esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Impact of Different Water Occurrences  
The overflight statistics presented in Section 4.1 change as the lake shapes vary: at a high water level, a lake will cover a larger area and will have a higher probability of being crossed by an altimeter track. In this study, occurrence masks are used to extract lake shapes for different water levels (Section 3.2). When increasing the water occurrence threshold from 50% to 95% the number of detected lakes decreases (see Figure 3) since only permanent water is counted. At the same time, the mean size of the detected lakes increases (from 6.4 to 11.4 km\({}^{2}\)) as more larger lakes have permanent flooded parts. Consequently, fewer lakes are missed by the altimetry missions when the occurrence threshold is increased. Instead, a lower occurrence threshold means that more lakes cannot be monitored since they are no longer crossed by a satellite's track. Figure 8 illustrates the percentage of missed lakes for different water occurrences for lakes larger than 0.1 km\({}^{2}\) and lakes larger than 10 km\({}^{2}\). Whereas nearly 90% of the smaller non-permanent lakes are missed by the past altimetry configuration at the 5% occurrence threshold (and 85% by the current threshold), these numbers decrease to 75% (and 63%) at a 95% occurrence threshold. When limiting the analysis to lakes larger than 10 km\({}^{2}\), the number of missed lakes is smaller for all occurrence thresholds but shows the same behavior.  
Figure 7: Lake Barkley and Kentucky, separated in the GLWD data (purple and orange) and merged in the isolated results of this study (green). Additionally, the GSW water occurrence is shown as a background. The inset shows the area close to the dams and the Barkley Canal connecting both lakes.  
The current configuration of altimeter satellites is able to monitor about 84% of these lakes when taking all occurrences into account.  
It may be worth mentioning that, with decreasing water occurrence, not only are altimetry observations less frequent but also of lower quality due to the expected increased land contamination (see Section 5.4).
## 2 Analysis of High-Speed Maglev Noise Source Distribution
### Analysis of Simulation and Experimental Results  
Figure 7 compares the time-domain curves of the pass-by noise at measuring point Y1 at different train operating speeds, the vertical axis denotes the A-weighted fast time weighted sound pressure level \(L_{A,F}\). During the passage of the Maglev train, the \(L_{A,F}\) initially exhibited a rapid increase and maintained relative stability over a brief duration, followed by a gradual decrease, with the curve demonstrating asymmetric characteristics.  
Figure 5: Schematic of external pass-by noise measurement points.  
Figure 6: Photograph of external pass-by noise measurement points.  
Figure 8 shows the equivalent continuous A-weighted SPL \(L_{Acap,T}\) variation curves in logarithmic coordinates with lateral distance at measurement points X1-X6 at different train operating speeds. The \(L_{Acap,T}\) continuously decreased with increasing lateral distance, with essentially consistent attenuation patterns across all tested speeds. A strong linear relationship was observed between the \(L_{Acap,T}\) and logarithm of the lateral distance. The fitted curves for each train operating speed exhibited coefficients of determination >0.99, with attenuation coefficients ranging from 12.3 to 14.8 dB/log\({}_{10}\)(m). The measured sound level reduction per distance doubling reached 3.7-4.5 dB(A), indicating that the external noise radiation characteristics of the Maglev train correspond to those of an incoherent line source.  
Figure 9 shows the numerically simulated three-dimensional vortex structures visualized using the velocity-colored Q-criterion around the Maglev train operating at 430 km/h. The results reveal that (1) vortex formation occurred at the high-pressure zone of the leading train owing to premature airflow separation; (2) the streamlined profiles induced flow separation at both the head and tail sections, generating distinct detached vortex structures downstream; and (3) a pair of counter-rotating wake vortices developed at the tail section, evolving into stable trailing vortex structures during downstream propagation. With increasing train operating speed, the vortex distribution demonstrated enhanced spatial complexity, exhibiting larger spanwise/vertical scales of vortex pairs, which expanded the effective noise source distribution range. Therefore, regions with  
Figure 8: SPL versus lateral distance at 1.2 m above ground at different running speeds.  
Figure 7: Time-domain curves of pass-by noise at point Y1 at different running speeds.  
intense turbulent movement were taken as strong noise sources. As shown in Figure 9, the positive-pressure area in front of the train, the streamline part of the head, the middle part of the train, the streamline part of the tail, and the negative-pressure area in the rear of the train were divided into five parts: \(l_{1}\), \(l_{2}\), \(l_{3}\), \(l_{4}\), and \(l_{5}\).
## 2 Related Studies
The Cross-Layer Active Predictive Congestion Control (CL-APCC) scheme [21] for improving the performance of networks applied Queuing theory to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. Decentralized, Predictive Congestion Control [22] (DPCC) for wireless sensor networks (WSN) consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel estimator algorithm in DPC that predicts the channel quality. Then, an adaptive flow control scheme selects suitable rate which is enforced by the newly proposed adaptive back-off interval selection scheme. Auction-based mechanism for providing efficient and localized WSN transient congestion management based on applications' priorities and the utility of sensed information was proposed in [23]. An essential component of this mechanism is a partially user-defined information utility metric that jointly represents two components. One is the objective quality of information (QoI), at which targets are tracked. The other is the subjective priority of each application defined by the user to reflect the value of knowing the precise position of a target. The combined metric is defined as the product of the tracking imprecision at the source sensor, the delay with which target updates are received, and the application's priority. This relation is clear for the tracking imprecision at the source sensor or delay of the packet carrying the measurements. Inclusion of priority in the metric enables us to differentiate between utility losses of different missions. DiffPQ [29] provide the practical adaptation and implementation of differential backlog that involves a cross layer optimization of both congestion control and MAC scheduling in real multi-hop wireless. One of the main goals of DiffPQ is to measure the performance difference between differential back-log and the other existing practical, but ad hoc, solutions. DiffPQ adapts the original solution for practical implementation using several heuristics for scheduling and routing [32].
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 3 Method  
### Framework Overview  
As shown in Figure 1, the architecture of HCMUNet is structured around four core components: the encoder, decoder, bottleneck, and SCDA module. The encoder initiates the process by partitioning the input image of size \(H\times W\times 3\) into non-overlapping patches, which are subsequently projected into a fixed embedding dimension via a patch embedding operation. These embeddings are passed through Hybrid CNN-Mamba (HCM) blocks along with patch merging layers to construct a hierarchical multi-scale representation. While the patch merging layers down-sample the spatial resolution and expand the channel dimension, the HCM blocks focus on extracting rich and discriminative features. The bottleneck module preserves both the spatial resolution and feature dimensionality, enabling effective global representation learning without altering the scale. In the decoder, HCM blocks are combined with patch expanding layers to progressively restore spatial resolution while decreasing feature dimensionality. To mitigate the loss of spatial information caused by down-sampling, the SCDA module integrates contextual features with multi-level encoder outputs, enhancing cross-scale feature interactions and long-range dependencies. A final 4\(\times\) up-sampling operation performed by the last patch expanding layer restores the original input resolution, and a subsequent linear projection produces pixel-wise segmentation predictions. Each component is described in detail in the subsequent sections. The pseudo-code of the model is detailed in Algorithm 1.  
```
1:Image \(\{x_{i},i\in N\}\), Ground truth \(\{y^{\prime}_{i},i\in N\}\), \(T\)
2:Enum \(y^{\prime\prime\prime}\), Model parameters
3:\(\vdash\)/11:viter over the training steps*/
4:for\(k=1\) to \(T\)do
5:\(x=PatchEmbedding(x_{i})\)
6:\(\vdash\)/11:traffic through the stages*/
7:for\(i=1\) to \(num\_stage\_block\)do
8:for\(j=1\) to \(num\_stage\_block\)do
9:\(x=HCMBlock(x)\)
10:endfor
11:\(x_{m}=PatchMerging(x)\)
12:\(tmp[i]=x_{m}\)
13:endfor
14:for\(s=num\_stage\) downto 1 do
15:\(x=PatchExpanding(x)\)
16:\(/\)/Concatenate encoder and decoder features*/
17:\(x=Concept(tmp[sum\_stage-s],x)\)
18:\(x=SCDA(x)\)
19:for\(t=1\) to \(num\_stage\_block\)do
20:\(x=HCMBlock(x)\)
21:endfor
22:\(/\)/Apply a final 4x expansion to the output*/
23:\(x=PatchExpanding4x(x)\)
24:\(/\)/Apply linear projection to obtain the final output*/
25:\(y^{\prime\prime\prime}=LinearProjection(x)\)
26:\(/\)/Compute the loss between predicted and ground truth*/
27:\(L\leftarrow\textit{loss}(y^{\prime\prime\prime},y^{\prime}_{i})\)
28:\(\textit{Back propagation}\)
29:Update parameters
30:endfor
```  
**Algorithm 1** Pseudo code of HCMUNet.
## 4 Efficient Support of Online Data Publishing  
In this section, we describe the proposed algorithm for efficiently anonymizing the query results on the fly in a distributed environment to support online privacy-preserving data publishing. The proposed approach in this paper is summarized as follows (Figure 5):  
1. First, given a query, the master node estimates the generalization level of each quasi-identifier attribute to satisfy the \(k\)-anonymity property over the query result datasets, and then send it to each slave node along with the user query (Section 4.1);
2. Each slave node then executes the user query, anonymizes its own query results based on the generalization information received from the master node, and sends the anonymized query results to the master node (Section 4.2);
3. Finally, the master node aggregates the anonymized query results from every slave node and returns the aggregated results to the user (Section 4.3).  
It is well known that \(k\)-anonymity algorithms are generally computationally expensive and complex, making them difficult to perform well with large amounts of data [43]. Thus, several approximation methods requiring a trade-off between data utility and computing time have been proposed [44; 45; 46; 47]. We also note that the approach proposed in this study is an approximation-based algorithm in that it trades off between data utility and computing time. We will now describe each of the above three steps in detail.
## 3 Results and Discussion  
### Pharmacokinetic and Toxicology Prediction  
The distribution, metabolism, and absorption characteristics of \(\alpha\)-mangostin and its glycine conjugates were assessed using the PreADMET server. The predicted pharmacokinetic and toxicology are summarized in Table 1.  
The predictive pharmacokinetic is represented in Figure 2. It was found that the anticipated absorption values for the Caco-2 cell were between 15 and 21 nm/s. Consequently, the permeability of each conjugate gathered for analysis was moderate [48]. The conjugates might also be well absorbed into the intestinal cells, as evidenced by the expected HIA values for each component being greater than 90%. Although \(\alpha\)-mangostin has a predicted PPB value of 96%, indicating it is a well-bound compound, all of the glycine-\(\alpha\)-mangostin conjugates had expected PPB values of less than 90%, suggesting that these compounds are weakly bound molecules. When \(\alpha\)-mangostin has a \(\text{C}_{\text{brain}}/\text{C}_{\text{blood}}\) value greater than 2.0, it signifies that the compound is absorbed more deeply into the central nervous system, indicating its ability to penetrate the blood-brain barrier. Because the results ranged from  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Molecules**} & \multicolumn{4}{c}{**Pharmacokinetics Prediction**} & \multicolumn{4}{c}{**Toxicity Prediction**} \\ \cline{2-9}  & **PCaco-2 (nm/s)** & **BBB (C\({}_{\text{brain}}/\text{C}_{\text{blood}}\))** & **HIA (\%)** & **PBB (\%)** & **Rat** & **Mice** & **Ames** \\ \hline am & 20.69 & 3.94 & 91.81 & 96.62 & + & - & - \\ Am1 Gly & 18.05 & 0.11 & 92.49 & 81.68 & - & + & - \\ Am2 Gly & 17.35 & 0.11 & 92.5 & 78.35 & - & - & - \\ Am3 Gly & 15.94 & 0.02 & 86.7 & 31.44 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predictive pharmacokinetics of \(\alpha\)-mangostin and its glycine conjugates.  
2.0 to 0.1 and were less than 0.1, the conjugates' \(\mathrm{C_{brain}}/\mathrm{C_{blood}}\) ratio was determined to have moderate to low absorption. On the rodent toxicity test (mice and rats), the \"negative\" sign designates compounds having clear indications of carcinogenic activity, while the \"positive\" sign indicates the absence of such evidence. However, the only conjugate that did not show carcinogenic activity in mice was Am1 Gly. Otherwise, on the Ames test, all of the models showed negative signs, which means none of the models induced mutagenicity toward the _Salmonella typhimurium_ population in [PERSON]'s model.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 2 Data
### Meteorological Data  
Meteorological data gathered from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasting system includes: global radiation, air temperature, precipitation, and potential reference evapotranspiration. Data are from ERA-Interim reanalysis model and are produced at 3-hourly time-step at a spatial resolution of approximately 80 km. Variables are then temporally aggregated to daily mean air temperature and cumulative precipitation and evapotranspiration values. Weather data are taken from the JRC MARSOP project repository ([PERSON] et al., 2018), where ERA-Interim is down-scaled onto a 25 km reference grid, including bias correction for all elements except precipitation. It is possible that the modelled and downscaled meteorological data may not perfectly represent local meteorology ([PERSON] et al., 2018) and thus contribute to the mismatch between modelled and measured GPP. However, this topic is not specifically addressed here also because the main aim of the study is to develop and test a methodology for GPP calculation at continental and regional scale and therefore should rely on meteorology from reanalysis, which is therefore here consider as one of the source of uncertainty.
## 5 Laboratory Tests and Real-Time Validation
### FPGA Resources and Computational Cost  
In the Simulink environment, the Xilinx System Generator library facilities hardware design, which could be synthesized and implemented in Xilinx FPGAs. Xilinx library provides various digital signal processing (DSP) blocks and supports floating- and fixed-point format to represent all numerical values in the system [38]. Proposed detector and  
Figure 19: SNR improvement by adaptive filtering; (**a**) Received symbol without filtering; (**b**) Corrected symbols after notch filtering; (**c**) Received SNR by receiver in different situation.  
mitigation design in this article are implemented based on the Xilinx system generator. This section analysis computational cost and required FPGA resources based on the generated Xilinx reports after compiling each part of project. It is useful to first look at the definition of fundamental building blocks inside a FPGA [39]:  
* LookUp table (LUT): An actual table determines what the output is for any given input and defines combinatorial logic behavior. Instead of connecting several NAND or NOR gates, a LUT can define all possible combinations.
* Block RAM (BRAM): or block memory, is a RAM that is embedded throughout the FPGA for storing data.
* Flip-flop (FF): is a circuit representing a single bit and capable of two stable states. Consider that the desired anti-interference system must be capable of detecting \(N_{f}\) individual narrowband interferences. Then, let us look at the computational cost of each main piece of design separately.
* FFT Block: The radix-2 Cooley-Tukey algorithm, for \(N\) a power of 2, can compute the same result of DFT with only the \((N/2)log2(N)\) complex multiplication and \(N\ log2(N)\) complex additions [24].
* Notch filter: Based on the lattice structure of first order notch filter [40], it contains seven complex editions, three complex multiplications, and one multiplexer for selection target. It should be mentioned that final design needs \(N_{F}\) detector and notch filter.
* Simplified Welch algorithm [16]: This block contains one RAM with FFT size \(N\), one real addition, two multiplications, and a Co-ordinate Rotation Digital Computer (CORDIC) division block [41] for gain correction. The main cost of this block is the RAM with the same size of calculated FFT. FFT and PSD estimation are the heaviest blocks in the design and contain most of the computational cost.
* Thresholder activator: Simply contains one counter, \(N_{F}+1\) comparator to fulfill the thresholding task and pulse generating, \(N_{F}+1\) logical AND, and \(N_{F}+1\) constant value.
* A detector: Each detector contains one accumulator, one counter, two real additions, a CORDIC division block, and maximum seven registers.
* To support the adaptive bandwidth notch filtering, two lookup tables that save the optimum \(r\) are required.  
Table 4 represents the required FPGA resource for each mentioned piece (for 16-bit data/phase factor) based on Xilinx DSP generator reports.
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 1 Introduction
Our method begins with the development of a noise attenuation model based on LRTA, with the total variation (TV) regularization [39] as a constraint and the weighted tensor nuclear norm minimization (WTNNM) [40]. We then apply the alternating direction method of multipliers (ADMM) to solve the model and further unfold the iterative schemes into layers, with one iteration being one layer, as a DL network. Unlike traditional LRTA methods, which require manual parameter tuning, LRTNet leverages DL to automatically optimize critical parameters such as weights and thresholds with a labeled dataset. Furthermore, LRTNet retains the underlying LRTA modeling framework, ensuring high interpretability and alignment with the physical principles of seismic data. Experimental results on both synthetic and real datasets demonstrate that LRTNet can effectively suppress random noise while preserving valuable seismic information. The remainder of this paper is organized as follows. Section 2 introduces the theoretical background and methodology of the proposed LRTNet. Section 3 presents the experimental setup and demonstrates the results on both synthetic and real-field datasets. Section 4 discusses the limitations of the proposed method and suggests potential directions for future research. Finally, Section 5 concludes this paper by summarizing the key contributions and findings.
# Study of the Constraint Pillar Test and the Application in Gob-Side Entry Retaining
## 4 Application Practice of Confined Concrete Pillar
### Ground Pressure Monitoring and Test Effect Analysis
#### 4.3.2 Ground Pressure Monitoring and Effect during Reuse of Roadway Retaining  
During the mining of the neighboring 50,107 working face, the stress of bolt, surface displacement of roadway, and stress of leading support were monitored, and the roadway reuse was in good condition, as shown in Figure 15. The stress monitoring of bolt shows that the stress of bolt begins to increase about 70 m away from the working face, and it increases when it is 20 m away from the working face, and the stress of some bolts reaches 75 kN, which is close to the yield load of bolt. The monitoring curve of roadway surface displacement is shown in Figure 16. Six groups of advanced supports are arranged in the working face, and the supporting distance is 45 m. The monitoring shows that the three groups of supports far away from the working face have no obvious leading pressure, while the three groups of supports close to the working face have the phenomenon of stress growth, and the closer to the working face, the greater the stress of the supports, with the maximum value of 37 MPa. The arrangement of advanced support is long, which damages the original anchor rod and anchor cable repeatedly, and the roof at the end sinks obviously. After entering the goat, the pillar is easily skewed and dumped to the goat side, and it is difficult to form a large triangle.  
Figure 14: Surrounding rock displacement curve during gob-side entry retaining.  
Figure 15: Reuse of gob-side entry retaining.  
The cross-sectional size of the gob-side entry retaining of the 50108 working face transportation channel is 4.7 \(\times\) 3.2 m. The maximum displacement of the top and bottom plates during reuse is 115 mm, and the maximum displacement of the two sides is 20 mm. The cross-section of the roadway during reuse is 97% of the original roadway cross-section. The surrounding rock of the transportation gateway is stable, the deformation is small, and all kinds of support are in good condition. The test shows that the confined concrete pillar roadway support has obvious technical advantages, such as large resistance, fast resistance increasing speed, high construction efficiency, good stability, certain deformation ability to adapt to roof subsidence, and low comprehensive cost, and it is suitable for the technical requirements of gob-side entry retaining roadway support in a fast-advancing working face.