esa-sceva/satcom-chunk-collection huggingface dataset:
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 5 Conclusions  
In this paper, we proposed a frequency-domain estimation scheme for frequency offset with large range in OFDM systems. The proposed scheme takes advantage of the autocorrelation of the pilot sequence, and the \(2\pi\)-periodicity of the phase offset between the two pilot OFDM symbols. It enables a large estimation range, independent of the symbol gap between the two pilot OFDM symbols. Moreover, the low-complexity frequency-domain implementation of the estimation scheme was proposed, and the simulation results based on 5G NR PUSCH show the effectiveness of the proposed scheme. Compared to the conventional time-domain estimation method, the provided low-complexity implementation of the estimation scheme still needs additional computing resources for frequency offset compensation and correlation operations. Further improvements in the complexity reduction of the scheme would help to implement it in a practical NR system. Additionally, one limitation of the proposed scheme is that at least two pilot OFDM symbols need to be configured in the radio frame. Future work on extending the estimation idea with only one pilot OFDM symbol could also be considered.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(N_{rb}=20\)} & \multicolumn{2}{c}{\(N_{rb}=30\)} & \multicolumn{2}{c}{\(N_{rb}=40\)} \\ \hline  & \(\epsilon=-0.3\) & \(\epsilon=0.1\) & \(\epsilon=-0.3\) & \(\epsilon=0.1\) & \(\epsilon=-0.3\) & \(\epsilon=0.1\) \\ Algorithm 1 & 132,120 & 132,120 & 198,180 & 198,180 & 264,240 & 264,240 \\ The time-domain method & 512 & 512 & 512 & 512 & 512 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean numbers of complex-multiplex operations of Algorithm 1 and the time-domain estimation method.  
Figure 9: The MSE performance of Algorithm 1 in multi-user and single-user scenarios.  
**Author Contributions:** Conceptualization, T.W.; methodology, T.W.; software, T.W. and H.J.; validation, T.W., D.K. and H.J.; formal analysis, T.W. and D.K.; investigation, T.W., H.J. and H.C.; resources, T.W., D.K., H.J. and H.C.; data curation, T.W.; writing--original draft preparation, T.W.; writing--review and editing, T.W., D.K. and H.J.; visualization, T.W.; supervision, H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported in part by the Scientific Research Project of Education Department of Hubei Province under Grant number D20221702, and the Cheung Kong Scholars Programme of China under Grant 21069000222.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the companies Guangzhou Digital Technology Group Co., Ltd. and Guangzhou Haige Communications Group Incorporated Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Cellular Self-Digestion and Persistence in Bacteria
## 5 Links between Metabolism and Cellular Self-Digestion
Metabolism involves a highly complex enzymatic network that is controlled by a number of transcriptional regulators, including ArcA, Cra, Crp, DksA, Fnr, Lrp, and Rpos, whose expression levels are drastically altered when cells enter stationary phase [246; 247; 248; 249; 250; 251; 252]. Critically, these regulators may be involved in self-digestion and in mediating the persister metabolism. Mutants deficient in _arcA_, for example, lose their culturability rapidly after a few days in stationary phase [101]. Cyclic AMP (a product of Cya enzyme) along with its receptor protein, Crp, may also play an important role in bacterial persistence. Persister cells in _E. coli_ and _S. aureus_ were previously shown to metabolize specific carbon sources and become susceptible to aminoglycos (AG), which inhibits protein synthesis [234; 235; 235]. The aminoglycos  
Figure 1: **Self-digestion mediated stationary-phase metabolism in bacteria. ** Self-digestion enables cells to transiently tolerate starvation conditions by recycling essential energy molecules. Perturbing the proposed metabolic mechanism genetically (deleting TCA cycle enzymes) [55], chemically (chlorpromazine (CPZ) [232], potassium cyanide (KCN) [55], and nitric oxide (NO) [245] treatments), and environmentally (removing O\({}_{2}\)) [55] can reduce persister formation during the stationary phase. potentiation was further extended to Gram-negative pathogens, _Salmonella enterica_ and _Klebsiella pneumoniae_, in a subsequent study [236] that also confirms earlier evidence of nascent protein synthesis in persisters [254]. Using the AG-potentiation assay, we found that a panel of carbon sources could not potentiate the AG-mediated killing of persisters derived from \(\Delta\)_crp_ and \(\Delta\)_cya_ strains, indicating a role for these regulators in this process [47]. AG uptake is a unique, energy-requiring process, requiring both the electrochemical potential and the proton gradient across the cytoplasmic membrane [234]. Thus, the fact that persisters can efficiently metabolize certain substrates (e.g., glucose or glycerol) and generate PMF [234; 235] supports the existence of active energy metabolism in these cells. However, we note that existence of an active mechanism does not necessarily imply \"an upregulation\" in that mechanism. The metabolism of persister cells is still likely to be lower than that of exponentially growing cells, which are metabolically highly active [13; 37; 102; 233; 103]. Regardless, this does not refute the proposed metabolic model highlighting the reliance of persister cell survival on energy metabolism. In fact, the addiction of some non-proliferating persister types (e.g., cancer persisters) to oxidative phosphorylation has repeatedly been shown [226; 227; 255; 256; 257; 258].
# Nonlinear Solid-Fluid Coupled Seismic Response Analysis of Layered Liquefiable Deposit
## 1 Introduction
This method was then used to simulate the seismic response of non-liquefied and liquefied sites with a downhole seismic array in Japan. The simulated results are in good agreement with the recorded spectral acceleration curves of the non-liquefied and liquefied sites. [PERSON] et al. (2016) [10] established a three-dimensional finite element dynamic analysis model of piles in a liquefiable site based on the OpenSees software platform and conducted numerical simulations for the centrifuge shaking table tests of pile in liquefiable horizontal foundations. Special attention was mainly dedicated towards the ground acceleration, EPWP, and the bending moment of the pile shaft during the shaking. However, the settlement and pore water pressure dissipation process of the soil strata after the earthquake were not mentioned in the study. In view of the above, the dynamic response of saturated soils under earthquake excitations is a complex mechanical process that includes the nonlinear hysteretic behavior of the soil and the coupled volumetric-distortional deformation. The former is composed of a decrease in the shear modulus associated with an increase in energy dissipation due to the damping. The latter is related to plastic irreversible strain under high confinement. The effects of liquefaction on geotechnical structures are increasingly being evaluated in practice using nonlinear time history analyses, but a 'loosely coupled' approach that predicts pore pressure by adopting relationships used in combination with total stress constitutive models (e.g., pore pressure prediction based on accumulated strains or stresses) is often used in the previous studies [3; 9; 11]. This approach fails to effectively consider the influence of dilatancy and the post-liquefaction shear deformation of the soil strata. In this study, by concentrating on the effects of liquefaction, a 'fully coupled' approach is adopted to evaluate the nonlinear seismic response of liquefiable multi-layered soils under earthquake loading and post-earthquake site response. The advanced soil constitutive model is used to characterize the complicated nonlinear dynamic behavior of sand. Basedon the numerical analyses, the dynamic response characteristics associated with the soil liquefaction are described in detail in this study.
## 2 System Description
### System Modeling
The precise values of the model's parameters are estimated by using the MATLAB software and provided in \[y_{P}=y_{PN}-R\theta, \tag{1b}\]0 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & \(y_{0}\) [\(in\)] & \(y_{N}\) [\(in\)] & \(P_{0}\) [MPa] \\ \hline
**Values** & 22 & 15 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Initial parameters of pneumatic artificial muscles (PAMs). Figure 2 shows identification results: (a) the control inputs are step of 0.4 MPa, (b) 0.5 Hz sine wave signal, and (c) time-varying amplitude and frequency sinusoidal signal. The discrete-time SOPDT mathematical model depicts a good approximation of nonlinear behaviour of the antagonistic actuator. The maximum error of the estimated angle (dash red line) from the measured one (blue line) was less than 5.0\({}^{\circ}\), and the root mean square error did not exceed 2.5\({}^{\circ}\). The mean values and standard deviations (SD) of the model parameters obtained by different types of control signals are provided in Table 2. As seen in Table 2, the standard deviations of the model parameters were much smaller than their mean values. Therefore, we can conclude that the model parameters obtained by different methods have similar values. As a result, we can use any aforementioned method for the identification purpose. The model parameters, which were identified by time-varying amplitude and frequency, are chosen to design the controller in Section 3 of this paper. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model Parameters** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{d}\) \\ \hline Value (Mean \(\pm\) SD) & \(-\)1.9139 \(\pm\) 0.0182 & 0.9164 \(\pm\) 0.0180 & 0.0472 \(\pm\) 0.0064 & 0.0460 \(\pm\) 0.0061 & \(22\pm 3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Identified parameters of the antagonistic actuator. Figure 2: Identification results of the antagonistic actuator: (**a**) the step input of 0.4 MPa, (**b**) the 0.5 Hz sinusoidal signal, and (**c**) the time-varying amplitude and frequency control input. Upper sub-figures show measured (blue line) and estimated (dash red line) show value of the actuator angle. Lower sub-figures show the estimation error of the mathematical model.
## 4 Attacks and Misbehaviors
### Bad Packet Attacks  
This type of attacks is divided into two main parts. The first is when a node is initiating malicious packets intended to harm the nodes or the BS. This type is directly detected by the BS, as it checks all the received packets for any maliciousness. The BS definitely bans this node from the system and isolates its harm.  
The second type is a simple attack or misbehavior either by a malfunctioning node or a bad node intending to just flood the network with erroneous packets. Such packets may include an invalid message, an invalid signature, or an unverified signature. This type of attack is directly dropped by the neighboring nodes and thus its effect is localized and minimized.
# Differential Signalling in Free-Space Optical Communication Systems
## 3 Lossy Channel and Background Noise
### Geometrical Loss  
At the Rx, the received optical beam radius \(w_{\text{Rx}}=w_{\text{Tx}}+\theta_{0}L\), and the geometrical loss is defined as [28]  
\[h_{geo}=1-exp\bigg{(}\frac{{D_{Rx}}^{2}}{2w_{Rx}}^{2}\bigg{)}, \tag{9}\]  
where \(w_{\text{TX}}\), and \(\theta_{0}\) are the laser beam radius, beam divergence angle at the transmitter (Tx), respectively. \(L\) is the link span and \(D_{\text{Rx}}\) is the aperture diameter at the Rx. Note that, if the maximum Tx lens radius is known, then \(\theta_{0}=\frac{\lambda_{\text{PSO}}}{\pi w_{\text{Tx}}}\) can be readily determined [29].
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 5 Experimental Evaluation
### Evaluation of TD-DNN Model  
The following well-known and state-of-art recommendation algorithms are used to compare the performance of the proposed TD-DNN model. These approaches are selected as they are implemented in RS.  
1. Item-based CF (IBCF) [77]: In the CF technique, the Adjusted Cosine (ACOS) measure is used to compute the similarity between items. It is a modified version of vector-based similarity that accounts for the fact that various users have varying rating methods; in other words, some people may rate items highly in general, while others may prefer to rate them lower.
2. User-based (UBCF) CF [20]: The performance of the proposed model is compared with the user-based CF approach. To compute the similarity among users, a recently developed MPIP measure is used. It combines the modified proximity, modified impact, and modified popularity factors in order to compute the similarity.
3. SVD [28]: The SVD method is used to reduce the dimensionality for prediction tasks. In this algorithm, the rating matrix is decomposed and is utilized to make predictions. The SVD technique has the advantage that it can handle matrices with different numbers of columns and rows.
4. DLCRS [44]: In the DLCRS approach, embedding vectors of users and movies are fed into layers which consist of element-wise products of users and items. It then outputs a linear interaction between user and movie. In contrast, in the basic MF model, the dot products of the embedding vectors of users and items are faded.
5. Deep Edu [11]: This algorithm map features into the N-dimensional dense vector before inserting them into a Multi-Layer Perceptron. It is applied to the movie's dataset to compare its performance with the proposed model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **ML-100k** & **ML-1M** & **Epinions** \\ \hline \# of users (M) & 943 & 6040 & 40,163 \\ \# of items (N) & 1682 & 3952 & 139,738 \\ \# of ratings (R) & 100,000 & 1,000,000 & 664,824 \\ \# of noisy ratings & 10,602 & 112,938 & 53,247 \\ \# of corrected noisy ratings & 3092 & 32,807 & 12,780 \\ \# of ratings per user & 106.6 & 165.8 & 16.55 \\ \# of ratings per movie & 59.5 & 253.03 & 4.75 \\ Density Index & 6.30 & 4.18 & 0.01 \\ Time range & Sep’97–Apr’98 & Apr’00–Feb’03 & Jul’99–May’11 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Dataset Description.  
Neural Network using Batch Normalization (NNBN) [78]: This approach applies batch processing on each layer to prevent the model from overfitting.
* NCF [39]: NCF is the most popular deep learning technique used for movie recommendations. Neural Collaborative Filtering replaces the user-item inner product with a neural architecture. It combines the predicted ratings of Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) approaches.
* Neural Matrix Factorization (NMF) [45]: In this approach, pairs of users-movies embeddings are combined using Generalized Matrix Factorization (GMF), and then, MLP is applied to generate the prediction.
## 3 Path-Tracking Control Strategy  
The vehicle has five DOFs and ten control inputs (six axle steering angles and four wheel drive torques) for the path-tracking control of a four-module six-axle SRT. The end-to-end control algorithms have a complex structure, low computational efficiency, and poor robustness [35; 36]. To achieve high path-tracking accuracy and reduce hinge forces and wheel sideslip, referring to the elementary control method in [33], this paper established a completed controller based on improved MPC and a hierarchical framework, and in response to the particularity of the SRT structure, the CG generalized force redistribution and \"virtual axle\" method are proposed, as shown in Figure 4.  
Firstly, the vehicle status and target path information are obtained by the measure or estimation module and then transmitted to the cooperative controller, which calculates the path-t  
Figure 4: The proposed control strategy based on improved MPC and hierarchical framework.  
Then, based on the improved MPC algorithm, the CG generalized forces of each module required for path tracking are calculated.  
The redistribution of CG generalized forces is carried out. The required CG generalized forces for the third module are provided by the fourth module and the second module through the hinge plates, and the sideslip of the tires of the third module is eliminated (the fourth axle) so that it achieves the effect of a \"virtual axle\".  
Finally, the control allocations of the first, second, and fourth modules are carried out to meet the CG generalized force requirements for path tracking. The second module only meets the yaw moment requirements, ignoring the longitudinal and lateral force errors.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.3 Discrete Laminate Model  
The general discrete laminate model (DLM) as described by [PERSON] et al. [79] with [PERSON] and [PERSON] [102] combines the attributes of discrete sandwich model and the symmetric laminate model. While the former assumes thin laminate skins and a shear bearing core, the latter assumes each laminate layer thick. The use of DLM permits both the skins and core to have thick laminates such that each laminate layer thickness has smeared features. Moreover, it can handle both the symmetrical and asymmetrical behavior of composite and sandwich shells having thin or thick laminate skins.
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 4 Data Classification
### K-Nearest Neighbor Algorithm  
K-Nearest Neighbor (KNN) is a simple data classification method that takes \(k\) number of nearest training samples in the feature space. KNN algorithm works on a dataset that is classified by its neighbors based on majority vote. The dataset is assigned to a particular class that is common in the \(k\) nearest neighbor defined by the distance function. For example, when \(k=1\), the dataset is assigned to the nearest neighbor class. There are three main distance functions used in KNN algorithm considering continuous variables.  
\[Manhattan=\sum\limits_{i=3}^{k}\left|x_{i}-y_{i}\right| \tag{10}\]  
\[Minkowski=(\sum\limits_{i=1}^{k}\left|x_{i}-y_{i}\right|^{q})^{1/q} \tag{11}\]  
\[Euclidean=\sqrt{\sum\limits_{i=1}^{k}\left(x_{i}-y_{i}\right)^{2}} \tag{12}\]  
\[Hamming\ Distance=D_{H}=\sum\limits_{i=1}^{k}\left|xi-yi\right| \tag{13}\]  
In a case where categorical variables exist, Hamming distance as in Equation (13) should be used. When there is a combination of numerical values and categorical variables in a given dataset, the issue of standardization arises as the numerical variables are between 0 and 1. The optimum value for \(k\) is chosen by thorough inspection of the given datasets. Generally, when the value of \(k\) is large, the overall noise is reduced. Cross validation or rotation estimation is another method to identify the optimal value for \(k\).
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
###### Abstract  
Accurate electrocardiogram (ECG) interpretation is crucial in the clinical ECG workflow because it is most likely associated with a disease that can cause major problems in the body. In this study, we proposed an ECG-signal multi-classification model using deep learning. We used a squeeze-and-excitation residual network (SE-ResNet), which is a residual network(ResNet) with a squeeze-and-excitation block. Experiments were performed for seven different types of lead-II ECG data obtained from the Korea University Anam Hospital in South Korea. These seven types are normal sinus rhythm, atrial fibrillation, atrial flutter, sinus bradycardia, sinus tachycardia, premature ventricular contraction and first-degree atrioventricular block. We compared the SE-ResNet with a ResNet, as a baseline model, for various depths of layer (18/34/50/101/152). We confirmed that the SE-ResNet had better classification performance than the ResNet, for all layers. The SE-ResNet classifier with 152 layers achieved F1 scores of 97.05% for seven-class classifications. Our model surpassed the baseline model, ResNet, by +1.40% for the seven-class classifications. For ECG-signal multi-classification, considering the F1 scores, the SE-ResNet might be better than the ResNet baseline model.  
ECG signal multi-classification; deep learning; convolutional neural network; arrhythmia +
Footnote †: journal: Applied Sciences
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 5 Conclusions
### Empirical Findings  
Our comprehensive experimental evaluation across three representative seasonal periods demonstrates the substantial performance advantages of weighted fusion over direct aggregation. The methodology achieved a 24.67% overall improvement in MAPE (from 3.09% to 2.33%), with particularly significant enhancements during critical peak periods and extreme weather conditions. Under high-temperature conditions in August, weighted fusion reduced peak-hour MAPE from 16.3% to 6.1.  
The time-varying weight analysis revealed important seasonal patterns in provincial contributions, with weight distributions shifting from Jiangsu dominance in summer months to increased Anhui contribution in winter. These weight redistributions aligned with the changing load characteristics and forecasting challenges across seasons, confirming the adaptive capability of our methodology to recognize and leverage regional specialization.  
The multi-temporal scale analysis demonstrated the robust performance of weighted fusion across forecasting horizons ranging from ultra-short-term (1 h) to medium-term (168 h). This consistent outperformance across all temporal scales validates the methodology's adaptability to diverse operational requirements, from real-time dispatch decisions to day-ahead market operations.