esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 6 Conclusions  
The proposed robust observer based on Attractive Ellipsoid Method shows a good performance under parameter uncertainty in the boost power converter and noise in the  
Figure 14: Generated power.  
Figure 13: Error signals for MPPT test.  
Figure 15: Power error between measured and sensorless based INC algorithm.  
output signal. The computation of the observer gains is done by means of solving an LMI optimization problem derived from the AEM, which guarantees the convergence of the estimation error under the considered uncertainties and external disturbances. The estimation error of the designed observer is low and has enough convergence speed to track the real state values, which allows the utilization of the estimated current for MPPT operation with the drawback of be limited to implementation of current based MPPT techniques. We demonstrated the appropriate operation of a conventional Incremental Conductance algorithm for MPPT in the proposed sensorless form under a test based on European standard EN50530 with small power losses due to current estimation error. Nevertheless, such power loss represents a good trade-off for the elimination of current sensor's cost mainly when operating small scale domestic PV systems. Although the observer is robust against parameter uncertainties, the variable nature of PV systems operating conditions as temperature, humidity or solar irradiance level may affect its performance and should be considered in the design stage. The effect of such conditions in the PV system performance and the experimental stage of the proposed scheme are considered as future research works.  
Conceptualization, D.C.-V.; Investigation, J.L.R.-L.; Resources, J.L.R.-L.; Supervision, H.A.; Writing--original draft, D.C.-V.; Writing--review & editing, H.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest.
# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 4 Evaluation
### Realization  
The realization of the evaluation was conducted over a period of two weeks--immediately after the completion of the protype. This process was vital to ensure enough sentiment data could be obtained for each asset to identify trends in the data that could then be compared with the stock market performance of that particular asset. Note, that this period took place from the 8 March to the 22 March.  
Figure 15: SA Model evaluation.  
For testing purposes, S&P 500 stocks (assets) were selected to ensure there was a larger quantity of tweets to obtain sentiment. The selected stocks were Apple, Tesla, Facebook, Google, and Amazon.  
Once the selected assets were chosen, the prototype was used to obtain the sentiment. Fortunately, this process was simplified because the deep learning system was implemented as a daily CRON job on Azure Functions, which meant the entire process was automated. Using this setup, the system pulled around 200 tweets down from the Twitter API every day for each asset, calculated the sentiment score, and then saved the results to the database along with a date time stamp. At the end of the two-week period, the web apps sentiment chart was displaying the trend of sentiment for each asset over the course of two weeks.  
Another requirement to realize the evaluation was to obtain the stock market data for each asset over the same period. This was accomplished using Yahoo Finance.  
However, it proved harder to compare the trend of the two datasets as no feature was designed in the prototype to visualize the two datasets in the same chart. Instead, images of the two charts were obtained and then compared with one another (See Figures 16-20). Although this means of comparison was still an effective solution, it was more time consuming, and it would have been more useful to have the stock market data in the same chart as the sentiment data.
## 3 Results
### Application Domains
#### 3.2.3 Localization  
Three articles [72; 81; 82] were considered within the scope of applications to determine the localization of older adults.  
Considering that the city should provide outdoor monitoring for older adults while promoting their ability to perform leisure activities, the final aim of the study reported by [PERSON] et al. [72] was to ensure a localization service for older adults that should be provided in time and when it is necessary. According to the authors, they took advantage of the IoT paradigm for collecting heterogeneous data that are broadcast in the city. The assessment of the location performance was performed through a set of experimental simulations using the MATLAB environment.  
[PERSON] et al. [81] presented an outdoor positioning system that might be used to protect older adults, via estimating their locations in a city. Particularly, locations were estimated using machine learning algorithms from the data measured at multiple base-stations that are densely deployed over a city to construct an ad hoc network. A prototype system consisting of nine base-stations was deployed on a university campus to conduct an experiment to validate the proposed approach in terms of the performance of the communication networks and machine learning algorithms.  
[PERSON] et al. [82] argued that wandering is among the most problematic, dangerous, and frequent behaviors of individuals with dementia and that frequent wanders are more vulnerable to experiencing adverse events than the healthy ones, ranging from falling, getting lost, elopement or boundary transgression to emotional distress. To minimize wandering-related adverse consequences, the authors proposed a geofence based in a virtual boundary delineated around an area of interest that can be created with a variety of different technologies, such as WiFi, cellular mobile, RFID and GPS. Moreover, a data mining-based approach was used to construct a personalized safe geofence by mining older adults' historical GPS trajectories. In terms of validation, an open dataset of older adults' GPS traces was used and, according to the authors, the qualitative results showed that the method is workable for constructing personalized safe geofences by mining older adults' GPS trajectories [82].
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 3 Preliminaries on Channel Estimation
### Existing ML Method
#### 3.2.2 Activation Function  
The \(m^{\prime}\)-th node of the \(k\)-th hidden layer performs the following calculations:  
\[\begin{split} o_{k,m^{\prime}}=f_{m^{\prime}}(z)& =f_{m^{\prime}}(\sum_{m=1}^{M}w_{m}x_{m}+b_{m}),\\ k&=\{1,2,3\},\\ m^{\prime}&=\{1,\ldots,M^{\prime}\},\end{split} \tag{9}\]  
where \(M\) is the number of inputs \((m=1,\ \ldots,\ M)\) to the \(m\)-th node in the previous layer, and \(M^{\prime}\) is the number of nodes in the current hidden layer. \(w_{m}\) is the \(m\)-th weight and \(b_{m}\) is the bias. \(f(\cdot)\) is the activation function used to characterize the nonlinearity of the channel  
Figure 3: The structure of the existing ML-based channel estimator.  
data, and \(o_{k,m^{\prime}}\) is the output for the \(m^{\prime}\)-th node of the \(k\)-th hidden layer. In this model, the activation function of the hidden layer adopts the tanh function as  
\[f(z)=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}, \tag{10}\]  
where \(e\) is [PERSON]'s number. The output \(\hat{\mathbf{G}}_{\text{ML},d}\) for the \(d\)-th data is obtained in ML from the input \(\hat{\mathbf{G}}_{\text{LS},d}\) and uses the loss function to minimize the MSE between the prediction channel and the actual channel as follows  
\[\mathcal{L}(\mathcal{W},\mathcal{B})=\frac{1}{DN}\sum_{d=1}^{D}\sum_{n=0}^{N- 1}\|G_{d}(n)-\hat{G}_{\text{ML},d}(n)\|^{2}, \tag{11}\]  
where \(G_{d}(n)\) is the actual channel value associated with \(\hat{G}_{\text{ML},d}(n)\), and \(\mathcal{W}\) and \(\mathcal{B}\) include all the weights and biases, respectively. From a set of initial values, the weights and biases are updated by minimizing the loss function shown in Equation (10).  
Since ML-aided channel estimation is based on LS estimates, it minimizes the MSE between actual channels, so it performs better than the LS method, but there are three hidden layers, and each node is designed twice the input size, resulting in high complexity. Therefore, we propose a low-complexity ML-based channel estimation where ML designed the DNN with a single hidden layer. We also propose weight quantization that can save memory stored by reducing the capacity of weights.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description
### Hardware of the Transmitter
#### 3.1.5 Control System  
An embedded PC carries out the tasks of controlling and configuring the system with a DSP (Digital Signal Processing) unit inside. The XTremeDSP-IV by Nallatech includes three Xilinx FPGAs (Field-Programmable Gate Array): (i) the Spartan-II connects the interface with the Peripheral Component Interconnect (PCI) bus; (ii) the Virtex-II is in charge of the clock and, finally; (iii) the Virtex-4 conducts the software radio processing with two 14-bit ADCs, two 14-bit DACs (Digital-to-Analog Converter) and the arithmetic and peripheral drivers.  
In order to make our measurement system accurate enough, a 100 MHz OCXO with a frequency precision of 30 ppm was installed. This improves the frequency synchronization for OFDM modulations and makes possible the measurement of the Doppler shift. The GPS unit enables the time synchronization using the PPS (Pulse Per Second) signal with an accuracy of 1 \(\upmu\)s, so the absolute propagation time from Antarctica to Spain can be measured [11].
## 4 Experiment and Analysis
### Dense Building Maps Experiment  
In this paper, the walking_xyz sequence is selected as the image sequence for dense map construction, in which two men walk around a computer desk. If we do not eliminate the dynamic objects to build the map as shown in Figure 11a, we can observe that the dynamic objects in the scene have an impact on the map construction, resulting in a large number of characters in the map residual shadow, which seriously reduces the effect of dense map construction. The algorithm used in this study creates the map that is depicted in Figure 11b, removing the impact of dynamic objects and creating a good map that is suitable for use in robot navigation and path planning.
## 3 Results and Discussion
### Experimental Results of Single-Crack Specimens with Different Angles
#### 3.1.3 Fixed Confining Pressure  
Experimental data collecting under the conditions confining pressures of 20 and 10 MPa (Figure 14) were selected for analysis, as shown in Figure 21a,b, respectively. When the confining pressure was 20 MPa (Figure 21a), with 0 MPa of axial pressure, the seepage flow increments for the 40 to 90-degree cracks were 3.30 \(\times\) 10\({}^{-7}\), 1.43 \(\times\) 10\({}^{-6}\), 9.75 \(\times\) 10\({}^{-6}\), -3.00 \(\times\) 10\({}^{-7}\), and 1.00 \(\times\) 10\({}^{-7}\), respectively. When the axial pressure decreased from 45 MPa to 0 MPa, the seepage flow increments for the 40-60-degree cracks were 1.94 \(\times\) 10\({}^{-6}\), 2.26 \(\times\) 10\({}^{-6}\), and 3.65 \(\times\) 10\({}^{-6}\), respectively. The seepage flow increments of the 70-90-degree cracks were 3.30 \(\times\) 10\({}^{-6}\), 2.00 \(\times\) 10\({}^{-6}\), and 1.60 \(\times\) 10\({}^{-6}\), respectively. When the confining pressure was 10 MPa (Figure 2b) with 0 MPa of axial pressure, the seepage flow increments for the 40-90-degree cracks were 7.20 \(\times\) 10\({}^{-7}\), 2.77 \(\times\) 10\({}^{-6}\), 1.06 \(\times\) 10\({}^{-5}\), 2.00 \(\times\) 10\({}^{-7}\), and 2.00 \(\times\) 10\({}^{-7}\). When the axial pressure decreases from 45 MPa to 0 MPa, the seepage flow increments for the 40-60-degree cracks were 5.44 \(\times\) 10\({}^{-6}\), 6.12 \(\times\) 10\({}^{-6}\), and 8.65 \(\times\) 10\({}^{-6}\). As the crack angle increased, the seepage flow increment also increased. The seepage flow increments for the 70-90-degree cracks were 6.60 \(\times\) 10\({}^{-6}\), 5.30 \(\times\) 10\({}^{-6}\), and 4.60 \(\times\) 10\({}^{-6}\). The flow rate change rule was similar under confining pressures of 20 MPa and 10 MPa, but the flow rate value at 10 MPa was significantly higher than that at 20 MPa.  
When the crack angle was less than 60 degrees, with an increase in the angle, the flow rate increment caused by the decrease of axial pressure increased. When the crack angle was greater than  
Figure 21: Flow rate change curves of different angles: (**a**) confining pressure of 20 MPa and (**b**) confining pressure of 10 MPa.  
Figure 20: Flow rate change curves of different angles: (**a**) axial pressure 40 MPa and (**b**) axial pressure 10 MPa.  
70 degrees, with an increase in the angle, the flow increment caused by the decrease in axial pressure decreased. This indicates that when the crack angle is less than 60 degrees, the sensitivity of the single crack model to the axial pressure increases with an increase in the angle, and when the crack angle is greater than 70 degrees, the sensitivity of the single crack model to the axial pressure decreases gradually with an increase in the angle. The axial stress sensitivity of the 40-60-degree cracks is slightly greater than that of the 70-90-degree cracks. As the confining pressure decreases, the sensitivity of the single crack model to axial stress increases.
## 3 Results and Discussion  
### Unconfined Compressive Strength (UCS) Test  
#### 3.1.1 Effects of M-RHA on UCS  
Figures 3 and 4 depict the effects of L and M additions on the UCS values after 7, 28, and 90 days of curing for the gypseous soil samples. The UCS values for M-treated samples increased from 885.33 to 1108.67 kPa when the M content was increased from 10% to 20% without the presence of sulfate at 90 days of curing. Samples treated with L and M (10% and 20%) showed a decrease in UCS values in the presence of sulfate (see Figures 3 and 4). Furthermore, after soaking, the UCS values deteriorated from 885.33 to 635 kPa for M10%, (reducing by 28.27%) and 1108.67 to 1035.33 kPa with 20% M content in the presence of sulfate (reducing by 6.62%). However, this deterioration, as compared to that of the lime stabilizer, was much less. The observed deterioration in the UCS results can be attributed to ettrinigite production, the growth of which between the particles of the sample soil would lead to the destruction of the soil structure. In addition, the residual sulfate chelates formed calcium in CSH through a decalcification process, which accounted for the lower strengths of the soaked and unsoaked samples. However, the higher strength of the M-treated samples might have been due to the hydration process and formation of pozzolanic products that might have improved the strength of the bonds between the soil particles.  
Figures 3 and 4 show the effects of the RHA content on enhancing the USC values for the M-treated samples (soaked and unsoaked) after 7, 28, and 90 days of curing in the presence of sulfate. For example, Figure 4b indicates that the UCS values of the samples treated with 7.5M-12.5 RHA increased from 2510 kPa to 2753 kPa when immersed in water after 28 days of curing, where UCS increased by 9.68%. This impact was also observed for 5M-15 RHA (increased from 1830 to 2148 kPa), which was improved by 17.37%, and 5M-5 RHA (increased from 884 to 929 kPa), which was enhanced by 5.09%. These improvements could be due to pozzolanic product (CSH) formation, which would have improved the USC value for the samples with higher M-RHA content.
## 8 Results and Discussion  
For a given linear problem \(\mathbf{A}\mathbf{x}=\mathbf{b}\) with size \(n\), the first step is constructing \(\mathbf{D}=\{D_{ij}\}\), \(\mathbf{U}=\{U_{ij}\}\) and \(\mathbf{L}=\{L_{ij}\}\) from \(\mathbf{A}\) (refer to Algorithm 7), which are used in all algorithms. The number of operations is \(n^{2}\).  
In order to compare the proposed NSIS to the methods in the existent literature, some simple examples are taken and the sizes \(n\) of the linear systems are the same to the literature.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
By contrast, for previous no-global-trajectory-optimization based algorithms, in a control circle, there is only one candidate trajectory can be used for an approaching CAV. As shown in Figure 1, the proposed VB algorithm consists of an online algorithm and an offline algorithm. As indicated by the name, the offline algorithm is used before the online application of the algorithm. The offline algorithm is comprised of VBs modeling and collision checking. By the method of VBs modeling, the considered intersection is modeled as several VBs. There are several VGs that fixed on each VB. The collision relations among these VGs can be obtained by collision checking. The online algorithm is used for the online application of the VB algorithm, which is designed based on the principle of \"first-in-first-service\" [1]. The online algorithm consists of VG selecting and trajectory planning. Several suitable VGs can be obtained by the method of VG selecting. By the trajectory planning method, the most suitable VG and the collision free trajectory can be found for each approaching CAV. Following the planned trajectory, an approaching CAV can pass an intersection without collisions. The remainders of the paper are organized as follows: In Section 2, the problem of the considered intersection management is illustrated. In Section 3, several basic conceptions of the VB algorithm are introduced, which lay the foundation of this algorithm. In Section 4, the method to obtain the collision relation among the VGs in different VBs is proposed. In Section 5, the initial ideal of VB algorithm is discussed, which provides a simple demonstration of the VB algorithm. In Section 6, the VB algorithm designed for actual application is presented. In Section 7, the numerical simulations of the VB algorithm are conducted to demonstrate the effectiveness of this algorithm. At last conclusion is given in Section 8.
## 2 System Model
The signal transmitted at [PERSON] can be expressed as  
\[\mathbf{x}_{a}=\sqrt{g_{b}}\mathbf{w}_{b}s_{b}+\sqrt{g_{\mathrm{AN}}}\mathbf{ w}_{\mathrm{AN}}s_{\mathrm{AN}} \tag{7}\]  
where \(s_{b}\) is the normalized information signal, \(s_{\mathrm{AN}}\) is the normalized jamming signal, i.e., \(\mathbb{E}\left[|s_{b}|^{2}\right]=\mathbb{E}\left[|s_{\mathrm{AN}}|^{2}\right] =1\), \(\mathbf{w}_{b}\in\mathcal{C}^{M\times 1}\) is the normalized beamformer for Bob, \(\mathbf{w}_{\mathrm{AN}}\in\mathcal{C}^{M\times 1}\) is the normalized beamformer for jamming signal, i.e., \(\|\mathbf{w}_{b}\|^{2}=\|\mathbf{w}_{\mathrm{AN}}\|^{2}=1\), \(g_{b}\) is the power allocated to the information signal, and \(g_{\mathrm{AN}}\) is the power allocated to the jamming signal. Without loss of generality, the total transmit power of Alice is normalized to 1 and hence we have \(g_{b}+g_{\mathrm{AN}}=1\). Therefore, the signal received at Bob can be expressed as  
\[y_{b}=\sqrt{g_{b}}\mathbf{H}_{b}\mathbf{w}_{b}s_{b}+\sqrt{g_{\mathrm{AN}}} \mathbf{H}_{b}\mathbf{w}_{\mathrm{AN}}s_{\mathrm{AN}}+n_{b} \tag{8}\]  
where \(n_{b}\) represents the complex baseband thermal noise at Bob, such that \(n_{b}\sim\mathcal{CN}\left(0,\sigma_{b}^{2}\right)\). Likewise, the signal received at Eve can be expressed as  
\[\mathbf{y}_{e}=\sqrt{g_{b}}\mathbf{H}_{e}\mathbf{w}_{b}s_{b}+\sqrt{g_{ \mathrm{AN}}}\mathbf{H}_{e}\mathbf{w}_{\mathrm{AN}}s_{\mathrm{AN}}+\mathbf{n}_ {e} \tag{9}\]  
where \(\mathbf{n}_{e}\) represents the complex baseband thermal noise at Eve, such that \(n_{e}\sim\mathcal{CN}\left(0,\sigma_{e}^{2}\mathbf{I}_{N}\right)\). Then, the SINR at Bob can be written as  
\[ \begin{split}\text{SINR}_{b}&=\frac{g_{b}|\mathbf{H}_{b} \mathbf{w}_{b}|^{2}}{g_{\text{AN}}|\mathbf{H}_{b}\mathbf{w}_{\text{AN}}|^{2}+ \sigma_{b}^{2}}\\ &=\frac{g_{b}|\mathbf{H}_{b}\mathbf{w}_{b}|^{2}}{\tilde{g}_{\text {AN}}|\mathbf{H}_{b}\mathbf{w}_{\text{AN}}|^{2}+1}\end{split}  \tag{10}\]  
where \(\tilde{g}_{b}=\frac{\tilde{g}_{b}}{\sigma_{b}^{2}}\) and \(\tilde{g}_{\text{AN}}=\frac{g_{\text{AN}}}{\sigma_{b}^{2}}\). Moreover, assuming Eve applies maximum ratio combining (MRC) to combine the signals received from different antennas, the SINR at Eve can be written as  
\[ \begin{split}\text{SINR}_{e}&=\frac{g_{b}\|\mathbf{H }_{e}\mathbf{w}_{b}\|^{2}}{g_{\text{AN}}\|\mathbf{H}_{e}\mathbf{w}_{\text{AN}} \|^{2}+\sigma_{e}^{2}}\\ &=\frac{\tilde{g}_{b}\|\mathbf{H}_{e}\mathbf{w}_{b}\|^{2}}{\tilde {g}_{\text{AN}}\|\mathbf{H}_{e}\mathbf{w}_{\text{AN}}\|^{2}+1}\end{split}  \tag{11}\]  
where \(\tilde{g}_{b}=\frac{\tilde{g}_{b}}{\sigma_{e}^{2}}\) and \(\tilde{g}_{\text{AN}}=\frac{g_{\text{AN}}}{\sigma_{e}^{2}}\), \(\|\cdot\|^{2}\) denotes the square of the norm of a vector.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 3 Powdered Food Production Processes
### Freeze-Drying  
Freeze-drying is defined as the process of removing water from a frozen product via sublimation, followed by desorption. The product, initially frozen, goes through the phase transition between ice and vapor at low temperatures and pressure. The second stage of drying occurs when the remaining water in the product is removed via desorption. Due to its operation at low temperatures and under vacuum conditions, this process is recommended for foods sensitive to higher temperatures and oxidation [31; 32].  
This method has the advantages of generating a final product with high yields (little or no deterioration reaction); flavors and nutritional characteristics similar to the original product (retarding or inhibiting chemical, physical, and microbiological processes); and preserved bioactive properties (little or no influence of temperature and oxygen) [33]. In this sense, the authors have reported that the freeze-drying process prevents the action of enzymatic browning, thereby preserving the color of the powdered product.  
As a bottleneck, the freeze-drying process is associated with a high cost (about 4 times higher than spray-drying) due to the entire process occurring with the frozen product, demanding high energy consumption. Another disadvantage is the time required to produce powdered foods, as the food must undergo the sublimation and desorption process for a correct duration to achieve low moisture content [24; 31].  
[PERSON] et al. [34] studied the effects of different drying techniques on the physicochemical and bioactive characteristics of papaya pulp, with no significant changes in color and pH between the treatments applied. The study revealed that due to the oxidation process, the spray-drying process reduced vitamin C content by approximately 15% compared to fresh and freeze-dried pulp. Additionally, the freeze-drying technology significantly increased the antioxidant activity of the pulp, while both drying methods were able to preserve the antioxidant characteristics of papaya pulp [34]. [PERSON] et al. [35] also compared the freeze- and spray-drying methods to obtain a functional powdered tea using ginseng roots. The authors found that the freeze-dried powder had a shorter dispersion time in water than the spray-dried powder, justified by the greater porosity of the structure. Lastly, the solubility, moisture content, and hygroscopicity parameters did not show statistically significant differences between the two processes [35]. Lastly, in a study by [PERSON] et al. [36], microencapsulation of a natural pigment (curcurcumin) revealed that despite the spray-drying process yielding particles with smaller size and more regular shapes, the freeze-drying method demonstrated greater efficiency in the microencapsulation process.