esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Adaptive OFDM-IM Method
### Establishment of the Energy Efficiency Maximization Equation  
Due to the dynamic and complex nature of vehicular network environments, traditional fixed-weight evaluation methods cannot accurately assess systems' energy efficiency. Furthermore, the interdependence among performance metrics makes finding the optimal configuration more challenging. To address these issues, we use the spectral efficiency, BER, and energy consumption as evaluation criteria for the system's energy efficiency. By establishing the energy efficiency maximization equation, as shown in (22), it is possible to optimize the multi-carrier index modulation system model for vehicular network scenarios based on the practical setting and user requirements.  
\[\max Y\{H_{i}(R_{i},\mu_{i})\}=a_{i}\eta+b_{i}BER+c_{i}NH, \tag{22}\]  
where \(i\) represents different scenarios \((i=\{1,2\}):i=1\) refers to the urban traffic scenario with scenario parameters \(R=10\) dB, \(\mu=0.5\); \(i=2\) refers to the highway scenario with scenario parameters \(R=20\) dB, \(\mu=0.2\). Different values of \(i\) define different channels based on the scenario parameters. \(Y\) is the output value of fuzzy inference under different typical channel conditions and is used to represent the system's energy efficiency. \(a_{i}\), \(b_{i}\), and \(c_{i}\) are the logical output weights of three logical factors under different channel scenarios, and \(\eta\) is the ratio of the spectral efficiency of the OFDM-IM system to that of the OFDM system, which is used for comparison with traditional OFDM systems. \(BER\) is the system's bit error rate, and \(NH\) is the system's energy consumption.  
Figure 5: OFDM-IM communication in urban traffic scenarios.  
It is important to note that the metrics \(\eta\),\(BER\), and \(NH\) in (22) have been normalized during the fuzzification step using membership functions, ensuring dimensional consistency and enabling a fair comparison of the weighted contributions of each metric.
# On the Hardware-Software Integration in Cryptographic Accelerators for Industrial IoT
## 2 Preliminaries and Related Work  
### Preliminaries  
The Advanced Encryption Standard (AES) is a symmetric-key encryption algorithm standardized by the U.S. National Institute of Standards and Technology (NIST) in 2001 [10]. AES is a variant of the Rijndael cipher [11], which can work with different key lengths and block sizes. In standardizing AES, NIST fixed the block size to 128 bits, while the possible lengths of the key are 128, 192, and 256 bits. AES is included in the ISO/IEC 18033-3 standard, and it is currently the only cipher publicly approved by the U.S. National Security Agency (NSA) for the protection of top secret information.  
SHA-2 (Secure Hash Algorithm 2) is a family of cryptographic hash functions that use the Merkle-Damgard construction with a one-way compression function that uses the Davies-Meyer structure. SHA-2 family has been standardized by NIST in 2001 [12], and it includes four hash functions with different digest sizes: SHA-224, SHA-256, SHA-384, SHA-512. SHA-224 is a truncated version of SHA-256 with a different set of initial values. The same holds for SHA-384 relatively to SHA-512.  
SHA-3 (Secure Hash Algorithm 3), also called Keccak, is a family of cryptographic hash functions that use a sponge construction [13]. SHA-3 has been standardized by NIST in 2015 [14], with the aim not to supersede SHA-2, but rather to provide a backup hash function family for it. Indeed, SHA-3 provides for the same digest sizes of SHA-2 (SHA3-224, SHA3-256, SHA3-384, SHA3-512), but it uses a completely different approach than SHA-2 in such a way that it is unlikely that new attacks discovered against SHA-2 also apply to SHA-3.  
From the standardization of the SHA-3 family in 2015 to now, the security industry has not shown a wide adoption of it, and the majority of applications still relies on the SHA-2 hash family. The first reason for this is that SHA-2 has not been shown to have structural weaknesses big enough to justify the switch, as instead happened in the transition from MD5 to SHA-1 and from SHA-1 to SHA-2. The second reason is that SHA-3 performs slower than SHA-2 when implemented in software. This is confirmed also by our experiments (see Section 4). However, the high level of parallelization of SHA-3 allows us to implement it on hardware accelerators (ASIC or FPGA) with better performance than SHA-2, both in terms of processing time [15] and energy consumption [16]. This is the reason why SHA-3 is expected to slowly replace SHA-2 in new developments, especially when SHA-3 hardware accelerators will be widely available on processors.
## 4 Conclusions
\begin{tabular}{l l} \multicolumn{2}{l}{**Nomenclature**} \\ \(A_{b}\) & Base amplitude (m) \\ \(b\) & Fin spacing (m) \\ \(Cp\) & Constant specific heat (J/(kg K)) \\ \(f\) & Vibrational frequency (Hz) \\ \(f_{n}\) & Natural frequency of thin fin (Hz) \\ \(H\) & Height of the fins (m) \\ \(k\) & Thermal conductivity of fluid (W/(m K)) \\ \(L\) & Length of the fins (m) \\ \(Nu_{without}\) & Nusselt number of heat sink in the no-vibration conditions \\ \(Nu_{with}\) & Nusselt number of heat sink at vibration conditions \\ \(p\) & Hydrostatic pressure (Pa) \\ \(Pr\) & Prandtl number \\ \(Re\) & Reynolds number (\(\rho\)Ub/\(\mu\)) \\ \(T\) & Temperature (K) \\ \(t\) & Time (s) \\ \(U_{i}\) & Velocity in \(i\)th direction (m/s) \\ \(X_{i}\) & Cartesian coordinates (m) \\ \(Y\) & Horizontal displacement at any time (m) \\ Z & Height between any point on the fin to the base of the fin (m) \\ Greek Symbols & \\ \(\mu\) & Fluid viscosity (Pa.s) \\ \(\rho\) & Fluid density (Kg/m\({}^{3}\)) \\ \(\tau\) & Cycle time (s) \\ \(\Delta t\) & Time step (s) \\ \(\alpha\) & Thermal diffusivity of the fin material (m\({}^{2}\)/s) \\ \(\tau_{ij}\) & Sub-grid scale stress (Pa) \\ \(\lambda\) & The solutions of the equation \(\cos\lambda\) cosh \(\lambda\) + 1 = 0 \\ Abbreviations & \\ \(PR_{Re}\) & Percentage reduction in Reynolds number \\ \end{tabular}
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 3 Component-Based IoT Architecture
### Central Control System Component
The topic and message structure of the MQTT protocol enables devices to receive data only from the designated party(s) based on the topic and level of subscription. * External data handler: This sub-component is in-charge of passing the data from the external data input component to the software application components for visualization and data storage. The main functions include external data source identification, database creation, and sensor data logging. * Device ID registry: The device ID registry is a data registry that stores all the IDs of sensors and external data sources that have previously been processed by the system. To effectively support multiple applications under one IoT system, the registry stores the device IDs and external data sources according to their applications and data types. It is used by the device identification manager to cross-reference against the incoming device ID from the end device component. The data of this registry are stored within the database of the system, together with all the sensors and external data for instant referencing.
# Dynamic Torsional Stiffness of Reducers and Its Testing Method
## 6 Conclusions  
1. The reducer's torsional stiffness is not static but dynamic. A novel concept called \"dynamic torsional stiffness\" was introduced to accurately represent the reducer's torsional stiffness under real working conditions. An analytical formula for this dynamic torsional stiffness was provided.
2. A testing method for dynamic torsional stiffness in reducers, based on the transmission error method, was proposed. The theoretical demonstration confirmed its feasibility and its consistency with the hysteresis curve method.
3. Experimental research was conducted to explain the limitations of the hysteresis curve method. Additionally, the dynamic characteristics of torsional stiffness and the effectiveness of the transmission error method were verified. As a result, the torsional stiffness information of the reducer under all operating conditions was obtained.
4. The transmission error method takes into account the actual working conditions of the reducer, and its results comprehensively consider the influences of torque and speed, while also avoiding the effects of loading rate dependency. Therefore, the test results are more accurate and reliable, making it more valuable in both theoretical analysis and practical engineering applications.  
Figure 19: Application of evaluation results.  
**Author Contributions:** Conceptualization, Z.S. and H.C.; methodology, H.C.; software, Z.Y.; validation, G.Z., H.C. and Z.Y.; formal analysis, H.C.; writing--review and editing, H.C.; supervision, Z.S.; project administration, Z.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by supported by the Zhongguancun open laboratory concept project (grant number Q7001004202101).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw/processed data required to reproduce the above findings cannot be shared at this time as the data also form part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Use Cases
### Temporal Insights Through ADR Variability: Violin Plot  
This visualization implements the framework's temporal analysis component while demonstrating how institutional structures (as discussed in our theoretical framework) can be maintained through consistent visual communication modes. The violin plot visualizes the distribution of average daily rate (ADR) across different time periods (e.g., months,seasons, or years) and by hotel type (city versus resort). This plot combines features of boxplots and kernel density plots, showing not only summary statistics (e.g., medians, interquartile ranges) but also the full distribution of the ADR.  
The violin plot highlights variability in pricing strategies, which is essential for:  
* Temporal optimization: identifying high-variance periods where dynamic pricing strategies could stabilize revenue.
* Seasonal trends: revealing peak and off-peak pricing patterns.
* Economic implications: supporting models of temporal resource allocation by quantifying demand elasticity.
## 4 Discussion
This would facilitate the processing of images (after being converted into spectral point clouds as described in Section 2.1.2) and make full use of the kernel points set, requiring only structural information for point feature fusion to co-locate features spatially. In conclusion, this work has introduced two HSPC representations and a novel point convolution-based composite fusion neural network that leverages these HSPC representations. Our results demonstrate the efficacy of these representations in both unimodal and multimodal networks and show that the proposed multimodal network delivers competitive performance compared to previous research. This work represents a significant stride towards the development of fusion neural networks capable of processing both native image type data and point cloud data, specifically in the realm of multimodal remote sensing. We recognize the limitations associated with point convolution neural networks like KPConv, including their substantial computational cost and added complexity. However, these challenges are difficult to overcome given the inherently large size and dimensionality of point cloud data. In light of these findings, our work paves the way for innovative approaches to multimodal remote sensing exploitation, unlocking new possibilities for enhanced data analysis and interpretation. **Author Contributions:** Conceptualization, K.T.D. and B.J.B. ; methodology, K.T.D. ; software, K.T.D. ; validation, K.T.D. and B.J.B. ; formal analysis, K.T.D. ; investigation, K.T.D. ; resources, K.T.D. ; data curation, K.T.D. ; writing--original draft preparation, K.T.D. ; writing--review and editing, K.T.D. and B.J.B. ; visualization, K.T.D. ; supervision, B.J.B. ; project administration, K.T.D. and B.J.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Original dataset acquired from IEEE GRSS IADF and the Hyperspectral Image Analysis Lab at the University of Houston: [[https://hyperspectra.Lee.uh.edu/?page_id=1075](https://hyperspectra.Lee.uh.edu/?page_id=1075)]([https://hyperspectra.Lee.uh.edu/?page_id=1075](https://hyperspectra.Lee.uh.edu/?page_id=1075)) (accessed on 1 October 2021). Altered dataset available upon request to [PERSON]. **Acknowledgments:** [PERSON] thanks [PERSON] for their guidance along with their parents and fiance for continued support. The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense, or the U.S. Government. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A.1**: _Superclass Generation from GRSS18 Classes_
## 2 CRKG in Three Typical Duplex Modes  
The reciprocity in the propagation of radio waves is the basic premise of CRKG to enable legitimate users to have a common source of randomness, from which they can produce identical secret keys. The duplex mode is essential to the design of CRKG, because channel measurements are carried out by the wireless platforms running in different duplex modes. Figure 3 shows CRKG model in TDD, FDD and IBFD duplex modes. Most CRKG implementations are realized in the TDD mode, in which channel probing is performed alternately. In the FDD mode, [PERSON] and [PERSON] probe the channel at the same time slot, but most of the channel parameters can be quite different due to the frequency separation. For this reason, FDD has received little investigation for CRKG. The drawbacks of half-duplex motivate an exploration of CRKG in IBFD mode, which enable users to probe their channels simultaneously over the same frequency band. Next, we discuss the specific challenges and opportunities of CRKG when it is applied to practical communication systems operating at TDD, FDD and IBFD modes.  
Figure 2: New air interference technologies and potential applications of CRKG in 5G and beyond.
# The BER Performance of the LDPC-Coded MPPM over Turbulence UWOC Channels
## 3 LLR Calculation
Thus, we obtain:  
\[lr(y)=\frac{P_{s}(y)}{P_{n}(y)}=\exp\left(\frac{2 yI-I^{2}}{N_{0}}\right) \tag{14}\]  
where \(I=RhMP_{av}/w\). Substituting (14) into (12), we have:  
\[L(a_{l})=\ln\left[\sum_{ \begin{subarray}{c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=0\end{subarray} }\exp\left(\sum_{e\in\mathbf{H}_{i}}\frac{2y_{e}I-I^{2}}{N_{0} }\right)\right]-\ln\left[\sum_{ \begin{subarray}{c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=1\end{subarray} }\exp\left(\sum_{e\in\mathbf{H}_{i}}\frac{2y_{e}I-I^{2}}{N_{0} }\right)\right] \tag{15}\]  
The calculation of (15) is very difficult for a practical system because of the complex operation (even appearing infinity) and requirement of CSI. Thus, a simple and low computational complexity expression should be applied. Based on the approximation of \(\ln(e^{m}+e^{n})\approx\max(m,n)\) (Jacobian logarithm), (15) can be written as:  
\[L(a_{l})=\max_{ \begin{subarray}{c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=0\end{subarray} }\left(\sum_{e\in\mathbf{H}_{i}}\frac{2y_{e}I-I^{2}}{N_{0} }\right)-\max_{ \begin{subarray}{c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=1\end{subarray} }\left(\sum_{e\in\mathbf{H}_{i}}\frac{2y_{e}I-I^{2}}{N_{0}}\right) \tag{16}\]  
Furthermore, it can be simplified to:  
\[L(a_{l})=\max_{ \begin{subarray}{c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=0\end{subarray} }\left(\sum_{e\in\mathbf{H}_{i}}y_{e}\right)-\max_{ \begin{subarray} {c}\mathbf{C}_{i}\in\mathbf{C}\\ a_{l}=1\end{subarray} }\left(\sum_{e\in\mathbf{H}_{i}}y_{e}\right) \tag{17}\]  
In the simplification from (16) to (17), the absolute value of LLR is changed while its sign (plus or minus), which depends on the largest received slots and decides the bit, keeps the same due to monotonicity. Therefore, the simplification satisfies the requirement of the iterative soft decision but does not need CSI.
# Study of Cyclic Plasticity and Creep Ratchet Behavior of PTFE
## 1 Introduction  
Polytetrafluoroethylene (PTFE), which DuPont developed in 1938 [1], has since been widely applied in the aerospace, mechanical, chemical, and pharmaceutical industries due to its exceptional corrosion resistance and low coefficient of friction. At the same time, it exhibits excellent high- and low-temperature resistance, and can maintain good mechanical properties in the range of \(-\)196 to 260 \({}^{\circ}\)C [2].  
In the materials and structures subjected to a cyclic stressing with non-zero mean stress, a cyclic accumulation of inelastic deformation will occur if the applied loading is high enough (ensuring that a yielding occurs), which is called ratcheting, or the ratcheting effect (some researchers have also called it \"cyclic creep\") [3]. The effects of average stress, stress amplitude, loading rate, loading path, and temperature on the ratchet behavior of metallic materials have been extensively investigated by researchers since the beginning of the 20 th century [4; 5; 6; 7; 8; 9; 10; 11; 12]. Numerous researchers [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] have conducted a series of uniaxial and multiaxial ratchet tests on a large number of polymer materials--NBR (nitrile butadiene rubber), H-NBR (hydrogenated nitrile butadiene rubber), NR (natural rubber), PC (plastic), PEI (polyetherimide), PMMA (polymethyl methacrylate), UHMWPE (ultra-high-molecular-weight polyethylene), POM (polyoxymethylene), and ACFs (activated carbon fiber). The ratchet behavior of polymers is, to a certain extent, dependent on the polymer's molecular structure. The response to the average and amplitude stresses is comparable for different polymers. For example, they are all similar, with ratcheting being more pronounced at higher stress levels.  
When PTFE is used in gaskets under preload and alternating pressure load [23], creep and ratchet would often occur. [PERSON] and [PERSON] [24] and [PERSON] et al. [25] studied the ratcheeting behavior of PTFE under cyclic compression, and [PERSON] et al. [23] and [PERSON] and [PERSON] [26] studied the ratcheting behavior of PTFE at room temperature and high temperature. [PERSON] et al. [27; 28] examined the ratcheting behavior of PTFE gaskets under cyclic compressive loading, with small stress amplitude, and creep ratchet deformation under compressive creep-fatigue loading, with small stress amplitude at various temperatures. However, PTFE membranes and PTFE-lined tubes are also subjected to tensile stresses in service, and little is known about the relationship between PTFE's tensile creep and ratcheting effects at various temperatures.  
In this study, PTFE dog-bone tensile specimen were experimentally studied by applying constant tension and cyclic uniaxial loading at temperatures ranging from 23 to 200 \({}^{\circ}\)C. The ratchet and creep behaviors of PTFE at different temperatures were investigated.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 3 Proposed Method
### IoT-Based ECG Framework
#### 3.1.2 REL Protocol  
REL protocol is commonly used in wireless sensor networks (WSNs) and IoT applications such as smart cities, environmental monitoring, healthcare, and comfortable offices and homes. It uses residual energy and link quality to find routes that improve the system's QoS (quality of service) reliability and support and incorporates an event-driven mechanism for load balancing and the prevention of early node death [36]. In WSN communication, links are often unreliable due to low-powered radios that are vulnerable to multipath distortion, noise, and interference. The effectiveness of route selection depends on the accuracy of the link quality estimate to improve reliability. Generally, a LQE (single link quality estimator) value such as LQI (link quality indicator) or RSSI (received signal strength indicator) is used to represent the link quality at a specific time, but it does not provide additional information about the end-to-end link quality, hop count, or residual energy.  
The REL protocol has a number of advantages over purely reactive protocols like DSR. It has lower latency and overheads, since routes are maintained proactively rather than being discovered on an as-needed basis. It also has better scalability, since it can handle larger networks more efficiently. However, it does have some drawbacks, such as higher energy consumption and the potential for routing loops if the proactive flooding mechanism is not implemented carefully.  
Thus, a hybrid approach of routing protocol is used, which combines elements of both DSR and REL. The hybrid protocol uses a REL to maintain routes to commonly used destinations, while using a DSR to discover routes to less frequently-used destinations on an as-needed basis.