperature losses factor using the following equation,  
\[V_{t}=\frac{Akt_{stc}}{q} \tag{2}\]1  
where \(\gamma_{pump}\) is power coefficient (\(\%/\)C\({}^{-1}\)), \(T_{cell}\) is temperature measured from the back of the module, and \(T_{STC}\) is temperature in standard test condition [4].
## 2 Monitoring Scheme
### Instantaneous Baseline Establishment for Comparative Monitoring
If the feature values of one segment are significantly different from those of the other segment in the current stage, distinct damage may occur. The detailed validation process is described in Section 4.1. The interlayer debonding is a gap between track slab and CA mortar layer, longitudinally along the track slab in length, transversely along the track slab in width, and vertically along the track slab in height. The length of debonding is measured directly by a meter ruler. The end of the meter ruler is inserted into the gap until it stops moving; the scale of the meter ruler is the width of debonding. The height is measured by a thickness gauge, which consists of a number of pieces with different thicknesses, and the thickness of the piece inserted into the gap determines the height of debonding. In this manuscript, the debonding refers to the height of the interlayer debonding. As the structural inspection of the high-speed railway must be carried out in the skylight and the number of skylights is limited, we had to measure the size of the debonding at irregular intervals. A total of eight tests were carried out in March, June, August, October in 2019, and January, June, August, October in 2020. In October 2020, the health condition of Segment 2 had deteriorated, and the size of interlayer debonding in Segment 2 was about 6.45 m in length, the maximum height was about 2 mm (Figure 3), and the maximum width was about 155 mm. The location of the interlayer debonding was under a track slab near the middle of the viaduct. The discrimination of multiple time-domain, frequency-domain, and time-frequency features at cross-sections 1-1, 2-2, and 3-3 between the two segments will be examined in Section 4 to identify debonding-sensitive features and constructing a new indicator combining multiple debonding-sensitive features together. Figure 3: (**a**) Debonding area. (**b**) Measurement by thickness gauge. Figure 2: Longitudinal sketch of CRTS-II-slab ballastless track structure on the viaduct.
## 3 Results and Analysis
### Evaluation of Indoor Thermal Environment  
Section 3.2 evaluates the indoor thermal environment by varying MLSP. Figure 8 shows the indoor air dry-bulb temperature and relative humidity for each case.  
Figure 6: Cooling coil loads by varying MLSP.  
Figure 7: Cooling energy consumption by varying MLSP.  
In all simulation cases, the indoor air dry-bulb temperature was maintained below the indoor cooling set-point temperature owing to the supply air reaching the set-point in all scenarios. The relative humidity of indoor air decreased progressively from Cases #1 to #7, attributed to the corresponding decrease in MA absolute humidity across these cases. Consequently, as more OA was introduced, the relative humidity of indoor air decreased. This evaluation demonstrated that the indoor thermal environment remained stable despite variations in MLSP. Based on these findings, the optimal MLSP for this evaluation period was determined to be Case #5 (22 \({}^{\circ}\)C), which maintained stable indoor thermal conditions while achieving the lowest cooling energy consumption.
## 2 AR/VR Applications  
In this section, we review the main categories of applications that are using AR and VR technology as summarized in Figure 3. In Section 2.1, we highlight the benefits gained by Industry 4.0 from the implementation of virtual reality and augmented reality. In Section 2.2, we describe the ways in which AR and VR are transforming the entertainment industry by making it more immersive and engaging for the users. Section 2.3 discusses several possibilities for the use of AR/VR technology in healthcare and its evolution. In Section 2.4, the contribution of AR and VR to the automotive industry by creating a safe environment for the driver and making the assembly of cars more efficient is discussed. Section 2.5 presents the ways in which the increasing demand for or awareness of AR/VR technologies is benefiting the travel and tourism industry. In Section 2.6, the applications of virtual education to provide personalized learning or regulated environments for the learners in creative, innovative, and fun ways are highlighted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline fps & Frames per second & QoE & Quality of experience \\ HARQ & Hybrid automatic repeat request & QoS & Quality of service \\ HDD & Heads down display & RTT & Round-trip time \\ HDR & High dynamic range & SOC & System-on-a-chip \\ HMD & Head-mounted display & TI & Tactile Internet \\ HUD & Heads up display & URLLC & Ultra-reliable low-latency \\ IoT & Internet of Things & UWB & communication \\ IMU & Inertial measurement unit & VIO & Vistal-inertial odometry \\ KPI & Key performance indicator & VLC & Visible-light communication \\ MAC & Multiply–accumulate & VR & Virtual reality \\ MAR & Mobile augmented reality & WLAN & Wireless local area network \\ MEC & Mobile edge computing & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: The major use cases of AR/VR.
## 2 Materials and Methods
### Description of the Proposed Decision-Support Tool
#### 2.2.3 GIS-Based Routing Analysis  
Once the routing design criteria have been identified, the MCDA has been performed, and the corresponding weight factors have been derived, a multi-criteria routing analysis is conducted using the LCPA technique in GIS. LCPA constitutes an efficient methodology that can manage a variety of spatial criteria to enhance the effectiveness and reduce the costs of large-scale engineering projects, such as CI. LCPA is based on a variation of [PERSON]'s algorithm [58] and is a highly effective way to identify the shortest and most cost-efficient route on a cost surface, such as a raster surface, between the starting and destination points.  
The algorithm first examines all the cells that are adjacent to the starting point in the horizontal, vertical, and diagonal directions, and the one with the lowest cost is identified. This cell is then considered the new starting point, and the next cell with the lowest cost is determined. This process is repeated iteratively until the user-defined origin and destination points are connected. Subsequently, a back-propagation process from the destination to the origin point is conducted using the previously identified cells with the lowest cost, which results in the least-cost path. It is worth noting that one of the most important advantages of [PERSON]'s algorithm and its variations is that they guarantee the solution of the shortest path problem in a computationally efficient manner compared to other route planning algorithms. This is attributed to the fact that they examine all possible cells in the cost surface, identify all suitable paths, and ultimately determine the most cost-effective one between two geographic points.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results  
This section starts by analyzing the WuRx power consumption. Then, results of the microbenchmarks performed to provide detailed insights into the energy cost of the transmission and reception of a packet using the evaluated protocols are exposed, and these results are used to compute the \(H\) and \(\xi_{\infty}\) values related to the EUC metric defined in Section 3. The energy consumption of the sink incurred by the evaluated protocols is then studied. Next, the benefits of SNW-MAC are shown by comparing it to the two state-of-the-art MAC protocols introduced in the previous section. Finally, our scheme is evaluated under variable energy harvesting conditions to show the benefits of the EM in collaboration with the MAC protocols and the higher performance of the proposed approach.