esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Construction and Evaluation of Interpretable Machine Learning Models  
Compared to logistic regression, machine learning algorithms generally offer superior capabilities in modeling nonlinear relationships, exhibit stronger predictive performance, possess built-in mechanisms for assessing feature importance, and demonstrate greater fault tolerance and robustness. To ensure methodological comprehensiveness and enhance the robustness of our findings, we selected nine machine learning models representing diverse algorithmic families: linear models (LR, RR), tree-based models (DT, RF, and GBDT), kernel-based models (SVM), instance-based models (KNN), ensemble strategies (AB and VC), and deep learning architectures (MLP). This selection enabled a systematic comparison across models varying in interpretability, capacity for nonlinear modeling, and sensitivity to data structure. For reference, a logistic regression model was also employed as a baseline. All models were implemented using Python 3.10.9. The dataset was partitioned into a training set (80%) and a testing set (20%), comprising 926 and 232 samples respectively. Hyperparameters for each model were optimized via grid search with five-fold cross-validation on the training set. To ensure reproducibility, random seeds were fixed during model initialization. Model performance and robustness were assessed using several evaluation metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity/recall. Hyperparameter configurations for each model are provided in Table S1. To enhance model interpretability, the study employed both permutation feature importance and an SHAP analysis. Permutation importance quantified the contribution of each feature to the model's predictive accuracy. The SHAP analysis offered a comprehensive view by providing both global insights into feature impact and directionality, as well as local explanations of how individual feature values influenced model output. This combined approach also allowed for the identification of threshold effects in feature contributions.
###### Abstract  
This paper proposes a new vibration isolation mounting system, with spherical balls and vertical spring dampers, that provides seismic protection from horizontal and vertical ground excitation. To characterize the system, nonlinear governing equations are derived by considering the kinematics and interaction forces of the structures, and the dynamic characteristics of the design parameters are investigated by numerical analysis. The condition of contact stability for sustaining continuous friction of the isolation device is discussed, along with the design parameters. The vibration transfer characteristics are analyzed for the displacement transfer ratio obtained for the design parameters satisfying the sustainable contact condition, when the base is harmonically excited. The results confirm that the relative motion occurrence satisfies the conditional expression, and a jump in which the transfer ratio suddenly increases at a specific frequency is identified. Finally, design variables that are suitable for the horizontal and vertical acceleration of the El Centro earthquake are set, and a simulation confirms the vibration reduction effect in both the horizontal and vertical motions.  
vibration isolation system; vertical damper; relative motion; friction; contact stability; transfer ratio; nonlinear vibration
# Onboard Processing in Satellite Communications Using AI Accelerators
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACM & Adaptive Coding and Modulation \\ AG-DPA & Assignment-Game-based Dynamic Power Allocation \\ AI & Artificial Intelligence \\ ANN & Artificial Neural Network \\ BP & Belief Propagation \\ CNN & Convolutional Neural Network \\ COTS & Commercial off-the-Shelf \\ CPU & Central Processing Units \\ CR & Cognitive Radio \\ CSI & Channel State Information \\ CU & Centralized Unit \\ DCA & Dynamic Channel Allocation \\ \end{tabular}  
\begin{tabular}{l l} DDQL & Double Deep Q-Learning \\ DL & Deep Learning \\ DQL & Deep Q-Learning \\ DRA & Direct Radiating Array \\ DRL & Deep Reinforcement Learning \\ DSP & Digital Signal Processor \\ DTP & Digital Transparent Payload \\ DU & Distributed Unit \\ DVB-S2X & Digital Video Broadcasting-Second Generation Satellite Extensions \\ ED & Energy Detector \\ ESA & European Space Agency \\ FEC & Forward Error Correction \\ FOV & Field of Vision \\ FP & Floating point \\ GPU & Graphics Processing Unit \\ GSO & Geostationary Satellite Orbit \\ HSN & Heterogeneous Satellite Network \\ KPI & Key Performance Indicators \\ LDPC & Low-Density Parity Check \\ LEO & Low Earth Orbit \\ LSTM & Long Short-Term Memory Network \\ MEO & Medium Earth Orbit \\ MICP & Mixed-Integer Convex Programming \\ ML & Machine Learning \\ MLSAT & Machine Learning and Artificial Intelligence for Satellite Communication \\ MODCOM & Modulation and Coding \\ NGSO & Non-Geostationary Satellite Orbit \\ NVDLA & NVIDIA Deep Learning Accelerator \\ OPEX & Operational Expenditure \\ OPs & Operations per second \\ PHY & Physical layer \\ PL & Programmable Logic \\ PPA & Proportional Power Allocation \\ PVA & Programmable Vision Accelerator \\ QL & Q-Learning \\ QoS & Quality of Service \\ RAN & Radio Access Network \\ RNN & Recurrent Neural Network \\ RRM & Radio Resource Management \\ RU & Radio Unit \\ SAGIN & Space-Air-Ground Integrated Networks \\ SatCom & Satellite Communication \\ SATAI & Machine Learning and Artificial Intelligence for Satellite Communications \\ SHAVE & Streaming Hybrid Architecture Vector Engine \\ SINR & Signal-to-Interference-plus-Noise Ratio \\ SLL & Side Lobe Level \\ SNR & Signal-to-Noise Ratio \\ SVM & Support Vector Machine \\ UHTS & Ultra-High-Throughput Satellite \\ \end{tabular}
## 6 Conclusions  
It has been demonstrated via computer simulations that BCH-encoded CPM-OFDM is a promising option for underwater acoustic communication. Without using a complex equalization method, this system provides good error performance. There are several values of modulation index \(h\) that yield good error performance. Furthermore, for a channel distance of 100 meters between the transmitter and the receiver, the error performance is so good that no equalization is required. This is at the expense of a complex receiver due to CPM and the reduction in effective information rate due to the use of BCH encoding, which can be overcome by increasing the overall bit rate. An analysis of the computational complexity of the proposed system has also been presented and compared with that of conventional OFDM. In this study, as proof of concept, we assessed the performance of a binary CPM-OFDM system using a BCH encoder as an example. The performance of an M-ary BCH-encoded CPM-OFDM system remains to be explored. Moreover, several other  
Figure 8: (**a**) OOB comparison of uncoded CPM-OFDM with that of PSK-OFDM and (**b**) OOB comparison of BCH-encoded CPM-OFDM with that of PSK-OFDM.  
Figure 7: Comparison of BER performance of the proposed system using four different BCH codes when \(h=7/16\).  
variants of BCH encoding schemes can be used to evaluate the system performance and identify more effective encoding schemes for reliable underwater acoustic communication.  
Conceptualization, I.A.T. and M.M.; methodology, M.M.; modeling and simulation, I.A.T.; validation, M.M., I.A.T. and P.O.; investigation, M.M. and I.A.T.; resources, P.O.; data curation, I.A.T.; writing--original draft preparation, M.M. and M.-A.L.-N.; writing--review and editing, I.A.T.; visualization, I.A.T.; supervision, P.O. and M.-A.L.-N.; project administration, P.O. and M.J.A.; funding acquisition, P.O. and M.-A.L.-N. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by the Institute of Oceanic Engineering Research, University of Malaga, Malaga, Spain.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank Escuela Tecnica Superior de Ingenieria de Telecomunicacion, and the Instituto de Ingenieria Oceanica, Universidad de Malaga, Malaga, Spain for their support. We also pay our gratitude to Smart Lab at the College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia.  
The authors declare no conflicts of interest.
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 1 Introduction
For this frequency, the study of the body shadowing effect was presented in the literature [14; 21; 22; 23; 24]. In [21], a statistical approach was applied for the analysis and modeling of the impact of path loss on the wireless link that is operating in the 2.4 GHz band. Also in [22], a statistical signal reception model for shadowed body-centric communications was proposed. The parameters of channel models proposed in the literature are extracted from channel measurements and can be used in statistical analysis of wireless system performance [23; 24]. Figure 1: Influence of body shadowing on wireless transmission between sensors: (**a**) No shadowing, successful transmission; (**b**) body shadowing that affects transmission  
This paper presents an analysis using computer modeling of the effect of body shadowing on wireless link performance in the very popular 2.4 GHz band. The effect of body shadowing was first analyzed in full wave computer simulations using the finite-difference time-domain (FDTD) method. Due to the high numerical burden of this method, the analysis was limited to only a small region around the human body. To analyze the performance of wireless sensor networks across larger distances, including indoor propagation environments, a simplified human body model is proposed that can be used in simulations made with a ray-based computer program utilizing the unified theory of diffraction (UTD). A similar model of the human body was first proposed by the author for simulations of the human body shadowing effect in 5 G wireless systems operating in the 3.4 GHz band [25]. The model required reconfiguration for simulations in the 2.4 GHz band, which is particularly useful for wireless sensor networks. The results of the simulations were found to be in good agreement with measurements, suggesting that the model is suitable for analyzing wireless sensor networks operating across large areas, including in buildings with obstacles. The main contribution of this paper is the proposed human body model dedicated for a ray-based computer program that can be successfully used for body shadowing simulations in the 2.4 GHz band. The proposed model has practical value because the design of wireless systems that operate in the indoor environment is often performed with a ray-based software that is computationally effective in the modelling of large objects. In this kind of simulation software (in contrast to full-wave simulators), there were no human body models that could be used to model the shadowing effect.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 1 Introduction
### FREEDM Architecture with Multiple Levels
#### 2.1.1 Plug-And-Play (PnP) Level  
The PnP level of operation integrates the power devices, especially DRERs and DESDs, anytime and anywhere, with the existing smart grid [33]. It helps with the easy and instant recognition of power devices, as soon as they get connected. The PnP infrastructure connects all of the devices of FREEDM to the EI, using wired or wireless protocols [34], such as fiber optics, power line communication (PLC) [35], ZigBee [36], worldwide interoperability for microwave access (WiMAX) [37], cognitive radio (CR) [38], long-term evolution (LTE) network [39], and cellular communications [40].  
Figure 2: Levels of operation in Future Renewable Electric Energy Delivery and Management (FREEDM) design in the energy internet (EI).
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background
### Algorithm of NTRU
#### Key Generation  
The private key \(f\) is a trinary polynomial selected from \(\mathcal{L}_{f}\) and the public key \(h\) satisfies \(h=pf_{q}^{-1}\cdot g\pmod{q}\), \(g\in\mathcal{B}_{g}\). The public key is used in the data encryption and private key is used in the data decryption.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 3 PQ Mitigation Techniques
### Future Mitigation Trends  
This section illustrates the current and future paths in mitigating PQ issues in the grid subject to wind energy integration. Such trends demonstrate the advancements in energy storage technologies and control strategies of ESS and transmission technologies of transmitting wind energy.
## 3 Results and Discussion
### FTIR Analysis  
FTIR analysis assisted in identifying functional groups present in plant extracts that contribute to the mechanism of bonding with Zn-O NPs. Figure 2 shows the FTIR spectra of KKD-Zn-NPs, which were acquired in the range 400-4000 cm\({}^{-1}\) using the KBr method. The broad peak at 3289.80 cm\({}^{-1}\) resulted from the O-H stretching of Kakadu plum adsorbed phenolic compounds on the surface of KKD-ZnO-NPs. Due to the complexation of functional groups present in Kakadu plum and the surface of KKD-ZnO-NPs, some transmitted peaks that appeared at 2910 and 1710 cm\({}^{-1}\) of KKD-ZnO-NPs exhibited shifts to lower wavenumber. Because of the asymmetric and symmetric stretching of C-H and C=O (ketones) bonds, these peaks were associated with the presence of aromatic compounds. Transmission peaks observed at 1320 cm\({}^{-1}\) were proposed to be contributed by the stretching of C-O bonds of carbonyl groups and/or C-O of alcoholic groups and glycosidic linkages of the phytochemical constituents of Kakadu plum. Finally, there was a strong transmission peak that appeared around 500 cm\({}^{-1}\) is correlated to Zn-O [35; 36; 37; 38; 39].  
Figure 1: Visible spectra of KKD-ZnO-NPs with Kakadu plum extract.
## 2 Methodology
### Extraction and Constraints of Atmospheric Delay
#### 2.2.2 User-End Atmospheric Delay Constraints  
In medium-scale and long-scale reference station networks, the spatial correlation of atmospheric delay is greatly reduced. If the user uses the atmospheric delay of reference stations directly, the residuals are absorbed by the ambiguity and affect fast IAR. To eliminate the influence of receiver-related errors in the ionospheric delay, the SD ionospheric and zenith tropospheric delay corrections are introduced into the undifferenced and uncombined (UDUC) PPP as virtual observations. If the receiver code hardware delay cannot be reasonably handled, the performance in a PPP application may be degraded [21; 22; 23]. By adding the ionospheric a priori correction information constraint, the receiver code hardware delay can be estimated. Assuming that \(m\) satellites are observed and that the \(f\)th satellite is selected as the reference satellite, the dual-frequency UDUC PPP functions with external atmospheric corrections after error correction and reparameterization can be expressed as follows:  
\[\left\{\begin{array}{l}p_{u,1}^{\mathrm{s1}}=\mu_{u}^{\mathrm{s1}}\cdot x+c \overline{t}_{u}-c\overline{t}^{\mathrm{s1}}+M_{w,u}^{\mathrm{s1}}d_{w,u}+l_{ u,1}^{\mathrm{s1}}+\varepsilon_{P,u,1}^{\mathrm{s1}}+\gamma_{2}^{2}/\left( \gamma_{2}^{2}-1\right)DCB_{u}\\ p_{u,2}^{\mathrm{s1}}=\mu_{u}^{\mathrm{s1}}\cdot x+c\overline{t}_{uIn the formula,  
\[\lambda_{i}\overline{N}_{u,i}^{s}=\lambda_{1}N_{r,i}^{s}+b_{r,i}-b_{i}^{s}+d_{1F}^ {s}\]  
where \(u\) represents the user station, \(\Delta\overline{I}\) is the SD ionospheric delay, \(\overline{d}_{w,u}\) is the tropospheric zenith wet delay, and the other terms in the equation are as defined previously. The users and servers use the same data processing method to ensure consistency.
# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 3 The Proposed Bascs
### The Time Complexity Analysis  
Taking one iteration as an example, the worst-case time complexity analysis of BASCS assumes that the dimension of the test function is d, the population size is n, and the time complexity of the [PERSON] flight operation is \(O(Levy)\), which is a constant factor \(O(1)\). The time complexity of initializing population positions is \(O(n\times d)\), tent chaotic mapping combined with random walking is \(O(d)\), calculating the fitness value of the objective function is \(O(f(d))\), dynamic updating of the Levy flight step size and discovery probability are both \(O(n\times d)\), Levy flight operation based on Equation (16) and direction judgment of the BAS during the global search phase is \(O(n\times d\times O(Levy))\), local search using Equation (12) in later iterations is \(O(n\times d)\), and updating solutions using Equation (6) during the random walking phase is \(O(n+d)\).  
Therefore, the total time complexity of T iterations is \(T\times O(n\times(d+f(d)))\).  
Compared to CS and its variants, BASCS achieves an acceptable level of complexity while delivering superior search performance. This balance between complexity and efficiency is vital for practical applications. In real-time scenarios like robotic path planning  
Figure 1: The flowchart of the BASCS.  
and dynamic optimization, the efficiency of the search process and convergence speed often outweigh the need for extensive computation time.  
The BASCS enhances convergence speed and search accuracy by integrating various optimization strategies, including tent chaotic mapping, random migration, and Levy flight, along with dynamic parameter adjustments. This improvement is particularly evident in high-dimensional complex problems. However, these enhancements come at the cost of increased computational workload, significantly raising the time and resources required for each iteration. The need to execute multiple optimization strategies and make real-time parameter adjustments adds extra computational demands, which may impact the algorithm's practicality in resource-limited or real-time scenarios. Thus, while BASCS demonstrates superior theoretical performance, it is crucial to balance the computational burden against performance gains in practical applications to ensure the algorithm's effectiveness and efficiency across different contexts.
## 4 Discussion
### Mechanical Properties of Diatomite Soil in Direct Shear Test  
Through the direct shear test, 24 groups of diatomite soil samples with different water contents are used. It is found that the mechanical strength index of diatomite soil has obvious characteristics with 20% water content and 40% water content. Comparing the strength index of diatomite soil with 20% water content and that of diatomite soil with 40% water content, it can be seen that when the water content rises from 20% to 40%, the cohesive force \(C\) of diatomite soil greatly decreases, the cohesive force \(C\) of diatomite soil with 20% water content is about twice than that of diatomite soil with 40% water content, the internal friction angle of diatomite soil with 40% water content diatomite soil is 3\({}^{\circ}\) lower than that of diatomite soil with 20% water content. The test shows that the water content in diatomite soil will affect its shear strength--it will change the cohesion and internal friction angle of the soil.  
The undisturbed diatomite soil has a strong structure, it shows better mechanical strength properties. The diatomite soil has special element mineral composition, strong water absorption, and softening characteristics; the original stable silicon aluminum oxide laminated crystal cell structure will be affected by the water molecules; it will show the phenomenon of softening and volume expansion on the macro level; its mechanical strength characteristics will be affected. At the same time, water can reduce the cohesion of soil by influencing the bite force between particles and the friction force generated by the particles. It has a tremendously adverse effect on the stress of diatomite soil due to its expansion characteristics when encountering water.
## 2 Results and Discussion  
In Silico Analysis of RSH Genes and Proteins in B. napus and Selected Close Relatives from the Brassicaceae Family  
Over 20 years ago, _RelA/SpoT_ homologs (_RSH_) were discovered in plants [21], and the occurrence of the stringent response in plants was also proposed. Subsequently, _RSH_ genes have been characterized in other plant species, and it has been shown that the stringent response plays a critical role in the regulation of plant growth and development, and in adaptation to different environmental niches [23; 24]. The nature of the evolutionary basis of the stringent response raises questions regarding the complexity of plant _RSH_ gene families including their number, and the structure of plant _RSH_ proteins in various plant species. The plant _RSH_ proteins have been divided into three groups (_RSH1_, _RSH2/3_, and _CRSH_), based mostly on protein primary structure. The members of these three groups of _RSH_ proteins vary in their expression patterns and catalytic activities and, therefore, they probably fulfil distinct physiological roles. It seems that the diversification in plant _RSH_ genes occurred when plants adapted to terrestrial conditions, and resulted either in the loss or acquisition of some structural and functional features [35; 36]. Here, in order to reveal the complexity of the _RSH_ gene family, and to further predict relations between sequence and function, we have analysed in silico _RSH_ genes and RSH proteins in _B. napus_, and in selected relatives from the _Brassicaceae_ family.