esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Patterns of Land Use Land Cover
The visual spatial change distribution indicates that the gains in agricultural land of 11.7% (Table 4) occurred mainly in eastern Dedza as well as central Dedza around Chongoni Forest Reserve \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dedza** & **2001** & \multicolumn{3}{c}{**2009**} & \multicolumn{3}{c}{**2019**} \\ \hline
**Land Use** & **Ha** & **Percent** & **Ha** & **Percent** & **Ha** & **Percent** \\ \hline Agriculture & 186,776 & 48.0 & 203,915 & 52.4 & 232,291 & 59.7 \\ \hline Built-up & 43,726.5 & 11.2 & 30,916 & 7.9 & 19,575 & 5 \\ \hline Forest-mixed & 95,061 & 24.4 & 97,972 & 25.2 & 67,621 & 17.4 \\ \hline Barren & 18,232 & 4.7 & 21,200 & 5.4 & 37,492 & 9.6 \\ \hline Wetland & 44,377 & 11.4 & 34,243 & 8.8 & 31,136 & 8.0 \\ \hline Water & 1025 & 0.3 & 950 & 0.2 & 1082 & 0.3 \\ \hline Total Area & 389,197 & 100 & 389,196 & 100 & 389,197 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Land cover distribution and statistics summary for Dedza district (Ha = Hectares). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ntcheu** & **2001** & \multicolumn{3}{c}{**2009**} & \multicolumn{3}{c}{**2019**} \\ \hline
**Land Use** & **Ha** & **\%** & **Ha** & **\%** & **Ha** & **\%** \\ \hline Agriculture & 124,479 & 36.8 & 130,697 & 38.7 & 120,057 & 35.5 \\ \hline Built-up & 42,694 & 12.6 & 35,320 & 10.5 & 31,280 & 9.3 \\ \hline Barren & 27,520 & 8.1 & 32,062 & 9.5 & 35,811 & 10.6 \\ \hline Other & 21,440 & 6.3 & 21,281 & 6.3 & 10,573 & 3.1 \\ \hline Wetland & 51,435 & 15.2 & 41,161 & 12.2 & 23,832 & 7.1 \\ \hline Forest-mixed & 41,311 & 12.2 & 26,262 & 7.8 & 34,993 & 10.4 \\ \hline Shrub- & & & & & \\ woodland & 29,012 & 8.6 & 51,110 & 15.1 & 81,346 & 24.1 \\ \hline Total Area & 337,891 & 100 & 337,893 & 100 & 337,892 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Land-cover distribution and statistics summary in Ntcheu district (Ha = Hectares). and river valleys. The 7.1% loss in the forest-mixed class was also largely in the same areas of the district. Gains in agricultural land cover were mainly on the western side while losses were prevalent on the eastern side of Ntcheu (Figure 7). Agricultural land is persistent on the eastern side and found mainly in the Shire River valley. Losses in forest-mixed cover occurred along a longitudinal gradient, with persistent forested lands found mainly in the western upland and plateau areas. Wetland cover had major losses across the district except in a stable area in the northeastern part. Shrub-woodland had gains across the region during this same time frame with increases of 15.5% (Table 5), suggesting significant forest degradation. Figure 6: Maps of land gains, persistence, and losses for agriculture, forest-mixed, and wetland classes in Dedza district (2001–2019 period) (black pixel colors contain no information). Figure 7: Maps of land gains, persistence and losses change classes from agriculture, forest-mixed wetland, shrub-woodland classes in Ntcheu district (2001–2019 period).
## 3 Results
### Drought Evolution and GPP Response to Drought in Yunnan  
#### 3.3.1 sc_PDSI Spatiotemporal Pattern Evolution  
According to Figure 9 and Table 2, it can be seen that this massive drought swept through Yunnan in June 2009, peaked in January 2010, gradually subsided in the second half of 2010, and largely returned to normal by March 2011. In June 2009, Western Yunnan and parts of Northeastern Yunnan experienced a slight drought. The drought in July and August from Western Yunnan and Northeastern Yunnan spread to Central Yunnan, and the drought area accounted for about 28.4% of the province. The drought in September and October spread rapidly and gradually spread to the province, and the drought accounted for 88.6%. By December, the province's drought accounted for 99%. The degree of drought at the border areas of Western Yunnan and parts of South-eastern Yunnan became serious and accounted for about 53.6%. The drought was still very severe by January 2010, with about 63% of the province having an sc_PDSI value below \(-3\), a severe drought situation, except for the three river basins in Northwest Yunnan and near Yuanjiang in Southern Yunnan. In April 2010, the drought started to ease from Northwest Yunnan. In October 2010, except for the drought in Southern Yunnan and some parts of Northeast Yunnan, the drought situation in the rest of the region improved, and the severe drought area dropped to 31.72%. By March 2011, Yunnan Province basically returned to normal.  
Figure 8: Spatial distribution of GPPSIF data and the other two products data in 2009. (**a**) GPPSIF; (**b**) GPPSIF; (**c**) GPPSIF. (The numbers below the title are the range of values for this raster plot).  
Figure 9: Spatial distribution of monthly average values of sc_PDSI from 2009 to 2011.  
From the drought recovery, Western and Northwestern Yunnan recovered earlier, basically returning to normal at the end of 2010, with a drought duration of about 16-20 months. Eastern and Central Yunnan recovered later, returning to mild and moderate drought, and drought continued by June 2011, with a duration of about 22-26 months.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 4 Conclusions  
In summary, this study proposes an innovative Dual-View Knowledge Transfer (DVKT) framework to tackle the challenges of efficiency and dataset scarcity in RSID. The framework includes two innovative modules: Intra-layer Knowledge Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT), which can successfully distill hierarchical features and cross-layer correlations from a pre-trained teacher network to a lightweight student network. Specifically, the Intra-KT module can correct the learning bias of the student network by the guidance of representative features from a well-trained teacher network. The Inter-KT module can extract compact and effective features for RSID by distilling and transferring knowledge about cross-layer correlations. Comprehensive evaluations confirm that the proposed DVKT framework successfully compresses RSID models, yielding dual enhancements in both dehazing performance and efficiency.  
While the proposed DVKT framework exhibits promising results, it currently relies on a pre-trained teacher model trained on natural-image-dehazing datasets. This may introduce a domain gap between the teacher and student networks, limiting the optimal performance for remote-sensing images. Future work could explore domain adaptation techniques to better bridge this gap.  
Conceptualization, L.Y. and J.C.; methodology, L.Y.; software, H.B.; validation, R.Q.; formal analysis, H.G.; investigation, L.Y.; resources, H.B.; data curation, R.Q.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y.; visualization, H.G.; supervision, J.C. and H.N.; project administration, J.C.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China under Grant 62201452.  
Not applicable.  
No new data were created or analyzed in this study.  
We would also like to express our gratitude to the anonymous reviewers and the editors for their valuable advice and assistance.  
The authors declare no conflicts of interest.
## 4 Experiments
### Comparison with Other Methods
#### 4.3.3 Lab Color-Space Analysis  
From the perspective of the Lab color space, as shown in Figure 11, the a-channel represents the red-green component, while the b-channel represents the yellow-blue component. In the a-channel, the original hazy image appears relatively smooth, with much of the red-green information obscured by the haze. In contrast, the dehazed image exhibits significantly enhanced a-channel details, with clearer building edges and richer red-green contrast. Notably, in the region marked by the red box, the dehazed a-channel reveals more distinct details compared to the hazy image and closely resembles the ground truth, indicating that the model effectively restores red-green color information. The changes in the b-channel further support this observation. Overall, our method's impact in the Lab color space is primarily reflected in restoring brightness distribution, enhancing contrast, and recovering color information, making the dehazed image visually clearer and more natural, bringing it closer to the real scene.  
Figure 11: Visualization of dehazed image and corresponding ground-truth image in the Lab color space.
## 3 Description of System Performance Indicators
### Performance Metrics for Complete Systems  
In the domain of wireless communication systems, system reliability and outage probability are pivotal performance metrics that have a substantial impact on communication quality. System reliability ensures that the system can maintain a certain level of performance consistently over a prolonged duration, encompassing correct information transmission, stable connection maintenance, and uninterrupted operation. Outage probability measures the probability that the system can sustain the desired communication quality during operation. It can be seen that system reliability and outage probability play a crucial role in determining the overall performance of a wireless communication system, and we will introduce them next.
## 3 Results and Discussion
### Analytical Performance of Water-Based Microwave-Assisted Digestion Method  
Under the optimum conditions, analytical figures of merit for the proposed WB-MAD method were investigated. The studied parameters include sensitivity, correlation coefficient (R\({}^{2}\)), limit of detection (LOD), Equation (2), limit of quantification (LOQ), Equation (3), method detection limit (MDL), Equation (4), method quantification limit (MQL), Equation (5), precision and accuracy. The LOD is the lowest signal that can be detected using the instrument, the LOQ is the lowest concentration that can be quantified using the instrument or using MQL and the MDL is the minimum measured concentration [37]. These analytical figures of merit are extremely important to determine whether the newly developed analytical method is more efficient than the ones used in previous studies [1]. Firstly, the calibration curve was obtained by analyzing a series of seven standard solutions prepared through WB-MAD followed by F-ISE analysis.  
Figure 5 shows a linear least-squares fit plot of E_meas (mV) versus log [F\({}^{-}\)], and the slope was found to be 56.397 mV. Therefore, the results suggest that the sensitivity for F\({}^{-}\) was 56.397 mV. It is pertinent to note that for an ideal F-ISE, the cell potential is linearly related to the logarithm of the fluoride ion concentration ([F\({}^{-}\)]) and should increase by 59.16 mV for every 10-fold decrease in the [F\({}^{-}\)] [38; 39]. In the present study, an increase of 59 mV in the cell potential was observed, and this is acceptable [38]. The other figures of merit were determined by using the standard deviation (SD) of 20 blank samples and their responses for F\({}^{-}\). The average of the twenty measurements was 0.0668 \(\upmu\)g/kg and the standard deviation (SD) was 0.0104 mV. By comparing the obtained slope (56.397 mV) with the theoretical (57.93 mV) at 19 \({}^{\circ}\)C, it can be clearly seen that there is a deviation of around 2%, which is acceptable [39]. Furthermore, the observed analytical figures of merit  
Figure 4: Influence of digestion time on fluoride determination in toothpaste samples using F-ISE after WB-MAD. Digestion conditions: sample amount, 0.05 g; digestion temperature, 180 \({}^{\circ}\)C; water volume, 10 mL and \(n=3\).  
presented in Table 2 were comparable with the literature reports, but with better accuracy and precision [39,40].  
\[\textbf{Limit of detection }(\textbf{ LOD})=\frac{3*\textrm{SD}}{\textrm{Method calibration slope}} \tag{1}\]  
\[\textbf{Limit of quantification }(\textbf{LOQ})=\frac{10*\textrm{SD}}{\textrm{Method calibration slope}} \tag{2}\]  
\[\textbf{Method detection limit }(\textbf{MDL})=\frac{\textrm{LOD }*\textrm{Final volume}}{\textrm{Optimum mass}} \tag{3}\]  
\[\textbf{Method quantification limit }(\textbf{MQL})=\frac{\textrm{LOD }*\textrm{Final volume}}{\textrm{Optimum mass}} \tag{4}\]
## 1 Introduction
### Oxygen Carriers as Bed Material in FBC
Essentially, commercial deployment of OCAC contributes to creating experience with and acceptance for using oxygen carriers in combustion applications. The development from FBC to OCAC and finally to CLC is illustrated schematically in Figure 2. The figure shows that the development from FBC to OCAC requires few changes and results in better oxygen availability throughout the boiler and takes advantage of oxygen carrier--fuel interactions. Further development to CLC requires more significant changes in the equipment and relies solely on oxygen carrier--fuel interactions. Even though applying OCAC can lead to immediate improvements in plant performance, it has yet to become a widespread technology. To date, OCAC is only used commercially in a few boilers in Sweden. One reason is that the oxygen carriers currently available are more expensive than conventional bed material. However, Sweden and also neighboring countries Finland and Norway have large metallurgical industries that handle huge amounts of ones and industrial by-products, many of which contain metal oxides (mainly iron oxide), potentially making them feasible as oxygen carriers [32]. Some of these products are inherently produced as particles and have properties that make them suitable to use more or less directly as bed materials. Cost aspects of oxygen carriers will be thoroughly discussed in Section 4.1, where it's made clear that there are significant costs associated with bed material preparation. Concluding this introductory section: utilizing oxygen carriers in biomass combustion has been proven to reduce practical operational problems in the boiler and increase efficiency. It has also been shown that the concept can be implemented with no or little adaptation of the available equipment. However, costs and other practical issues to some extent hinder large-scale implementation.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
###### Abstract  
A simple data-aided carrier synchronization scheme is proposed for variable modulation (VM) communication systems under the initial conditions of a low signal-to-noise ratio (SNR) and normalized carrier frequency offset (CFO) symbol rate of 20%. The proposed carrier synchronization scheme is simplified into two steps; a reconfigurable L&R (RLR) algorithm and pilot-aided (PA) phase linear interpolation algorithm is applied for carrier frequency recovery (CFR) and carrier phase recovery (CPR), respectively. Furthermore, the autocorrelation values of multi-pilot blocks are superimposed to improve the accuracy of the CFR algorithm, and the algorithm formulas are decomposed and modularized to simplify the implementation complexity of the RLR algorithm. Simulation results show that the RLR algorithm can track and lock the CFO up to a 33.2% symbol rate and reduce the CFO to 0.024%. The bit error rate (BER) performance of the carrier synchronization scheme almost coincides with the theoretical curve results. Comparison of hardware complexity shows that the multiplication resource consumption can be reduced by at least 72.47%.  
variable modulation systems; carrier synchronization; RLR; pilot-aided phase linear interpolation algorithm +
Footnote †: journal: Electronics
## 2 Problem Description
### Extended Logistic Problem  
The second model we are considering has been named an extended logistic problem (ELP). The model additionally takes into account journey times and associated delays. The following additional constants are used in the ELP model:  
Constants:  
\[\lambda(a)\ \text{ travel time on arc }a\in\mathcal{A};\]  
\[\xi(s,v,f)\ \text{ cost for stream }s\in\mathcal{S}\text{ being subjected to logistic function }f\in\mathcal{F}\text{ at vertex }v\in\mathcal{V};\]  
\[\lambda(s,v,f)\ \text{ time needed for stream }s\in\mathcal{S}\text{ to be subjected to logistic function }f\in\mathcal{F}\text{ at vertex }v\in\mathcal{V};\]  
\[g(s)\ \text{ stream that is gathered together with stream }s\text{, i.e., both streams have to travel together before being subjected to logistic function }EDN;g(s)\in\mathcal{S},\ \text{if }s\text{ is independent, then }g(s)=s;\]  
\[h(s,d)\ \text{ time available to stream }s\text{ with acceptable delay }d\text{; e.g., for D+2 stream }s\text{ available at 7 a.m. that has to be delivered before 4 p.m., we have }h(s,D{3})=0,\,h(s,D{2})=17,\]\[h(s,D1)=24\text{, }h(s,D0)=16\text{.}\]  
In this model, the cost function is represented by the expression (11). In addition, two constraints are needed.  
Objective cost function:  
\[\min\sum_{t\in\mathcal{T}}\sum_{a\in\mathcal{A}}y_{ta}\xi(t,a)+\sum_{s\in \mathcal{S}}\sum_{v\in\mathcal{V}}\sum_{d\in\mathcal{D}}\sum_{f\in\mathcal{F}} w_{svdf}\xi(s,v,f) \tag{11}\]  
Constraints:  
\[x_{sadNEN}=x_{g(s)adNEN}\quad\forall s\in\mathcal{S},\ \forall a\in \mathcal{A},\ \forall d\in\mathcal{D} \tag{12}\] \[\sum_{a\in\mathcal{A}}\sum_{f\in\mathcal{F}}x_{sadf}\lambda(a)+ \sum_{v\in\mathcal{V}}\sum_{f\in\mathcal{F}}w_{svdf}\lambda(s,v,f)\leq h(s,d) \quad\forall s\in\mathcal{S},\ \forall d\in\mathcal{D}.\] (13) \[y_{ta}\in\mathbb{Z}^{+}\quad\forall t\in\mathcal{T},\ \forall a\in \mathcal{A}\] (14) \[x_{sadf}\in\mathbb{Z}^{+}\quad\forall s\in\mathcal{S},\ \forall a\in \mathcal{A},\ \forall d\in\mathcal{D},\ \forall f\in\mathcal{F}\] (15) \[w_{svdf}\in\mathbb{Z}^{+}\quad\forall s\in\mathcal{S},\ \forall v\in \mathcal{V},\ \forall d\in\mathcal{D},\ \forall f\in\mathcal{F}. \tag{16}\]  
Objective function (11) is to minimise the total travel cost. Constraints (12) assure that streams that were gathered together share their paths before being subjected to logistic function \(EDN\). Notice that the relation resulting from (12) is transitive. Therefore, it is possible to gather more than two streams together. Finally, constraints (13) help the model to obtain solutions that are less likely to break real-world delay constraints. They sum up all incurred travel and logistic function times and assure that the sum is below the assigned threshold. Notice that these constraints assume that the throughput of each vertex is infinite; thus, streams do not have to wait before being served. Moreover, they consider weighted averages of incurred delays when streams are split. Constraints (14)-(16) describe the admissible domain of the sets of variables used.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 2 Basic Design of N-Path Notch Filter  
Figure 2a illustrates the fundamental operation of the N-path BRF, which consists of a high-pass filter connected in series with a switch. This configuration shifts the high-pass filtering characteristics to the local oscillator (LO) frequency, generating the desired notch response. The mechanism effectively extends the high-pass filter's utility to the target frequency band defined by the LO. Figure 2b shows that the N-path bandpass filter (BPF) operates with clock signals driven by a non-overlapping eight-phase clock waveform featuring a 12.5% duty cycle. The switching elements are modeled as ideal switches with a small series resistance, \(R_{SW}\), representing the on-resistance. This design assumption simplifies the analysis and highlights the impact of switching behavior on the overall filter characteristics [13,14]. Figure 2b shows that the N-path BPF and its clock waveforms are driven by a non-overlapping clock with an eight-phase 12.5% duty cycle. The switch is considered an ideal switch with a small series resistance \(R_{SW}\), which is the on-resistance.  
The input impedance of the system is defined as \(R_{S}\). When a signal is applied, it passes through the series combination of the source resistance \(R_{S}\) and the switch on-resistance \(R_{SW}\), with only one branch conducting at a time. Subsequently, the signal is connected in parallel with the eight ideal switches, as illustrated in Figure 3. Based on the derivation in other papers [15,16], the impedance equation for the N-channel band-stop filter can be obtained as:  
\[Z_{in}=2R_{SW}+R_{L}+(2R_{SW}+R_{L}+R_{a})\frac{8\gamma}{1-8\gamma}//\gamma Z _{BB}(\omega-\omega_{0}) \tag{1}\]  
where \(R_{L}\) is the load impedance, and \(Z_{BB}\) (\(\omega-\omega_{0}\)) is the frequency-dependent base-band impedance.  
The conventional N-path BRF exhibits a large impedance at the center frequency of the notch, corresponding to low block linearity, while presenting matched impedance characteristics within the passband, as illustrated in Figure 4. Under identical input power conditions, the large impedance state at the center frequency produces a significant voltage amplitude across the signal. This elevated voltage amplitude is directly applied across the source and drain terminals of the N-path filter's switch. Consequently, this affects the V\({}_{\text{gs}}\) of the MOS switch during both its on and off states, inducing nonlinearities in the filter's operation.  
Figure 3: N-path filter with switched impedance equivalent modeling.
###### Abstract  
This paper proposes an improved frequency domain turbo equalization (IFDTE) with iterative channel estimation and feedback to achieve both a good performance and low complexity in underwater acoustic communications (UWACs). A selective zero-attracting (SZA) improved proportionate normal least mean square (SZA-IPNLMS) algorithm is adopted by utilizing the sparsity of the UWAC channel to estimate it using a training sequence. Simultaneously, a set-membership (SM) SZA differential IPNLMS (SM SZA-DIPNLMS) with variable step size is adopted to estimate the channel status information (CSI) in the iterative channel estimation with soft feedback. In this way, the computational complexity for iterative channel estimation is reduced effectively with minimal performance loss. Different from traditional schemes in UWACs, an IFDTE with expectation propagation (EP) interference cancellation is adopted to estimate the a posteriori probability of transmitted symbols iteratively. A bidirectional IFDTE with the EP interference cancellation is proposed to further accelerate the convergence. THe simulation results show that the proposed channel estimation obtains 1.9 and 0.5 dB performance gains, when compared with those of the IFNLMS and the I\({}_{Q}\)-IPNLMS at a bit error rate (BER) of \(10^{-3}\). The proposed channel estimation also effectively reduces the unnecessary updating of the coefficients of the UWAC channel. Compared with traditional time-domain turbo equalization and FDTE in UWACs, the IFDTE obtains 0.5 and 1 dB gains in the environment of SPACE'08 and it obtains 0.5 and 0.4 dB gains in the environment of MACE'04 at a BER of \(10^{-3}\). Therefore, the proposed scheme obtains a good BER performance and low complexity and it is suitable for efficient use in UWACs.  
underwater acoustic communication; expectation propagation; adaptive sparse channel estimation; a posteriori soft decision; frequency domain turbo equalization 2023  
232378011390/s231878011390/023/18/01