esa-sceva/satcom-chunk-collection huggingface dataset:
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology  
Figure 1 shows the methodology's flowchart to obtain long-term series of BRDF-corrected NDVI from VEGETATION sensors radiometry. An atmospheric correction is first performed on RED and NIR TOA reflectances to obtain Top of Canopy (TOC) reflectances. Then, a spectral BRDF retrieval is performed on a pixel basis accumulating TOC values over a short time period. The retrieved BRDF models serve to set the RED and NIR VEGETATION TOC reflectances and NDVI values to a standard Sun-sensor geometry. A detailed description of the processing stages is shown in Figure 1 and detailed in the next subsections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**VGT1**} & \multicolumn{2}{c}{**VGT2**} & \multicolumn{2}{c}{**VGTPV**} \\  & \(\mathbf{\lambda_{0}}\) & **FWHM** & \(\mathbf{\lambda_{0}}\) & **FWHM** & \(\mathbf{\lambda_{0}}\) & **FWHM** \\  & [\(\mathbf{\mu}\)m] & [\(\mathbf{\mu}\)m] & [\(\mathbf{\mu}\)m] & [\(\mathbf{\mu}\)m] & [\(\mathbf{\mu}\)m] & [\(\mathbf{\mu}\)m] \\ \hline RED (B2) & 0.615 & 0.615–0.700 & 0.653 & 0.616–0.690 & 0.655 & 0.614–0.696 \\ NIR (B3) & 0.773 & 0.773–0.894 & 0.838 & 0.783–0.892 & 0.837 & 0.772–0.902 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RED and NIR Spectral ranges of the VEGETATION instruments. \(\lambda_{0}\) and FWHM are the center wavelength and full width at half maximum, respectively. Information taken from [[http://www.spot-vegetation.com/faqnew/index.html](http://www.spot-vegetation.com/faqnew/index.html)]([http://www.spot-vegetation.com/faqnew/index.html](http://www.spot-vegetation.com/faqnew/index.html)) accessed on 15 March 2021 and [[http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf)]([http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf](http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf)) accessed on 15 March 2021.
###### Abstract  
This study aimed to develop a simple but effective mechanical-to-electrical energy conversion for harvesting hydrokinetic energy based on triboelectric nanogenerator (TENG) technology. Here, a direct-current fluid-flow-based TENG is reported as a potential solution to solve the inconvenience of directly powering electronic devices where direct-current (DC) power is required. The falling of a water droplet (about 1.06 mL) from an elastomeric pipe can generate an open-circuit voltage of -35 V, short-circuit current of 3.7 uA, and peak power of 57.6 uW by passing through a separated electrode. Notably, the electrical responses have the distinct characteristics of pulsed direct current. The ability to generate DC outputs enables the TENG to directly drive electronic devices. Our experimental results prove that this TENG can act as a power source to directly light up 50 light-emitting diodes without requiring a rectifier, and, also, the produced electric energy was demonstrated that can be stored directly in a capacitor to power commercial temperature and humidity IoT sensors. Furthermore, the device shows a greatly varied output voltage based on the droplet flow rate, with a linearity R\({}^{2}\) = 0.998. This work highlights a promising potential for applications in harvesting hydrokinetic energy and self-powered sensors and systems.  
direct-current electricity generation; fluid-flow-based triboelectric nanogenerator; direct charge transfer; self-powered IoT sensors +
Footnote †: journal: _sciences_  
_Applied_  
_Acticle_  
A Direct-Current Triboelectric Nanogenerator Energy Harvesting System Based on Water Electrification for Self-Powered Electronics
## 3 Results and Discussion  
### Radiometric Calibration  
The empirical line approach used for sensor calibration produces a set of 12 linear relationships between DNs and ground measured reflectance of a white and black calibration panel. Figure 12 shows the empirical lines for bands b\({}_{471}\)-b\({}_{713}\) (Figure 12a) and bands b\({}_{761}\)-b\({}_{953}\) (Figure 12b).  
Raw imagery DNs of the white panels for bands 1-6 showed values close to saturation (DN 1024). After flat-field correction those pixels showed values higher than 1024. This is caused by flat-field correction factors \(>\)1, in cases where the white panel was situated close to the periphery of an image. Image DNs of the calibration panels and the corresponding ground measured reflectance are listed in Table 2 together with the respective regression.  
The spatial subset depicted in Figure 13 shows a composite from MCA b\({}_{658}\) (R), b\({}_{551}\) (G) and b\({}_{471}\) (B) after calibration, band alignment, layer stacking and transformation from geographical coordinates (WGS 84) to projected coordinates (ETRS 89 UTM 33).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Band** & \multicolumn{2}{c}{**DN Mini-MCA 12**} & \multicolumn{2}{c}{**Reflectance ASD Fieldspec**} & \\ \hline  & **White Panel** & **Black Panel** & **White Panel** & **Black Panel** & **Regression** \\ \hline b\({}_{471}\) & 1299.5 & 87.1 & 0.968 & 0.062 & R = 0.000748 \({}^{*}\)DN \(-\) 0.003615 \\ b\({}_{515}\) & 1269.1 & 55.4 & 0.917 & 0.05
## 5 NB-IoT Cell Search
### Brief Summary of Cell Search  
The whole cell search procedure has been discussed adequately in the two subsections above, and we will briefly summarize it here. We have proposed a noise power estimator and a FFO pre-estimator to improve the performance of the NPSS detector with low SNR and large frequency offset. IFO and fine timing joint estimation is conducted in the time domain with an existing ML cross-correlation solution. Then, we promote a divide-and-conquer cell ID identification method. To make the whole process clearer, a cell search algorithm flowchart is given in Figure 7.  
Figure 7: Algorithm flowchart of the proposed cell search scheme.
## 3 The Proposed Method
### Attribute Branch  
In addition to category and location information, abundant attribute information is contained in the feature map generated by the backbone network. In contrast to [17], which solely focuses on learning the density attribute, an attribute branch (ATTB) is proposed to simultaneously extract both the density and count attributes of pedestrians, guiding the adjustment of the suppression intervals and intensity in MA-NMS. The structure of ATTB is illustrated in the orange box in Figure 3.  
In the attribute branch (ATTB), a context extraction module (CEM), consisting of a \(5\times 5\) dilated convolution followed by a \(1\times 1\) standard convolution, is designed. The CEM is applied to capture the contextual information of pedestrians. The extracted context is then fed into the Region Proposal Network (RPN) to generate regions of interest (RoIs). These RoIs are subsequently concatenated with that from the detection branch, enabling a more comprehensive representation of pedestrians and their surrounding occluded pedestrians. Moreover, the concatenated ROIs are fed into two fully connected layers (FC layers) to obtain the density and count attributes. Notably, the proposed ATTB can be easily embedded into generic pedestrian detectors to acquire the density and count attributes, which, in turn, guide the adjustment of the NMS. Consequently, the incorporation of ATTB enhances the capabilities of NMS-based detectors in mitigating the impact of intra-class occlusion.  
Figure 3: The structure of the pedestrian detector for crowded scenes. The context extraction module (CEM) consists of a \(5\times 5\) dilated convolution and a \(1\times 1\) standard convolution in series. The detector generates a tuple of attributes for each pedestrian, including confidence score (\(s\)), bounding box (\(b\)), density (\(d\)), and count (\(c\)). To remove duplicate proposals (highlighted in red), MA-NMS applies strong suppression using a score threshold of 0.01. Meanwhile, weak suppression is employed to retain the occluded pedestrians (highlighted in green). Color printing is recommended.
## 2 Methodologies
### Field Test
From the above analysis, it can be found that, for the soil layers in which the utility tunnel is located, there are mainly two parts, which are soft soil in lower layers and filling soil in upper layers. The soft soil layers mainly include the sullage silty clay and the silty clay. Therefore, the soil layers in which the utility tunnel is located belongs to the typical soft soil. Because the utility tunnel engineering, which is a typical soft foundation engineering, is shallowly buried under the green isolation areas, it will be seriously affected by the ground traffic load during the operation period, which will cause large deformation for the utility tunnel. Moreover, for locations near multiple ground transportation routes, this engineering will be repeatedly affected by the loads of incoming and outgoing ground vehicles. Therefore, for this utility tunnel engineering with its big size and complex structure during the operation period, the ground traffic load will significantly influence its safety. It is very important work to monitor and predict the safety for this utility tunnel during the operation period. To monitor the response of the utility tunnel structure during the operation period in-site, the steel stress gauge and concrete strain gauge are placed at the middle of the floor, the side wall and the roof of the structure for the directly buried outlet shaft. Moreover, the contact pressure monitoring sensors are embedded in soil at different depths along the side wall of the utility tunnel and along the floor. The layout of field monitoring points is shown in Figure 9.  
In this study, the used data acquisition instrument is the dynamic and static signal testing system of Chentu CT5808W, whose adjustable maximum frequency is 50 Hz. This instrument can capture the slight changes in sensors within a short period of time. In the field test, to simulate the vehicle load under normal two-way traffic and one-way traffic conditions, on the second lane from east to west and the opposite lane, vehicle load action tests have been conducted, with rear axle loads of 8.3 tons, 9 tons and 11 tons. The vehicle load, which is a one-time action, is applied by a dump truck.
## 3 Correlated Mode Auto-Pairing and Adaptive Evolution Screening Method
### Population Evolution Screening Mechanism Provided by an Evolutionary Algorithm
The evolution process will stop until the evolutionary algorithm meets the termination condition such as the maximum number of generations has been reached or the best individual in the population has not been improved for several consecutive generations. The best individual with the minimum objective function value in the last generation is selected as the final updated result. The procedure of the CMPES method is illustrated in Figure 2. The one-to-one \(\mathbf{CMP}^{(r)}\) are determined adaptively based on the given MAC threshold. \(\mathbf{J}^{(r)}\) constructed by correlated and penalized subitems are calculated based on the correlated mode auto-pairing strategy. Then, the population evolutionary screening mechanism is employed to screen the CMPs further and search the global minimum by urging \(\theta\) and the CMPs towards the ideal results. Combined with the auto-pairing strategy, the adaptive switch from penalized to correlated subitem during iteration can ensure that all the potential of analytical or experimental modes can be fully exploited. The updated results will be obtained when the evolutionary algorithm meets the termination condition. Figure 2: Procedure of the proposed CMPES method.
## 3 Our Approach
### LS Interest Modeling  
The behavioral motivation of users is often complex. Even users with stable long-term interests may occasionally click on popular items unrelated to their interests. As a result, short-term user interests are considered unstable and contain a significant amount of noise data. [PERSON] et al. [21] have emphasized that accurately predicting a user's long-term interests can increase click rates, while accurate short-term interest prediction is critical for platform profitability and user experience. In sequence modeling, RNN structures are frequently employed because they take into account the time factor of sequence data. Although a user's interests may differ significantly in a short period of time, they are still linked, and the RNN structure captures the weight of the relationship between items before and after.  
Table 1 displays the prediction results of our DIN model on different types of users with varying sequence lengths, trained on Amazon Electric data. The findings indicate that when users have few click sequences, the model recommends more diverse item types and popular items to explore their interests, resulting in fluctuating hit rates. As the number of user interactions increases, the proportion of recommended item types matching the user's click history increases, and the proportion of popular items decreases, indicating that more data can extract stable user interests. For users with diverse interests, the long-sequence model has minimal impact on the diversity of recommended items, while users with narrower interests experience the opposite effect, with longer models resulting in greater bias. A long time span of item interaction can be fatal to a short-term sequence model, leading to recommended popular items to compensate for the deficiency. However, the long-term interest model has minimal effect on short-term interest prediction and should be used judiciously to avoid impairing the prediction accuracy of the model.  
In summary, we can answer the first question (Q1): long sequence models are more suitable for users with diverse interests, while short sequence models are better for users with less diverse interests. Long sequence models prioritize diversity over accuracy, while short sequence models prioritize accuracy over diversity. To overcome these challenges, we propose decoupling the short-length interest model, redesigning the feature enhancement module, and introducing a loss function to account for differences in interests.
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 6 Analysis of the CIR under Different Power Spectral Density (PSD)
For co-channel beam group 2 (beams 2, 6, 12, and 14), the simulation results change very weakly under different PSD compared to uniform PSD, due to the unchanged PSD of beams 2/6/12/14. For co-channel beam group 3 (beams 3, 5, 8, and 18), the simulation results also change weakly because the carrier signal power and interference power decrease at almost the same level for any beam therein. For co-channel beam group 4 (beams 4, 7, 9, 11, and 17), the situations are diverse. The user downlink CIR level of beam 4 decreases  
Figure 13: CDF of the CIR in the user link of beam 1 concerning different or uniform PSD: (**a**) user uplink and (**b**) user downlink. significantly under different PSD compared to uniform PSD because beam 4 suffers from a significantly downward carrier signal power while it benefits from downward interference power slightly. The user downlink CIR level of beams 9/15 experiences an increase under different PSD compared to uniform PSD due to the unchanged carrier signal power and downward interference power. The user downlink CIR level of beams 7/11/17 shows an uncertain and slight change under different PSD compared to uniform PSD. It depends on which one of the carrier signal power and interference power decreases more, although neither of them decreases significantly. Furthermore, we can conclude the importance of the estimation of the CIR with PSD, which accords with our method very well, for satellite operators, especially in the present day's flexible payload applications. Flexible payload architectures can support configurable power, bandwidth, and/or coverage, which provides operators with flexibilities but also challenges. For example, in order to change the in-orbit capacity of one beam according  
Figure 14: Comparison diagram of the user downlink CIR in all beams for 85% coverage. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Beam Number** & **Uniform PSD (100\%)** & **Different PSD (100\%)** & **Uniform PSD (85\%)** & **Different PSD (85\%)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table of the carrier-to-interference ratio in the user downlink for all beams. to real-time demand, the operator can reallocate the channel power among several beams. However, it will affect the PSD of beams, and then yield variation in the beam's CIR, which can even lead to link interruption. Therefore, the operators will be overcautious during the power allocation. Our work is so meaningful in that it provides operators with prediction of beams' CIR after power allocation and helps them to find a possible allocation scheme guaranteeing the level of beams' CIR.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
### Notation  
The notation adopted is as follows: scalars are represented by italic lowercase characters as in \(b\), vectors are represented by bold lowercase characters as in \(\mathbf{b}\), and matrices are represented by bold uppercase characters as in \(\mathbf{B}\). The transpose operation is indicated by the superscript \({}^{T}\), the conjugate transpose by the superscript \({}^{H}\), and the conjugate operation by the superscript \({}^{*}\). \(\mathbf{I}_{n}\) represents the identity matrix of dimension \(n\), \(\text{Re}\{\cdot\}\) and \(\text{Im}\{\cdot\}\) refer to the real part and the imaginary part.
## 4 Evaluation
### Results Analysis
#### 4.3.5 Comparative Analysis
However, with respect to the average final accuracy, both groups performed exceptionally well, with only a 0.4% difference between them. This indicates that even students with limited initial understanding were able to successfully correct their errors and achieve the learning objectives through the system's iterative practices and real-time feedback. These results further demonstrate the platform's capacity to support learners of varying proficiency levels, providing strong guidance for self-directed study and a forgiving learning environment. A closer examination of the average number of submissions shows that Group 2 students averaged 3.13 submissions per question, substantially higher than Group 1's average of 2.12. This suggests that Group 2 students required more attempts and corrections to arrive at the correct answers, likely reflecting greater challenges in understanding the intent of the questions and/or mastering the syntactic details. This phenomenon reinforces the importance of recognizing differences in students' backgrounds and highlights the potential benefits of differentiated instructional strategies to shorten learning curves and reduce error rates. In summary, this analysis not only uncovers disparities in initial comprehensions and problem-solving processes between the two groups but also confirms that, with effective learning interfaces and instructional designs, students from different backgrounds can ultimately achieve high learning standards. The findings provide valuable insights for the future development of individualized tutoring approaches and targeted adjustments in the section design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **FirstAvg Acc.** & **LastAvg Acc.** & **SubmitAvg Times** \\ \hline Section 1 _Verilog_ & 86.57\% & 99.76\% & 2.28 \\ Introduction & & & \\ Section 2 Basic & 77.64\% & 99.72\% & 3.13 \\ Architecture & & & \\ Section 3 Data & 81.18\% & 99.75\% & 1.92 \\ Conversion & & & \\ Section 4 _Verilog_ & 71.17\% & 97.32\% & 3.18 \\ Operator & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance analysis across sections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **FirstAvg Acc.** & **LastAvg Acc.** & **SubmitAvg Times** \\ \hline Group 1 & 84.82\% & 99.34\% & 2.12 \\ Group 2 & 74.01\% & 98.94\% & 3.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison between two groups.
# Enhanced Beacons Dynamic Transmission over TSCH
## 5 Experimental Results and Discussion  
To assess the performance of the proposed method, comparisons are made by performing multiple simulations. For this purpose, the Cooja simulator, included in the Contiki-NG kernel (The Contiki-NG version used for this research is the Develop v4.0, available at [[https://github.com/contiki-ng/contiki-ng/tree/develop/v4.0](https://github.com/contiki-ng/contiki-ng/tree/develop/v4.0)]([https://github.com/contiki-ng/contiki-ng/tree/develop/v4.0](https://github.com/contiki-ng/contiki-ng/tree/develop/v4.0))), is used to emulate the communication among Zolertia Z1 nodes. A computer with 16 GB of RAM and an Intel Core i7-1185G7 processor was used to run 500 independent simulations, ensuring a maximum mean error of 10% for a 95% confidence interval. Three different scenarios are used to evaluate the performance of the proposed EBDT-TSCH. The parameters used in the simulation are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Value/Calculation** \\ \hline Sloftrame length & \(L\) & 11 slots \\ Number of network channels & \(m\) & 4, 8, 16 \\ Maximum time between beacons & \(\mathit{Teb}\) & 4 s \\ Minimum time between beacons & \(\rho\mathit{Teb}\) & 0.75\(\mathit{Teb}\) \\ Maximum time between beacons in the intensive phase & \(\mathit{Tebi}\) & \(\alpha\mathit{Teb}\), \(\alpha=0.5\) \\ Minimum time between beacons in the intensive phase & \(\rho\mathit{Tebi}\) & 0.75\(\mathit{Tebi}\) \\ Number of EBs sent in the intensive phase & \(u\) & \(|\beta m|\), 0 \(\leq\beta\leq 1.8\) \\ Join-seeker scan period & - & \(\mathit{Teb-\rho\mathit{Teb}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Probability of getting associated at the intensive phase for different network available channels, represented by the symbol “\(\chi\)” in discrete values of \(\beta\).