esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Applications of BIVA in Sports  
Some examples of BIA, BIVA, and somatotype characteristics applied to sports are presented below along with corresponding interpretation. This section presents a series of cases, by way of example, in which SF-BIA was applied and interpreted according to the two previously reviewed patterns as well as other techniques widely used in athlete BC research. The sports included are:  
* Endurance sports: rhythmic gymnastics.
* Velocity/power sports: athletics (team sprints and relay races) and canoeing.
* Team sports: rugby.  
Anthropometric measurements were performed according to standard procedures [44]. An electronic scale and a SECA stadiometer (Hamburg, Germany) were used to measure body weight (W, kg; precision: 100 g) and height (H, m; precision: 0.1 cm). Body circumferences were determined using a flexible and nonextendable steel tape (to the nearest 1 cm). Somatotypes were calculated by employing the Heath-Carter method [12].  
Bioimpedance analysis (BIA) was conducted by single-frequency mode (at 50 kHz, 400 \(\mu\)ARMS) using a tetrapolar electrode configuration with a BIA 101, AKERN-SrI biompedance measuring device (Florence, Italy). The measurement method was standardized in accordance with the protocol used by [PERSON] [18]. Measurement errors of the system were determined using a precision resistor and capacitor, with <1% for R and <2% for capacitance. Fat-free mass (FFM) was estimated using the equation of [PERSON] et al. [25], and fat mass (FM) was calculated as the difference between body weight and FFM. For bioelectrical impedance vector analysis, the components of the impedance vector (R and Xc) were normalized by the subject's height (R/H (ohm/m) and Xc/H (ohm/m), respectively) and were expressed on the R-Xc graph (abscissa axis: R/H; ordinate axis: Xc/H). Individual impedance vectors were compared with the vector distribution of [PERSON] et al.'s reference sports population [28] (50%, 75%, and 95% tolerance ellipses) (software provided by [PERSON]--BIVA Programs, released in 2002).  
The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Clinical Research Ethics Committee (CEIC) of Valladolid-East Health Area (Protocol code: PI 16-512 of 17 January 2017).  
Systematized exposure of the casework was carried out. Athlete data are presented in tabular form:  
* BMI, classified according to WHO criteria [45; 46].  
BC, determined by conventional BIA (FFM and FM). Reference values were obtained by the author who validated the equation [25].
* Somatotype. Values and interpretations thereof are included in the table. Reference values for each discipline are also included [47].
* Phase angle value.
* BIVA graph.
# A Review on Methods and Systems for Remote Collaboration
## 4 Visual Display Technologies  
In this section, we focus on methods that are used to share the visual space between two distant collaborators.  
The most common method used in remote assistance and collaboration is a simple video camera with the ability to stream a 2D video, which is shown on a monitor at the helper's side. In order to give the remote collaborator the feeling of being collocated with the local user, the researchers also experimented with the use of 3D videos, 360-degree panoramas, virtual reality, augmented reality, and mixed reality. For each of the last three technologies, we will also provide a short definition within our discussion for a better understanding.
## 1 Introduction
We aim to minimize the delay of FL by jointly designing the computation and communication resource allocation;
* Due to the non-convex form of the objective function, the coupling effect of multiple variables and the binary constraint brought by the user selection vector, the optimization problem is difficult to solve. By introducing additional variables and invoking the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) techniques, we convert it into a convex optimization problem. Then, a low-complexity algorithm is proposed to optimize the computation and communication resources allocation;
* The simulation results demonstrate that the effectiveness of proposed algorithm in reducing the training time of the FL system base on imperfect CSI. The rest of this paper is organized as follows. The system model and channel model are described in Section 2. In Section 3, we formulate the latency minimization problem. The iterative algorithm to find the local optimal solution of the latency minimization problem is given in Section 4. In Section 5, we discuss the simulation results. Finally, our conclusions are drawn in Section 6. _Notations_: In the paper, scalars, vectors and matrices are denote by lowercase letters, lowercase bold letters and uppercase bold letters, respectively. \(C^{M\times N}\) denotes the space of \(M\times N\) complex matrix. \(j\) denotes the imaginary unit. \(I\) denotes the identity matrix with appropriate dimensions. \((\cdot)^{H}\) and \(Tr(\cdot)\) represent the Hermitian transpose and the trace, respectively. \(F_{X}(\cdot)\) denotes the cumulative distribution function (CDF) of the variable \(X\). Finally, \(CN(0,\sigma^{2})\) denotes the circularly symmetric complex Gaussian (CSCG) associated with zero-mean and variance \(\sigma^{2}\). The other parameters used in the paper are listed in Table 1. The acronyms used in the paper are listed in Table 2.
# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### Reuse of Low-Level Features in Medical Images
#### 4.1.2 Dense Connection Reuse of Medical Image Low-Level Features  
Due to the characteristics of large differences in the shape of medical image objects and weak organizational boundary information, high-level features cannot reflect accurate edge information. [PERSON] et al. [78] found that since the two adjacent layers of ResNet can be directly connected, they can be directly connected to any two layers. The architecture comprises multiple dense blocks with transition layers between them. Each dense block consists of multiple convolutional layers, where each layer receives feature maps from all preceding layers and passes on its own feature maps to all subsequent layers. Specifically, each layer implements the operations: BatchNorm -> ReLU -> Convolution. A transition  
Figure 6: U-shape: It can be seen from the network structure that U-Net combines low-resolution information (providing the basis for object category recognition) and high-resolution information (providing the basis for accurate segmentation and positioning) and is perfectly suitable for medical image segmentation.  
layer is used between two consecutive dense blocks, which consists of BatchNorm, ReLU, Convolution, and then a 2 \(\times\) 2 average pooling. As shown in Figure 7, the dense block makes the gradient propagation more direct, and even the gradient of the last layer can be directly transmitted to the first layer. The low-level features of medical images are very important features [79]. Compared with the residual network, which can only use high-level features, the dense block can reuse the low-level features to better retain the original features of the input image and extract feature information to the maximum through specific network design due to the direct connection of any two layers. At the same time, in terms of computing power, the dense block adds up the low-level features and high-level features so that the number of channels is greater, the parameters are more effective, and each layer can use fewer parameters.
## 2 Recent Works
### Coverage of UAV  
In 2019, [PERSON] et al. [11] studied the problem of intelligently tracking ground users with UAVs without access to user-side information, such as user location. The authors established a reinforcement learning model and applied the deterministic policy gradient (DPG) algorithm to the model but ignored the relationship between the coverage range of UAVs and the width and height of antenna beams. [PERSON] et al. [12] revealed and analyzed new trade-offs between UAV design space dimensions in different scenarios but did not consider the uplink link scenarios for the UAV coverage of disaster-affected areas. [PERSON] et al. [13] proposed a gradient projection-based algorithm to find the optimal position of UAVs, thus maximizing the duration of uplink transmission while covering users, but only considered the two-layer network structure between UAVs and users.  
In 2020, [PERSON] et al. [14] proposed a three-layer network system of satellites, UAVs, and ground to enhance network coverage and solved the problem using problem decomposition, continuous convex optimization, and bisection search tools. However, they did not consider that different ground users have different network quality requirements. [PERSON] et al. [15] classified ground users with different network requirements and studied the UAV coverage problem in terms of maximizing energy efficiency and user experience quality but did not consider non-intentional interference from the ground on UAVs. [PERSON] et al. [16] studied the problem of the uninterrupted coverage of UAVs in an environment that allows ground interference and evaluated the impact of external interference on the connectivity of UAV groups.  
In 2021, [PERSON] et al. [17] designed a greedy algorithm based on DRL to determine the optimal trajectory of UAVs in order to maximize the coverage of ground users for a higher coverage rate. [PERSON] et al. [18] introduced a SI-EDTL technique and used it to construct an accurate and tunable deep transfer learning model for multiple object detection by UAV.  
In 2022, [PERSON] et al. [19] studied the problem of UAV coverage under partially observable conditions and introduced a new network architecture based on deep recursive graphs in order to deal with information loss caused by partial observability.
## 3 Materials and Methods
### Image Fusion Methods  
We selected two DL-based methods [29; 34] to generate the fused images employed in the present study; we chose these methods because they present several desirable features. The method proposed by [PERSON] et al. [29] uses a pre-trained _VGG19_ DCNN as a part of its process to extract multi-layer features of the detail content of the source images. Since [PERSON] et al. employ only specific layers of the said pre-trained network, no further training on new datasets is needed. The _Fire-GAN_ model proposed by [PERSON] et al. [34] is, to the best of our knowledge, the only DL-based method that addresses the fusion of visible-NIR images for fire imagery. It is based on a Generative Adversarial Network  
Figure 3: Sample images of the Corsican Fire Database.  
(GAN) that expands on the one proposed by [PERSON] et al. in [31] to allow for the preservation of color in the generated fused images, the processing of higher resolution images, and to control the amount of visible or thermal information included in the fusion process, to account for the particular thermal characteristics of fire NIR images.  
In the following subsections, we present both methods in greater detail, succinctly addressing their characteristics and their associated advantages and disadvantages.
## 3 The Proposed Delay-Doppler Block Division Multiplexing  
This section proposes a waveform named delay-Doppler block division multiplexing (DDBDM). We first introduce the signal generation process of the DDBDM, and then explain how it addresses the issues present in existing INAC waveforms for PNT applications.
## 2 Methodology
### Description of the Dataset  
In this study, therefore, field drilling data acquired through a high-speed (wired drill pipe) telemetry system was used for modeling equivalent circulating density (ECD) based on drilling parameters. Data quality determined the model accuracy performance of the data-driven machine learning modeling. Therefore, the raw data was preprocessed to ensure cleanliness and appropriate feature selection using a correlation heat map to meticulously identify the most suitable features for the modeling process.
# Automated Test Creation Using Large Language Models: A Practical Application
## 6 Future Work  
The initial experiments showed that the developed software prototype can be a useful assistant in the teacher's work. To assess its usability, effectiveness, and impact on the learning process, experiments in a real educational environment need to be conducted. Therefore, the work is planned to continue in several directions:  
* Empirical study: The application will be used for generating and conducting tests in existing courses in computer sciences, allowing it to be evaluated by both teachers and students. Through observation and conducting surveys, the usability of the application, its impact on the teachers' work, the learning process, and the learning outcomes will be assessed.
* Comparative analysis with traditional test creation methods: Studies will be conducted to gather opinions from both teachers and students regarding the quality, relevance, adaptability, and evaluation of questions generated with the help of AI.
* Exploration of the application's potential in different fields of education: This research will focus on the adaptability of the application across various disciplines outside of computer and technical sciences. Experiments will be conducted with courses in social and humanities fields to test its ability to generate questions in different contexts and at varying levels of complexity.
* Evaluation of applicability in different teaching strategies: The application will be studied in various teaching strategies and approaches, such as problem-based and project-based learning, self-learning, group work, etc. Experiments and analyses will be conducted to explore how the application can be used to support different teaching strategies and forms of learning.  
These planned studies will provide a clearer understanding of the applicability and effectiveness of the developed prototype and offer ideas and directions for its improvement.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 2 Dynamic Model of Load Frequency Control  
### Multi-Area Interconnected Power System  
To ensure the stable control of frequency in the power system, it is imperative to accurately represent the fundamental dynamic characteristics of the system frequency through the construction of a suitable model. When the changes in load are insignificant, the LFC system can be effectively analyzed using a linear model. Therefore, the simplified linear models for all dynamic simulations are adopted in this paper.  
In the regulation of a power system's frequency, the primary control variable is the active power output of the generators. During periods of steady-state operation, it is expected that the active power will be synchronized with the load power. The interconnected power system is commonly composed of multiple control areas, which are interconnected through tie-lines to facilitate power exchange during regular operations. This interconnected arrangement helps address any discrepancies between power generation and demand. When confronted with a load change disturbance, the operation of the interconnected power system model aims to achieve two primary control objectives [26]. The primary goal is to maintain the load frequency deviation at or close to zero. The second objective is to maintain the tie-line power within the desired set value.  
Taking a three-area interconnected power system, for instance, as shown in Figure 1, the LFC mechanism has the responsibility of maintaining power exchange between these areas within the scheduled limits. In essence, it is imperative that any alterations in the tie-line flow \(\Delta P_{ij}\) remain at zero.
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 2 Related Work
### Uncertainty Quantification  
Uncertainty quantification is essential in multimodal learning, particularly for dynamic fusion and model calibration. [PERSON] (2019) proposed an uncertainty-aware multimodal learning approach that uses cross-modal random network predictions to adjust fusion weights, ensuring consistency across modality predictions. [PERSON] (2019) provided a comprehensive review of uncertainty quantification in deep learning, highlighting the efficacy of Bayesian neural networks, Monte Carlo dropout, and ensemble learning in enhancing model robustness. Specifically, [PERSON] (2019) introduced Monte Carlo dropout, which estimates prediction uncertainty through multiple forward passes, while [PERSON] (2019) demonstrated that by aggregating predictions from multiple models, ensemble learning improves robustness and uncertainty estimates.  
Furthermore, [PERSON] et al. (2020) reviewed deep learning-based information fusion techniques for multimodal medical image classification, discussing strategies such as input fusion, intermediate fusion (including single-layer, hierarchical, and attention-based fusion), and output fusion (ScienceDirect). These general fusion strategies provide a foundation for effectively integrating multimodal data and can be enhanced through uncertainty quantification to improve model robustness and accuracy. For instance, attention-based intermediate fusion can dynamically adjust modality weights based on uncertainty metrics, optimizing the fusion process. Although these strategies have not yet coalesced into a unified framework, they offer valuable insights for dynamic fusion in multimodal learning. The PAMRL framework proposed in this paper introduces a hybrid uncertainty metric to address the limitations of entropy-based metrics in MLA, optimizing cross-modal fusion and significantly improving robustness in noisy and imbalanced scenarios.  
Despite their strengths, these methods encounter limitations in multimodal contexts. In MLA, the entropy-based uncertainty measure struggles to distinguish between \"correct and confident\" and \"incorrect and confident\" predictions, risking overreliance on low-quality modalities. While Monte Carlo dropout and ensemble learning are effective, their high computational cost makes them impractical for large-scale multimodal models or datasets. Furthermore, these techniques were primarily designed for unimodal settings, and their adaptation to ensure cross-modal uncertainty consistency remains underexplored.  
Addressing these gaps is critical for improving the reliability and scalability of uncertainty quantification in multimodal learning.
## 2 Background and Related Work
### Consensus Mechanisms
#### 2.4.1 PoW  
During mining, a new block is created by computing the block's cryptographic hash. To prove its validity on the blockchain, a block hash must meet certain conditions. The  
Figure 3: Execution of a 51% attack with new hash power.  
Bitcoin blockchain, for example, starts each hash block with four trailing zeroes. Given that block data, which are transactional data, cannot be changed, the miner must modify the predefined hash pattern at every occurrence. The two network partners compete for the right nonce to create a valid block hash. Initially, the miner who seeks a solution attaches the block to the chain. As a reward for the miner's efforts, the system produces a certain number of coins and provides the newly produced coins to the miner.  
The PoW mechanism entirely relies on the computer power of the miner. The more computing power a miner has, the greater the chance of finding blocks and earning rewards [46]. In PoW consensus, half of the network's nodes are assumed to remain trustworthy. As a result, this consensus is vulnerable because more than half the hashing power is owned by a single party. The cost of resources and hardware is one of the significant disadvantages of PoW. Several studies have reported that the energy consumption of Bitcoin mining is considerably higher than the energy consumption of 159 countries [47]. By contrast, the mining requirements and the mining time can differ depending on the algorithm used by each cryptocurrency. PoW mining is relatively slower than other consensus protocols. Given that a small number of mining pools dominate the Bitcoin network, attacks on these pools may result in severe disruptions. Recent attacks have demonstrated that PoW is vulnerable to 51% attacks. Low-hacking crypto coins based on PoW consensus are susceptible to 51% attacks because the requisite hash is easy to obtain. With the appropriate budget, the P+ epsilon attack can be conducted at no cost [48]. In addition, the researchers in [49] studied blockchain security and performance-based PoW. They presented a novel quantitative approach to examine the security and performance implications of various consensus and network parameters applied to PoW blockchains. Therefore, the approach proposed in [49] is solely based on PoW consensus, as opposed to hybrid mechanism approaches that offer higher levels of security and performance. Another study was conducted by [50] to review the role of blockchain in preventing future pandemics. Several applications of blockchain technology were also discussed, and these may assist in fighting the COVID-19 pandemic.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 2 Overview on the IEEE 802.15.4 TSCH and RPL
### Time-Slotted Channel Hopping
Nodes that wish to join the network must request the information from the nodes that are already part of the network. For this reason, Enhanced Beacons (EB) are used to broadcast information on synchronization, sending broadcast messages that contain, as their main form of information, the ASN value of the device itself to serve as a time reference to new devices. Thus, the synchronized nodes will be broadcasting EBs periodically, while the nodes that wish to join will be listening for a considerably longer time, in comparison to the slot length, in different channels until they get a signalling message to correctly adjust their time origin.
# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 5 Use Cases from the CELLS ALBA Synchrotron Radiation Facility
### Scientific Assessment of the LLM Analyses  
The identification of peaks around 528 eV and 530 eV and their association with lattice oxygen in metal oxides and adsorbed oxygen species or contamination effects is consistent with known XPS analysis phenomena. These binding energy values are within the expected range for oxygen species on metal surfaces, reflecting the versatility of XPS in detecting surface chemical states.  
The consideration of sulfur contamination affecting peak interpretation is a realistic concern in XPS studies. Sulfur species can indeed contribute to the complexity of the spectrum, necessitating careful analysis to differentiate between overlapping peaks and chemical states.  
The detailed analysis capturing the potential for the formation of silver oxide (Ag2O or AgO) during the oxidation process is a plausible outcome expected in such experiments. The discussion around various oxygen species, including adsorbed oxygen and hydroxyl groups, aligns with the surface chemistry changes one would anticipate during the oxidation of silver surfaces.