esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 5 IoT LiDAR-based Collaborative Mapping - A Case Study
### LiDAR Self-testing Application
#### 5.2.2 Model Parametrization and Validation  
With the aim of determining which model training strategy based on AI provides the best reliability prediction model, an experimental validation was performed. The training dataset was composed of 998 scenes for the model training and 250 scenes for the model validation. All of them were obtained from a simulation procedure. The data input consisted of geospatial statistics [70; 96] that were extracted from the point cloud supplied by the LiDAR sensor, so that the models could generate the figures of merit in terms of accuracy error: DRMS and MRSE.  
The four AI-based strategies considered can be summarized as follows. First, a multilayer perceptron neural network with backpropagation error (MLP) is selected with two hidden layers, each with five neurons and sigmoid activation functions, and an output layer with a lineal activation function, two neurons, and 5000 epochs. The initial value of the learning rate (\(\mu\)) is \(10^{-3}\) with a decrease factor ratio of \(10^{-1}\), an increase factor ratio of 10, and a maximum \(\mu\) value of 1010. The minimum performance gradient was \(10^{-7}\). The training process stop criteria is as follows: the maximum number of epochs (repetitions); goal performance minimization; the performance gradient below a minimum gradient; or, a \(\mu\) value in excess of the maximum value. The second modeling technique is \(k\)-nearest neighbors (k-NN), with \(k\) = 5 and using Euclidean distance as the distance function. The third method is a lineal regression that was also obtained by minimizing the sum of squared differences between the predicted and the observed values. Finally, a support vector machine model is also obtained by means of data standardization and the radial basis function kernel.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 4 Using ECADLL Information for Spoofing Detection
### Classification as Spoofer Presence
#### 4.2.3 Additional Remarks for Spoofing Classification  
One additional remark that can be made is based on the knowledge of the dynamic behavior of the receiver, e.g., checking for the continuity of the impairment signal over time if the user is in a dynamic environment. In dynamic scenarios, multipath rays will be appearing and disappearing as the physical scenario (objects, buildings) changes around the receiver. In these cases, having an impairment signal that is persistent for several seconds is highly improbable, so a spoofing attack could be declared.  
On the other hand, in a static scenario where the receiver is fixed, this assumption does not hold anymore, because a reflection from a nearby object can be persistent over time. Nonetheless, in a static case, the receiver can be declared as spoofed once the error in the position, velocity or time surpasses a certain value that a multipath signal will unlikely produce; this assumption only holds if the antenna position is georeferenced. In static cases, it is also important to remember that multipath signals can be observed and classified a priori if the receiver is in a known and fix environment, so anything that is not the known signals can be also classified as spoofing. All of these considerations are heavily case dependent, and that is why they were not included in the general classification algorithm; but they could be effectively used to adapt the algorithm to a given scenario.
## 3 Results and Discussion
### Effect of Polyamine  
Polyamines, including spermine (Spm), putrescine (Put), and spermidine (Spd), represent a category of low molecular weight aliphatic nitrogenous organic compounds containing two or more amino groups [77]. These compounds are associated with various biological processes, including tissue growth, cell division, and cell differentiation [78]. Polyamines have been linked to higher callus induction and growth [79], while also playing a significant role in responding to both biotic and abiotic stresses [80]. Previous studies have indicated an increase in arginine decarboxylase (ADC) activity in rice seedlings when exposed to salinity [81]. Similarly, [PERSON] and [PERSON] [82] observed an increase in ADC and S-adenosylmethionine decarboxylase (SAMDC) expression in citrus embryonic callus under high salinity and both low and high temperatures. Furthermore, various abiotic stresses have been shown to trigger the up regulation of SAMDC expression at the transcriptional level in transgenic tobacco plants [83]. Moreover, [PERSON] et al. [78] observed higher free polyamine levels and the expression of polyamine biosynthesis enzyme genes in young rice spikelets under heat stress, thereby increasing endogenous Spd and Spm levels. This correlation was associated with higher yield and resistance to heat stress, providing insights for rice production under high temperatures. This involvement in stress response is just one aspect of their intricate physiological functions.  
Callus induction in meristem and stipe was monitored in PESI solid medium with different polyamines (Spm, Put, Spd) at 12 \({}^{\circ}\)C in dark (Figure 11). The selected calluses are shown in Figure 12. Overall, an increase in callus induction was observed in meristem tissue when culture medium was supplemented with 1 \(\upmu\)M Spm (60.55 \(\pm\) 3.05%). Except for this, all other conditions did not show a favorable impact on callus induction in meristem and stipe of _E. cava_ compared to the callus development in standard PESI medium.  
Figure 11: Callus induction in PESI medium supplemented with different polyamines. Values are means \(\pm\) standard errors (S.E.) of 10 individual replicates per experiment (\(n\) = 10). Spm: Spermie; Put: Putrescine; Spd: Spermidine. *: Indicates significant differences using [PERSON]’s HSD test using a multiple-way ANOVA test at \(p\) < 0.05.
## 5 FGI Challenges and NGN Solutions for Future Networks
### Openness, Interconnection and Third-Party Applications  
A fundamental difference between the telco and internet approaches is the implementation of \"openness\".  
Telco networks are open in the sense that any phone-service user can send phone calls and SMS to any other phone-service user. This openness is achieved through interconnection of telephony and IP networks worldwide. Rich Communication Suite [21] is an NGN-based telco initiative to extend this openness to other services, including multi-media messaging and presence. However, the openness is limited to those services that are standardized, and there is little room for third-party service providers to extend and build upon those services.  
Internet services, social networking services in particular, are open in the sense that anyone with internet access can subscribe to and use these services. Also third parties can offer additional service functionality through open APIs. However, the openness is limited to users within the same social network.  
Convergence of these two types of openness between NGN and FGI is becoming visible. On the one hand, telcos are working on open APIs for Rich Communications in Converged Networks [22]. On the other hand, social networking services are becoming more interconnected, enabling a user on one social network to interact with a user on another. Still, there is a lot of room for further convergence between telco and internet services.
## 3 Results and Discussion
### Sterility  
The starch/3%MCC film was securely sealed on all four sides utilizing a heat-sealing machine, demonstrating excellent sealing integrity, as shown in Figure 9a. After storing the starch/3%MCC sealing film at room temperature for 7 days, the sterilized gauze pad was incubated in SCDM at 25 \({}^{\circ}\)C and FTM at 37 \({}^{\circ}\)C for 7 days and then investigated any growth of microorganisms by the TPC method. In this study, the commercial product (sterilized gauze pad and sterilized sealing film) was used in the control group (Figure 9b). In the sterility tests, it was found that there was no microbial growth after incubation in both the SCDM and the FTM at the 7 days with the gauze pad samples in the sterilized starch/3%MCC and commercial sealing film (Figure 10). The absence of microbial growth in the sterilized gauze pad with sealing in the sterilized starch/MCC film confirms that the UV-C sterilization, 30 min for each side at 254 nm, effectively eliminated any microorganisms present on the starch/MCC film in the thickness of 139 \(\pm\) 6 \(\upmu\)m, ensuring their sterility. This reinforces the effectiveness of the sterilization process in maintaining the sterility of the medical packaging, which is crucial for ensuring their safety and efficacy in medical applications.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Formulation** & **Moisture Content (\%)** & **Water Activity Index** \\ \hline Starch & 14.20 \(\pm\) 1.42 & 0.50 \(\pm\) 0.003 \\ Starch/1\%MCC & 13.76 \(\pm\) 1.55 & 0.48 \(\pm\) 0.004 \\ Starch/3\%MCC & 14.07 \(\pm\) 1.05 & 0.51 \(\pm\) 0.004 \\ Starch/5\%MCC & 12.98 \(\pm\) 0.51 & 0.54 \(\pm\) 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Moisture content and water activity index of films (_n_ = 3).
## 5 Simulation Setup and Result Analysis
### Latency  
In this section, we introduce the latency by using O-MAC and other competing hybrid MAC protocols. We measure the latency in terms of how much time one packet takes to travel from the sender to the destination point.  
In addition, we measure and display different types of latencies, including the propagation delay, transmission delay, router delay and storage delay. These four types of delays are collectively shown in Figure 13. We also used the combined mobile and static scenario for determining the latency.  
The latency at the different speeds is shown in Figure 13. The simulation illustrates that the latency increases with increasing mobility. O-MAC has 0.0012-0.058 s of latency at speeds of 0-18 m/s with 25% mobility, whereas other competing MAC protocols show higher latency, namely, 0.0013-0.094 s for the same number of mobile nodes. CM-MAC produces higher latency than the other hybrid MAC  
Figure 12: Throughput of O-MAC and other competing hybrid MAC protocols with a varying number of transmitters.  
protocols. O-MAC achieves 9.23%-37.07% lower latency than the other MAC protocols. We validate that O-MAC can be used for different types of applications that require a faster delivery of data. Based on the simulation, our modular energy efficient paradigms based on MAC and network layers substantially improved the energy efficiency and QoS provisioning. The improvement of our proposed paradigms is shown in Table 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Improvement in Percentage** \\ \hline Energy consumed in accessing the channel & 5.4\% to 8.2\% \\ Energy consumed in sending the short preamble & 13.5\% to 18.4\% \\ Energy consumed in forwarding 5 MB of data & 3\% to 4.2\% \\ Energy efficiency & 3.88\% to 31.84\% \\ Successful packet delivery rate & 8.2\% to 15.4\% \\ Throughput with a varying number of transmitters & 8.4\% to 23\% \\ Latency & 9.23\% to 37.07\% \\ Channel access time & 18.9\% \\ Data frame transfer time & 2.3\% \\ Retry transmission time & 27.3\% \\ Retry transmission time & 11.1\% \\ Total channel access, frame transfer and acknowledgment and & 8.9\% \\ \multicolumn{2}{l}{turnaround time in an ideal scenario} \\ Total channel access, frame transfer and acknowledgment and & 8.7\% \\ \multicolumn{2}{l}{turnaround time in the worst-case scenario} \\ \hline \hline \end{tabular}
\end{table}
Table 8: The improvement of the proposed O-MAC with the PT model compared with other competing hybrid protocols.  
Figure 13: Latency of O-MAC and other competing hybrid MAC protocols with varying mobile speeds.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### Factor Analysis of Telecom Customer Calls
#### 5.2.2 Research Hypotheses Testing: KMO and Bartlett Sphericity Tests  
The KMO and Bartlett test results for call data are shown in Table 9. The KMO measures of sampling adequacy were 0.555 > 0.5, and the value of Sig was 0.000 < 0.05. It was concluded that the data were suitable for factor analysis.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 3 Analysis of Simulation Results
### System Robustness Analysis in Specific Application Scenarios  
For the specific application scenarios of fixed UWA communication nodes, the robustness of the system needs to be analyzed. In the previous section, the test channel samples were taken outside the range of the training samples. This is because the underwater nodes will be affected by ocean currents and tides, which will lead to the change of node position. In order to analyze the impact of the sample mismatch between the offline training stage and the online test stage on the system performance, this section will first select the training channel sample space and use it as the test channel to obtain the system performance when the two-stage samples match each other. Secondly, by changing the transmitting transducer depth (\(d_{t}\)), receiving hydrophone depth (\(d_{r}\)), and the distance (\(d_{l}\)), the CIR outside the training sample space is obtained to analyze the system performance when the two-stage samples mismatch. Only the robustness of the BiLSTM model is analyzed in this section. The simulation results are shown in Figure 14. By comparing the BER curves, it can be seen that based on the simulation results in this application scenario, due to the change of underwater node position, the UWA channel sample mismatch between the training stage and the test stage does not have significant damage to the performance of the DL-based CSK-SS UWA communication system.  
Figure 13: BER curve of DL-based system with or without preprocessing.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 2 Related Work
### Color-Analysis-Based Approach  
Numerous methods leveraging color information have emerged to enhance image visibility and quality across diverse environmental scenarios. [PERSON] [2] proposes a method centered on estimating the scene's albedo and inferring medium transmission. However, its reliability diminishes in scenarios where the method necessitates a diversity in the independent components of local patches. Moreover, it is ineffective for grayscale images or heavily foggy conditions characterized by low variability or signal-to-noise ratios.  
Conversely, [PERSON] et al. [7] utilize the Color Attenuation Prior (CAP) to devise a linear model for scene depth. Their approach aids in restoring luminance in fog-laden images, effectively mitigating fog and thereby significantly enhancing image quality in foggy environments.  
Additionally, [PERSON] et al. [9] propose a method using a color ellipsoid in RGB space for dehazing foggy images. This method simultaneously performs the calculation of the transmission map and image improvement through fuzzy segmentation, minimizing the occurrence of unnatural artifacts.  
[PERSON] et al. [10] propose a method that combines color-based approaches and transmission map techniques to reconstruct the 3D structure of scenes under adverse weather conditions. This approach has the advantage of effectively restoring the color and structure of scenes in various weather conditions by simultaneously utilizing color changes and depth information.  
While these methodologies demonstrate efficacy under specific conditions, [PERSON]'s method [2] may encounter challenges with low variability or signal-to-noise ratios. On the other hand, [PERSON] et al.'s [7] machine-learning-based approach demands substantial data for training, and inadequate data may result in ineffective haze removal. Furthermore, [PERSON] et al.'s method [9] acknowledges the potential drawbacks of reduced boundary accuracy in fuzzy segmentation and the high computational complexity of the implementation. [PERSON] et al.'s method [10] may have limited effectiveness in cases of grayscale images or low color contrast, and it requires high complexity for transmission map computation.
## 3 Preliminaries and Problem Definition  
### Preliminaries  
One of the main challenges in sentiment analysis is accurately classifying texts based on the sentiments they convey. Unlike aspect-level or sentence-level SA, document-level SA involves analyzing the sentiment expressed in a complete document rather than just a single sentence or aspect. This requires a deeper understanding of the complex relationships between sentiment and the phrases it depends on.  
Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art language representation model that utilizes a transformer architecture [8] and contextualized word representations [47] to generate contextual representations of words. By utilizing pretraining on a vast text corpus, BERT can better understand the meaning of words in the context of a sentence and effectively capture sequential features. Additionally, BERT is highly adaptable thanks to its capacity for fine-tuning to specific subtasks or datasets, making it a powerful tool in various natural language processing scenarios.  
The attention mechanism in neural networks is employed to enhance the model's comprehension of input data and improve its performance by focusing on certain parts of the input. The interpretability of the attention mechanism refers to the ability to understand and explain how the model uses the attention mechanism to process input data. This can be achieved by studying the attention weights (the level of focus) and the attention paths (the sequence of attention) that the model assigns to each input part.
## 3 Methodology
### Reflectance Solution based on NDVI and MODIS BRDF Model Parameters  
In order to solve Equation (6), get the value of bi-directional reflectance factor and accomplish topographic correction process, surface BRDF model data are required. Due to the lack of BRDF model information in TM scale, MODIS BRDF products with 500 m spatial resolution are used as surrogates. Three weighted coefficients for the semi-empirical BRDF model are stored in MCD43A1 data [20,24],and they are isotropic scattering coefficient \(f_{iso}\), volume scattering coefficient \(f_{vol}\) and geometric scattering coefficient \(f_{geo}\), respectively. With these three coefficients, kernel-driven BRDF model can be reconstructed and bi-directional reflectance factor under any geometric condition can be expressed as [45]:  
\[ \begin{split}\rho_{s}(\theta_{s},\theta_{v},\delta\varphi)& =f_{iso}+f_{vol}K_{vol}(\theta_{s},\theta_{v},\delta\varphi)+f_{ geo}(\theta_{s},\theta_{v},\delta\varphi)\\ &=f_{iso}\left(1+\frac{f_{vol}}{f_{iso}}K_{vol}(\theta_{s}, \theta_{v},\delta\varphi)+\frac{f_{geo}}{f_{iso}}K_{geo}(\theta_{s},\theta_{v },\delta\varphi)\right)\\ &=f_{iso}\Omega(\theta_{s},\theta_{v},\delta\varphi,\alpha_{1}, \alpha_{2})\end{split}  \tag{7}\]  
where \(\alpha_{1}=\frac{f_{vol}}{f_{iso}}\), \(\alpha_{2}=\frac{f_{geo}}{f_{iso}}\) ; \(\Omega\) represents BRDF shape function [24], which indicates the three-dimensional structural information for surface targets and is believed to remain relatively stable for the same land cover type [46]. Due to the large difference of spatial resolution between TM and MODIS, it is likely that different land cover types in TM will be synthetically displayed as one mixed MODIS pixel, and it will be problematic to directly employ the MODIS BRDF data for corresponding TM pixel. Averaging the BRDF data for the usage of whole TM scene, however, is also inappropriate since different land cover types often possess variable BRDF characteristics due to their structural and spectral variability [20]. Considering that MODIS BRDF model used to produce MCD43A1 products is a linear superposition of scattering kernels [45], we assume that MCD43A1 parameters can be averaged for the similar terrestrial targets and employed to account for BRDF effect of corresponding TM pixels. Based on the insensitivity of NDVI to terrain effect [47] and the principle of employing as less ancillary data as possible, we employed NDVI thresholds to divide land targets into eleven groups (Table 1), and averaged MCD43A1 data within each group. Then, parameters of BRDF shape function \(\Omega\)  for each group (Table 1) can be obtained and used to account for the BRDF effect for that group of land targets. Hemispherical-directional reflectance can be calculated by integrals of bi-directional reflectance over the illuminating hemisphere, and bi-hemispherical reflectance can be computed by integrating over both the illuminating and viewing hemispheres. According to [48], the related kernel functions can be integrated in advance and stored as known terms.
## 3 ECG Signal Processing  
If we look for a classifier that can obtain a satisfactory result of detecting \(VF\) and its differentiation from \(VT\), it is necessary that the data provided as input to this classifier are properly treated. For this reason, different stages of data conditioning are performed to both the temporal signal of ECG and its time-frequency representation \(TFR\). Figure 1 shows the general scheme of the followed methodology, from the reading of the records of the database to the results obtained by the classifier.  
The developed methodology is composed of three fundamental phases.  
* First phase: Data filtering in order to reduce the baseline that affects the ECG. Once filtered, obtain the Window Reference Mark (\(WRM\)) of the ECG signal. Each \(WRM\) indicates the beginning of a time window (\(t_{w}\)) of the ECG signal.
* Second phase: Extraction of information through the implementation of the Hilbert transform to each window \(t_{w}\) obtained in the first phase, then, assesment of the \(TFR\) matrix using the Pseudo Wigner-Ville, and the Time-Frequency Representation Image (\(TFRI\)).
* Third phase: The classification phase is carried out considering both the individual and combined classifiers used. In this phase, the previously obtained \(TFRI\) matrices are used as input. The success in the detection of \(VF\) depends on the processing of the signal and the structure of the classifiers used. In order to better adapt to the data, we must adjust the parameters of the classifier to obtain the best performance.