esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Results  
Next, microscale and macroscale numerical examples are studied with the proposed meshless technique, and the produced results are analysed and discussed. Firstly, following the homogenisation procedure described in Section 2.4, the homogenised mechanical properties of a polyurethane foam (PUF) with circular voids are calculated. Afterwards, the obtained homogenised mechanical properties are then applied to a macroscale sandwich cantilever beam with aluminium top layers and PUF core. In order to study the influence of the PUF core density, distinct PUF cores are analysed: PUF cores with constant density and approximated functionally graded PUF cores.  
In this work, the following two NNRPIM formulations are considered:  
* NNRPIMv1: first-degree influence cell.
* NNRPIMv2: second-degree influence cell.  
Regarding the integration scheme, for both NNRPIM formulations, one (\(1\times 1\)) integration point per quadrilateral sub-area was considered in all NNRPIM analyses. Concerning the construction of the shape functions, for both NNRPIM formulations, we considered the following: shape parameters \(c=0.0001\) and \(p=0.9999\) and a constant polynomial basis.  
For comparison purposes, finite element formulations are applied [47], such as:  
* FEM-3n: constant-strain linear triangular elements (three-node elements), with one integration point per element.
* FEM-6n: quadratic triangular elements (six-node elements), with full integration.
* FEM-4n: Lagrangian four-node elements, with full integration.  
Regarding the software and hardware resources used in this work, all the NNRPIM and finite element method codes were fully written by the author using MatlabTM R2017b, and the nodal and element meshes were built with Simcenter Femap Student Edition SoftwareTM. All the analyses were performed with a standard laptop computer (Samsung, Suwon, Korea), with an Intel\({}^{\copyright}\)CoreTM i5-3230M CPU, 2.60 GHz, and 8 GB of RAM.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.2.2 Scenario 2: Dual Satellite Visibility  
In the Scenario depicted in Figure 9, when the terminal detects only one additional NPSS within 10 ms after detecting the first NPSS, it can conclude that the system is in a dual-satellite visible scenario. At this point, there are fewer visible satellites, and the terminal is unable to immediately calculate its position. The terminal records the arrival times of the NPSSs, denoted as \(L_{1}\) and \(L_{2}\), and the satellite real-time ephemeris \(S_{1}(x_{1},y_{1},z_{1})\), \(S_{2}(x_{2},y_{2},z_{2})\). It then enters the Power Saving Mode (PSM) for a duration of \(T\). After the PSM sleep period, the terminal re-detects the NPSSs. If three satellites are now visible, it  
Figure 8: Receiver timing diagram.  
directly uses the method from the three-satellite visible scenario to calculate the position. If the three-satellite visible scenario is not detected, the terminal records the arrival time \(L_{3}\) of the first NPSS and the satellite ephemeris \(S_{3}(x_{3},y_{3},z_{3})\). The time difference for the dual-satellite visible scenario is given by:  
\[\begin{array}{l}\Delta t_{1}=L_{2}-L_{1}\\ \Delta t_{2}=L_{3}-L_{1}-T\end{array} \tag{25}\]  
In dual-satellite and single-satellite visible scenarios, it is necessary to detect downlink satellite signals at different time instances in order to obtain observational values. The longer the interval between observations, the better the geometric configuration of the satellites becomes, resulting in higher positioning accuracy. However, NB-IoT terminals are power-constrained IoT devices, operating in four general power states: transmission, reception, light sleep, and deep sleep. Between two consecutive signal detection events for acquiring observations, the terminal must enter deep sleep mode for a prolonged interval denoted as \(T\). A longer observation interval \(T\) leads to higher energy consumption and longer synchronization latency but improves positioning accuracy. Conversely, a shorter \(T\) reduces power consumption but may result in insufficient positioning and TA estimation accuracy, potentially failing to meet the synchronization requirements for NB-IoT uplink. Therefore, depending on the accuracy requirements, each terminal can adaptively adjust its observation interval \(T\) to strike a balance between positioning accuracy and power consumption. To ensure better satellite geometry for localization, this paper sets 100 s.  
Accordingly, the TDOA equations with surface constraints can be formulated as:  
\[\left\{\begin{array}{l}r_{2}-r_{1}=c\Delta t_{1}\\ r_{3}-r_{1}=c\Delta t_{2}\\ x^{2}+y^{2}+z^{2}=\left(R+h_{n}\right)^{2}\end{array}\right. \tag{26}\]  
The terminal's coordinates can be determined using the method outlined earlier.
# Addressing Chemophobia: Bridging Misconceptions in Food Chemistry
## 3 Chemophobia in Food Advertising
### Analyzing the \"Pingo DOC\" Campaign  
The central claim of the advertisement--Pingo DOC's products being \"free from chemicals\"--is scientifically inaccurate and perpetuates misunderstanding. Every food product, whether processed or raw, contains chemicals because all matter is composed of chemicals. For example, (1) water (H\({}_{2}\)O), a fundamental ingredient in yogurt, is a chemical; (2) lactose, the natural sugar in milk, is a chemical compound; (3) even the natural flavor compounds in fruits, like limonene in citrus, are chemicals.  
By equating \"chemicals\" with harmful artificial substances, the ad exploits the public's misunderstanding of the term while failing to educate consumers about the nuances of food chemistry.  
The campaign taps into the \"natural fallacy\": the belief that natural substances are inherently good while synthetic substances are harmful. This false dichotomy ignores the reality that many natural substances can be toxic (e.g., cyanide in almonds or botulinum toxin), while synthetic compounds can be safe and beneficial. For example, ascorbic acid (vitamin C), often added to preserve food, is chemically identical whether it is derived from natural sources or synthesized in a laboratory.  
The ad's focus on the absence of \"artificial colorants and flavor enhancers\" implies that these additives are unnecessary or unsafe, ignoring their role in improving taste, shelf life, and appearance while meeting rigorous safety standards set by food regulatory bodies.  
The conversational tone of the advertisement makes it relatable and reinforces a sense of shared mistrust of \"chemicals\". This emotional appeal is highly effective in influencing consumer behavior, as it validates existing fears and provides a simple solution: buy Pingo DOC products to avoid harmful \"chemicals\". However, this approach prioritizes marketing goals over consumer education, leaving viewers with a skewed understanding of food safety and chemistry.  
It is important to notice that the sentiments and level of chemophobia vary from country to country and depend strongly on its representation in the mass media, educational system, and other specific features of a certain country. For instance, the levels of chemophobia are higher in France, Italy, and Poland compared to Switzerland, Austria, Germany, Sweden, and the UK [38].
## 2 Related Work
In contrast, the subspace pursuit (SP) [37], stagewise-OMP (StOMP) [38], compressive sampling matching pursuit (CoSaMP) [19], regularized-OMP (ROMP) [39], generalized-OMP (GOMP) [40], generalized orthogonal adaptive matching pursuit (GOAMP) [41], constrained backtracking matching pursuit (CBMP) [42], generalized backtracking regularized adaptive matching pursuit (GBRAMP) [43], OMP algorithm based on singular value decomposition [44], enhanced block-based OMP pursuit [45], gradient pursuit (GP) [46], and multipath matching pursuit (MMP) [47], involve columns with projected values that exceed the thresholds selected by the algorithm. Other differences between the algorithms include the way the residual vector \(\gamma_{iter}\) is calculated and how the non-zero values of \(x_{est}\) are estimated. For instance, the MPLS and the subspace pursuit (SP) algorithms do not estimate \(x_{est}\) until the last step of their respective processes. As the aforementioned discussion has highlighted, all the existing greedy algorithms use either single or groups of column selection. For every frame of the signal, iterations are required to find the dominating columns set (\(\tilde{q}_{iter}^{dominant}\)), as described in Figure 2. This makes the algorithm complex and time-consuming. To overcome this problem, we proposed an optimized measurement matrix generation in such a way that all dominating columns consisting of the original signal components are known in advance. Furthermore, the optimization minimizes the presence of noise components in these columns to achieve better signal enhancement. Based on these investigations, this paper proposes a CS-based speech enhancement technique called the dominant columns group orthogonalization of the sensing matrix (DCGOSM). Figure 1: Sparse recovery algorithms.
# Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
## 4 Analysis of Experimental Results
### Computer Simulations and Ocean Experiments
\begin{table}
\begin{tabular}{c c} \hline \hline
**Demodulation Scheme** & **Data Rate (bps)** \\ \hline CV-CFM, Ref. [12] & 309.3 \\ TFSK, Ref. [13] & 18.2 \\ The proposed method \(L_{c}:2\) & 288.9 \\ The proposed method \(L_{c}:3\) & 284.1 \\ The proposed method \(L_{c}:4\) & 278.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of bits for bio-mimetic modulation scheme. Figure 7: The simulation and theoretical BER results. maximum Doppler shift of the modeled channel was 2 Hz. The delay profile and sound speed profile (SSP) used to model the channel are shown in Figure 8.  
The modulation parameters for the proposed method and the compared conventional methods were the same as in AWGN channel simulations, and the \(L_{c}\) was set to 3. All compared methods were tested with and without using a 1/3 turbo code. The transmission rates for each scheme were the same as in Table 1. The BER results in the modeled underwater channel environments in Figure 8 are shown in Figure 9.  
In Figure 9, the BERs of the proposed method were similar to those in Figure 7 for AWGN. The conventional method in [12] showed an error floor at around a BER of \(2\times 10^{-2}\). The proposed method, however, achieved a BER of \(10^{-3}\) at an SNR below \(-15\) dB. The proposed method demonstrated a 7 dB SNR gain over the conventional method at a BER of \(10^{-3}\). To verify the BER performance of the proposed method through practical ocean experiments, an experiment was conducted on 20 October 2019, in an area located 7 km away from the Shinjin-do coast in Taean, South Korea. The transmitter was deployed at a depth of five meters from the sea surface and used a Neptune-D17 BB with a frequency range of 12.5 kHz to 19.5 kHz. The receiver with two TC4032 was deployed at depths of 5 m and 7 m with two channels each. The communication modulation parameters for the proposed and conventional methods were the same as those used in computational simulations, and the code length of \(L_{c}\) was set to three. The distance between the transmitter  
Figure 8: The simulation channel (**a**) delay profile, (**b**) SSP. Figure 9: The UWA Channel simulation BER results. and the receiver was 1 km. Figure 10a-c provides the experiment location, experimental setup, and SSP. Figure 11 represents a portion of the received signal using the proposed method. Both proposed and conventional methods transmitted whistles of 70 s and were repeated 100 times. The results of the ocean experiments are provided in Table 3.  
The received SNR of the ocean experiment data were estimated to be approximately \(-20\) dB. In Figure 9, the BER with an SNR of \(-21\) dB and turbo coding yielded around \(10^{-4}\), which is close to the results obtained from the ocean experiments. The maximum Doppler shift was estimated to be approximately \(1.8\) Hz. The proposed method consistently exhibited the lowest BER with a greater data rate in both the computational simulations and the real ocean experiments.
## 6 Discussion
### Forecasting Error Analysis  
The proposed hybrid approach outperforms the conventional methods. The forecasting results and precision of the proposed model in four different wind turbines in multi-step forecasting are shown in Table 6. Then, Figures 5-7 show the forecasting results of multi-step forecasting from the proposed approach and different traditional methods. The following conclusions are obtained from these results: (1) From the results of three-step forecasting, we can see that the hybrid method is more accurate than other conventional models because the error is the lowest many times. (2) The degree of fitting between the output series and the actual data from different models is shown in Figures 5-7. The proposed approach is superior to the corresponding traditional methods, with a higher precision.  
Figure 5: One-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).  
Figure 6: Two-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).  
Figure 7: Three-step forecasting results of the proposed model and other traditional models (BP RBF ARIMA).  
Multi-step forecasting is employed to further test the validity of the new hybrid approach. From Table 6, the results of multi-step forecasting of four wind turbines are shown. For wind turbine 1, the MAPE values of multi-step forecasting are 4.79%, 6.19%, and 10.14%, respectively. The other three wind turbines obtain similar results. As the results show, the new hybrid model is valid in multi-step forecasting.  
**Remark 5**.: _From the analysis, the performance of the hybrid method is better than those of the traditional models. The proposed model is able to adapt to the fluctuation of the input data so that it can obtain a more accurate result. Therefore, the new hybrid approach is more effective in wind speed prediction._
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### System Defense Assessment Technology
#### 5.5.1 Resilience Assessment Methods  
Resilience assessment methods include qualitative and quantitative assessments [213]. Qualitative assessments include questionnaire surveys [214], the analytic hierarchy process [215], etc. Such methods rely on the cognitive level of the assessment subject. The assessment results obtained by different assessment subjects vary greatly, and the objectivity of the assessment is slightly weak. In addition, the complex coupling mechanism and a  
Figure 17: Schematic diagram of honeypot technology.  
large number of uncertain factors within the integrated energy system further reduce the practicality of qualitative assessment.  
Quantitative assessments mainly include statistical analysis and simulation. The statistical analysis method is to infer and analyze the system's resilience in a certain period in the future by statistically analyzing the frequency and severity of system failures in extreme events in history. For example, by statistically analyzing the historical failure data of the system, using probability density distribution fitting methods [216] or establishing Bayesian networks [217] to reasonably analyze system failures under extreme events, we can then evaluate the system resilience. This method is highly dependent on historical data, and it is difficult to ensure the accuracy of the assessment for extreme events with low probability. The simulation method establishes a disaster and system model, simulates the entire process of the system responding to extreme disasters, collects system operation status data for resilience index calculation, and evaluates the resilience of the system based on the value of the index. In [218], researchers established an earthquake fault model and a system repair model to simulate the system's resistance and recovery process under earthquake scenarios, and established robustness, rapidity, and redundancy indicators to evaluate the resilience of the integrated energy system. In [219], the authors established an electric-gas coupling system model under a hurricane scenario to evaluate the system resilience from the aspects of system operation, infrastructure failure, and economic losses. The evaluation results obtained by the simulation method are highly interpretable and easy to understand and accept. The main steps of the simulation method include fault scenario modeling, system response and recovery modeling, system operation simulation, and resilience indicator calculation and evaluation.
## 6 Implementation and Case Study
### Adversarial Conditions
#### 6.4.1 Data Poisoning  
Data poisoning attacks aim to degrade the FL model by manipulating a fraction of local updates.  
**Threat Model:** Let \(W_{i}\) denote the local model parameters from client \(i\). In a data poisoning scenario, a malicious client sends corrupted updates:  
\[W_{i}^{\prime}=W_{i}+\delta_{i} \tag{15}\]  
where \(\delta_{i}\) represents the injected perturbation. Two attack vectors are modeled:  
* **Gaussian Noise Injection**: \(\delta_{i}\sim\mathcal{N}(0,\sigma^{2}I)\) with \(\sigma=0.5\).
* **Label Flipping**: Labels \(y\) are flipped with probability \(p=0.3\): \[y^{\prime}=1-y\] (16)  
The global model aggregation under poisoning can be presented as Equation (7), as presented in Section 4.10.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Expected Range** & **Unit** \\ \hline CPU Utilization & 0–100 & \% \\ \hline DU Energy Consumption & 0–500 & W \\ \hline Communication Latency & 0–20 & ms \\ \hline \hline \end{tabular}
\end{table}
Table 12: Key metrics in OpenRAN Gym.  
\(\mathcal{B}\) is the set of benign clients and \(\mathcal{M}\) the set of malicious clients.  
To limit the impact, our framework applies an \(\ell_{2}\)-norm clipping:  
\[\|\Delta W_{i}\|_{2}\leq 3.0 \tag{17}\]  
thus ensuring that large deviations are discarded.  
**Detection Strategy:** The system flags clients with \(\|\Delta W_{i}\|_{2}>2.5\) as suspicious. Using standard hypothesis testing,  
\[\mathcal{H}_{0}:\text{Client is benign}\quad\mathcal{H}_{1}:\text{Client is malicious} \tag{18}\]  
we achieve a detection rate of 92.4% with a false alarm rate (FAR) of 0.7%.
## 3 Proposed Techniques
### Competitive Analysis  
Based on the logistic equation, the Lotka-Volterra model (LVM) is adopted to capture the interactions between competing firms [14,18,23]. Differential equations are given as:  
\[\frac{\mathrm{d}x_{1}}{\mathrm{dt}}=a_{1}x_{1}-b_{1}{x_{1}}^{2}-c_{1}x_{1}x_{2}, \tag{13}\]  
Figure 4: Deep neural network (DNN).  
Figure 3: Support vector regression (SVR). Stars mean representative data samples, a solid line is a central fitting, and dashed lines denote the upper and lower limit.  
\[\frac{\mathrm{d}x_{2}}{\mathrm{dt}}=a_{2}x_{2}-b_{2}{x_{2}}^{2}-c_{2}x_{1}x_{2}, \tag{14}\]  
where \(x_{i}\) can be modeled by adopting users, shipments, revenues, etc., \(a_{i}\) denotes the ability of the equation itself, \(b_{i}\) refers to the limitation of the firm during market expansion, \(c_{i}\) describes the interaction between the firm and its competitor. In equilibriums, the differential values in Equations (13) and (14) are zeros, and the two objects can be mutually estimated as: \(x_{1}=(a_{1}-c_{1}x_{2})/b_{1}\) and \(x_{2}=(a_{2}-c_{2}x_{1})/b_{2}\). To use discrete data, differential equations are converted into difference equations:  
\[x_{1}(t+1)=\frac{\alpha_{1}x_{1}(t)}{1+\beta_{1}x_{1}(t)+\gamma_{1}x_{2}(t)}, \tag{15}\]  
\[x_{2}(t+1)=\frac{\alpha_{2}x_{2}(t)}{1+\beta_{2}x_{2}(t)+\gamma_{2}x_{1}(t)}, \tag{16}\]  
where \(a_{i}=ln\ \alpha_{i}\), \(b_{i}=\beta_{i}\ ln\ \alpha_{i}/(\alpha_{i}-1)\), and \(c_{i}=\gamma_{i}\ ln\ \alpha_{i}/(\alpha_{i}-1)\) are used to estimate three important parameters, \(\alpha_{i},\beta_{i},\gamma_{i}\).  
The original LVM can be generalized to include more objects at a time. For clarity, managerial insights regarding the parameters in LVM are described in Table 2. The relationships between a firm and its rivals can be one of the six types: pure competition (mutually harmful), mutualism (win-win), predator-prey (win-loss), amensalism (one-side harmful), commensalism (one-side beneficial), and neutralism (independent). Hence, LVM can clearly explain the market dynamics between firms [18; 19]. Further, stable equilibriums occur when neither of the population levels are changing: differential equations are equal to 0: \(\frac{\mathrm{d}x_{1}}{\mathrm{dt}}=0\), \(\frac{\mathrm{d}x_{2}}{\mathrm{dt}}=0\). In this case, four possible equilibriums, \(\left(x_{1}^{*},x_{2}^{*}\right)\), are derived: (1) \(\left(x_{1}^{*},x_{2}^{*}\right)=0\) meaning both species disappear, (2) \(\left(x_{1}^{*},x_{2}^{*}\right)=(a_{1}/b_{1},0)\) meaning specie 1 will survive while specie 2 will disappear, (3) \(\left(x_{1}^{*},x_{2}^{*}\right)=(0,a_{2}/b_{2})\) meaning specie 2 will survive while specie 1 will disappear, and (4) \(\left(x_{1}^{*},x_{2}^{*}\right)=\left(\frac{a_{1}b_{2}-a_{2}c_{1}}{b_{1}b_{2}- c_{1}c_{2}},\frac{a_{2}b_{1}-a_{1}c_{2}}{b_{1}b_{2}-c_{1}c_{2}}\right)\) meaning both species can survive. Each equilibrium point can be stable only if the real parts of the eigenvalues of the Jacobian matrix, \(\mathrm{J}(x_{1},x_{2})=\begin{pmatrix}a_{1}-2b_{1}x_{1}-c_{1}x_{2}&-c_{1}x_{1} \\ -c_{2}x_{2}&a_{2}-2b_{2}x_{2}-c_{2}x_{1}\end{pmatrix}\), are negative.