esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The upcoming promising technology for 5G smartphone communication is the use of MIMO, which uses two or more antennas for simultaneous transmission and reception over a communication channel. With the increasing number of mobile users, there is a need to increase channel capacity, which is possible through MIMO, where multiple antennas can be employed in the mobile terminals and base stations [1, 2]. Presently, various countries are focusing on sub-6 GHz Long Term Evolution (LTE) bands such as 3.4-3.6 GHz and 3.6-3.8 GHz. In addition, 3.5-4.2 GHz, 3.3-3.4 GHz/4.8-5 GHz, and 3.6-4.2 GHz/ 4.4-4.9 GHz are also incorporated by the USA, Japan, and China, respectively, for 5G applications [3]. The allocation of the 5G spectrum for countries is different, stretching from 3.3-6 GHz, and for future 5G applications, the multiple licensed and unlicensed bands will be amalgamated. Therefore, a multi-port antenna operating between 3.3 and 6 GHz is essential for smartphone applications [4, 5, 6]. Employing multiple antennas with MIMOand 5G capability requires high inter-elemental isolation to ensure smooth communication between the wireless channels along with high data rates in handheld devices. Due to the use of multipath technology, higher data rates using MIMO technology are possible, leading to an increase in the range and reliability without extra bandwidth usage. Thus, spectral efficiency significantly improves and ultimately helps in coping with the necessity of higher data rates for various services [7]. To achieve the desired Electro-Magnetic (EM) signal, the antennas are placed in the smartphones. However, if the antennas are non-conformable, they consume extra space. Therefore, conformal antennas are designed to be accommodated anywhere within the limited space available inside smartphones. Thus, conformal antennas save space, which can then be utilized for increasing the functionality of the smartphones [8].
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 4 Numerical Results  
We show the performance of the uplink cooperative system in LPWAN while varying the total received SNR (the effective SNR at the cloud server), which has three GWs (\(N=3\)), each with the different number of the antennas. We also generalize for a distance from ID to each GW; i.e., \(d_{1}=1\), \(d_{2}=4\), and \(d_{3}=8\).  
Figure 2 shows the outage performances of uplink LPWAN with non-cooperative and cooperative techniques when \((K_{1},K_{2},K_{3})\in\{(1,3,5)(5,3,1)\}\), \(d_{1}=1\), \(d_{2}=4\), \(d_{3}=8\), \(R=1.5\), and \(\alpha=3\). In this figure, the analytic results derived from Equations (8) and (11), are exactly matched with the Monte-Carlo simulation results for all cases of antenna combination. They show that the cooperative technique always outperforms the non-cooperative technique. It also has better performances when the number of antennas of the near GW from the ID is more than the number of antennas of the far GW.  
Figure 2: Outage probability performance of the LPWAN while varying the total received SNR when \(N=3\), \((K_{1},K_{2},K_{3})\in\{(1,3,5)(5,3,1)\}\), \(d_{1}=1\), \(d_{2}=4\), \(d_{3}=8\), \(R=1.5\), and \(\alpha=3\).  
Figure 3 shows the BER performances on the same system as the simulation environment in Figure 2. The analyzed BER performances in this paper, Equation (13) and (15), are verified to match well with the simulation results by Monte-Carlo. The detailed interpretation of the results is omitted because it is the same as the interpretation of the result of outage probability.  
Figure 4 validates the diversity order analysis of the uplink cooperative technique in LPWAN when \((K_{1},K_{2},K_{3})\in\{(1,3,5)(5,3,1)\}\); i.e., \(K=K_{1}+K_{2}+K_{3}=9\). As expected in Section 3.3, the asymptotic analysis results (17) are matched well with the exact analysis results in the form of an upper boundary curve as the SNR increases.
## 2 Materials and Methods
### Model Structure  
The core of the methodology lies in the model of the engine and its after-treatment system. Dynamic programming was used and, in our case, involved two state variables: the first representing the state of energy of the battery and the second representing the catalyst temperature. There were three control parameters in our algorithm, one being the intake manifold pressure that drives engine torque and the two others being the relative spark advance, \(\Delta_{SA}\), and the air to fuel equivalence ratio, \(\phi\).  
The vehicle dynamics and the resulting power demand are modeled using a backward approach. The velocity as a function of time is known in advance and this driving cycle corresponds to a necessary torque at the wheels to overcome the inertia of the vehicle as well as the resistive forces acting on it. The mechanical transmission components have constant efficiencies and the engine speed is saturated to its idle speed. All numerical values of the equations and parameters are presented in [23]. The three-way catalyst (TWC) efficiency model has also been described in [23]. This latter model uses two Wiebe functions to model global efficiency:  
* As a function of catalyst temperature, \(T_{cat}\);
* Regarding the air to fuel equivalence ratio, \(\phi\)  
Figure 3: Speed profile and gearbox ratios for the Class 3 WLTC driving cycle.  
\[\eta_{x}=\eta_{T}(T_{cat})\cdot\eta_{Y}(\phi) \tag{1}\]  
In which \(x\) represents the different species: CO, HC, and NO\({}_{\rm x}\). Figure 4 presents the shape of the curves.  
Finally, the engine model, fully described in [24] (equations, parameters values and measurements), comprises the following different parts:  
* CO, HC, and NO tailpipe concentration;
* Fuel flow behavior;
* Brake torque model;
* Exhaust temperature depending on operating parameters.  
For clear comprehension of the results, it is mandatory to recall the equations of engine emissions (see Equations (2)-(4)) and illustrate their shape for an arbitrary operating point (\(N=2000\) rpm; \(P_{intake}=0.9\) bar) and various relative spark advance values (see Figure 5). A zero relative spark advance (\(\Delta_{SA}=0\)) is optimal regarding engine efficiency; however, emission models are defined in absolute spark advance with the following relationship: \(SA=\Delta_{SA}+SA_{opti}\).  
\[[\text{CO}]=co_{1}\bigg{(}\phi-1+\sqrt{(\phi-1)^{2}+co_{2}}\bigg{)} \tag{2}\]  
\[[\text{HC}]=\max(0,\;\;hc_{1}\cdot N+hc_{2}\cdot(\phi-0.9)^{2}+hc_{3}\cdot SA+hc _{4}\cdot P_{intake}+hc_{5}) \tag{3}\]  
\[[\text{NO}]=\max([\text{NO}]_{min},\;\;no_{1}\cdot(\phi-0.9)^{2}+no_{2}\cdot SA +no_{3}\cdot P_{intake}+no_{4}) \tag{4}\]  
In Equations (2)-(4), \(co_{x}\), \(hc_{x}\), and \(no_{x}\) denote adjusting parameters determined by experiments in [24].  
Figure 4: Catalyst conversion efficiency model (**a**): regarding air to fuel equivalence ratio; (**b**): function of catalyst temperature.
## 3 A Survey on Wavelet Transform-based EEG Denoising Techniques
### Other Wavelet Transform-Based Method  
Besides ICA, empirical ensemble mode decomposition (EEMD) and canonical correlation analysis (CCA) can also be combined with wavelet transform for successful removal of artifacts from EEG signals [33; 34; 35; 36]. When SWT and EEMD are studied separately, they both appear to be effective in artifact removal from EEG recordings. One cause could be that the EEMD approach decomposes the signal into frequency and amplitude components [33]. This method does not confine the artifacts to a specific level. As a result, the EEMD's assumption of artifacts with larger amplitude at a specific decomposition level may not be correct. The CCA algorithms are used to segregate components depending on the EEG sources, such that the artifact component is considered as a single CC with a random distribution that can be removed easily [33; 34]. [PERSON] et al. [33] showed that the combination of CCA-SWT coupled with a second-order blind identification and SWT had significant improved performance in removing EOG and EMG artifacts. A combination of CCA-WT can yield improved results compared to using them separately; however, adding in an initial decomposition using EEMD yields improved performance when coupled with a support vector machine (SVM), leading to EEMD-CCA-DWT [36].  
[PERSON] et al. [37] presented an OA removal technique where Kalman filter is combined with DWT. In their work, DWT is applied to the raw EEG in identified OA zones to reconstruct a rough OAs approximation. The Kalman filter is used to optimize the OAs approximations from the previous step. Finally, the optimized OAs are subtracted from raw EEG. [PERSON] et al. [38] proposed a tunable algorithm for artifacts removal from EEG signals using wavelet packet decomposition and wavelet filtering. [PERSON] et al. [39] proposed a multi-stage EEG denoising method that combines wavelet packet decomposition (WPD) with a modified non-local means (NLM) algorithm for muscle artifact identification and removal. [PERSON] et al. [40] introduced two novel methods for the removal of EEG artifacts. In the first method, the common components among EEG channels were extracted and eliminated as artifacts, called common component rejection (CCR). In the second method, wavelet decomposition was employed to decompose the EEG signals, then the CCR method was applied to remove artifacts in the time-frequency domain, referred to as automatic wavelet CCR (AWCCR). [PERSON] et al. [41] developed a flexible technique to remove EEG artifacts in the context with minimal supervision. They proposed a new wavelet-based method that allows to remove artifacts from single-channel EEG based on a data-driven renormalization of the wavelet coefficients. Their method is capable of adaptively attenuating artifacts of a different nature.  
There are many other robust wavelet decomposition methods whose performances are yet to be investigated in processing EEG signals. The reader is referred to [14] for more details of some of these methods.
## 6 Conclusions  
In order to explore the evolution characteristics of WUE and the driving factors influencing it in the Yunnan-Guizhou Plateau of China from 2003 to 2020, this study used Theil-Sen slope estimation and Mann-Kendall significance analysis to investigate the temporal trends and spatial significance of WUE changes in the study area. XGBoost was then used to establish the driving relationships, and the SHAP model was applied to explain the importance of the driving factors and their specific relationships with WUE.  
(1) Overall, WUE showed an increasing trend, with a growth slope of 0.002, indicating that the vegetation's water absorption and utilization capacity in the study area has improved and water resource management has made progress.  
(2) The spatial distribution of WUE exhibits a \"high-low-high\" pattern from southwest to northeast, with lower WUE values in the northwest and central regions. Notably, the areas in the southwest with higher WUE showed a decreasing trend, while the areas in the central region with lower WUE showed an increasing trend.  
(3) WUE showed a decreasing trend in the western and southeastern regions, while other regions displayed an increasing trend. Specifically, 6.68% of the area showed a significant increase in WUE, 50.80% showed a slight increase, 4.60% showed a significant decrease, and 37.92% showed a slight decrease. In the future, stronger water resource protection and management should be implemented in the western and southeastern regions.  
(4) The importance of the driving factors is ranked as follows: NDVI, TMAX, SRAD, PDSI, VPD, and PRE. The NDVI exhibited a linear positive relationship with WUE. SRAD had a quadratic decreasing impact on WUE, and its inhibitory effect weakened at higher values. TMAX, PRE, the PDSI, and VPD all showed a non-monotonic relationship with WUE, exhibiting a pattern of increasing first and then decreasing. The patterns of these driving factors provide a theoretical basis for regional water resource management and can also serve as a reference for other regions with similar ecological conditions.  
P.W., methodology and writing. X.Z., coding and analysis. Y.L., data sources and investigation. W.N., supervision. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Science and Technology Program of Zhejiang Province (grant No. 2022C35070).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no competing interests.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 4 Simulation Results
### The Simulation Results of BER Performance
Specifically, the bigger \(\frac{N_{s}}{N_{T}}\) was, the lower the BER was in high SNRs. In other words, the less bits used in IM, the higher the BER was in high SNRs. Thus, we could control the BER performance in other positions by selecting a proper value of \(\frac{N_{s}}{N_{T}}\) so that Eve could not restore the original signal correctly, even if error correction coding was employed. Figure 10: 3-D performance surface of BER versus the direction angle and distance (\(N_{s}=1024\)). (**a**) 3-D performance surface of BER using PSS Plus RP; (**b**) 3-D performance surface of BER using RSCS-IM.
## 5 Discussion
### Robustness and Scalability  
Robustness and scalability are typical characteristics of swarm systems. Robustness means the system could operate normally in the case of disturbance or individual failure. Since the designed system is fully distributed with high redundancy, some members failing will be compensated by others. The distributed scheme also resists disturbance from surroundings.  
Scalability is another essential property of swarm robotics, which implies the system has the ability to work under arbitrary populations. As shown in the simulations with different populations and circumstances, the proposed method is identical for every member of the swarm, so it has high robustness and scalability.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Challenges of Achieving Energy Efficiency in the IoT
#### 2.2.2 Heterogeneity  
The heterogeneity of the IoT poses multiple obstacles to achieving energy efficiency. IoT environments typically consist of a wide variety of devices, technologies, platforms, and protocols [17]. Although this heterogeneity provides configuration flexibility for various applications, it also brings about issues such as integration complexity, interoperability, management, and security. For example, IoT systems are normally comprised of different types of devices like sensors, smart cameras, air pollution monitors, communication protocols, and others [18]; as a result, these devices differ broadly in design and functionality, energy consumption, and battery life. Such differences make it complicated to implement a generic energy-saving strategy.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 4 Experiments  
To verify the feasibility and practical effect of GPS spoofing on the APF method in the previous section, we first use HackRF ONE as a GPS spoofer to simulate GPS spoofing, as shown in Figure 12. Then we use GPS spoofing data to simulate the path planning of the APF method. The hardware system of the GPS spoofing test includes HackRF ONE, GPS external clock, and a 700-2700 Mhz antenna, as shown in Figure 13. The software platform is Ubuntu 64 bit (18.04), and the GPS receiving terminal is Xiaomi 6 mobile phone, as shown in Table 1. We use Matlab to simulate the path planning of the APF method to test the influence of GPS spoofing on path planning.  
Figure 11: The influence of \(\delta\) change rate on the resultant force of attraction and repulsion.  
Figure 10: The influence of \(\delta\) change rate on \(U_{rep_{1}}\).  
Figure 9: The influence of \(\delta\) change rate on \(U_{att}\).
## 5 Conclusions  
The block thermal network method was used to study the temperature rise in a DPRS at different ambient temperatures and determine the thermal deformation of the screw. A DPRS wear prediction model that considers the roughness and ambient temperature was then established, and the DPRS wear at different ambient temperatures was predicted. The influences of the thread numbers, roughness, and pitch on the DPRS bearing characteristics and wear depth were also considered. The wear prediction method was validated through experiments. The main conclusions drawn from the study are listed below.  
1. The temperature rise and thermal deformation of DPRSs under different environmental temperatures were analyzed. The temperature of each part of the DPRS reaches a steady state after a period of operation. The steady-state temperature of each part is influenced by the ambient temperature. The steady-state temperature of the roller is higher than the temperature of the screw, and both parts have uneven temperature distributions. The nut temperature is the lowest. The magnitude of the thermal deformation of each part at different temperatures and the temperature differences between the parts are consistent;
2. The load distribution and wear depth of DPRSs for different thermal deformation were analyzed. The load on DPRS threaded teeth and the depth of wear decrease as the thread number increases for the same temperature environment. The ambient temperature has a significant effect on the DPRS load distribution and wear depth of the thread teeth. Centered on the threaded tooth in the middle serial number, at higher temperatures, the load distribution is less uniform, and the wear depth distribution is more uneven. This indicates that reducing the ambient temperature is beneficial for enhancing the bearing capacity and wear resistance of the DPRS;
3. The effect of parameters such as number of threads, pitch and surface roughness on the depth of wear of DPRS at different ambient temperatures was analyzed. Increasing the number of threads and reducing the pitch can improve the bearing capacity of the DPRS and reduce its wear depth. In addition, reducing the surface roughness is also beneficial to improving the wear resistance of the DPRS. Therefore, the design of different parameters for DPRSs working at different ambient temperatures is beneficial for improving the wear resistance of the DPRS and prolonging its service life.  
W.G.: analysis, methodology, writing--original manuscript; C.L.: supervision, review, project administration, funding supporting; J.M.: methodology, writing--review and editing; P.W.: collecting literatures, writing--editing; B.C.: collecting literature and discussion. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52075053.  
The study does not require ethical approval.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 3 Experimental Result and Accuracy Evaluation  
### Experimental Results  
In this experiment, a CNN model was initially constructed using geographic covariates and OCO2 XCO2 data to extract deterministic trends. The stationarity test conducted on the residuals showed that the CNN model has the capability to capture the primary trends and relationships within the data, with the residuals exhibiting stationarity at a 1% confidence level, which meets the prerequisite for Kriging interpolation.  
Subsequently, the sample variogram and the theoretical variogram model were calculated for the residuals. The quality of the sample variogram depends on the number and distribution of available sample points. A sufficient number and good distribution of sample points can reduce the lag distance as much as possible and obtain more reliable calculation results. We aggregated discrete OCO-2 XCO2 retrievals into 0.25\({}^{\circ}\) grids at a monthly time scale, greatly reducing data gaps, but over 60% of the positions still have less than 10% valid values within a 72-unit time frame. Therefore, we attempted to fit residual semi-variogram models using all grid positions and subsets with at least 12, 24, 36, or 48 valid values as sample points. It was found that when using all positions and the subset with at least 12 valid values as sample points, we were unable to obtain the semi-variogram fitting curve converged to a stable value over space and time at certain distances. In other words, to achieve stable spatial and temporal variation scales when distances between spatiotemporal sample points are excessively large, it is necessary to screen the points to reduce uncertainties. After several experimental comparisons, it was recommended to select grid points with at least one-third (24-time units) of valid values as sample points to participate in the calculation and modeling of the variational function. The least squares method was used for parameter estimation, and the product-sum modelwas used to fit the empirical spatiotemporal variogram. The resulting model achieved relatively stable spatiotemporal sill values within certain time lags and spatial distances. Detailed descriptions of the stationarity test of residuals and the process and results of constructing the theoretical spatiotemporal variogram are given in Supplementary Materials Sections S1 and S2.  
Finally, the trend values estimated by the CNN model were combined with the residuals optimized through the spatiotemporal Kriging method to obtain the spatiotemporal distribution of monthly XCO2 for the study area from 2015 to 2020 at a resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), as shown in Figure 3.
## 3 Results
### Diagnostic Accuracy  
The results for the diagnostic accuracy and cutoff thresholds for key jumping-based metrics are shown in Figure 2. Importantly, gender was controlled for as a potential confounder in the logistic regression analysis to focus on the fundamental between-group differences. The coefficients for each term in the logistic regression equations are provided in the Supplementary Materials, which practitioners can use to enhance their decision making.  
Figure 1: Non-parametric inferential results for between-group differences in kinetic and kinematic parameters. (**A**) shows peak force; (**B**) shows jump height; (**C**) shows the jump impulse; (**D**) shows the reactive strength index (RSI); (**E**) shows the ground contact time; (**F**) shows the peak isometric torque. Note: \(M_{\text{diff}}\) = median difference; ND = non-dominant limb; Dom = dominant limb; Con = control group; PFP = patellofemoral pain group; RSI = reactive strength index; horizontal bars indicate the median value for each metric; d = Cohen’s d with a 95% confidence interval.  
Both baseline and post-intervention performances indicate that the RSI and GCT metrics have considerable diagnostic utility for differentiating PFP from healthy performances (AUC > 0.80) [28]. Jumping height and impulse seem to show adequate utility (AUC: 0.70-0.80).