esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Decoding the Intricacies of Blind Source Separation (BSS): A Deep Dive into Core Principles
### Independent Component Analysis (ICA)  
ICA is an advanced BSS technique focused on the isolation of statistically independent components in EEGs, distinguishing itself by its ability to separate signals into subcomponents with less statistical overlap [94; 95]. This makes it particularly suitable for removing complex artifacts, such as ocular or cardiac ones, improving the distinction between real brain activity and interference. The accuracy of ICA in identifying independent signal sources facilitates the elimination of artifacts and improves the interpretation of EEG data, making it essential in both neuroscientific research and clinical applications.  
Mathematically, the ICA derives a demixing matrix, \(\mathbf{W}=\mathbf{A}^{-1}\), such that [96; 97]:  
\[\mathbf{S}_{est}=\mathbf{W}\cdot\mathbf{X}, \tag{2}\]  
where \(\mathbf{S}_{est}\) estimates the separate EEGs (artifact-free signals), and the artifacts, \(\mathbf{X}\), optimize \(\mathbf{W}\) to maximize the statistical independence between the components of \(\mathbf{S}_{est}\).  
ICA calculates \(\mathbf{W}\) by optimizing a contrast function, such as negentropy, which measures the non-Gaussianity (and therefore independence) of the components. Once \(\mathbf{W}\) is calculated, the EEG signals are separated from the artifacts by identifying and removing the corresponding components in \(\mathbf{S}_{est}\). Choosing which components to remove often requires manual analysis or automatic criteria based on known characteristics of the artifacts, such as frequency, topography, or temporal behavior. After removing artifacts, clean EEG signals are obtained as:  
\[\mathbf{X}_{clean}=\mathbf{A}_{clean}\cdot\mathbf{S}_{clean} \tag{3}\]  
where \(\mathbf{S}_{clean}\) are the components of \(\mathbf{S}_{est}\) that represent the clean brain activity, and \(\mathbf{A}_{clean}\) is the matrix of corresponding reduced mixing.
## 2 Materials and Methods
### Statistical Analysis  
In order to compare nutritional profile and fatty acids content between eggs from hens fed with standard and supplemented diet, a \(t\)-test was conducted [34]. All requirements for its execution (namely, normality of data and homogeneity of variances) were validated. Additionally, in order to compare physical parameters, nutritional profile and EPA, DHA and ALA content between control eggs and eggs from hens fed with supplemented diet with 3% of _Nannochloropsis oceanica_, during 4 weeks of trial, a one-way analysis of variance (ANOVA) was performed. Also, the same analysis was used to compare physical parameters, nutritional profile and fatty acids contents between control eggs and eggs from hens fed with supplemented diet (W4), at zero (SI-0) and 28 days (SI-28) of storage. Whenever the assumptions for performing the ANOVA were not met (namely, normality of data and homogeneity of variances), the non-parametric Kruskal-Wallis test was performed [34]. Whenever applicable, multiple comparisons between samples and the control group were performed using the Dunnett test [34]. For the remaining comparisons, Tukey's honestly significant difference (HSD) or least significant difference (LSD) and Games-Howell multiple comparison tests were performed, according to the fulfillment (or not) of the ANOVA requirements (respectively) [34]. The results were considered statistically significant with a significance level of 5%. Where applicable, all results are presented as mean \(\pm\) standard deviation (SD). Finally, principal components analysis (PCA), based on correlation matrices, was applied to identify the main associations among physical parameters, nutritional pro file, and fatty acids of egg samples along the shelf life. The PCA provides information on the most meaningful parameters, which describe a whole data set, affording data reduction with minimum loss of original information. Although the results concerning the first two components (PC1 and PC2) were presented, the others were also analyzed. When applicable, data were standardized, and log (x + 1) transformed [35]. All calculations were performed with the CANOCO version 4.5 package.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 3 Equalizers for SDM
### Multi-Mode 2M \(\times\) 2M Dynamic Equalization  
For the generalized equalization, we considered \(M\) interfering propagating modes, resulting in an equalizer of 2\(M\times\) 2\(M\). The \(x\) and \(y\) polarizations of the \(v\)-mode of the received signal are denoted by \(\mathbf{x}_{v}^{(n)}\) and \(\mathbf{y}_{v}^{(n)}\). Similar to the 2 \(\times\) 2 case, the equalization of the received signals can be expressed as [8]:  
\[\begin{split}\overline{x}_{v}(n)&=\sum_{j=1}^{M}\mathbf{x }_{j}^{(n)}.\mathbf{h}_{x_{v}x_{j}}^{(n)}+\mathbf{y}_{j}^{(n)}.\mathbf{h}_{x_{v}y_{j}}^{(n) }\,,\\ \overline{y}_{v}(n)&=\sum_{j=1}^{M}\mathbf{x}_{j}^{(n)}. \mathbf{h}_{y_{v}x_{j}}^{(n)}+\mathbf{y}_{j}^{(n)}.\mathbf{h}_{y_{v}y_{j}}^{(n)}\,,\end{split} \tag{10}\]  
where the filter coefficients are:  
\[\begin{split}\mathbf{h}_{x_{v}x_{u}}^{(n)}&=\left[h_{x_ {v}x_{u}}^{(n)}(1),h_{x_{v}x_{u}}^{(n)}(2)\dots h_{x_{v}x_{u}}^{(n)}(N)\right] \!\!\mathbf{h}_{y_{v}x_{u}}^{(n)}&=\left[h_{y_{v}x_{u}}^{(n)}(1),h_{y _{v}x_{u}}^{(n)}(2)\dots h_{y_{v}x_{u}}^{(n)}(N)\right]\,,\\ \mathbf{h}_{x_{v}y_{u}}^{(n)}&=\left[h_{x_{v}y_{u}}^{(n) }(1),h_{x_{v}y_{u}}^{(n)}(2)\dots h_{x_{v}y_{u}}^{(n)}(N)\right]\!\!\mathbf{h}_{y_ {v}y_{u}}^{(n)}&=\left[h_{y_{v}y_{u}}^{(n)}(1),h_{y_{v}y_{u}}^{(n) }(2)\dots h_{y_{v}y_{u}}^{(n)}(N)\right]\,.\end{split} \tag{11}\]  
The input signals consist of a vector accounting for \(N\) samples of the received signal:  
\[\begin{split}\mathbf{x}_{v}^{(n)}&=\left[x_{v}(n),x_{v} (n-1),\dots,x_{v}(n-N+1)\right]\,,\\ \mathbf{y}_{v}^{(n)}&=\left[y_{v}(n),y_{v}(n-1),\dots,y_{ v}(n-N+1)\right]\,.\end{split} \tag{12}\]  
Similar to the single-mode equalizer, the filter coefficients are updated based on the stochastic gradient update rule, where the generalized equations describing the filter update are:  
\[\begin{split}\mathbf{h}_{l_{d}m_{g}}^{(n+1)}&=\mathbf{h}_{ l_{d}m_{g}}^{(n)}-\mu\
abla_{\mathbf{h}_{l_{d}m_{g}}^{(n)}}f(\mathbf{h}^{(n)});l,m\in\{x,y\},\\ \
abla_{\mathbf{h}_{d}^{(n)}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot} \mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\  
where \(\hat{x}_{v}(n)\) and \(\hat{y}_{v}(n)\) are the expected signals of each mode \(v\). Similar to the \(2\times 2\) case, the LMS equalizer can operate in training mode or decision-directed (DD) mode. According to our description of the stochastic gradient descent, the \(2M\times 2M\) LMS algorithm has the following error coefficients:  
\[C_{1v}(n)=2(\overline{x}_{v}(n)-\hat{x}_{v}(n))\overline{x_{v}}(n);C_{2v}(n)=2( \overline{y}_{v}(n)-\hat{y}_{v}(n))\overline{y}_{v}(n). \tag{15}\]
## 4 Results
In these, the times taken to track and identify all the RFID tags that should have been read effectively (except the tag glued to the metal surface, for justified reasons, in the influence of electromagnetic interference for this type of tags in use in the tests) were the lowest, compared to the other alternatives (where in the latter two it was not possible to read all the tags in all the rounds carried out), with values of \(t_{1}\) = 1.06, \(t_{2}\) = 1.59, and \(t_{3}\) = 2.10 for the average of the reading data for the position horizontally and inclined to the side of the start of the trolley's trajectory under the gantry and the antenna's detection field, horizontally without any inclination and horizontally with an angle of inclination in the opposite direction to the start of the trolley's passage, respectively, obtained through several calculations made. In turn, the average reading time of the asset sample by the RFID system (\(t_{F}\)) is taken as the average of the three values considered above (with \(k\) = 3), using Equation (1):  
\[\text{RFID Reading Time }(t_{F})=\frac{t_{1}\ +\ t_{2}\ +\ t_{3}}{k}\ =\ \frac{1.06\ +\ 1.59\ +\ 2.10}{3}\ =\ 1.58\ \text{s} \tag{1}\]  
And finally, it turns out that the improvement in asset-tracking time with RFID tags is given by Equation (2):  
\[\text{Reading Time Optimization }(14\ \text{assets})\ =\ t_{0}-t_{F}\ =\ 45.2-1.58\ =\ 4 3.62\ \text{s} \tag{2}\]  
In this context, metal surfaces reflect the wave emitted by the RFID reader and cause interference that normal RFID tags cannot withstand. To overcome this obstacle, special RFID tags that use an additional layer of material that separates the RFID antenna from the metal object on which the tag is applied can be incorporated, a type of tag that could address the situation of products with metal-only surfaces in the system's future implementation scenario. Figure 22 illustrates the various results and relevant parameters in the context of the RFID process, according to the prototype under study. These results were generated using the RFID software and were displayed on the company computer's interface. A total of 56 simulation rounds were conducted, during which the set of assets passed under the  
antenna reading region on the gantry. It is important to note that this represents one of the 140 total test rounds carried out, considering seven different coupling positions of the reading system on the gantry. The data of interest obtained was displayed in three different tabs--the window with the list of detected tags (tag list view), the performance graph (performance graph), and the simple view of the tags read (tag view)--within the scope of the software's functionality option corresponding to the advanced test inventory. This option of the RFID reading programme provided as many input settings as possible to configure the device with the most appropriate instructions and the best RFID-tag-detection properties (passing under the system's reading region). Figure 22: Results generated in the RFID software according to the test procedure carried out.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 1 Introduction
Therefore, the optimal switching frequency will always depend on its use, whether it needs high density, efficiency, or both, whereas, in [43], authors have reported a bidirectional totem pole PFC with resonant dual active bridge converter using SiC WBG devices. However, the complexity of the overall system has increased due to resonant dual bridge converter. Similarly, [PERSON] et al. [44] have presented an on-board charging infrastructure with SiC WBG devices for bidirectional LLC DC-DC converter. However, PFC operation is performed with an interleaved bridge-less totem pole PFC which adds an extra burden on the overall system. Based on the studies mentioned earlier, it is evident that the totem pole PFC converter is most suitable for charging EV. With few topology modifications, it is capable of working in bidirectional mode. The use of SiC WBG devices in bidirectional totem pole converter makes it more beneficial due to improved efficiency and power density. Additionally, the combination of WBG devices with totem pole topology enables the use of a converter at high switching frequency, which benefits in reducing the size of EMI filters and lowering the total harmonic distortion (THD) content. Therefore, this paper intends to apply an SiC-based bidirectional totem pole charging infrastructure for EV application in G2V and V2G mode. The significant contribution of this paper is summarized as follows. * A CCM-control-based totem pole PFC offers simple structure with higher efficiency in bidirection mode to charge and discharge the EV batteries in G2V and V2G modes, respectively. * Due to the use of SiC device, totem pole converter is capable of operating at the high switching frequency, which reduces the size of the interfacing inductor and achieve higher efficiency as well. * An optimized switching frequency has been analysed to obtain the high efficiency and power density. * At an optimized switching frequency, a small sized interfacing inductor achieves lower THD. Furthermore, the detailed implementation of the BDCI is structured as follows. The design parameters of the BDCI is presented in Section 2. Whereas Section 3 presents the G2V and V2G mode control. The performances of the BDCI is discussed in Section 4 and finally, the conclusions are drawn in Section 5.
# An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication
## 3 Related Work  
In this section, we present some works related to empathy in the area of computing and machine learning.  
[PERSON] et al. [2] carried out a study that aimed to detect empathy present in spoken conversations in call centers. To do this, they present an operational definition of empathy based on the modal model of emotions. A sample of 905 spoken conversations were obtained from a corpus of 1894 randomly selected customer agent interactions. Subsequently, interactions that were annotated with at least one empathetic interaction between the customer and the agent were considered to have empathy. Empathy presence was used as a binary class in their research. [PERSON] et al. [2] considered Acoustic, Lexical, and Psycholinguistic features for their model. Binary classification models were designed using Support Vector Machines (SVM) [2]. The experiments were carried out used a combination of the available features, with the Un-weighted Average (UA) as the performance metric [2]. The UA is the average recall of positive and negative classes [2]. Using the Leave-One-Speaker-Group-Out (LOSGO) cross-validation method, [PERSON] et al. [2] obtained a maximum of 63.9 average UA using feature selection methods.  
[PERSON] et al. [31] carried out research aimed at obtaining an estimation of emotional interactions in a meeting with four participants. The types of emotional interaction targeted were empathy, antipathy, and unconcern. The features used were gaze, facial expressions, and speech-silence features. The estimation was based on the Bayesian approach, and obtained promising results.  
[PERSON] et al. [32] present a study on the behavior of a social robot capable of detecting the affective state of humans and acting in a limited empathetic manner. In this study, an iCat robot observes a chess match between two players, and behaves in an empathic manner by commenting the game and disclosing its affective state. Results showed that the participants recognized the empathetic behavior of the robot and were willing to interact with it in the future.  
The Doctoral thesis of [PERSON] [4] presents the most comprehensive study on the Cognitive aspect of empathy on AI. This research explores the various elements that comprise behavior consistent with cognitive empathy in AI Healthcare systems. The AI Cognitive Empathy Scale (AICES) was developed to test the elements of empathy in interactions between AI focused on healthcare and patients. Furthermore, a conceptual model of cognitive empathy for patient-AI interactions was created.  
[PERSON] et al. [33] developed a framework for integrating artificial empathy in AI marketing agents. They explore various components of empathy in these agents. Furthermore, they present the advantages and disadvantages of using artificial empathy in marketing. Mainly, they show the value of bridging the AI-human gap in affective and social customer experience. However, they also present situations in which artificial empathy can be unnecessary, or harmful.
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 3 Results
### Quantitative Analysis
#### 3.2.2 Electron Number Density  
For determination the electron number density (n\({}_{\mathrm{e}}\)) of the plasma, the Stark broadened line profile technique was utilized for the selected spectral lines, emitted from either atomic or singly ionized atoms. The following simplified expression was used for calculation of n\({}_{\mathrm{e}}\)[34; 35]:  
\[\mathrm{n_{e}}=\frac{1}{2\omega}\Delta\lambda_{\frac{1}{2}}\times\mathrm{n_{r}} \tag{2}\]  
Here \(\omega\) is the stark broadening parameter, \(\Delta\lambda_{\frac{1}{2}}\) is the value of full width at half maximum (FWHM) of selected spectral line and n\({}_{\mathrm{r}}\) is the reference value of density, which is \(10^{16}\) for atomic lines and \(10^{17}\) for singly ionized lines. FWHM values of used lines were calculated by fitting the Lorentzian function and values so obtained were used in Equation (2) after subtracting the instrumental width of 0.06 nm from these values. The stark broadening parameters were taken from reported literature [36]. The lines of Ca-I at 422.85 nm, Ca-II at 397.01 nm, Fe-I at 438.58 nm and Cu-I at 578.19 nm were selected for n\({}_{\mathrm{e}}\) calculations and an average value of \(1.01\times 10^{18}\) cm\({}^{-3}\) was obtained. The stark broadened profile of one of the selected lines, 438.58 nm of Fe-I, is shown in Figure 5.  
Figure 4: Boltzmann plot drawn using spectral lines of Cu-I.
## 1 Introduction
### Survey of GNSS Signals and Receiver Architectures  
This section is split into three: GNSS signals, their modulation and the resulting receiver tracking channels.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction
### Motivation
evaluated ongoing efforts to integrate LLMs with UAV systems, discussing (in detail) the applicability and potential of LLMs in UAV communication, data processing, decision-making, and spectrum sensing. Their work highlighted the advantages of LLMs in data processing and decision-making [10]. For example, several existing flight control models for UAV swarms have been developed based on GPT-4, such as FlockGPT [11] and Aeroverse [12]. UAV path planning models based on Gemini, such as LeviOSA [13], have been introduced, and emergency rescue response models have been established based on LLaMA3 and Claude3, such as Say-REAPEx [14]. These models interpret and respond to natural language commands through LLMs, simplifying the control process of UAVs and enhancing their autonomous decision-making capabilities [15; 16]. Moreover, [PERSON] et al. thoroughly explored the integration of LLMs and UAVs, indicating that LLMs can enhance the autonomy and intelligence of UAVs in low-altitude mobile systems and proposed a UAV architecture that realizes autonomous sensing, reasoning, memory, and tool use [17]. These papers either focus on applying LLMs to specific UAV scenarios or comprehensively summarize the integration of LLMs with UAV communication or UAV network architectures. However, exploration remains limited in areas involving the combination and application of LLMs for UAV safety and security. Considering the current research gap regarding the use of LLMs in UAV safety and security, this paper summarizes the present state of AI applications in this domain. Moreover, this paper comprehensively studies and examines the advantages and challenges of LLMs in achieving UAV safety and security. Section 1 outlines the motivation behind this paper, investigates the current body of research, summarizes the existing surveys, and presents the contributions of this paper. Section 2 presents an analysis of the safety and security threats associated with UAVs, reviews the development of AI technology, and discusses the potential applications of LLMs in UAV safety and security. Section 3 discusses the application of traditional AI techniques in UAV safety in detail, including physical safety design, battery safety management, sensor spoofing, collision avoidance, path planning, and flight control. It also highlights the limitations of traditional AI technologies and discusses the potential advantages of LLMs. Section 4 focuses on UAV security, discussing the applications of traditional AI techniques, including sensing security technology, radio interference defense, identity authentication, cryptography technology, and self-organizing network (SON) communication security. In addition, it presents an analysis of the shortcomings of traditional AI methods and highlights the potential application space of LLMs. Section 5 discusses the challenges in deploying LLMs for UAV safety and security and outlines future research directions. Section 6 summarizes and concludes this paper. The structure of our survey is presented in Figure 1.
## 5 Discussion
### Analysis of Typical Landslides
#### 5.4.3 Sanxingwan Landslide  
The time series of P1 on the back and P2 on the toe of the Sanxingwan landslide (Figure 18c) were extracted for further analysis. As shown in Figure 18a,b, P2 on the leading edge remained stable during monitoring. At P1 on the back of the slope, the Sentinel\(-\)1A data showed continuous deformation (Figure 18a), while the deformation time-series data of ALOS/PALSAR\(-\)2 oscillated back and forth without any obvious pattern (Figure 18b). Figure 18d was obtained by removing the time difference between the two time series at P1. It can be seen that the cumulative deformation of the two has some consistency before December 18, 2021, but after that time, ALOS/PALSAR\(-\)2 shows irregular oscillations, indicating that the results are distorted by errors. The first reason is that radar satellites are more susceptible to atmospheric delay in mountainous areas with steep terrains [71; 72] and that L\(-\)band sensors will be affected by the ionosphere six times more than X-band ones [70] (atmospheric error is divided into tropospheric error and ionospheric error). Secondly, the spatio-temporal filtering method adopted in this paper requires a large number of original SAR images (usually no fewer than 15 scenes) to be superimposed and calculated when the atmospheric delay phase is separated and removed. The effective data volume of ALOS/PALSAR\(-\)2 is only nine scenes, so the ability to remove atmospheric errors will be greatly reduced. Thirdly, ALOS/PALSAR\(-\)2 data are not as sensitive to small deformations as Sentinel\(-\)1A data are. With the combination of these sensors, a few  
Figure 17: InSAR time-series results of Lianfeng landslide, (**a**) is the optical image of the landslide, and the positions of PI and P2 points are marked on the image; (**b,c**) are the time-series results of ALOS/PALSAR\(-\)2 and Sentinel\(-\)1A at P1 and P2, respectively; (**d,e**) are comparison of time series between P1 and P2 after removing the time difference.  
potential landslides are missed, but the system can still carry out more than 90% of the required monitoring.