esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Circuit with ASK Modulation
### Operation Principle  
To achieve a high data rate, utilizing a relatively high carrier frequency to extend the bandwidth for data transmission is vital. Meanwhile, it should be noted that the chosen carrier frequency needs to meet human safety requirements (<20 MHz). Therefore, this proposed circuit employs a 16 MHz carrier frequency to drive the Class-E PA. A 16 MHz active crystal oscillator with a fixed duty cycle of 0.5 is utilized to maintain a stable carrier signal. Simultaneously, the input data signal is synchronized with the carrier signal to prevent glitch impulses from occurring at the gate of \(S_{1}\). When the data transmission signal is at a high level, \(S_{2}\) and \(S_{3}\) turn on, enabling the circuit to operate in the Class-E PA's optimally tuned state. In other words, the Class-E PA has the highest efficiency in this state.  
When the data transmission signal is at a low level, \(S_{2}\) and \(S_{3}\) turn off, and \(L_{\mathrm{a}}\) is incorporated into the Class-E PA's load network, the carrier signal periodically turns \(S_{1}\) on and off. During this time, the load network contains the \(L_{\mathrm{a}}\) and the reactance of the load network is increased, resulting in a reduction in the voltage amplitude of the transmitter coil. As a result, the ASK modulation scheme is achieved. To enhance power transmission efficiency, it is crucial that this load network operates in the high-efficiency region shown in Figure 7. Consequently, this reconstructed load network can be denoted as the Class-E PA's sub-optimally tuned state.  
The purpose of adding \(S_{3}\) is to prevent the flow of AC from the ground to the transmitter coil when the data are at a high level. If \(S_{3}\) is removed, the voltage from the ground to the transmitter coil will exceed 0.7 V, causing the current to flow through the body diode of \(S_{2}\). As a result, achieving ASK modulation becomes challenging due to the slight change in the transmitter's voltage amplitude.
# Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation
## 1 Introduction
This technique allows for easy implementation of algorithms to create sparse structures by removing wires with low current magnitudes from the structure. The application of OCGA not only significantly reduces the mass and surface of the structures but also decreases computational costs, including calculation time and the necessary computer memory. When it is necessary to reduce the mass and surface of scattering devices, the use of OCGA to design sparse scatterer structures becomes truly essential. However, the biggest difference between antenna and scattering structures is the characteristics of the excitation source. In antennas, the source is typically fixed [6], while in scattering devices, the source depends on many parameters of the incident wave, for example, direction, polarization, and wave front (spherical, plane, etc.) [48; 49]. Therefore, applying OCGA to scattering structure presents more challenges. CR structures are composed of flat metal plates with various shapes [50; 51]. Therefore, reducing the mass and surface of rectangular flat scattering plates will be considered a preliminary study to apply the OCGA algorithm to more complex scattering structures in the future. In addition, the synthesis and review of the results related to the reduction in antenna mass and surface based on OCGA will provide more insight into the effectiveness of this algorithm. This will facilitate the application of OCGA to reduce the mass and surface of scattering structures. Therefore, the aim of this paper is to present the results of sparse antennas generation summarized from previous studies and developed algorithm for generating sparse scattering structures based on OCGA. This paper is organized as follows. Section 2 presents a comprehensive review of approaches for reducing mass and surface of various antenna structures using the OCGA. Section 3 presents the results of validating scattering characteristics for the plate scatterer structure, obtained using MoM with pulse basis function (PBF) based on WG. Section 4 introduces an algorithm to sparse the scattering structure when it is excited by an incident wave at a specific direction. Section 5 presents another algorithm to generate sparse scattering structures and analyze their scattering properties when the specific excitation region of the incident wave is unknown through the scattering plate example. Section 6 analyzes it when this region is known. Section 7 presents the results of generating a sparse structure of a dihedral and circular trihedral corner reflector. Section 8 summarizes and discusses the results obtained and highlights the points to consider in future studies, followed by conclusions in Section 9.
# A Kinematic Model of the Shoulder Complex Obtained from a Wearable Detection System
## 2 Materials and Methods  
### Mechanism Model of the Shoulder Complex  
To obtain a kinematic model of the shoulder complex, it is necessary to first understand the anthropotomy. Generally, the shoulder complex consists of the shoulder girdle and the humerus, and the humerus connects to the scapula of the shoulder girdle through the GH joint [27]. The shoulder girdle includes the scapula, sternum, clavicle, sternoclavicular joint (SC), scapulothoracic joint, and acromiodavicular joint (AC) [20; 21]. As shown in Figure 1, the GH joint is composed of the humeral head and the glenoid cavity of the scapula, which is usually equivalent to a spherical joint, and the kinematics of the AC, scapulothoracic (ST), and SC joints are not clear [28; 29]. Thus, it is difficult to understand the motion characteristics of the shoulder girdle.  
Theoretically, all the joints and bones of the shoulder complex are particularly complex [24]. Although some researchers have been looking for an equivalent kinematic structure considering the overall motion characteristics, they do not wholly replicate the shoulder girdle [25]. The mechanistic theory of joint physiology is helpful to understand the shoulder complex kinematics [15].  
The GH joint has three revolute DOFs with axes intersecting vertically in the GH joint center and moves with the functional relevance of the shoulder girdle during humeral movements. According to the knowledge of theoretical mechanics, the general motion of a rigid body can be decomposed into translation following any of the base points and rotation relative to the base point. Thus, the shoulder girdle is assumed to be a platform. The mobile reference frame is established on the shoulder girdle, and the origin of the mobile reference frame is \(G\). Relative to the thorax coordinate system \(O\)-\(XYZ\) (i.e., the coordinate directions are taken from Reference [7]: the \(x\)-axis is equal to the frontal axis, and the direction is from the sternum to the GH joint center, the \(z\)-axis is equal to the vertical axis, and the direction is upward, \(y\) is the cross product of vector \(z\) and \(x\), and the coordinate origin is the deepest point of the incisura jugularis [25]), the \(G\) point has three translational DOFs. The fixed connection coordinate frame is established on the humerus head, and the origin represented by \(g\) is a point on the humerus head coinciding with the GH joint center. Relative to the mobile reference frame, the humerus has three rotational DOFs with axes intersecting vertically at the point \(g\) in space. In this way, a generalized GH joint with a floating center (i.e., a 3-DOF spherical joint with a floating center, the center displacement variable of which relative to the thorax coordinate system is coupled with its rotation) is presented, as shown in Figure 2.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis  
HS-SPME-GC-MS analysis was performed as described by [PERSON] et al. [27] with some modifications. Powder samples (0.8 g) with saturated NaCl solution (4 mL) were hermetically sealed in a 20 mL headspace glass having a PTFE/silicon septum (Supelco, Bellefonte, PA, USA) and a magnetic screw cap. Then the samples were incubated at 40 \({}^{\circ}\)C for 40 min. A stainless-steel needle containing divinylbenzene-carboxen-polyddimethylsiloxane (DVB/CAR/PDMS, 50/30 \(\upmu\)m) SPME fiber (Supelco, Bellefonte, PA, USA) was inserted through the septum of the sample vial to extract the volatile compounds at 40 \({}^{\circ}\)C for 40 min.  
The volatile compounds were determined by the Agilent 7890B/5977B GC-MS instrument (Agilent Technologies Inc., Palo Alto, CA, USA) with the VF-WAXms capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies Inc., Palo Alto, CA, USA). The sample extracted by HS-SPME was injected in splitless mode, with the inlet at 250 \({}^{\circ}\)C. The carrier gas was 99.999% pure helium at a fixed flow rate of 1.68 mL/min. The initial oven temperature was 35 \({}^{\circ}\)C, ramped at 2 \({}^{\circ}\)C/min to 130 \({}^{\circ}\)C, then increased to 250 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min and held for 10 min. Mass detector was conducted in an electron impact mode at 70 eV and the ion source temperature was 230 \({}^{\circ}\)C. Mass spectra (MS) were scanned from 50 to 550 amu. The retention index (RI) of each compound was calculated using n-alkanes C7-C30 (Sigma-Aldrich, St. Louis, MO, USA) as external references. The RIs were calculated according to the retention time of n-alkanes. The volatile compounds were identified both by comparing RIs with those of the authentic compounds and by matching the mass spectra in the standard NIST 14 library. The reverse match factors of identified compounds were bigger than 900, whose RI value differed within 20 from those in the database. Volatile compounds were approximately quantitated according to the Equation (3) by adding 50 \(\upmu\)L, 50 mg/mL cyclohexanone as the internal standard [33]. The concentration of the volatile compound in the shiitake mushrooms samples was calculated according to the Equation (4).  
\[\frac{m_{\text{is}}}{m_{\text{tc}}}=\frac{A_{\text{is}}}{A_{\text{tc}}} \tag{3}\]  
\[c=\frac{m_{\text{tc}}}{0.8} \tag{4}\]  
In Equation (3), \(m_{\text{is}}\) and \(m_{\text{tc}}\) were the concentrations of internal standard and target compound in the mixture of powder samples and NaCl solution, respectively, \(A_{\text{is}}\) and \(A_{\text{tc}}\) were the peak areas of internal standard and target compound, respectively.  
In Equation (4), \(c\) was the concentration of the volatile compound in the shiitake mushrooms samples and expressed by \(\upmu\)g/g.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 5 Literature Review
### Blockchain-Based Solutions  
In this section, we delve into various papers that have explored blockchain-based solutions for enhancing privacy and security in healthcare.  
[PERSON] et al. [28] introduce HealthChain, a blockchain-based approach to enhance the security and interoperability of electronic health records (EHRs). By incorporating AES-256 encryption and Elliptic Curve Cryptography (ECC), it safeguards sensitive medical data while enabling efficient and seamless data exchange between healthcare providers. The framework leverages smart contracts to automate access control and maintain data integrity. Designed for privacy, resilience, and improved security, HealthChain surpasses conventional centralized EHR systems in both data protection and interoperability.  
In their paper, [PERSON] [29] suggests using a blockchain-based approach to ensure privacy and identity allocation in IoT devices. The paper covers the basics of the IoT and blockchains and various consensus models. The authors propose a secure identity management system that leverages public key cryptography. The paper presents an approach to preserving privacy in IoT devices utilizing blockchain technology.  
[PERSON] et al. [30] proposed a blockchain-based technique for ensuring the security of medical certificates in IoT healthcare. Their architecture utilizes Ethereum with a Proof of Stake algorithm and IPFS for secure storage. The system ensures privacy, performance, and security through smart contract rules, outperforming existing schemes. Future improvements will aim to address scalability and certificate updates.  
[PERSON] et al. [31] proposed a blockchain-based access control framework for the secure sharing of electronic medical records (EMRs) in smart healthcare systems. The framework employs smart contracts for user verification, access authorization, misbehavior detection, and revocation. EMRs are encrypted and stored in the cloud, and their hashes are recorded in the blockchain. An evaluation with a private Ethereum system demonstrated its efficiency in real-time healthcare scenarios.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Feature Fusion Module
#### 3.3.2 Fusion at the Score Level  
Score-level fusion integrates the output scores or ranks from different models, focusing on the distance matrix that measures the similarity between the query and gallery sets in re-identification tests. This approach is particularly efficient when each model captures unique aspects. It is important to note that in our scoring system, a lower score represents a better match. This means that the Re-ID system is more confident in the identity match when the score is minimized. Consequently, during the evaluation phase, our aim is to achieve the lowest mean score across all identities, indicating the highest overall system accuracy.  
In the weighted averaging method, similarity scores or ranks from different models are averaged, with specific weights assigned to each score. The final score for a person (\(i\)), denoted as \(S_{\text{final}}^{i}\), is calculated using the following equation:  
\[S_{\text{final}}^{i}=\alpha\cdot S_{\text{appearance}}^{i}+(1-\alpha)\cdot S_{ \text{gait}}^{i} \tag{9}\]where \(S^{i}_{\text{gatt}}\) represents the gait score and \(S^{i}_{\text{appearance}}\) stands for the appearance score. The variable \(\alpha\) serves as a weight factor that balances the significance of the appearance and gait scores in the final score.  
Another approach is threshold-based conditional fusion. In this technique, gait and appearance are always utilized, but appearance features are only considered if the gait score is above the threshold. The score for a person (\(i\)), expressed as \(S^{i}_{\text{conditional}}\), is determined by the following conditions:  
\[S^{i}_{\text{conditional}}=\begin{cases}w\cdot S^{i}_{\text{appearance}}+(1-w) \cdot S^{i}_{\text{gait}}&\text{if }S^{i}_{\text{gait}}<\text{threshold}\\ S^{i}_{\text{appearance}}&\text{otherwise}\end{cases} \tag{10}\]  
In this case, \(w\) is the weight factor that influences the contribution of the appearance score when the fusion is conditional on the gait score being above the threshold.  
This paper aims to conduct a comparative analysis of these feature-level and score-level fusion methods to assess their distinct and collective impacts on re-identification (Re-ID) performance.
# A Spectrum Management Platform Architecture to Enable a Sharing Economy in 6G
## 3 Trading and Allocating Spectrum as Part of a Sharing Economy in 6G
### Requirements of the Spectrum Trading Platform  
Realising this vision requires the functionality of wireless devices and Radio Access Networks (RANs) to be enhanced such that resources become discoverable, sharable, and accessible almost in real time. The key functionality required for a spectrum-sharing economy platform is, therefore:  
1. Providing Connectivity. This is the ability to offer connectivity to wireless devices at (at least) the same or (preferably) better quality and efficiency (e.g., energy consumption) than existing radio access schemes. In other words, the platform should be able to connect to every device offering and/or demanding resources and should have access to fine-grained radio- and link-control functionality in order to offer the optimal (i.e., agreed) connectivity;
2. Scalability. The platform needs to support the anticipated scale and density of networks and devices in both current and future deployments, and maximise their capacity based on new and existing Radio Access Technologies (RATs). In other words, dynamic spectrum trading is particularly important when multiple devices via for resources, and the platform should be able to deal with this, and in near real time: deals may have to be renewed within minutes, with changing offers and demand of devices in range;
3. Heterogeneous Access. This is the ability to offer suitable network access to devices that are heterogeneous in terms of capabilities, requirements, and limitations. Particularly in unlicensed networks, devices sporting different RATs (e.g., Wi-Fi or Bluetooth)  
Figure 1: Vision of spectrum trading and allocating as part of a sharing economy in 6G with an online broker.  
may compete for the same spectral resources. The platform should be able to deal with that.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 5 Discussion
### Implications for Streaming Architecture and Data Sovereignty  
VidBlock's architecture represents a promising advancement in decentralized streaming by providing enhanced data sovereignty and user control. Unlike traditional centralized streaming systems, VidBlock only stores metadata on a blockchain-based ledger, while the actual content remains exclusively in the creator's local storage. This approach ensures that sensitive content data never leaves the creator's device, minimizing the risk of unauthorized access and reducing the need for extensive, centralized storage facilities.  
By storing only metadata and relaying connection details on the blockchain, VidBlock achieves a high level of security and transparency. The metadata include essential information such as the content title, stream status, and relevant hash values, enabling the system to verify the authenticity and integrity of content without accessing the actual media files. This distributed, blockchain-based metadata ledger also strengthens VidBlock's resistance to data breaches, as no central authority has control over the user data, and all records are encrypted and distributed across the network.  
Furthermore, VidBlock's serverless design and reliance on P2P connections drastically reduce infrastructure costs and operational overheads. Traditional CDNs depend on centralized servers and data centers, which require substantial investment and maintenance. In contrast, VidBlock's architecture leverages client and relay nodes to handle data distribution, creating a more economically sustainable model that scales efficiently with user growth. This design not only makes VidBlock a cost-effective alternative to centralized systems but also minimizes the impact of server failures, as the network relies on a distributed set of nodes rather than a single point of control.  
Additionally, VidBlock's robust failover mechanisms enhance service availability by dynamically switching to alternative relay nodes in the event of a node failure. This capability is crucial for real-time applications, where interruptions in service can disrupt user experience. VidBlock's failover strategy ensures that even if a relay node fails, the system quickly re-establishes the connection via an alternative node, maintaining an uninterrupted streaming experience. This resilience positions VidBlock as an ideal solution not only for live streaming but also for a range of real-time, decentralized applications, including remote collaboration, online gaming, and other interactive media platforms.  
In summary, VidBlock's architecture offers a unique combination of data security, cost-efficiency, and high availability, making it a compelling candidate for decentralized, user-controlled streaming in the Web3.0 era. By preserving content privacy on local devices while leveraging blockchain for metadata management and P2P for content distribution, VidBlock provides a scalable, resilient platform that aligns with the growing demand for decentralized, user-driven media solutions.
## Materials and Methods
### Dataset Partition and Parameter Settings  
MATLAB (MathWorks, Natick, MA, USA) with PLS Toolbox v.8.2.1 (Eigenvector Research, Inc., Manson, WA, USA) was used to conduct the NIR spectral data analysis. The independent test set samples were selected using the Kennard and Stone duplex method [28]; for each class, 50% of the samples were assigned to the training set and 50% were assigned to the test set (a split ratio of nearly 1:1). The sample composition of four classification models for each class (positive or negative) is shown in Table 1. The preprocessing method applied to the NIR spectral datasets was the first Savitzky-Golay [29] derivative (second polynomial with a filter width of 15) followed by mean centering to remove offsets from the spectral data.  
Cross-validation is a very useful tool that enables an assessment of the optimal complexity of a model such as the number of principal components in a PCA model. In this study, samples of each class randomly compose its correlated dataset; thus, it is appropriate to adopt the Venetian blinds method to perform cross-validation. Moreover, because this method requires that the variables are correlated, it is particularly suited to NIR data [30]. This approach includes a set of subvalidation procedures, each of which involves the removal of 10% of the samples from a total dataset; then, a model is built with the remaining 90% samples in the dataset and is applied to the removed samples. The optimal number of latent variables (LVs) in the classification model was determined using the root mean square error of cross-validation (RMSECV).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SIMCA**} & \multicolumn{2}{c}{**PLS-DA 1**} & \multicolumn{2}{c}{**PLS-DA 2**} & \multicolumn{2}{c}{**PLS-DA 3**} \\ \hline  & **P** & **N** & **P** & **N** & **P** & **N** & **P** & **N** \\ \hline  & **MS1** & **MS2,3,4,5** & **MS2** & **MS3,4,5** & **MS3** & **MS4,5** & **MS4** & **MS5** \\ \hline training & 90 & 239 & 52 & 187 & 61 & 126 & 49 & 77 \\ test & 90 & 237 & 52 & 186 & 60 & 125 & 49 & 76 \\ \hline \hline \end{tabular}  
* P: positive class; N: negative class.  
\end{table}
Table 1: The number of samples used in four classification models in both training and test phases. PLS-DA: partial least squares discriminant analysis, SIMCA: soft independent modeling of class analogy.