esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 5 Materials and Methods
### Performance Parameters  
As QoS parameters, we choose the delay, PDR, duty cycle, number of parent switches, and number of packets dropped from the queue.  
The **delay** is measured at the application layer. The **average delay** is the average of the delay measured for all unicast data packets over all simulation runs related to a given scenario.  
The **PDR** is the total number of unicast data packets received at the application layer of the receiver (the root) divided by the number of unicast data packets sent from the application layer of all packet sources. The **average PDR** is the average of the PDR over all simulation runs related to a given scenario.  
The **number of parent switches** is the number of times each node changes parent during the simulation run. By averaging the values obtained for all sender nodes in the network across all the simulations for a given scenario, we obtain the **average number of parent switches** during a simulation run. In Contiki-NG, logging can be activated. In this case, each node prints helpful information in the output. The number of parent switches is retrieved from the nodes' output by filtering relevant keywords and counting the number of times they appear in the course of the simulation run. Frequent parent switches will result in frequent route modifications, making RPL operate in an unstable manner. Reconstructing the routes generates additional control traffic, which uses energy and shortens the network lifetime [18]. Thus, we can consider the average number of parent switches as a performance parameter to assess network's stability.  
Conclusions regarding the average power consumption of a node can be drawn based on the **radio duty cycle**, which estimates the fraction of time the radio of a node is on in a defined period. The **average radio duty cycle** is obtained by averaging the radio duty cycle of all nodes in the network across all the simulations for a given scenario.  
The **number of packets dropped from the queue** refers to all the messages dropped regardless if they are control packets or data packets. By averaging the dropped packets of all sender nodes in the network, across all the simulations for a given scenario, we obtain the **average number of dropped packets from the queue**.  
The QoS parameters are analyzed during regular traffic and burst traffic.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Normal load & 1 and 2 packets/minute \\
**Traffic** & Burst & 5, 10 and 15 packets/minute \\  & \# Packets sent by each node & 1000: 250 normal traffic + 500 burst traffic \\  & & + 250 normal traffic \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Satellite Operations and Management Systems
#### 2.2.2 Operations in Persistent Connectivity Constellations
The Control Data Service manages the incoming and outgoing control data. The Network State Analysis module evaluates and predicts network resources, taking the expected service requirements from the service requirements repository as input. The Network Resource Allocation, Management, and Optimization module uses AI/ML to allocate resources for the future expected network requirements. Finally, the Network Control and Orchestration module generates the control commands to be sent to the NTN segment based on the resource allocation. Even though HCC enables near-real-time feedback from the network status, the authors identify that control messages should be scheduled reasonably so as not to flood the network with constant control signaling. This same concept is explored in [81], where the authors propose a pre-operations solver (before ops) and an operations solver (during ops) for the management of a satellite network under uncertainties. Recent research has also focused on optimizing constellation design for mega-constellation scenarios, with approaches achieving significant reductions in required satellites while maintaining performance requirements [82]. These optimization frameworks are essential for addressing the scalability challenges identified in traditional satellite operations management. The tools and frameworks reviewed in this section, spanning both sporadic and persistent connectivity operations, exhibit several common limitations that highlight the need for an integrated management approach. Table 3 summarizes the key characteristics and shortcomings of the major solutions identified in the literature, categorized by their operational focus and deployment context. \begin{table}
\begin{tabular}{c c c} \hline
**Tool/Framework** & **Category** & **Limitations** & **CMS Contribution** \\ \hline \hline \multirow{6}{*}{APSI [40], ASPEN [41], EUROPA [42], SPIRE [43], SPIRE [46]} & \multirow{6}{*}{Space agency tools} & Mission-specific designs tailored to particular space agency requirements and procedures. Many frameworks have been abandoned due to funding interruptions, resulting in outdated and unsupported code. Limited extensibility and lack of standardization across different missions. \\  & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cl
# A High-Efficiency K-band MMIC Linear Amplifier Using Diode Compensation
###### Abstract  
This paper describes the design and measured performance of a high-efficiency and linearity-enhanced K-band MMIC amplifier fabricated with a 0.15 \(\upmu\)m GaAs pHEMT processing technology. The linearization enhancement method utilizing a parallel nonlinear capacitance compensation diode was analyzed and verified. The three-stage MMIC operating at 20-22 GHz obtained an improved third-order intermodulation ratio (IM3) of 20 dBc at a 27 dBm per carrier output power while demonstrating higher than a 27 dB small signal gain and 1-dB compression point output power of 30 dBm with 33% power added efficiency (PAE). The chip dimension was 2.00 mm \(\times\) 1.40 mm.  
Keywords:high-efficiency; K-band; linearity enhancement; power amplifier; GaAs pHEMT; diode compensation +
Footnote †: journal: Electronics Electronics
## 3 Results
### Diffuse Transmission Spectroscopy Analysis
#### 3.1.2 Monte Carlo (MC) Outlier Elimination in Transmittance Spectra  
When conducting data analysis, removing outliers is essential to ensure the accuracy of model development. In this study, the MC outlier detection method was employed as a crucial step for this purpose. This method effectively identifies various types of outliers, including those in both (X) and (Y) datasets. MC outlier detection involves multiple rounds of random sampling and statistical analysis to pinpoint data points that significantly deviate from other samples. The method utilizes a large number of simulations to assess each data point performance, determining its status as an outlier based on the distribution of the mean and standard deviation. In this study, the MC outlier detection method was applied to the collected spectral data and SSC measurements. As illustrated in Figure 5, this approach identified samples with unusually high means and standard deviations as outliers. Consequently, the samples labeled 45, 74, 115, and 166 were removed based on the results of the outlier detection.  
Figure 4: Diffuse transmittance raw spectra.  
Figure 5: Outliers in diffuse transmittance spectra.  
As shown in Table 1, the analysis using the PLSR model demonstrates that the removal of outlier samples significantly improved the model performance. Specifically, after removing outliers, the coefficient of determination \(R^{2}\) increased notably. Although there was a slight decrease in the \(R^{2}\) for the calibration set from 0.95 to 0.96, the prediction set \(R^{2}\) improved markedly. Additionally, the root-mean-square error (RMSE) decreased significantly. The calibration set RMSE increased marginally from 0.34 to 0.39, while the prediction set RMSE decreased from 0.87 to 0.79.  
These changes indicated that outlier removal had effectively enhanced the model fit and predictive capabilities. The results underscored the importance and efficacy of outlier removal, as cleaning the dataset mitigated the interference from anomalies and rendered the model more accurate and reliable in both calibration and prediction. This improvement not only boosted the overall performance of the model but also strengthened its generalization ability to new data. The method of outlier removal was applicable not only to PLSR models but also to other regression analysis techniques. In practical applications, data cleaning and preprocessing to eliminate outliers could significantly enhance model performance and predictive accuracy. This process highlighted the critical role of data quality in the modeling process and demonstrated how appropriate data processing techniques could substantially optimize model outcomes.
## 4 Discussion
Interestingly, however, neither HA nor PN alone exerted such an effect on fibroblasts, suggesting that their combination may have synergic, non-linear effects that should be further investigated. Similarly, our confocal microscopy observations (Figure 4) attest to the increased cell viability in the presence of both PN and HA, which would explain why cells are capable of growing around spheroids under those conditions. Furthermore, the re-plating experiment (Figure 5 and Figure S2) offered some important insights into the biology of cells in spheroid models under PN stimuli. One of the most striking observations is that control spheroids preserved their integrity over a 15-day culture period. This may be interpreted as an indication of a lack of proliferative potential in these spheroids, which were not able to give rise to a new cell progeny and colonize the culture plate. HA-treated samples were instead capable to do that, although spheroids were maintained. This suggests that only a small fraction of the cells that made up the spheroids participated in the plate colonization process. Cell sprouts appeared quite late in culture, possibly because only a few cell clones were activated and were this capable of spreading on the plate once they divided long enough and could migrate, possibly using the available HA as an aid to enhance their motility. Most of the cells in HA-treated spheroids, however, remained where they were, and the spheroids maintained their size and density, as observed by staining intensity. In the presence of PN, on the contrary, and even more so in the presence of a combination of PN + HA, the spheroids lost some of their density or were even disassembled completely (Figure 5). This can possibly mean that the majority of (or all) the cells forming the spheroids were reactivated and participated in plate colonization. As such, these data can be interpreted as proof that cells in PN, HA-treated spheroids maintained a higher degree of viability and were available for further adaptation and migration. Based on the available data it is not possible to identify how the combination of these stimuli may act this way, possibly by providing both a scaffold for spheroids and cells to attach and a pro-survival stimulus but the synergy of these compounds appears capable of maintaining cell viability across the whole spheroid, unlike the other tested treatments. It can be hypothesized that a combination of PN + HA provides both an extracellular matrix-like support to cells and a viability stimulus that, by working in synergy, have an overall positive trophic effect on cultured cells, maintaining their regeneration potential for a longer time.
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
## 3 Conventional Estimation Scheme
### Conventional Scheme C  
Since (4) and (7) are based on the limited FRCs, the basic concept reported in [22; 23] can be straightforwardly extended to the DRM system for further enhancing the estimation performance, which uses the GRC as the pilot symbol [14]. Depending on DRM robustness mode and OFDM symbol index \(l\), GRC pattern is repeated in the frequency and time directions. Without the loss of generality, the GRCs with length \(N_{p}\) are assumed to be either uniformly or symmetrically inserted at the subcarrier positions \(\{-k_{N_{p}/2},-k_{N_{p}/2-1},\,\cdots,-k_{1},k_{1},k_{2},\,\cdots,k_{N_{p}/2}\}\). Denote \(\Lambda_{l,1}\) and \(\Lambda_{l,2}\) the temporal correlations for the first and the second half of pilots around DC, respectively.  
For the simple description, we assume that \(H_{l}(k)\approx H_{l+D_{l}}(k)\). By using the structure of the post-FFT pilot-aided estimator [22; 23], the temporal correlation between two pilot symbols is of the form  
\[\begin{split}\Lambda_{l,l}&=\sum_{m=1}^{N_{p}/2}b_{ l}((-1)^{i}k_{m})R_{l}^{*}((-1)^{i}k_{m})R_{l+D_{l}}((-1)^{i}k_{m})\\ &\approx\sum_{i=1}^{N_{p}/2}|H_{l}((-1)^{i}k_{m})|^{2}E_{X}e^{2 \pi\phi((-1)^{i}k_{m})D_{l}N_{u}/N}+\sum_{m=1}^{N_{p}/2}b_{l}((-1)^{i}k_{m}) \check{I}_{l}((-1)^{i}k_{m})\\ &\quad+\sum_{m=1}^{N_{p}/2}b_{l}((-1)^{i}k_{m})\check{Z}_{l}((-1) ^{i}k_{m}),\quad i=1,2\end{split} \tag{11}\]  
where \(D_{l}\) is the distance between the two consecutive GRCs in time direction, \(\check{I}_{l}(k)\) denotes the combined ICI contribution given by  
\[\begin{split}\check{I}_{l}(k)&=H_{l}^{*}(k)X_{l}^{* }(k)I_{l+D_{l}}(k)e^{-2\pi\phi(k)((l+D_{l})N_{u}+N_{g})/N}+I_{l+D_{l}}(k)Z_{l} ^{*}(k)\\ &\quad+H_{l+D_{l}}(k)X_{l+D_{l}}(k)I_{l}^{*}(k)e^{2\pi\phi(k)(lN_ {u}+N_{g})/N}+I_{l}^{*}(k)Z_{l+D_{l}}^{*}(k)+I_{l+D_{l}}(k)I_{l}^{*}(k)\end{split} \tag{12}\]  
and \(\check{Z}_{l}(k)\) stands for the AWGN defined as  
\[\begin{split}\check{Z}_{l}(k)&=H_{l}^{*}(k)X_{l}^{* }(k)Z_{l+D_{l}}(k)e^{-2\pi\phi(k)((l+D_{l})N_{u}+N_{g})/N}\\ &\quad+H_{l+D_{l}}(k)X_{l+D_{l}}(k)Z_{l}^{*}(k)e^{2\pi\phi(k)(lN_ {u}+N_{g})/N}+Z_{l+D_{l}}(k)Z_{l}^{*}(k)\end{split} \tag{13}\]Then, the estimate of \(\Delta_{\rm s}\) takes the expression  
\[\hat{\Delta}_{\rm s}=\frac{N}{2\pi N_{u}D_{t}}\frac{\arg\left\{\Lambda_{l,2} \right\}-\arg\left\{\Lambda_{l,1}\right\}}{k_{N_{p}/2}+k_{1}} \tag{14}\]  
In [23], it has been proved that the estimation scheme (14) is asymptotically unbiased in AWGN channel. In this case, the MSE of (14) is expressed as  
\[E\left[|\hat{\Delta}_{\rm s}-\Delta_{\rm s}|^{2}\right]=\frac{N^{2}}{\pi^{2}N _{u}^{2}(k_{N_{p}/2}+k_{1})^{2}D_{t}^{2}N_{p}}\left(\frac{1}{\gamma}+\frac{1}{ 2\gamma^{2}}\right) \tag{15}\]  
As discussed in [22; 23], the condition of small SFO and less frequency selectivity ensures the unbiasedness of (14). To overcome this drawback, the robust SFO estimation scheme to the presence of the CFO along with frequency selectivity will be suggested in the following section.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results  
This section examines the experimental evaluation results of three ML classifiers based on two quantitative metrics: (i) an F1-score, which was used to compare the performance of the three evaluated classifiers in this paper (Section 7.1), and (ii) a confusion matrix that gave an insightful representation of the reported results for each individual classifier (Section 7.2).
###### Abstract  
Accurate quantification of ecosystem water use efficiency (eWUE) over agroecosystems is crucial for managing water resources and assuring food security. Currently, the uncoupled Moderate Resolution Imaging Spectroradiometer (MODIS) product is the most widely applied dataset for simulating local, regional, and global eWUE across different plant functional types. However, it has been rarely investigated as to whether the coupled product can outperform the uncoupled product in eWUE estimations for specific C4 and C3 crop species. Here, the eWUE as well as gross primary production (GPP) and evapotranspiration (ET) from the uncoupled MODIS product and the coupled Penman-Monteith-Leuning version 2 (PMLv2) product were evaluated against the in-situ observations on eight-day and annual scales (containing 1902 eight-day and 61 annual samples) for C4 maize and C3 soybean at the five cropland sites from the FLUXNET2015 and AmeriFlux datasets. Our results show the following: (1) For GPP estimates, the PMLv2 product showed paramount improvements for C4 maize and slight improvements for C3 soybean, relative to the MODIS product. (2) For ET estimates, both products performed similarly for both crop species. (3) For eWUE estimates, the coupled PMLv2 product achieved higher-accuracy eWUE estimates than the uncoupled MODIS product at both eight-day and annual scales. Taking the result at an eight-day scale for example, compared to the MODIS product, the PMLv2 product could reduce the root mean square error (RMSE) from 2.14 g C kg\({}^{-1}\) H\({}_{2}\)O to 1.36 g C kg\({}^{-1}\) H\({}_{2}\)O and increase the coefficient of determination (R\({}^{2}\)) from 0.06 to 0.52 for C4 maize, as well as reduce the RMSE from 1.33 g C kg\({}^{-1}\) H\({}_{2}\)O to 0.89 g C kg\({}^{-1}\) H\({}_{2}\)O and increase the R\({}^{2}\) from 0.05 to 0.49 for C3 soybean. (4) Despite the outperformance of the PMLv2 product in eWUE estimations, both two products failed to differentiate C4 and C3 crop species in their model calibration and validation processes, leading to a certain degree of uncertainties in eWUE estimates. Our study not only provides an important reference for applying remote sensing products to derive reliable eWUE estimates over cropland but also indicates the future modification of the current remote sensing models for C4 and C3 crop species.  
ecosystem water use efficiency; cropland; remote sensing; PMLv2; MODIS Article
###### Abstract  
Doppler wind lidar (DWL) demonstrates significant advantages in wind field detection under clear weather conditions and has been widely applied in airports with complex wind environments. However, its detection performance is highly susceptible to weather conditions and meteorological factors. To address this issue, this study analyzes the detection efficiency of DWL based on data collected at Lhasa Gonggar Airport from August 2023 to April 2024, along with ground-based meteorological observations. The results indicate that when the detection efficiency dropped to 40%, the average detection range for the plan position indicator (PPI) mode and Doppler beam swinging (DBS) mode were 5.3 km and 2.7 km, respectively. The influence of different underlying surface types on detection efficiency was minimal, with detection efficiency at a 270\({}^{\circ}\) azimuth slightly better than at a 90\({}^{\circ}\) azimuth. A 4\({}^{\circ}\) elevation angle performed better than a 6\({}^{\circ}\) elevation angle. During the study period, the detection efficiency generally improved, with the lowest detection efficiency being observed in August, suggesting that precipitation significantly impacts performance. In August, the detection efficiency of the PPI mode dropped below 50% at 4 km, while the highest detection efficiency occurred in April, where performance remained above 50% at 7 km. This is associated with enhanced thermal and dynamic activity in the lower atmosphere. Low-cloud activity also affected the detection performance of the DBS mode. The daily variation in the detection range in April was more pronounced than in January, with the detection range generally being larger. The increase in detection range was related to the more active vertical atmospheric mixing. The PPI mode was more sensitive to changes in meteorological factors, with its median detection range being 0.2-0.6 km shorter than that of the DBS mode when the meteorological optical range (MOR) was less than 4 km. Additionally, the PPI mode showed weaker stability than the DBS mode when relative humidity was below 75%. When relative humidity exceeded 80%, both modes showed a linear decrease in detection efficiency.  
Doppler wind lidar; detection performance; plateau airport; laser technology 202415530[[https://doi.org/10.3390/atmos15121530](https://doi.org/10.3390/atmos15121530) [https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere)]([https://doi.org/10.3390/atmos15121530](https://doi.org/10.3390/atmos15121530) [https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere))
## 5 Simulation
### MATLAB Simulation
As the spreading rate increases, there is an increase in the bit error rate. When the spreading factor is changed from 7 to 12, the bit error is minimized at SF = 6. Thus, there is a loss of data packets. It is preferred to send the data with the spreading factor so that bit error and packet error rate are minimized. To conclude, it is preferred to minimize the SF value to reduce the bit rate error. To maintain minimal collision among data packets, a lower value SF is preferred. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Antenna** & **Link Budget** & **Range in** & **Range in** \\
**(Up Link)** & **Tx Gain in dB** & **dBm** & **Meter** & **Square KM** \\ \hline Up Link & 0 & 141 & 945 & 3 \\ Up Link & 1 & 142 & 1001 & 3 \\ Up Link & 2 & 143 & 1061 & 3 \\ Up Link & 3 & 144 & 1123 & 4 \\ Up Link & 4 & 145 & 1190 & 4 \\ Up Link & 5 & 146 & 1260 & 4 \\ Up Link & 10 & 151 & 1681 & 8 \\ Up Link & 15 & 156 & 2241 & 16 \\ Up Link & 25 & 166 & 3986 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Link budget in dBm from 141 to 164 dBm for the transmitter LoRa module. Figure 12: Link budget variation with range. After computing all of the essential communication parameters of LoRa, it was found that increasing SNR affects the PER, SER, and BER. At \(-\)30 dB of SNR, BER is almost 1, which is not considered. It was also observed that with a decrease in the value of SNR to 0 dB, the error rate decreases. To calculate the data rate of LoRa, the input parameters such as CR, SF and BW are included in the equation. Figure 15 presents the data rate of LoRa from SF 7 to SF 12. The data rate is denoted in terms of bits per second (bps). In each SF, the data rate increases at BW 7, then increases exponentially after BW 8, and reaches a limit at BW 10. We can observe that SF 7 has an inverse effect on the data rate, because the data rate steadily decreases from SF 7 to SF 12. In SF 7, the data rate of the LoRa reached 22,000 bps, and in SF 12, the data rate was limited to 2000 bps. An increase in SF will lead to transmission of a low amount of data; thus, SF 7 is the optimal SF that needs to be considered for sending a large amount of data. Figure 13: Link budget variation with range. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode (Up Link)**} & \multirow{2}{*}{**Frequency**} & **Node Sensitivity** & \multirow{2}{*}{**Range in Meter**} & **Range in** \\  & & **in dBm** & & **Square KM** \\ \hline Up Link & 433 & \(-\)124 & 945 & 3 \\ Up Link & 433 & \(-\)125 & 1001 & 3 \\ Up Link & 433 & \(-\)126 & 1061 & 3 \\ Up Link & 433 & \(-\)127 & 1123 & 4 \\ Up Link & 433 & \(-\)128 & 1190 & 4 \\ Up Link & 433 & \(-\)129 & 1260 & 4 \\ Up Link & 433 & \(-\)130 & 1681 & 8 \\ Up Link & 433 & \(-\)131 & 2241 & 16 \\ Up Link & 433 & \(-\)132 & 3986 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Range for the node sensitivity from \(-\)124 to \(-\)132 dBm. Figure 14: (**a**,**b**) PER, SER, and BER versus the signal to noise ratio and BER versus SF = 6. Figure 15: Data rate of LoRa from SF 7 to SF 12.