esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the process of high-concentration photovoltaic (HCPV) power generation, multijunction cells work in the conditions of high radiation and high current. Non-uniformity of focusing, the mismatch between the focusing spectrum caused by the dispersion effect and the spectrum of multijunction solar cell design and the increase in cell temperature are the key factors affecting the photoelectric performance of the multijunction solar cell. The coupling effect of three factors on the performance of multijunction solar cell intensifies its negative impact. Based on the previous research, the light intensity and spectral characteristics under Fresnel lens focusing are calculated through the optical model, and the optical-electrical-thermal coupling model under non-uniform illumination is established. The results show that obvious changes exist in the concentration spectrum distribution, energy and non-uniformity along different optical axis positions. These changes lead to serious current mismatch and transverse current in the multijunction solar cell placed near the focal plane which decreases the output power. The lost energy makes the cell temperature highest near the focal plane. In the condition of passive heat dissipation with 500 times geometric concentration ratio, the output power of the solar cell near the focal plane decreases by 35% and the temperature increases by 15%. Therefore, optimizing the placement position of the multijunction cell in the optical axis direction can alleviate the negative effects of optical-electrical-thermal coupling caused by focusing non-uniformity, spectral mismatch and rising cell temperature, and improve the output performance of the cell. This conclusion is verified by the experimental result.  
optical-electrical-thermal coupling; performance of concentrating photovoltaic cell; non-uniform illumination; spectral mismatch +
Footnote †: journal: Applied Physics Letters
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Secure AODV
#### 3.2.1 SAODV Hash Chain  
The SAODV protocol specifies that the intermediate nodes need to complete hash operations in the hash fields in the message and increase the hop count before forwarding the routing message. In order to send an \(RREQ\) or \(RREQ\) message, each node needs to do the following two steps: (1) Randomly generate a hash seed, let the hash fields of the \(RREQ\) or \(RREP\) be the seed, and set the \(Max\_hop\) of the route so that it is equal to \(TTL\); (2) Select a hash function \(h(\bullet)\) for the hash fields, and complete hash operations on the seed \(Max\_hop\) times. After this, we get \(h^{Max\_hop}\), which is then written into the top of the hash fields (called \(Top\_hash\)). Each node receiving the \(RREQ\) or the \(RREP\) also completes the hash operations \(Max\_hop\) times on the hash fields by the \(h(\bullet)\) in the message, to check if the result is equal to \(Top\_hash\); if equal, it means that the number of hops is not maliciously reduced. The type of \(Hash\), \(Top\_hash\), \(Hop\), and \(Max\_hop\) are all defined as natural numbers.
# A Power Monitor System Cybersecurity Alarm-Tracing Method Based on Knowledge Graph and GCNN
## 2 Materials and Methods
### Cybersecurity Alarm-Tracing GCNN
#### 2.2.2 Cybersecurity Alarm Tracing
Given that the number of nodes in the adjacency matrix corresponding to different alert information can vary, the global pooling layer reduces the feature matrix \(H_{\text{GAT}}^{(m_{\text{GAT}},k)}\) to an \(n_{\text{GAT}}^{(m_{\text{GAT}})}\)-dimensional feature vector \(x^{(k)}\). Further, an alert cause traceability module based on \(m_{\text{FCN}}\) layers of fully connected networks has been constructed. After activation using the Softmax function, it generates an alert traceability vector \(Y_{\text{pre}}^{(k)}\) with dimensions \(m_{\text{alert}}\), as shown in Equation (10). \[Z^{(j,k)}=\sigma(W_{\text{FCN}}^{(j)}Z^{(j-1,k)}+b_{\text{FCN}}^{(j)}) \tag{10}\]  
When \(j\
eq\ m_{\text{FCN}}\), \(\sigma\) is the ReLU function; when \(j=m_{\text{FCN}}\), \(\sigma\) is the Softmax function, where \(j=1,\cdots,m_{\text{FCN}}\). \(Z^{(j,k)}\) is the output of the \(j\)-th fully connected layer. When \(j=1\), \(Z^{(0,k)}=x^{(k)};W_{\text{FCN}}^{(j)}\) is the weight matrix of the \(j\)-th fully connected layer, with dimensions \(n_{\text{FCN}}^{(j-1)}\times n_{\text{FCN}}^{(j)}\); \(b_{\text{FCN}}^{(j)}\) is the bias of the \(j\)-th fully connected layer, with dimensions \(1\times n_{\text{FCN}}^{(j)}\). For the alert label vector \(Y_{\text{Label}}^{(k)}\) and the alert traceability vector \(Y_{\text{pre}}^{(k)}\), the binary cross-entropy loss function is used to compute the loss. The weights \(W_{\text{GAT}}^{(i)}\) and \(W_{\text{FCN}}^{(j)}\), attention parameter \(a^{(i)}\), and biases \(b_{\text{GAT}}^{(i)}\) and \(b_{\text{FCN}}^{(j)}\) in the graph attention network and fully connected network, are optimized using the stochastic gradient descent (SGD) algorithm. Here, \(i=1,\cdots,\ m_{\text{GAT}}\) and \(j=1,\cdots,\ m_{\text{FCN}}\). This process ultimately completes the construction of the feature extraction module and the traceability module. This section constructs a cybersecurity alarm-tracing GCNN. This approach not only fully extracts the graph information features of PowerCyberSec alerts that traditional deep learning methods cannot directly utilize but also accounts for the varying importance of different entities in traceability analysis, thereby improving the accuracy of tracing PowerCyberSec alerts.
## 5 Results and Discussion
### Computational Efficiency Analysis
#### 5.7.2 Analysis of Computational Efficiency  
Table 7 highlights the stark differences in computational resources between 2D and 3D models:  
* **Processing Time and Memory Usage**:
* **2D Models**: Exhibit low ERT (2.8 to 3.6 h-c) and minimal RAM usage (6.7 to 8.2 GB), regardless of segmentation;
* **3D Models**: Require significantly higher ERT (72.0 to 160.0 h-c) and memory (154.1 to 1072.4 GB), especially with increased segmentation and model length;
* **Segmentation Impact**:
* The five-segment 3D model demands the highest computational resources due to its complexity and length (4 m), whereas 2D models maintain consistent efficiency across segmentations.
## 2 Uncertainty Quantification Methods in Radar-Based Precipitation Estimation
### How to Quantify Uncertainty
#### 2.2.3 Modified-Fractional Uncertainty Method  
The uncertainty quantification method proposed by [30] is referred to as the fractional uncertainty method (FUM) in this study. The temporal variability of fractional uncertainty can be represented by the prediction uncertainty divided by the expected mean change (\(\sigma/\mu=\sqrt{U_{T}}/\mu_{o}\)) over time for future prediction. This study defined the three sources for uncertainty in radar data as follows: (1) internal variability represents the difference between the simulation result of each method and the average simulation result of all methods. This study used radar-based precipitation estimates and observed values; hence, internal variability is newly conceptualized as the difference between the radar-based precipitation estimates of each method and the station-observed values; (2) scenario uncertainty represents the variance, which means the degree of a spread of various simulation results according to each scenario. This study revised the event uncertainty as the weighted spreading range of precipitation estimates at each event; and (3) method uncertainty represents the average of the variances of all simulation results, and this study used the same concept. With these, the FUM is expressed as the M-FUM, and the basic concepts of each uncertainty are summarized as follows:  
Basic concept at each stage  
\[U_{T,i}=\sum\
olimits_{i}^{N}U_{i}=\sum\
olimits_{i}(V_{i}+E_{i}+M_{i}) \tag{8}\]  
1. Internal uncertainty (\(V\)): variance of biases  
\[V_{i}=\sum\
olimits_{m}W_{i}\text{Var}(\varepsilon_{m,i});\ \varepsilon_{m,i}\ =\ o_{m,i}\ -\ x_{m,i} \tag{9}\]  
Figure 2: Basic concept of uncertainty Delta method (UDM): (**a**) uncertainty quantification and propagation in the precipitation estimation process, (**b**) quantification in each stage. QPE: quantitative precipitation estimation.  
2. Event uncertainty (\(E\)): variance of all precipitation events \[E_{i}=\text{Var}(\sum_{m}W_{i}x_{m,i})\] (10) 3. Model (method) uncertainty (\(M\)): average of variances of each method \[M_{i}=\frac{1}{N_{i}}\sum\text{Var}_{m}^{W}(x_{m,i})\] (11) where \(i\) and \(N\) represent the specific stages (e.g., quality control, signal processing, and precipitation estimation) and number of stages, respectively; \(m\) represents the number of methods; \(o\) and \(x\) are the observed precipitation and precipitation estimates by simulating method, respectively; \(W\) represents the weight of the method, and \(\varepsilon\) is the difference between the observed precipitation and precipitation estimates by simulating model.
## 4 Steady-State Performance Index and Optimal Gains
### Derivation of Tracking Performance Index  
The tracking performance index is derived using (34). During tracking with the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter, a steady-state bias error occurs for the target moving with a constant snap (time-derivative of jerk) because of the difference between the motion of the model (constant-jerk model) and the motion of the target. Thus, the tracking performance, that is, the steady-state predicted error, is calculated by (34)assuming the true target position \(x_{\text{tt},k}=s_{\text{c}}(kT)^{4}/24\) (\(s_{\text{c}}\) denotes a constant snap) and measurement errors are not considered, as  
\[e_{\text{fin},\alpha\beta\gamma\delta}=\lim_{k\to\infty}(s_{\text{c}}(kT)^{4}/24 -x_{\text{p},k}), \tag{42}\]  
Equation (42) is calculated using the transfer function of (9) and the final value theorem given in Appendix C. The derivation result is  
\[e_{\text{fin},\alpha\beta\gamma\delta}=\frac{s_{\text{c}}}{\delta}T^{4}, \tag{43}\]  
The important aspect here is that the tracking performance is determined by the gain \(\delta\), and it does not depend on other gains.
# Detecting the Speed Change Intention from EEG Signals:
## 2 Materials and Methods
### Offline BMI
#### 2.5.1 Features  
Temporal and frequency single features were extracted for each window class: The power spectrum density (PSD) (_pburg_ in MATLAB):  
\[S_{xx}(\Omega)\sum_{m=-\infty}^{\infty}R_{xx}[m]e^{-if\Omega m} \tag{1}\]  
where \(R_{xx}\) is the autocorrelation function that can be reconstructed from its power spectrum \(S_{xx}(\Omega)\), the PSD estimates the power distribution in each frequency band.  
The Shannon entropy:  
\[ShannonEntropy=-\sum_{i=1}^{N}\lvert C_{i}\rvert^{2}\log_{e}\lvert C_{i}\rvert ^{2} \tag{2}\]  
where \(N\) is the number of coefficients in the wavelet subband, \(C_{i}\) is the wavelet coefficient having index \(i\). The Shannon entropy measures the nonlinear properties of the signal, which indicates signal variation at each frequency scale, that could be related to the ERD/ERS process [21].  
The first Weibull parameter:  
\[y=f(x\lvert a,b)=ba^{-b}X^{b-1}e^{-\frac{b}{a}b} \tag{3}\]  
where \(b\) is the scale parameter of the distribution and \(a\) is the shape parameter and \(X\) is a continuous random variable. The Weibull distribution has been used as an indicator of a physiological neural process [22; 23].  
The three Hjorth parameters, where \(y\) is the signal of one channel and \(t\) is the time:  
Activity which represents the proportion of standard deviation of the power spectrum:  
\[HJ_{A}=var(y(t)) \tag{4}\]  
Mobility which represents the mean frequency:  
\[HJ_{M}=\sqrt{\frac{var(\frac{dy(t)}{dt})}{var(y(t))}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Valid Repetitions**} \\ \cline{2-4}  & **Increase** & **Decrease** & **Both** \\ \hline S1 & 31/43 & 32/37 & 63/80 \\ S2 & 45/48 & 31/32 & 76/80 \\ S3 & 44/45 & 32/35 & 76/80 \\ S4 & 45/46 & 34/34 & 79/80 \\ S5 & 39/41 & 39/39 & 78/80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of valid repetitions of each subject for the three types of models/total repetitions performed for each type of model.  
Complexity which represents the changes in frequency:  
\[HJ_{C}=\frac{Mobility(\frac{dy(t)}{dt})}{Mobility(y(t))} \tag{6}\]  
Features were selected based on its common use in these type of studies [6; 14; 24] and the possibility of being used with only a few samples per class.  
Each of the features were computed in the different bands extracted with the state variables band-pass filters: whole spectrum (8-40 Hz), alpha (8-14 Hz), low beta (15-22 Hz), high beta (23-30 Hz), low gamma (31-40 Hz) and high gamma (41-60 Hz).
## 2 Related Activities
### Physics of the Processes Initiated by Exposure of Living Cells to RF-EMF  
Dielectric dispersion is a dissipative (i.e., thermal) phenomenon. However, the non-thermal interaction of RF-EMF with biological systems may be based on dielectric dispersion as the permittivity decreases with increasing frequency [9]. For RF-EMF non-thermal effects of mobile communication carrier frequencies, the targets may include fast mobility of amino acids, lipids, and nucleotides, as well as proton hopping (3G/4G operation frequencies) or water (5G) [10]. As 3G-5G RF-EMFs have a minor effect on the dipole or charged groups' mobility, it is possible that the envelope of the modulated carrier frequencies affects the cells. Specifically, in this case, the action of the signal is expected on the organelles, membranes, and nuclei due to the slow migration of ions or deformation of ion clouds at the membrane surfaces, or polarization of cellular membranes [11]. A progressive increase in carrier frequencies used for telecommunication results in the corresponding reduction of the penetration depth through the human body. Although human skin penetrationdepth at 3G-5G operation frequencies (0.5-60 GHz) affects only the skin dermis layer [12], blood may respond to the RF-EMF when passing through the dermal microvasculature and thus transducing the signal to the peripheral tissues [13]. Indeed, previous studies indicate non-thermal effects of acute exposure to RF-EMF on erythroid precursors and red blood cells (RBCs) [14]. Significant knowledge advancement regarding the possible impact of RF-EMF on (i) hemoglobin function, (ii) the state of hydration shells and free water, and (iii) membrane properties will be accomplished in NextGEM. Frequency dependencies, response kinetics, and duration, as well as tissue-specificity, will be studied. Such a mechanistic understanding is crucial to understanding if non-thermal RF-EMF effects are possible and, if so, under which circumstances.
## 2 Materials and Methods
### Unmanned Aerial Vehicle  
The UAV aims to allow the transmission of information from the beacon to the central system. With this aim, a new device onboard that supports communication with both the beacon (LoRa communication) and the central system (Bluetooth communication) was developed (Figure 4). This new device was composed of an Arduino Mega 2560 board, a LoRa RFM95 device that provides communication with the beacon, a microSD module to temporarily store the information, and a Bluetooth module to connect to the central system.  
Figure 3: Beacon device along with the electronic nose. (**a**) Comparison of the components of the beacon with a coin; (**b**) presentation of the internal structure of the beacon.  
In this work, a quadcopter multirotor aerial vehicle was developed. The flight controller was a PIXHAWK module and integrated a 32-bit ARM Cortex M4 core with an FPU processor. The complete system contained acceleration sensors, gyroscopes, and GPS for stabilization and navigation control. Communication with the vehicle was supported via a 2.4 GHz radio channel, providing data transmission up to 1 to 2 km. A 3-cell and 5000 mAh battery supported flights of up to 20 min. Figure 5 shows the developed UAV with the LORA communication device integrated into its lower part.  
Figure 4: Structure of the communication system integrated in the UAV.  
Figure 5: Integration of LoRa communication in the UAV.  
The UAV algorithm controlled the system and started initializing LoRa and Bluetooth communication parameters. Once the UAV took off, the system sent specific messages to the beacon. When the beacon received a UAV message, the data transferring process was activated. As soon as the transmission was completed, the system was updated to standby mode until the drone returned home. Finally, once the vehicle landed, the Bluetooth connection was activated, and all the data were transferred to the central system.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Assessment Strategies
#### 3.3.2 Personal Measurements  
Data collected from spot measurements in indoor environments do not provide a good point of view about the exposure levels of individuals who are often exposed to multiple sources at the same time and, during their daily routine, move from one place to another. Data on the EMF exposure of individuals can only be obtained by using personal exposure meters (PEM), i.e., portable devices that are worn on the body to continuously measure for an interval of time the incident electric fields at the location of a subject wearing the device. These devices are calibrated in free space but are used on the body, therefore an uncertainty analysis is crucial, mainly based on: (1) body shielding, i.e., perturbation of real EMF exposure levels due to the presence of the human body in the immediate proximity; (2) residual uncertainties due to calibration; (3) how well the shaped frequency response of probes follows sensitivity variation with frequency; and (4) measurement artefacts. In addition, measurements with PEM tend to underestimate or overestimate the actual RF EMF exposure, depending on the frequency bands. A potential solution would be to determine correction factors based on the calibrations in order to modify the measurement results [21; 32].  
In indoor scenarios the shadow from the body, i.e., the perturbation of real EMF exposure levels due to the presence of the human body in the immediate proximity, is partly decreased by multi-pathing caused by reflections to walls and objects [33], thus the systematic measurement error depends on the size of the room: the smaller the room, the smaller the body shadow effect. Moreover, the response depends highly on the position of the exposimeter on the body, mainly on the separation distance between the exposimeter and the body and the tissue permittivity [34].  
Studies using personal measurements focused on the identification of typical exposure levels in indoor environments (see, e.g., [20]) and on population surveys by questionnaires in which the personal exposure distribution in the population of interest was determined (see, e.g., [3]). Even if some studies focused on developing ad hoc standard study protocols for personal measurements campaigns [35], a wide variety of strategies for the recruitment of the study participants, either trained researcher or volunteers of different ages, as well as the data analysis methods differ between studies, making a direct comparison of their results extremely difficult.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Data Logging  
Log files, including written records of some attribute as it changes over time and recorded responses to a questionnaire or an interview produced by either participants or observers, are the longest-serving modality for engagement estimation. For good reason, recording data in written format entails the simplest apparatus setup, ranging from interviewing participants and eliciting self-assessment of task engagement to logging behaviours through observers' eyes. Not only simple to conduct, log files support rich description coloured by subjectivity, cognition, emotion, and memory [1].  
Many studies to date actively adopt data logging for engagement analysis as they remain efficacious, if not being the only way to unveil engagement. For example, quizzes and assessment performance are typically used to reflect students' level of engagement in e-learning [3]. Nevertheless, logging engagement cannot circumvent the curse of perception bias. That is, participants are often imperfectly poised or motivated to reflect their true engagement state, undermining the authenticity of logged data. Bright learners may perform well in assessment without being engaged in learning. Observers could be deceived by behaviours as no one is able to read minds. Recently, mouse clicks, average time spent on a web page, and other user interaction audits have been exploited to reveal user engagement interacting with digital devices. Obtaining such data is on the rise thanks to a rapid growth in online working, meeting, and learning. However, such data have limitations as they offer rather indirect cues.
# Botanicals in Functional Foods and Food Supplements: Tradition, Efficacy and Regulatory Aspects
## 4 Botanicals in Functional Foods
### Phytosterols/Phytostanols  
Phytostenols and phytostanols are compounds of plant origin, which are commonly part of the human diet. Phytosterols can be transformed in phytostanols by hydrogenation and both class of molecules can be esterified with fatty acids from vegetable origin to the corresponding esters [26; 27]. Phytosterols and phytostanols are structurally similar to cholesterol, apart from their side chain. The most common phytosterols and phytostanols are sitosterol, sitostanol, campesterol, campestanol, stigmasterol and brassicasterol [26].  
In the EU, the use of phytosterols, phytostanols and their esters in foods refers to the Regulation (EC) No. 258/97 of the European Parliament and of the Council of 27 January 1997 concerning novel foods/novel food ingredients.  
These molecules reduce the intestinal absorption of cholesterol and, as a consequence, cause a decrease in blood cholesterol levels. For this property, phytosterols and phytostanols, in free or -asterified form, can be added to foods for their properties to maintain normal (claim under Art. 13) or reduce blood cholesterol levels (claim under Art. 14). The first wording is allowed for food providing 0.8 g of plant sterols/stanols with the daily dose and the second for daily doses ranging from 1.5 to 3 g. Doses of 1.5-2.4 g/day are associated with a cholesterol level reduction of 7-10%, while 2.4-3 g/day can produce a decrease of 10-12.5% [28].