esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 2 Motivation and Related Work
### MPPT Mechanism  
In a typical application of EH-WSN, power is harvested from the energy sources to a converter usually in DC-DC type and then from the converter to the energy storage. The charge controller with MPPT keeps track of the output of the energy sources and comparing it with the storage's voltage. Continuously, it figures out the best power that the sources can potentially put out in order to charge the storage. [PERSON] et al. presented a fully integrated photovoltaic power harvesting system with a low-overhead adaptive MPPT scheme for powering an IoT node [11]. The proposed scheme tracked the maximum power point within 12 us by utilizing an inherent negative feedback loop, within a tracking error of 0.6%. Using a commercially available solar cell of area 11.3 cm\({}^{2}\), the proposed system could provide 833 uW power with a light intensity of 600 lux. [PERSON] et al. introduced a Semi-Pilot Cell Fractional Open-Circuit Voltage MPPT concept, it featured less complicated configuration with reduced hardware requirements and lower cost, the comparison result has shown improvement in providing precise calculation of the new maximum power point whenever there is an irradiance change [12]. [PERSON] et al. designed a self-powered battery-less electric potential wireless sensor that harvests near-maximum energy from the avocado plant and transmits a signal tens of meters away. By designing the DC-DC power converter to operate in the input voltage range 0.5-0.7 V, the power generated by the plant kept close to the corresponding MPP in the time-varying power-voltage curves during the day [13]. Base on all those studies, we find that although the energy sources could work at its MPP, however, the DC-DC converter usually may not work with its maximum efficiency, so the overall energy harvested into the storage also may not be the maximal value. Hence, we need to solve an improved problem: Maximum Power Transfer Tracking (MPTT).
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 1 Introduction  
In these last years, the number of satellites, spacecrafts, astronaut crews, and other technological artifacts launched into space has grown significantly. In launch campaigns, for the tracking of rockets and their payloads, there is an essential element: the trajectography radar. It performs a Single-Target-Tracking (STT) [1] by automatically tracking a specific target, recording its trajectory accurately and in real time. Among its main applications stand out the tracking of rockets, missiles, satellites and space debris.  
Two trajectography radars are used at a Brazilian Air Force launch base named Centro de Lancamento da Barreira do Inferno (CLBI), located in Parnamirim city, state of Rio Grande do Norte. This base performs and provides support for launches, tracking activities and research. One of these activities is the tracking of rockets launched by the Guiana Space Centre at Kourou, in French Guiana, in cooperation with the European Space Agency (ESA). For the tracking activities, CLBI has the Adour and Beam trajectoryraders and a telemetry station.  
The radars are French-made, C-band operated, and designed to track targets over long distances. For that, the Beam radar has a high gain (about 44 dBi) narrow beam (about 0.92 deg half power beam width) antenna, a high-sensitivity receiver and a transmitter capable of delivering high-power pulsed electromagnetic signals, reaching levels around 1 kW of average power and 1 MW of peak power [2].  
Trajectography radars emit high power of non-ionizing radiation (NIR), concentrated in a narrow beam. This set of specifications constitutes a quite unusual radiofrequency emission scenario when compared to the general telecommunication broadcasts, such as TV signal transmission, personal mobile service coverage and satellite communication. Radars' high-power emissions cause concerns about the possible danger to human health, especially in public areas. For health safety reasons, there is interest in measuring the NIR levels that people who work daily with the radar and those who visit it are exposed toFrom the measured values, it is possible to make a comparative analysis with the human exposure limits to NIR specified by the International Commission for the Protection of Non-Ionizing Radiation (ICNIRP) [3] and also by the Brazilian National Telecommunication Agency (Anatel) [4]. Then, measures can be taken to prevent and control this exposure.  
In this work, field measurements are performed to obtain the power density values of Beam's emissions, which operate with a higher power than Adour radar and are located only 690 m from a public space museum. This is one of the chosen measurement points, as it receives daily visits from children and teenagers from local schools and tourists from various parts of Brazil and the world. The objective is to measure only radar emissions, avoiding capturing signals from other sources. For that, a specific setup for trajectography radars was proposed. Up to now, there are no records of RNI levels in CLBI's area.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm
### Impact of Antennas  
The spectral efficiency of the proposed and other algorithms was analyzed against the number of BS antennas in Figure 5. As can be seen from Figure 5, the spectral efficiency of all algorithms increases by increasing the number of BS antennas. Moreover, the relative comparison shows that the spectral efficiency of the proposed Alt-MMSE algorithm was better than that of other algorithms [12; 13; 14; 18], and it also had similar performance with the optimal digital precoding scheme, which makes it superior and useful for deploying in mmWave-MIMO systems. To further elaborate on the effectiveness of the proposed scheme, the spectral efficiency at 180 BS antennas was evaluated. As can be seen from the results, the spectral efficiency of the proposed scheme was about 48 bits/s/Hz, whereas the spectral efficiency of the PP [12], MO-AltMin [13], PC-SIC [18], and OMP [14] schemes was 45, 44, 42, and 41 bits/s/Hz, respectively.  
To analyze the impact of the number of user antennas on spectral efficiency, Figure 6 compares it against the number of user antennas with SNR = 0 dB. It can be seen from Figure 6 that the proposed Alt-MMSE algorithm showed better spectral efficiency than existing competing alternatives [12; 13; 14; 18] at each number of user antennas, which makes it robust for multiple-user scenarios. Moreover, the rate gap between the proposed scheme and the OMP [14] scheme was much larger, showing the poor performance of the OMP scheme when increasing the number of user antennas. To clarify it further, we checked the spectral efficiency of the proposed scheme and the OMP scheme when the number of user antennas is 60. As can be seen from Figure 6, the SE of the proposed algorithm was about 62 bits/s/Hz, whereas the SE of the OMP algorithm was about 50 bits/s/Hz, which means an increase of 12 bits/s/Hz.  
Figure 5: Comparison of spectral efficiency against number of BS antennas with 8 RF chains and SNR = 0 dB.
## 4 MPPT with the Modified Levy Flight Algorithm
### Mechanism of the Proposed MPPT Search  
Figure 5 shows the mechanism of the movement of the particles for the proposed modified Levy flight algorithm. The particles are considered to be distributed initially along the entire P-V curve. Four initial duty cycles are chosen [38,40]. The particles are distributed at locations D\({}_{1}\), D\({}_{2}\), D\({}_{3}\) and D\({}_{4}\). The power at each respective particle, D, is read. The highest value of power at the corresponding particle, D\({}_{\mathrm{pm}}\), is considered as the best particle value. Other particles are forced to move towards this particle, D\({}_{\mathrm{pm}}\), as the best particle is the mark point for the movement of all other particles. The movement of the particles is guided by Levy flight as in Equation (11). The step sizes are varied towards the best particles, either in small or large jumps depending upon the boundaries of the search area. The step size becomes small as the particle moves nearer to MPP. A randomized step size in Levy flight ensures that the search would cover all the points along the P-V curve. Once all the particles reach the best particle, another evaluation is executed by comparing it with the global best particle. The comparison between the best particle and global best particle is obtained as shown in Equation (12). The new global best particle is assigned if the current best particle exceeds the global best particle. The position and velocity as shown in Equations (11) and (12) are updated. The convergence stops once all particles reach their MPP. All particle step sizes reach zero once MPP is achieved.
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 4 Conclusions  
To effectively cover WLAN, C band, X band, Wi-Fi, Bluetooth, and WiMAX applications, this work presents the design and fabrication of an innovative ultra-wideband patch antenna. This antenna has compact dimensions of 40 \(\times\) 34 \(\times\) 1.6 mm\({}^{3}\), and it is supported by an FR-4 substrate to reduce manufacturing costs. The fractal resonator is designed on one side of the substrate. On the other side, a ground plane is implemented. The ground plane is composed of a rectangular part with a slot and a welded \"L\" shape in order to obtain UWB. After the measurements carried out on the manufactured prototype, dual-wideband operation was achieved; the first has a bandwidth of 1.84 [2.26, 4.10] GHz and the bandwidth of 3.9 GHz [6.10, 10] GHz is for the second band, with four resonant frequencies of 2.8 GHz, 3.51 GHz, 6.53 GHz and 9.37 GHz. In the operational band, this antenna can achieve a radiation efficiency of 92% and a peak gain of 9 dB.  
Conceptualization, M.M., Y.R., I.H.N. and M.H.; Methodology, M.M., Y.R. and I.H.N.; Validation, M.M., Y.R. and S.A.; Formal analysis, M.M. and F.-E.Z.; Resources, S.A., A.G. and M.H.; Data curation, M.S.; Writing--review & editing, F.-E.Z. and M.S.; Visualization, A.G.; Supervision, Y.R.; Funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 5 Experiments
### Effectiveness Test
#### 5.1.4 Effectiveness of Our Algorithm  
Here, we compared the sample data without attitude correction and the sample data with attitude correction to evaluate the effectiveness of our algorithm.  
It can be seen from the value of \(\varphi_{\text{error}}\) that the attitude error is eliminated by our algorithm. This can be clearly seen from Figures 8-10, the 3D point clouds without attitude correction (which is red) are obviously skew, and there are an obvious deviations between their \(\varphi_{\text{error}}\) and 0, while the attitudes of the 3D point clouds with attitude correction (which is blue) are correct, and their \(\varphi_{\text{error}}\) approach 0.  
It can be seen from the uncertainty of \(\varphi_{\text{error}}\) that, in general, in the same mode, the uncertainty of \(\varphi_{\text{error}}\) of a 3D point cloud with attitude correction is smaller than that of a 3D point cloud without attitude correction (as shown in Figure 10). Comparing the red part and blue part of Figure 10, our algorithm reduces the uncertainty of \(\varphi_{\text{error}}\), especially in low-resolution mode. In high-resolution mode, our algorithm does not show obvious performance in reducing the uncertainty of \(\varphi_{\text{error}}\). After all, in high-resolution mode, the uncertainty of the attitude of the 3D point cloud is very small--even without attitude correction. However, mutation point which is similar to the 22 nd point of the red line showed in Figure 9 cannot be excluded. Our algorithm can deal with this situation.  
Our algorithm is effective, whether it is to reduce the attitude error of the 3D point cloud, or to reduce the uncertainty of the 3D point cloud attitude. As for whether the accuracy of our algorithm can meet the application requirements, we will analyze it below.
## 4 Performance Evaluation  
In this section, we assess the performance of our proposed model by applying various prominent and widely recognized metrics, including accuracy, F1 score, recall, and precision [13]. These evaluation metrics are calculated from the confusion matrix, which is built upon the following four values:  
* False positive (_FP_): the actual data are negative, but the prediction indicates them as positive.
* False negative (_FN_): the actual data are positive, but the prediction incorrectly indicates them as negative.
* True negative (_TN_): both the actual data and the prediction are negative.
* True positive (_TP_): both the actual data and the prediction are positive.  
Accuracy is a measure of the model's performance, calculated as the ratio of correctly classified instances to the total number of instances in the dataset. It is calculated using the following formula:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
Recall is the ratio of correctly identified positive cases (_TP_) to the total number of true positive (_TP_) and false negative (_FN_) cases. This can be described as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
Precision represents the proportion of correctly predicted positives (_TP_s) out of all cases that were predicted as positive, which includes both true positives (_TP_s) and false positives (_FP_s). It is calculated using the following formula:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
The F1 score represents the harmonic mean of precision and recall. It is expressed as follows:  
\[F_{1}\;\text{score}=2\times\left(\frac{Precision\times Recall}{Precision+Recall}\right) \tag{8}\]
## 3 Results and Discussion
### Combustion Test  
The combustion properties of composites were studied by micro calorimetry. This study is used for ranking and comparing the fire behavior of a material. From this study, parameters such as PHRR, average heat release rate, total heat release (THR) and time to ignition (TTI) can be obtained. The PHRR values are indicated to evaluate fire safety. The results were obtained in the present work and presented in Table 3 and Figure 9.  
The combustion efficiency was increased in F1, F2, and F3 composites by adding ZrO\({}_{2}\) NPs. The pristine sample (F0) burned very quickly after ignition and showed a PHRR peak in the range of 180-450 s with 76.3 W/g. With the addition of 1%, 2%, and 3% ZrO\({}_{2}\) NPs to the composite, the PHRR values of the F1, F2, and F3 samples showed 28.7 W/g, 23.5 W/g and 18.9 W/g, respectively. The F0, F1, F2, and F3 composites obtained only single peaks. The ZrO\({}_{2}\) NPs were involved in endothermal decomposition and released the moisture to form a dense, boron trioxide protective layer that protected the underlying material and improved the flame retardance. The TTI is a measurement from the onset  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **LOI\%** & **Rating Results** \\ \hline F0 & 21.3 & V-1 \\ F1 & 31.8 & V-0 \\ F2 & 35.4 & V-0 \\ F3 & 38.7 & V-0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Limit of oxygen index (LOI) and UL-94V ratings of composites.  
Figure 8: Effect of ZrO\({}_{2}\) NPs on the LOI test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formulation** & **TTI (sec)** & **PHRR (W/g)** & **THR (k/g)** \\ \hline F0 & 188 & 76.3 & 22.1 \\ F1 & 276 & 28.7 & 7.0 \\ F2 & 278 & 23.5 & 5.5 \\ F3 & 281 & 18.9 & 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Microcalorimeter results of the epoxy/ZrO\({}_{2}\) NPs.  
of the PHRR curve of a sample. The TTI parameter is used to evaluate the flame retardant influence on the ignition of a material. Figure 10 and Table 3 clearly indicate that the F0 composite ignited at 188 s whereas the F1, F2, and F3 composites ignited at 276, 278, and 281 s, respectively. During the combustion process, the intumescent flame retardant (IFR) system decomposes gently and results in char formation. In addition to this, the loading of ZrO\({}_{2}\) NPs into the coating system the intumescent mechanism occurs gently. This might be the reason for the decrease in TTI values. The formation of char does not allow oxygen flow and heat transfer, hence the PHRR and THR values decrease. Among all composites, the F3 composite coating showed lower PHRR and THR values, which may be due to ZrO\({}_{2}\) NPs hastening the esterification reaction in the IFR system during the combustion process to form a protective char.
## 3 Evolution Towards Next Generation Networks (NGN)
### NGN Supported Services  
Several NGN services are already deployed and delivered over NGNs:  
* PSTN/ISDN simulation/emulation, legacy telephony
* Multimedia Telephony (MMtel) and Rich Communication Suite (presence, messaging, phonebook, video sharing)
* New content and Triple Play services such as Next Generation of IPTV services
* Business services (enterprises solutions)
* Regulatory services (emergency services, legal interception)
* Smart home services
## 2 Materials and Methods
### Development of General Thermal Comfort Predictive Model
#### 2.1.4 General Linear Regression (LR) Model Identification and Offline Parameter Estimation  
The main objective of this stage was to identify a general reduced-order and the most parametrically efficient (parsimonious) model structure with the minimum number of easily measured variables (based on the results of the previous stage) to predict the cyclist's under-helmet thermal comfort. For the sake of the main objective of the present work, the selected predictive model had to be suitable, concerning the computational cost, for wearable sensing technology. Due to the subjective nature of the thermal comfort data, it could not be performed in a continuous pattern, unlike the other input variables, which was a challenge for identifying the predictive model. Hence, in the present paper, we used a simple multivariate regression model with the following general form [25]:  
\[T_{ci}=\alpha+\beta_{1}u_{i1}+\beta_{2}u_{i2}+\ldots+\beta_{m}u_{im}+\epsilon _{i},\text{ for }i\in\{1,\ldots,\ n\},\]  
where \(T_{ci}\in\mathbb{R}\) is the response (thermal comfort) for the \(i\)th observation, \(\alpha\in\mathbb{R}\) is the regression intercept, \(\beta_{j}\in\mathbb{R}\) is the \(j\)th predictor's slope, \(u_{ij}\in\mathbb{R}\) is the \(j\)th predictor for the \(i\)th observation and \(\epsilon_{i}\sim N\big{(}0,\sigma^{2}\big{)}\) is an independent and identically distributed Gaussian error term. This can be formulated in matrix form as follows:  
\[\mathbf{T_{C}}=\mathbf{X}\beta+\epsilon,\text{ subjected to : }\mathbf{T_{C}}\in\mathbb{R}^{n\times 1} \text{ and }\mathbf{X}\in\mathbb{R}^{n\times m}\]  
where \(n\) and \(m\) are the number of samples and number of predictors (input variables), respectively. In the present paper, we used the ordinary least-squares (OLR) approach to find the regression coefficients estimates (\(\hat{\boldsymbol{\beta}}\)) that minimised the sum of the squared errors as follows:  
\[\hat{\boldsymbol{\beta}}=\text{arg }\underset{\beta}{\text{min}}(\mathbf{T_{C}}- \mathbf{X}\beta)^{\text{T}}(\mathbf{T_{C}}-\mathbf{X}\beta)=\big{(}\mathbf{X} ^{\text{T}}\mathbf{X}\big{)}^{-1}\mathbf{X}^{\text{T}}\mathbf{T_{C}}.\]
## 3 Results
### Assessment of the Optimal Dates and Required Observation Periods for Classification  
Figure 7 shows the time series changes in the importance of variables based on normalized reduction of Gini; the importance seemed to have a seasonal variation. The peak days of time series with 10-day intervals coincided with the timing of crop planting and harvesting (Figure 4). Figure 8 shows the normalized mean decrease of Gini reassessed by classification using only NDVI images of 14 peak days. We found that classification accuracy varied with the number of images used for crop type and practice classification (Figure 9), where the variables used were added from the date with the most variable importance (i.e., normalized mean decrease Gini) shown in Figure 8.  
Figure 6: Crop type and practice map based on classification using NDVI time series (adopt the maximum class of the class probability averages; n = 10). The left image in the inset was taken by an unmanned aerial vehicle in October 2019.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Reference Classes** & \multicolumn{6}{c}{**Classified**} & \multicolumn{6}{c}{**Total**} & **Producer’s Accuracy** \\ \hline  & **AF** & **Pasture** & **Spring Plant** & **Purple Yam** & **Ratoon** & **SP2018** & **SP2019** & **Pineapple** & & \\ \hline Agricultural facility & **100** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 1.00 \\ Pasture & 1 & **682** & 3 & 3 & 2 & 6 & 0 & 14 & 711 & 0.96 \\ Spring planting & 0 & 1 & **194** & 1 & 16 & 0 & 3 & 0 & 215 & 0.90 \\ Purple yam & 0 & 4 & 1 & **204** & 2 & 2 & 0 & 0 & 213 & 0.96 \\ Ratonon & 1 & 2 & 47 & 6 & **868** & 5 & 32 & 9 & 970 & 0.89 \\ Summer planting, & 2 & 1 & 2 & 0 & 9 & **245** & 0 & 3 & 262 & 0.94 \\ Summer planting, & 0 & 0 & 3 & 2 & 2 & 0 & **217** & 1 & 225 & 0.96 \\ Pineapple & 1 & 1 & 0 & 0 & 0 & 1 & 0 & **206** & 209 & 0.99 \\ Total & 105 & 691 & 250 & 216 & 899 & 259 & 252 & 233 & 2905 & \\ User’s accuracy & 0.95 & 0.99 & 0.78 & 0.94 & 0.97 & 0.95 & 0.86 & 0.88 & & \\
**Overall accuracy** & **0.93** & & & & & & & & \\
**Kappa** & **0.92** & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of the map shown in Figure 6. From the reference dataset based on the field survey, the reference data not used for training were used for validation. The number of samples in each class was determined by the area ratio, with the minimum set to 100.  
Figure 8: Normalized mean decrease of Gini index when NDVI images were classified with only 14 peak days.  
Figure 7: Changes in the importance of variables in NDVI time series. Y-axes show normalized mean decrease in Gini of variables. Gray bars, daily values; black circles, 10-day maxima; red lines, peak dates.
## 3 Overview of the VLC-IoT Paradigm
### IoT: An Overview  
Recently, IoT is moving from a limited usage M2M type communication system to a global IoT network service with a large impact on our daily life [29]. Furthermore, with AI and robotics, IoT constitutes one of the pillars of 4 IR with all the emerging smart environments and things that will be built and deployed to assist in dedicated application domains, ranging from smart cars, smart cities, smart campuses to smart manufacturing [30; 31]. Wireless communication technologies such as narrow band-IoT (NB-IoT), BLE, and Zig-Bee, as well as the protocols to support packet delivery, such as CoAP [RFC7252], MQTT [MQTT], MQTT-SN [MQTT-SN], and within the scope of hybrid communication support for the physical and transport layers. Moreover, as IoT is going to fully support the digitalization model, cloud computing will be the main support, and a set of standards and software have been designed to assist in developing IoT systems and products (Open Connectivity Foundation (OCF) or oneM2M). At the device level and regarding physical media access, the main challenge concerns scalability. Even if 5G is supposed to provide the needed frequency bands, supporting IoT only by the cellular network providers will not be possible due to different factors such as the legislation, country by country regarding frequency band allocation, and mainly the under 1G band [32].