esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 5 Experimental Setup and Methodology
Depending on whether the spoofed data or the real data had higher mean values, the left- or right-CDF was calculated for each metric. The CDFs were generated from the associated PDFs. The left-CDF \(c_{l}(x)\) is obtained by integrating the PDF \(p(x)\) from the left:  
\[c_{l}(x)=\int_{-\infty}^{x}p(x)dx\,. \tag{39}\]  
On the contrary, the right-CDF \(c_{r}(x)\) is obtained by integrating PDF \(p(x)\) from the right:  
\[c_{r}(x)=\int_{x}^{\infty}p(x)dx\,. \tag{40}\]  
Note that the left-CDF is always a monotone increasing function, whereas the right-CDF is always a monotone decreasing function. The left- and right-CDFs were used, as this allowed for a simple comparison of the distributions, and to illustrate how similar or disjointed the distributions were. Further, it allows for the separation between the distributions to be clearly visualized and analyzed. Lastly, a detection threshold was set for each metric to optimize correct detections, under the assumption, that a spoofing attack and no attack are equally likely. Note that this assumption is only valid for the test data and does not represent the likelihood of a spoofing attack in the real world, but is still adequate for research and development purposes. In this case, the optimal threshold \(\lambda\) would be the value by which the left- and right-CDFs for the spoofing and real data distributions are equal:  
\[c_{l}^{\text{spoof}}(\lambda)=c_{r}^{\text{real}}(\lambda)\,,\text{ or} \tag{41}\]  
\[c_{l}^{\text{real}}(\lambda)=c_{r}^{\text{spoof}}(\lambda)\,, \tag{42}\]  
where \((\cdot)^{\text{real}}\) denotes the real data distribution and \((\cdot)^{\text{spoof}}\) the spoofed data distribution. The assignment of the left- and right-CDF depends on the metric used: for some the spoofed distribution is larger than the real-data distribution, for others it is the opposite. This was indicated by the results. For some metrics, the detection values of others were used; hence, the analysis was done sequentially. To illustrate some observed effects and limitations in the data, results, where only two of the six antenna elements were used for acquisition and beamforming, are also shown. However, this is not the focus of this article and is not provided within the analysis.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 2 Related Work
The GA also exhibits superior robustness and inherently maintains a diverse population when compared to those of other evolutionary algorithms [24]. Some GA research tries to modify the population to achieve better performance. An improved genetic algorithm [25] tried to solve the population prematurity and slow convergence speed problem in USV path planning by managing the number of offspring. Other research focusing on an improved genetic algorithm [26] was carried out for cooperative collision avoidance. Through building fitness and iteratively optimizing the adjustment of velocity and heading, the researchers were able to use the algorithm to safely operate multiple USVs. Then, the improved genetic algorithm [1] was used to try to solve the convergence speed problem by maximizing the cumulative detection probability (CDP). Combining the GA with other algorithms provides better performance. Thus, the genetic algorithm was combined with the simulated annealing algorithm (improved hybrid GA) for path planning [27] in order to solve its lack of searching ability and the large amount of calculation required. Other research combined the GA with the artificial potential field (APF) algorithm [28] for multi-objective, multi-robot path planning in a continuous environment. A modified genetic algorithm [29] for a USV under environmental loads was proposed by integrating the three objective functions (minimizing travel time, reaching a target point, and avoiding obstacles). All GA modification research was carried out to produce better algorithm performance. In this research, we examine a neuronal GA--a combination of a genetic algorithm and a neural network--using neural network variable values as input to the genetic operator of the GA. The modification is carried out by implementing a partially exponential-based fitness function to produce a faster convergent solution (details of the proposed methodology are presented in Section 4). The proposed method aims to solve the same convergence problem as does other GA research, as well as other unresolved problems in the GA in the form of implementation in a dynamic environment.
## 3 Results and Discussions
### Influence of nMMA on the Adsorption Performance of MIPs  
Figure 6 presents a graph that reveals the effect of the variation of the amount of functional monomers on the adsorption performance of the polymer, wherein the amount of solanesol was 0.025 mmol, the amount of cross-linking agent was 6.25 mmol, the amount of KPS was 8 mg, and the synthesis temperature was 65 \({}^{\circ}\)C. It is evident that with the increase of the amount of functional monomer from 0.1 mmol to 0.35 mmol, the adsorption capacity of the SSO-MIPs first increased and then decreased. When the amount of functional monomer was 0.20 mmol, the adsorption capacity of  
Figure 4: Particle size distribution of SSO-MIPs.  
Figure 5: The FT-IR spectra of the MIPs and NIPs.  
the SSO-MIPs reached the maximum. This is because the amount of functional monomers was low, which resulted in the inability of some solanesol to participate in polymer formation; additionally, SSO-MIPs have low adsorption capacity because fewer stereo holes in the formed polymers can coordinate with solanesol. The figure of the pore volume also shows that there are fewer stereo holes. When the amount of the functional monomer increased, more solanesol was combined with the functional monomer to form a large amount of stable SSO-MIPs, thus increasing the adsorption capacity. The continued increase of the amount of functional monomers resulted in the excess of functional monomers, which were randomly arranged in the polymer. Therefore, SSO-MIPs were mixed with a large number of functional monomers, which leads to a decrease in pore volume and a decrease in adsorption effect.
# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
## 4 Experimental Study
### Benchmark Models  
In this section, we selected benchmarks including BPR, NeuMF, Caser, GRU4 Rec, and SASRec to evaluate the performance. We implemented them using TensorFlow and the RecBole library and considered latent dimensions [10, 20, 30, 40, 50] as the common hypermeter in all models. The maximum number of training epochs is 100. In addition, we adopted the NeuMF model with the hidden size h = [64, 32, 16]. We tuned the hyper-parameter using the validation set and terminated training if the validation performance did not improve at 40 epochs. The benchmark model is detailed as follows:  
* Bayesian Personalized Ranking (BPR): The model is an optimization technique applied to matrix factorization, explicitly tailored to the handling of implicit feedback to enhance the factorization process by incorporating a pairwise ranking loss function.
* NeuMF: This is a collaborative filtering model that uses user-item interactions with an MLP instead of the inner product in matrix factorization when calculating the relationship between the user and item.
* Caser: This adopts convolutional neural networks (CNNs) in horizontal and vertical dimensions to effectively model high-order Markov Chains (M.C.s) for sequential recommendations.
* GRU4 Rec: This is a unidirectional recurrent neural network (RNN)-based framework that is used to capture sequential dependencies and make predictions.
* SASRec: This is a state-of-the-art sequential recommendation model leveraging self-attention blocks to predict the next item for recommendation. Additionally, it employs the dot product computation between sequential latent features of the latest item and embeddings of the target item to establish the scoring mechanism.
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
## 3 Results and Discussion
### Effect of Adsorbent Dosage  
The efficiency of any adsorbent in the treatment of polluted wastewaters depends mainly on the number of available active adsorption sites [49]. To enhance the removal efficiency of RH and find the optimum dosage amounts, the adsorption capacity of RH and removal percentage were analyzed by varying the dosage in the range of 0.05-1.0 g at initial dye concentrations of 400 mg/L (MB) and 300 mg/L (CV). The results, as plotted in Figure 7, indicate that the MB removal percentage marked up from 53.74% to 97.74% while that of CV increased from 57.40% to 98.19%. The increase may be attributed to the increase in the number of adsorption sites [6; 19]. In contrast, with an increasing dose of RH, the adsorption capacity of MB and CV onto RH decreased from 160.41 mg/g to 25.6 mg/g and 68.81 mg/g to 11.78 mg/g, respectively. This decrease could be attributed to the split in the flux or concentration gradient between the solute in solution and solute at the surface of the adsorbent, or to the overlaying of adsorption sites following a reduction in the total adsorption surface-area available to the dye, and a rise in the diffusion pathway. Therefore, the amount of MB and CV adsorbed onto unit weight of RH is reduced, leading to a decrease in the adsorption capacity.  
Figure 6: Influence of solution pH (4–10) on (**a**) methylene blue (MB) adsorption and (**b**) crystal violet (CV) adsorption.
## 6 Discussion
### Validation of the Performance of the New Device
These perceptions are fundamental for the successful adoption of new technologies in industrial environments, as operator acceptance directly influences the effectiveness and sustainability of their implementation. Additionally, the suggestions received, such as the integration of an automatic data capture system, indicate clear opportunities for future improvements to the device, which could further optimize analytical procedures and reduce the risk of errors in manual data recording. The implementation of the new constant-pressure system effectively addresses the cost and efficiency challenges faced by the wine industry. By reducing processing times and the use of human resources, the device increases the laboratory's analytical capacity, allowing for a higher volume of analyses in less time and improving the response to internal demands. Additionally, by providing precise measurements of a fluid's clogging potential, the equipment not only benefits wine bottling companies but also has the potential to be applied in other industries that require this type of analysis for informed decision-making. This versatility makes the device a useful and potentially valuable tool for improving productivity, reducing operational costs, and strengthening competitiveness across various industrial sectors. The proposed device has broad application potential across various industries, standing out for its ability to operate without a pressurized tank and its versatile design that ensures safety and efficiency. Its use is particularly relevant in situations where samples cannot be handled under atmospheric conditions due to the risk of contamination or hazards to operators. For example, in industrial processes such as gas sweetening in refineries, it allows for samples to be extracted directly from the process line to monitor amine fouling levels, improving efficiency and reintegrating the filtered fluid into the system [38,39]. Similarly, in bioprocesses [40], the device protects sample integrity, enabling reliable analysis of larger volumes without compromising quality or increasing cleaning efforts. In the beverage industry, such as beer production [41], it offers significant advantages, as it does in wine filtration, to ensure microbiological and sensory standards [13]. These applications, combined with the ability to develop indicators tailored to the specific needs of each process, establish the developed device as a versatile solution that not only enhances operations but also drives innovation in decision-making and industrial planning. Furthermore, the methodological framework developed in this work contributes a robust and replicable structure for the evaluation of new indicators and different applications, strengthening the capacity for adaptability and continuous improvement across various industries.
## 4 Experimental Results
### Comprehensive Performance Evaluation  
To assess the advantages of the proposed model in terms of inference speed, power consumption, and deployment cost on embedded development boards, several key performance metrics are defined. Energy per frame (EPF) is calculated using the formula: \(\text{EPF}=\text{PC}\times\text{Inference Time}\), where PC is the device power consumption (in watts) and Inference Time is the per-frame inference time (in seconds). Cost per frame per second (CPFPS) is calculated using the formula: \(\text{CPFPS}=C\times\text{Inference Time}\), where \(C\) is the device cost (in RMB). Mean average precision (mAP) directly represents model accuracy: the higher, the better. To comprehensively reflect the model's advantages in inference speed, power consumption, and deployment cost, a comprehensive performance indicator (CPI) is defined that averages these three factors with weights. Assuming weights of \(\alpha\), \(\beta\), and \(\gamma\), where \(\alpha+\beta+\gamma=1\), the equation can be expressed as:  
\[\text{CPI}=0.4\times\frac{1}{\text{EPF}}+0.3\times\frac{1}{\text{CPFPS}}+0.3 \times\text{mAP}\]  
Assuming the following data--inference time for the proposed model at 352 resolution on RK3566 is 15.27 ms, RK3566 power consumption is 3.5 W, deployment cost is 299 RMB, and mAP is 11.5%--EPF, CPFPS, and CPI can be calculated using the above formulas.  
The CPI table, as shown in Table 7, highlights the trade-offs between inference speed, accuracy, energy efficiency, and cost-effectiveness. The proposed proposed models, particularly the one with a 320 \(\times\) 320 resolution, stand out with a CPI of 34.02, significantly higher than other models. This superior performance is due to its balanced metrics: an inference time of 12.13 ms, mAP of 10.8%, EPF of 0.0425 joules, and CPFPS of 3.63 RMB. The 352 \(\times\) 352 resolution variant also performs well with a CPI of 27.61, demonstrating a good balance between higher precision (mAP of 11.5%) and efficiency. Compared to models like YOLOv8s, which has the highest mAP but significantly lower CPI (6.20) due to high energy and cost. The proposed methods achieve faster inference speeds and are less sensitive to memory bandwidth variations. These results highlight the proposed model's efficiency in utilizing computational resources, making it ideal for real-time applications on cost-effective embedded platforms where both performance and operational costs are critical.
## 6 Discussion
### Challenges and Future Directions  
#### 6.3.1 Improve Model Interpretability  
Improving the interpretability of RNNs in traffic prediction is not only a technical requirement to enhance model transparency but also a key factor in achieving efficient traffic management and precise decision support. Interpretable models enable traffic managers and decision makers to understand the logic and influencing factors behind model predictions. For example, suppose a traffic prediction model can clearly show the reasons for traffic congestion during a certain period (such as weekday rush hours, holidays, or special events). In that case, decision makers can adjust traffic signal control or implement traffic control measures based on these insights. Additionally, when the model can provide easily understandable prediction processes and results, the trustworthiness of these predictions increases among the public and policymakers. This is crucial for promoting the application of intelligent transportation systems and the acceptance of new technologies. On the other hand, by enhancing the interpretability of models, developers can more easily identify biases in the data or algorithms, such as geographic biases or systematic biases in data collection, thereby improving the fairness and accuracy of the model. Improving models' interpretability is crucial for developing deep learning models in traffic prediction. However, enhancing the interpretability of deep learning models and further exploring their superiority and rationality remains a significant challenge.  
In future research, enhancing model interpretability can be pursued through two main directions. On the one hand, improvements can be made from within the model itself, such as simplifying model architectures and integrating interpretability mechanisms to ensure that while the model achieves high-performance traffic prediction, it maintains a transparent and explainable decision-making structure. On the other hand, developing visualization tools to represent the predictive model's internal workings and decision processes can reveal the underlying logic behind the model's predictions, thereby improving its interpretability.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Different Types of BackCom
#### 3.3.3. Bistatic Scatter  
[PERSON] et al. present the bistatic scatter (BiS) configuration for BackCom [50]. Figure 9 illustrates BiS BackCom, showing an RF receiver that is separated from the carrier emitter. BiS configuration increases the communication range by bringing the carrier emitter closer to the tag. The carrier emitter has an oscillator and power amplifier to generate and transmit the carrier signals. It is different from the traditional reader, such that it does not possess its own receiver. The signals generated from the adjacent carrier emitter in the BiS scenario reduce the round trip path loss by decreasing the distance between the tag and the power source (carrier emitter) [60]. In this way, the reflected signals have a higher signal-to-noise ratio (SNR) at the receiver end when compared to Amb-BackCom. Furthermore, this type of bistatic setup for communications shows better BER performance in the receiver than its counterpart [66]. Similarly, for an extended range, a full signal model for BiS radio is derived to demonstrate the experimental ranges of order 100 m [50]. However, it is not possible to consider a nearby signal generator in every scenario. Considering the size limitations of passive devices, another type is discussed below.  
Figure 8: Ambient BackCom.
## 2 Results and Discussion
### Antihydrotival Activity
This activity of EO and HY could be explained by the fact that essential oils, when inoculated simultaneously with the virus, are likely to cause direct inactivation of the viral particles, as opposed to stimulation of the host's defence response, as assumed for the activity of HY during pretreatment. It has been documented that the main mechanism of antiviral effects of many essential oils against respiratory viruses may be related to capsid or membrane disintegration, and that essential oils and their components may also inhibit the late stages of the viral life cycle by interfering with the redox signalling pathway [35]. Comparison of previous experiments dealing with the antiphytoviral activity of essential oils and hydrosols of different plant species [8] with the antiviral activity of _P. ramossissima_ extracts shows that EO and HY of this plant are a valuable source of compounds that can be further explored for the development of natural antiviral agents. In this context, we particularly highlight the activity of hydrosol, as our results have shown that volatile substances in the form of environmentally friendly water solutions are a promising new source of compounds that can be used to prevent the attack of viral pathogens. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**LLN \(\pm\) SD**} & \multicolumn{2}{c}{**LLN \(\pm\) SD**} \\ \cline{2-4} (a) & C & \(52.17\pm 14.54\) & Ch & \(8.84\pm 0.92\) \\ PT-EO & \(32.10\pm 9.53\) & SI-EO & \(4.98\pm 0.81\) * \\ PT-HY & \(16.67\pm 6.81\)* & Ch & \(14.98\pm 1.89\) \\ SI-HY & \(9.90\pm 1.40\)* \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of local lesions on: (a) leaves of local host plants treated with essential oil and hydrosol before virus inoculation; (b) leaf halves simultaneously inoculated with virus and essential oil or hydrosol. Figure 6: Antiphytoviral activity of _P. ramossissima_ against tobacco mosaic virus infection on local host plants. (**a**) Pretreatment of plants with essential oil (EO) or hydrosol (HY). (**b**) Simultaneous inoculation of EO or HY with virus. * statistically significant differences between essential oil and hydrosol treatment data (\(p<0.05\)).
## 5 Numerical Modelling for Form-Finding of TEL-Gridshells
### Residual Stresses
As the bracing is formed by curved transverse laths (not by cables or straight bars), the axial compressive forces produce bending moments in them which increase their curvature. Conversely, the elastic recovery moments of the laths tend to decrease the curvature. Figure 17 shows the final shape of the central traverse lath of the braced structure (model B) after relaxation. Since the transverse members are made up of three mechanically joined laths, the resulting bending stresses are translated into normal stresses distributed in each of them (Figure 18), whose maximum compression value reaches 3.34 kN in model B, producing a compressive stress of 7.0 N/mm\({}^{2}\). Figure 16: TEL-Gridshell geometry after relaxation, with bracing system (**left**) and without bracing system (**right**). Amplification factor = 5. Figure 17: Transverse lath before relaxation of the structure (_Stage 4_) in black, and deformed shape after relaxation in blue (amplification factor = 40). In the case of the structure with slip (model A), the distribution of normal stresses in the laths of the transverse members is very similar to the previous case without slip, but the values are reduced by around 80% producing a maximum stress of 5.5 N/mm\({}^{2}\), which represents a low value in relation to the material strength. It should be noted that the stresses produced by the curving moment (M\({}_{\rm y}\)) of the transverse member' laths are much more important. This is practically constant along the member (see Figure 16) with a value of 0.033 kNm (see Table 9), which produces a maximum bending stress of 53.1 N/mm\({}^{2}\), almost 10 times higher than those produced by the axial forces and close to the characteristic bending strength of the material. It should be kept in mind that the calculated stresses correspond to the initial instant of curving. However, as explained in Section 3.5, the load test was carried out one week after the prototype construction was completed, allowing part of the stresses due to curving to dissipate because of the rheological behaviour of the wood. A discussion on the long-term evolution of bending stresses and the consideration of rheological effects of wood in the sizing of laths under constant deformation can be found in [24].