esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 6 Conclusions  
This paper develops a low-overhead frame structure design and robust symbol timing and frequency synchronization methods for OFDM systems used in UAV TDD-TDMA communication networks. First, we achieve a high-efficiency transmission frame by eliminating the AGC guard interval. We also introduce a novel AGC circuit that is controlled by the results of symbol timing synchronization. Second, we propose a symbol timing synchronization algorithm based on 2-bit non-uniform quantization, where the training sequence is mapped into a 2-bit message, represented by a sign bit and a magnitude bit. We also introduce an optimal quantization parameter search algorithm based on minimum distortion. This approach allows the correlation operation in symbol synchronization to be implemented using bitwise XOR and addition operations, significantly reducing the hardware implementation complexity. Third, we present a robust frequency offset estimation algorithm based on AGC compensation to mitigate the destructive effects of AGC  
Figure 14: EVM of the preamble with and without AGC compensation.  
Figure 15: Performance comparison of frequency offset estimation with and without AGC compensation.  
adjustment on frequency synchronization. The simulation results demonstrate that the proposed 2-bit symbol timing synchronization achieves performance nearly equivalent to traditional 3-bit uniform quantization. Additionally, the frequency estimation performance is improved by approximately 2 dB using AGC compensation. Following the main results presented here, a near future work is to extend the current system to more realistic scenarios by considering the inclusion of multipath fading or interference.  
Conceptualization, L.L.; methodology, Y.H. and Z.L.; validation, Y.H., Z.L. and J.L.; formal analysis, H.L.; writing--original draft preparation, L.L.; writing--review and editing, Y.L. and J.L.; supervision, L.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under grant 62371037, in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2024A1515011186, in part by the China Postdoctoral Science Foundation under Grant Number 2023M740223, in part by the China Scholarship Council under Grant Number 202306460096, and in part by the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20230241.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 1 Introduction
The content of this paper is organized as follows: in Section 1, we introduce the current situation of OFDM technology and the limitations of CP-OFDM system, as well as the main contributions of this paper. In Section 2, the transmission model of OFDM signal and the influence of multipath effect are studied. In Section 3, we analyze the principle of traditional OFDM frequency-domain equalization algorithm and its main shortcomings. In Section 4, the principle of an improved frequency domain equalization algorithm for reducing cyclic prefixes is described in detail. Section 5 introduces the parameter setting and result analysis of the simulation experiment. Section 6 summarizes and looks forward to the future work.
## 2 Dynamic Response of a Multi-Story STMF with SMAs
### Description
Although in practical engineering, the same diameter of SMA X-bars in each special segment on all frame floors is incorporated (for convenient construction), in this work, an alternative and novel approach is first encountered in the relevant literature. Namely, the dimensioning of SMAs on each floor was performed by taking into account the maximum expected vertical shear strength developed in the midpoint of the special segment (\(V_{ne}\)). For this structure, one SMA per diagonal was placed to resist the \(V_{ne}\) developed in the special segment. The calculated SMA diameters on each floor are given in Table 1.  
Figure 1: The undeformed shape of the structure (conventional STMF). The SMA bars used are assumed to be equipped with buckling-restrained devices, or to be large enough in diameter (which is the case of the ones in Table 1) to prevent buckling in all stages of dynamic deformation.
## 3 c-di-AMP Degradation
### PgpH  
Another class of phosphodiesterase, PgpH, was first identified in _L monocytogenes_, and now appears to be widespread throughout multiple bacterial phyla [44; 48]. PgpH possesses a catalytic histidine-aspartate domain (HD) in its C-terminus that binds to c-di-AMP with high specificity and degrades it into 5'-pApA. HD is composed of two subdomains and two active sites that require iron for their catalytic activity [44]. In addition to an HD domain, PgpH also possesses an N-terminal extracellular domain and seven transmembrane helices [44; 48]. In _L. monocytogenes_, PgpH and PdeA, a homolog of GdpP, regulate the intracellular level of c-di-AMP in a cooperative manner. However, these enzymes are regulated by different external stimuli. PgpH expresses preferably in broth culture while PdeA is preferentially expressed during intracellular infection of eukaryotic host cells [44]. _L. monocytogenes_ mutants lacking either PgpH or PdeA exhibit slight growth defects compared to the wild type. However, mutants lacking both _pdeA_ and _pgpH_ exhibit higher levels of c-di-AMP, which is detrimental to bacterial growth and contributes to reduced virulence of _L. monocytogenes_ in a mouse model of infection, indicating the cooperative c-di-AMP degradative activity of these two enzymes [44]. The double mutant also elicits increased interferon (IFN) - \(\beta\) during intracellular infection of host cells [48]. Upon infection, _L. monocytogenes_ induces the expression of \(\beta\) -interferon and co-regulated genes following stimulation of the cytosolic surveillance pathway (CSP), STING, and DDX41 pathways in the host immune system [21]. In a screen designed to determine the Pathogen Associated Molecular Patterns (PAMPs) recognized by the infected mammalian cells, c-di-AMP was identified [16]. Indeed, bacterial mutants that secrete more c-di-AMP lead to an increase in \(\beta\)-interferon secretion [21].
## 3 Methodology
### Selection of the Waveform Portion  
Over inland water bodies, the on-board tracker may not properly locate the retrieval window to acquire the signal from the nadir corresponding to the water body. In addition, specific across-track surface contamination will be present in the waveforms, making it difficult to properly track the desired portion of the waveform. In our study, we used DEM information (SRTM DEM at a resolution of 30 m) to locate the waveform portion that comes from nadir within the receiving window, and then retracked the waveform using the selected portion only. This pre-processing approach, which prepares the portion of waveforms, can be applied and integrated with any kind of retracker.  
The waveform portion selection method isolates the nadir return within the receiving window as follows:  
* From the geo-located surface of interest within the water body and beneath the satellite track, the associated height was interpolated from the DEM information and referred to the geodetic ellipsoid, \(H_{DEM}\).
* This height was subtracted from the satellite height (\(H_{orb}\)) at the geo-located surface to obtain a rough estimation of the range (or equivalently window delay) from where the nadir returns were expected.
* This range was linked to a specific bin within the received waveform, by comparing it with the vector of ranges associated to each bin in the receiving window, which could be obtained from the measured range by the radar and the range sampling.
* The peak location closest to the previous range bin position was taken, and the portion of the waveform was selected around this peak considering the valley positions to the left and right of the peak plus some guard samples on top:
* A built-in Matlab function (findpeaks) was used to compute the prominent or outstanding peaks within the waveform. Prominent peaks are those peaks that drop more than a given threshold value on either side of the peak before the signal attains a higher value.
* The associated valley locations can be extracted using this built-in function, but taking as input the maximum of the whole waveform minus the waveform itself.  
The water level for each footprint was retrieved based on the selected portion of the waveform applying the three retrackers. The time series of the water levels were calculated using a strict water mask polygon as shown in Figure 2, within which the water levels of the altimeter footprints were considered, selected and averaged for each track. The water mask polygon was created manually according to the shoreline displayed in the Google Earth image.  
The workflow of the water level retrieval using the Sentinel-3 Level-1 product included four main steps: waveform exclusion, selecting the waveform portion using a DEM determining the retracking range using different retrackers, geophysical corrections and water level averaging, as shown in Figure 4. Geophysical corrections were applied to the retracked range using the Sentinel-3 Level-2 product, and the accuracy was assessed for each water body using in situ measurements from the gauging stations in the SAIH system.
## 3 5G and Beyond
### Cloud-Based 5G/6G Systems
#### 3.2.1 Multi-Access Edge Computing  
Multi-access edge computing (MEC), previously called mobile edge computing, is considered to be an extension of mobile computing through edge computing. MEC is defined by European Telecommunications Standards Institute (ETSI) as a platform that provides IT and cloud computing capabilities within RAN in 4G, 5G and beyond, in close proximity to mobile subscribers. It extends edge computing by providing resources close to low-resource mobile devices [18].
## 3 HGV Tracking with Multiple Model Algorithms
### IMM Tracking Algorithm
#### 3.1.1 Interaction of the Estimates  
The mixed state estimate for the CKF model \(M_{j}\) at discrete time \(k\!-\!1\) is calculated using outputs of the models \(\widehat{X}_{k-1/k-1}^{i}\), the corresponding model probability \(\omega_{k-1}^{i}\), the transition probability \(\rho_{ij}\) and is given by  
\[\widehat{X}_{k-1/k-1}^{oj}=\sum\limits_{i=1}^{N}\frac{\rho_{ij}\omega_{k-1}^ {i}}{\sum\limits_{l=1}^{N}\rho_{lj}\omega_{k-1}^{i}}\left[P_{k-1/k-1}^{i}+ \left(\widehat{X}_{k-1/k-1}^{i}-\widehat{X}_{k-1/k-1}^{oj}\right)\left( \widehat{X}_{k-1/k-1}^{i}-\widehat{X}_{k-1/k-1}^{oj}\right)^{T}\right]\right\} \tag{21}\]  
where \(N\) is the model number. The error covariance of the interactive state for the filter model \(M_{j}\) at discrete time \(k\!-\!1\) is  
\[P_{k-1/k-1}^{oj}=\sum\limits_{i=1}^{N}\left\{\frac{\rho_{ij}\omega_{k-1}^{i}} {\sum\limits_{l=1}^{N}\rho_{lj}\omega_{k-1}^{i}}\left[P_{k-1/k-1}^{i}+\left( \widehat{X}_{k-1/k-1}^{i}-\widehat{X}_{k-1/k-1}^{oj}\right)\left(\widehat{X} _{k-1/k-1}^{i}-\widehat{X}_{k-1/k-1}^{oj}\right)^{T}\right]\right\} \tag{22}\]  
where \(P_{k-1/k-1}^{i}\) is the estimate covariance for the \(i\)-th filter at discrete time \(k\!-\!1\).
## 2 Materials and Methods  
### Multi-Sensor Drought Indices  
#### 2.1.1 Standardized Precipitation Index (SPI)  
The SPI is a drought index developed with the idea that it is initiated by a decrease in precipitation, thereby causing water shortage (compared to the relative water demand). In other words, it was developed from the above assumption that decreased precipitation has different effects on groundwater, reservoir storage, soil moisture, and river runoff. The SPI is an efficient way to calculate the impact of individual water sources on droughts by setting time units accumulated over a given period of time (over 1, 3, 6, and 12 months), and calculating the drought index by using the amount of precipitation on a time basis [19; 28].  
The SPI is also recommended by the World Meteorological Organization (WMO) for tracking meteorological droughts [21; 25].
## 2 Materials and Methods
### Construction of the Compound Eye Spatiotemporal Tensor Model
This confirms the presence of a substantial low-rank structure across all dimensions. The singular value distributions are shown in Figure 2. Furthermore, in the context of infrared small target detection, the low-rank and sparsity priors have a clear physical meaning: the background tends to be smooth, slowly varying, and thus low rank, while small targets are localized, rare, and can be naturally modeled as sparse components. Analysis validates the low-rank nature of the background tensor: \(\text{rank}(\mathcal{B})<\epsilon\), where the constant \(\epsilon>0\) represents the complexity of the background and the target tensor is a sparse tensor, satisfying the following condition:  
\[\left\|\mathcal{T}\right\|_{0}<\tau \tag{4}\]  
Typically, it can be assumed that the random noise in the infrared image is additive Gaussian white noise with a noise intensity of \(\delta\), which satisfies the following condition:  
\[\left\|\mathcal{N}\right\|_{F}<\delta \tag{5}\]  
Figure 2: Illustration of the construction process of the infrared compound eye image tensor model. The original infrared image sequence is segmented to extract only the effective imaging regions corresponding to the ommatidia. These regions are then arranged to form a 4D tensor. Modes 1 to 4 correspond to the spatial dimensions \(N_{W},\ N_{h}\), the number of ommatidia \(N_{n}\), and the number of frames \(N_{t}\), respectively. Each unfolding operation along a specific mode is used for analyzing the tensor’s structural properties. Here, \(\|.\|_{F}\) denotes the Frobenius norm. Therefore, the low-rank background tensor and sparse target tensor can be separated by considering the following problem:  
\[ \begin{array}{l}\underset{\mathcal{B},\mathcal{T}}{\text{minrank}}(\mathcal{B} )+\lambda\|\mathcal{T}\|_{0}\\ \text{st.}\|\mathcal{D}-\mathcal{B}-\mathcal{T}\|_{F}<\delta\end{array}  \tag{6}\]  
Here, \(\lambda\) denotes the regularization weight parameter. However, since solving the \(l_{0}\)-norm is an NP-hard problem, the \(l_{1}\)-norm is typically used as a substitute. Therefore, it can be rewritten as:  
\[ \begin{array}{l}\underset{\mathcal{B},\mathcal{T}}{\text{min}}\|\mathcal{B} \|_{*}+\lambda\|\mathcal{T}\|_{1}\\ \text{st.}\|\mathcal{D}-\mathcal{B}-\mathcal{T}\|_{F}<\delta\end{array}  \tag{7}\]
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 5 Feature Analysis and Pattern Recognition
### Pattern Recognition  
Following feature extraction, our study applied a comprehensive ensemble of machine learning and deep learning techniques to accurately classify human activities. We implemented and rigorously evaluated eight distinct algorithms: traditional models (SVM, Decision Trees, and Random Forests) and advanced deep learning architectures (CNN, LSTM, Transformer, Swin Transformer, and TransUNet).Our classification framework integrates both traditional machine learning and deep learning paradigms, creating a hybrid approach that leverages their complementary strengths. Traditional algorithms excel in scenarios with limited data and offer interpretability, while deep learning models capture complex temporal patterns. For the traditional models, we employed feature vectors directly, whereas for the deep learning approaches, we preserved the temporal structure of the data:  
* CNN: Extracts spatial patterns from sensor signals through convolutional operations.  
LSTM: Captures long-term dependencies in sequential data via recurrent connections.
* Transformer: Leverages attention mechanisms to model global interactions.
* Swin Transformer: Employs hierarchical feature extraction with windowed attention.
* TransUNet: Combines CNN's local feature extraction with Transformer's global context. The hyperparameters were optimized through systematic grid search with cross-validation:
* CNN: Filter sizes \(\in\{32,64,128\}\), kernel sizes \(\in\{3,5,7\}\), dense units \(\in\{50,100,200\}\).
* LSTM: Units \(\in\{50,100,200\}\), dropout rates \(\in\{0.2,0.3,0.4\}\), learning rates \(\in\{0.01,\\ 0.001,0.0001\}\).
* Transformer-based models: Head sizes \(\in\{32,64,128\}\), number of heads \(\in\{2,4,8\}\), feedforward dimensions \(\in\{64,128,256\}\).  
This comprehensive evaluation framework addresses the need for robust comparison between traditional and emerging deep learning approaches, particularly advanced Transformer-based architectures that have shown promising results in time series classification tasks.
## 2 Related Work  
The application of MEC for joint task offloading and resource allocation in the IoV to enable real-time computing is gaining significant attention. The authors of [19] proposed a game-theory-based edge-to-edge collaborative computing mechanism to tackle the challenges of vehicle task offloading and communication resource allocation. In [20], the authors proposed a dynamic scheduling scheme based on genetic algorithms to prioritize the scheduling of subtasks to minimize task completion time. The authors of [21] jointly optimized task scheduling, channel allocation, and computing resources to minimize the total task processing delay for all vehicles. In [22], the authors considered scenarios with random traffic flow and dynamic network environments, using Deep Reinforcement Learning to address computation offloading and resource allocation in dynamic environments. In [23], a distributed task offloading and resource allocation algorithm is proposed for computation-intensive tasks to balance vehicle computation time and energy consumption. However, these works primarily focus on the trade-off between communication and computing resources, overlooking the impact of sensing on task offloading and resource allocation.  
ISCC integrates sensing, communication, and computing functions, optimizing resource allocation in the IoV system using sensing data. The authors of [24] proposed a novel extended Kalman filter framework that uses sensing echoes to predict vehicle positions, thereby reducing communication beam tracking overhead. In [25], the authors proposed an ISCC-based solution for vehicle target detection, aiming to maximize the effectiveness of the perceived information. The authors of [26] proposed a novel directional propagation strategy driven by ISCC, enabling traffic information dissemination with service awareness and on-demand scheduling capabilities. However, these works overlook the benefits of collaborative sensing in resource allocation within the IoV. The authors of [27] proposed a collaborative sensing data fusion method based on ISCC in which vehicles compress the sensed data and upload them to the roadside unit for fusion to extend the sensing range. The authors of [28] considered collaborative sensing between RSUs and vehicles to generate the same sensing data, with vehicles only needing to upload computation instructions to reduce communication resource consumption.  
However, the above studies assume that RSUs and vehicles generate identical sensing data, which is unlikely in practical environments. Therefore, this paper presents a collaborative sensing model for RSUs and vehicles and proposes a novel task offloading mechanism to enhance the utilization of communication resources.
## 4 Bringing CRKG into the Field: Securing Practical IoT Communications
### Long-Range IoT Communication Solutions  
LoRa is a new IoT technique and has been used widely in smart agriculture, smart cities, etc. It uses chirp spread spectrum (CSS) to modulate the signals, which is immune to multipath and Doppler shift, and has a very broad receive sensitivity, as low as \(-148\) dBm. Therefore, LoRa can support long range communications, namely up to 15 km when the channel quality is good.  
Similar to many other wireless techniques, LoRa is also working in the TDD mode, which is ideal for CRKG. The receiver calculates the power for each received transmission, through which the channel randomness can be exploited. LoRa extends key generation applications to unexplored long range communication scenarios [54; 55; 56; 57]. For example, the distance between [PERSON] and [PERSON] is as high as 1.5 km in the experiments of [56].  
A LoRa-based key generation system is designed in [54]. The LoRa node consists of an Arduino and a LoRa Shield which uses SX1276 as the LoRa transceiver. RadioHead [58], an Arduino-based LoRa library is used. A typical LoRa setup is considered, where one LoRa device remains stationary, emulating the gateway, while the other one moves around in an urban environment with a maximum communication range as 500 m. As shown in Figure 8, the received power varies from \(-50\) dBm to \(-122\) dBm. Compared to the results of WiFi-based key generation, LoRa-based key generation has a much larger variation of received power. The conventional absolute value-based quantizer, e.g., mean-based quantization, generates long series of 1s and 0s; however, an adaptive version requires learning the channel variation in real time, which results in additional overhead. A differential-based quantizer is used in [54], but each comparison only leads to one key bit.
## 4 Conclusions  
This paper puts forward an 18-DOF bending-torsion-swing coupled dynamic model of a pair of involute spur gears, considering a nonuniformly distributed meshing force, shaft bending deformation, dynamic center distance, and time-varying stiffness excitation. The influence of several important parameters, i.e., meshing frequency, stiffness excitation, damping, and error excitation, on the nonlinear dynamic characteristics of the gear system was researched from the perspective of system stability through global bifurcation diagrams, phase diagrams, Poincare maps, and time-domain diagrams.  
Compared to the related works in the literature review of this study, we have made considerable progress, as shown in Table 2. Several important conclusions were obtained:  
1. The proposed dynamic modeling method of the spur gear system considers a nonuniformly distributed meshing force, dynamic center distance, shaft bending deformation, and time-varying stiffness excitation. This can better reflect the actual dynamic characteristics of a gear system in complicated working situations;
2. When the meshing frequency is close to n/2 times the system's natural frequency, resonance will appear. An excessive meshing stiffness amplitude will cause the system to fall into a chaotic motion state, while a large supporting stiffness will improve the system's stability. Increasing meshing damping and supporting damping can improve system stability and decrease the vibration response. Moreover, the gear system's  
Figure 40: Bifurcation diagram with respect to \(e_{0}\) (\(T_{c}=21.6\) Nm).  
Figure 39: \(e_{0}=0.16\) mm; (a) phase diagram; (b) Poincaré map; (c) time-domain diagram.  
dynamic response is sensitive to transmission error when subjected to a light load. In order to evade the bifurcation points and the chaotic motion, the transmission error should be decreased by increasing manufacturing accuracy and assembly accuracy;
3. Traditionally, a gear system is designed in terms of strength theory, characterized by material selection, force analysis, stiffness verification, and fatigue-strength design. For gear systems that are used in complicated working conditions, design should be considered not in terms of only structural strength but also in terms of motion stability. By evading complicated nonlinear dynamic behaviors, such as bifurcation and chaos, the transmission performance of a gear system can effectively be improved.  
Writing--original draft preparation, B.J.; writing--review and editing, Y.B.; investigation, X.L.; simulation--review and editing, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Key R&D Program of China] grant number [2019 YFB2004602].  
The authors declare no conflict of interest.