esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The baseline LS measured in all patients was 14.9 \(\pm\) 13.2 kPa, and varied from 4.1 to 68.0 kPa. It was significantly (\(p\) = 0.003) higher in treatment experienced (median 12.6 kPa; IQR 10.1-20.7), than in treatment naive patients (median 7.9 kPa; IQR 6.6-9.2). According to the METAVIR score, 15 patients (44%) were classified as F4, 7 (21%) F3, 7 (21%) F2 and 5 (15%) F0/1. All patients achieved SVR and SSR. As shown in Table 2, a statistically significant increase of albumin levels, and a decrease of ALT activity and AFP level were observed during the treatment and follow-up period. A statistically significant decrease in LS was observed between baseline and all following time points after treatment termination (Table 3). Moreover, significant differences were also noted between EOT and FU96, as well as between FU24 and FU96. As shown in Figure 1, a decrease in LS between EOT and FU96 was observed in 27 (79%) patients. In analysis based on baseline fibrosis stage, statistically significant changes in LS were seen in patients with advanced fibrosis of F3 and F4 only (Figure 2), and the LS decline was significantly bigger in cirrhosis compared to patients with less advanced disease (Figure 3). LS reduction between baseline and FU96 was significantly (\(p\) = 0.047) bigger in experienced (median 3.5 kPa; IQR 1.2-8.1), compared to treatment naive patients (median 0.8 kPa; IQR 0.2-3). The decrease in LS was also significantly (\(p\) = 0.0006) bigger in patients with ALT exceeding 100 IU/mL (9.27 \(\pm\) 9.01 kPa) than in those with lower ALT activities (1.42 \(\pm\) 2.06 kPa). There was also  
Figure 4: Correlation (Spearman’s rank correlation coefficient) between ALT activity at baseline and changes in LS between baseline and FU96. ALT—alanine aminotransferase; LS—liver stiffness; kPa—kilopascal.  
Figure 3: Change in LS after 96 weeks in patients with fibrosis stage F4 and below F4 measured at baseline (unpaired \(t\)-test). F0/1–F4: METAVIR scores; LS—liver stiffness; kPa—kilopascal; data presented as mean; whiskers indicate standard deviations.  
Figure 2: Median values of sequential LS measurement according to baseline fibrosis stage. EOT—end of treatment; FU24—follow-up visit at week 24 after treatment; FU96—follow-up visit at week 96 after treatment; F0/1—F4: METAVIR scores; LS—liver stiffness; kPa—kilopascal; the upper and bottom side of each box represent 25 th and 75 th percentile (interquartile range); the line passing through the field indicates the median; whiskers indicate minimum and maximum. Statistical method: analysis of variance ANOVA.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 4 Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving Tag Identification
### Multi-Round Admission Control Algorithm
When \(n>N\), the number of tags that can be successfully recognized by the reader is \(m\), \(m<N\). Since more tags will worsen the collision situation, the actual number of recognized tags is smaller than the maximum value that a reader can recognize in a normal situation. In different positions of the reading range, the tag lost possibility varies. The first entering tag in a reading round will move for \(l_{1}=L-n\cdot d\) distance to leave the reading  
Figure 4: Recognition process in a single round. Figure 3: Multi-round tag recognition for conveyor belt systems. range. During this time, the average number of tags that can be identified by the reader is given by  
\[S_{1}=\frac{l_{1}}{T_{ave}\cdot r_{t}}=\frac{L-n\cdot d}{T_{ave}\cdot r_{t}}. \tag{3}\]  
Since all tags will move into this position and experience a similar identification process, for a tag in the first position, its lost probability is given by  
\[p_{1}=\frac{n-S_{1}}{n} \tag{4}\]  
Therefore, the average number of lost tags in the first position is \(n_{\mathit{lost}}^{1}=1\cdot p_{1}\). Similarly, in the \(i\)th position of the tag line, the tag will move for \(l_{i}=L-n\cdot d+(i-1)d\) distance to leave the reading range. Then, the average number of tags identified during this time is  
\[S_{i}=\frac{l_{i}}{T_{ave}\cdot r_{t}}=\frac{L-(n-i+1)\cdot d}{T_{ave}\cdot r_{ t}}. \tag{5}\]  
Then, when a tag is in the \(i\)th position, the probability of being unrecognized after it moves out of the reader's reading range is given by  
\[p_{i}=\frac{n-(i-1)+\left[(i-1)-\sum\limits_{j=1}^{i-1}n_{\mathit{lost}}^{j} \right]-S_{i}}{n-(i-1)}=\frac{n-\sum\limits_{j=1}^{i-1}n_{\mathit{lost}}^{j}- S_{i}}{n-(i-1)} \tag{6}\]  
Note that \(\mathcal{A}=(i-1)-\sum\limits_{j=1}^{i-1}n_{\mathit{lost}}^{j}\) is the number of lost tags prior to the \(i\)th position. The number of tags that will participate in the identification process of the \(i\)th position can be calculated by \(n-(i-1)-\mathcal{A}\). The average number of lost tags in the \(i\)th position is \(n_{\mathit{lost}}^{i}=1\cdot p_{i}\). Then, the tag lost ratio is  
\[\alpha=\frac{\sum\limits_{i=1}^{n-m}n_{\mathit{lost}}^{i}}{n}. \tag{7}\]  
Substituting (5) and (6) into (7), the tag lost ratio of a single reading round is obtained. By solving (7), the number of tags participating in each round is determined to meet the system requirement on the tag lost ratio. With MRAC, the reader waits for a short time to allow enough tags to enter the reading range and starts the identification process in each subsequent round.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Data Aggregations
#### 4.3.2 Aggregator Compression Matrix  
After the data \(\mathrm{D}_{n}\)\(\epsilon\)\(\mathbb{R}\)\((\mathrm{M}\times 1)\) are specified by sparse basis \(\delta_{n}\)\(\epsilon\)\(\mathbb{R}\)\((\mathrm{M}\ast\mathrm{M})\), it is now compressed as given in Equation (3). The use of this observation matrix is to reduce the dimension of data collected at the aggregator and also reduce the loss of information. The flow of aggregation is depicted in Algorithm 2.  
\[\mathrm{D}_{n}\ \epsilon\ \mathbb{R}\ (\mathrm{M}\times 1)\rightarrow\ \mathrm{CD}_{n}\ \epsilon\ \mathbb{R}\ (\mathrm{N}\times 1)\]  
\[\mathrm{CD}_{n}=\mathrm{\phi}_{n}\ \times\ \mathrm{D}_{n}\]\[\text{CD}_{\text{n}}=\text{I}_{\text{n}}\times\delta_{\text{n}}\times\phi_{ \text{n}} \tag{3}\]  
**Input**: Dn--data from environmental sensor  
**Output**: Compressed data CDn  
1. Compute sparse matrix for sensor data collected  
Dn = In \(\times\delta_{\text{n}}\) Where \(\delta_{\text{n}}=\text{I}_{\text{n}}\text{T}\times\text{D}_{\text{n}}\)  
2. Compute aggregator compression matrix  
CDn = In \(\times\delta_{\text{n}}\times\phi_{\text{n}}\)  
3. **Return** Aggregator compressed data CDn  
**Algorithm 2** Sensor data aggregation.
## 4 Discussion
### Comparison of Biometric and Eddy Covariance NPP Estimate
All of these facts do not point unanimously toward conclusion that there should be a significant trend in \(R_{h}\):\(R_{ECO}\) during the study period. Therefore, in the absence of sufficient evidence, we considered that the use of the constant \(R_{h}\):\(R_{ECO}\) ratio is the only justifiable approach. Nevertheless, further research at Jastrebarsko site is needed on the partitioning of the ecosystem respiration into autotrophic and heterotrophic in order to improve the estimates of \(NPP\) from flux measurements, which could also be beneficial for the \(NPP\) estimates from remote sensing [100, 101, 102]. Before comparing the seasonal dynamic of \(NPP\) from two independent estimates (Figure 12), it is important to recall the processes which drive each estimate. EC \(NPP\) estimate is primarily driven by canopy photosynthesis [4], therefore it reflects current accumulation of atmospheric C that can further be partitioned into structural growth and labile C storage [23]. On the other hand, biometric \(NPP\) estimate represents biomass growth that uses carbohydrates from current assimilation as well as previously stored NSC [103]. NSC is used in spring for the growth of leaves, roots and even wood in deciduous broadleaf trees [104]. e.g., [PERSON] et al. [105] reported that mean quantities of NSC, mobilized between October of the previous year and June of the current year, reached 2.8% and 1.2% of total carbon biomass in sampled, approx. 40-year-old, trees of _Quercus petrea_ and _Fagus sylvatica_, respectively. Figure 12 reveals clear difference in seasonal \(NPP\) dynamic from two independent estimates. In the spring, \(NPP_{BM}\) starts before \(NPP_{EC}\) on the account of C reserves stored in previous years [78, 106]. Later in the vegetation season, stem growth slows down and even ceases, but the forest ecosystem continued to absorb carbon, most likely in the NSC pool [6, 19, 94]. NSC represents very important storage/reserve pool of carbon with distinct seasonal pattern [23]. To account for seasonal changes in this carbon pool we used a simple modelling approach (see Section 2.5.2) aimed at addressing the replenishing of NSC pool in late summer and autumn. \(NPP\) estimated in this way greatly improved the agreement of \(NPP_{BM}\) with \(NPP_{EC}\) (Figure 12). We also note that the \(NPP_{LF}\), with its average contribution of 29.1% to the total \(NPP_{BM}\), is close to findings of [PERSON] et al. [22] which reported that >25% of net annual photosynthetic C assimilation occurred after growth had stopped in the autumn. Nevertheless, we are aware of the limitations of our approach due to the complexity of the NSC and wood formation dynamics [69, 78, 107]. Further development of used model is highly needed as the understanding and modelling of seasonal carbon allocation to the reserve pool is still being investigated [67, 68, 16].
## 5 Results
### Time Complexity Analysis with Different Approaches  
In this section, we compare the time complexity of our proposed variants 3 DCNN(MBSTRU), 3 DCNN(MBSTAL), and 3 DCNN(MBSTALRU) with previous baseline methods. In Table 6, we present the training and testing times. We utilized the Nvidia 2080 12 GB to process all the approaches. Our proposed method, 3 DCNN(MBSTALRU), achieved good results. We only compared the method with ResNet, STDN, and MGSTC. We noticed that the STDN and ResNet training and testing times are the longest. The reason for the longest training time is that STDN utilizes the local CNN and local CNN only predicts the center of the values. The second reason is STDN uses the sliding window across the whole city. In the sliding window train, the entire city then predicts every region of the city. For example, in the NYCBike dataset, it needs to repeat 8\(\times\)16 times traffic values to predict the entire city for the NYCBike dataset. If we consider the BJTaxi dataset, our proposed method, 3 DCNN(MBSTALRU), performs very well compared to ResNet. The main reason is that ResNet uses only two convolutional layers and 12 residual units. In our approach, we use two 3D CNN layers and 8 residual units. The approach MGSTC uses the multiple gated mechanisms, time delay occupied between input and output. Our proposed variant training and testing times are less, and we adopt 3D CNN with spatial-temporal convolutional branches, feature embedding, and external branches. Our proposed method achieves the best results and less training and testing time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**BJTaxi**} & \multicolumn{2}{c}{**NYCBike**} \\  & **Training Time (M)** & **Testing Time (S)** & **Training Time (M)** & **Testing Time (S)** \\ \hline STDN & 6327 & 207.4 & 317 & 89.9 \\ ST-ResNet & 84 & 1.92 & 3 & 0.75 \\ MGSTC & 64 & 1.45 & 3 & 0.19 \\
3 DCNN(MBSTRU) & 55 & 1.25 & 3 & 0.17 \\
3 DCNN(MBSTAL) & 53 & 1.14 & 2 & 0.13 \\
3 DCNN(MBSTALRU) & 60 & 1.21 & 2 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time complexity comparison of different methods.
## 2 Materials and Methods
### Deep Water Body Segmentation Network
#### 2.3.1 Learnable Kalman Filter Module
And calculate the observation prediction error covariance \(S_{k}\) to predict the comprehensive uncertainty of the error in the observation space, as shown in Equation (2). \[S_{k}=H_{k}P_{k|k-1}H_{k}^{T}+R_{k} \tag{2}\]  
Figure 4: Recursive spatial estimation pipeline based on the learnable Kalman filter. Based on \(S_{k}\), the Kalman gain \(K_{k}\) measures the balance weight between the predicted state and the observed value and adjusts the contribution of the prediction error to the state update through the inverse matrix \(S_{k}^{-1}\), as shown in Equation (3). \[K_{k}=P_{k|k-1}H_{k}^{T}S_{k}^{-1} \tag{3}\]  
By using the Kalman gain, the predicted state is corrected to obtain the updated state estimation:  
\[\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_{k}\left(z_{k}-H_{k}\hat{x}_{k|k-1}\right) \tag{4}\]  
where \(z_{k}\) represents the observation value at the current position, that is, the input feature map. \(z_{k}-H_{k}\hat{x}_{k|k-1}\) is the observation residual. \(\hat{x}_{k|k}\) represents the updated state estimation, which is a more accurate output obtained on the basis of correcting the predicted state. At the same time, the posterior estimation error covariance matrix is updated to \(P_{k|k}\), as shown in Equation (5), which is used to measure the precision of the estimated values. The update process fully takes into account the deviation between the observed data and the predicted values, effectively improving the accuracy of the state estimation. \[P_{k|k}=(I-K_{k}H_{k})P_{k|k-1} \tag{5}\]  
This recursive optimization mechanism enables the LKF module to gradually receive the inputs of adjacent pixels in spatial order and learn the dynamic patterns of their state transitions. In areas with interference such as vegetation occlusion, shadow coverage, or water body reflections, the model can, based on the predicted state of the previous spatial position, combine the current observation information with the surrounding context to adaptively infer the true state of the current pixel and maintain the continuity of the regional structure, thus effectively repairing the disturbed features. Different from traditional filtering methods that rely on prior noise statistical parameters, the LKF module conducts learnable modeling of the state transition matrix and the covariance matrix through end-to-end training, realizing a dynamic, data-driven adaptive optimization mechanism. In addition, the LKF module has a lightweight structure with a small number of parameters. It only depends on basic matrix operations and has good computational efficiency. It significantly improves the model's ability to suppress spatial noise and enhances the stability and consistency of feature representation in complex remote sensing environments.
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## 4 Discussion
When proceeding with the general K-means clustering, when two clusters are closely located or when cluster shape differs from the spherical shape, it fails to accurately distinguish the cluster boundaries [42]. Therefore, there needs further refinement of the K-means clustering results for good clustering performance. In this research, we have shown that a deep learning-based method can successfully improve the limited spike sorting results from K-means clustering. As shown in Table 1, although our model compared to other methods shows superior classification performance, it still shows an unsatisfactory accuracy of 71% on Difficult 2 with a noise level of 0.2. This can be explained by the structural similarity of the waveforms and high noise levels as seen in Figure 6A,B. Not only does this make the clustering process more difficult, it also makes template  
Figure 5: The accuracy of the proposed model depending on amount of training data (**A**) PCA feature plot of 5 cluster data with the case of two closely located clusters. The red dots represent the cluster centers calculated by K-means. (**B**) Change in accuracy depending on the amount of training data. extracting process challenging, which is where our model struggled most on. As shown in Figure 6C, template extraction method through PCA + K-means method fails even with human inspection. However, when given 10% of the data with labels as the training data, our MLP model shows near perfect classification accuracy even on Difficult 2 with noise level of 0.2 with and without overlapping spikes with score of 99.55% and 99.70%, respectively. This shows that if clustering techniques more advanced than PCA + K-means is utilized and the accuracy of training data increase, classification performance of our model can be expected to rise. For further work, we plan to modify the model and perform spike sorting on extracellular recordings using MEAs where the spatial information is utilized to further increase classification performance. The model performance will also be tested on other dimensionality reduction techniques as utilizing different domains can increase performance of models [43]. Moreover, different clustering algorithms, such as density-based clustering algorithms, will be implemented to further improve classification accuracy and make the procedure fully automatic.
# Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning
## 3 Problem Formulation
### Inequality Constraints  
Position constraint: The motion range in the workspace is physically limited by maximum position (\(p_{\max}\)) and minimum position (\(p_{\min}\)). Thus, given a single agent '\(i\)' at time step \(k\) (\(>\)1), we can write the position constraints as follows:  
\[p_{\min}\leq p_{i}^{t}\leq p_{\max} \tag{25}\]  
Velocity constraint: To ensure safe flight in a confined space and dynamic feasibility, the velocity of the UAVs must be limited by minimum velocity (\(v_{\min}\)) and maximum velocity (\(v_{\max}\)). Therefore, for a single agent '\(i\)' at time step \(k\) (\(>\)1), we can express velocity constraints as follows:  
\[v_{\min}\leq v_{i}^{t}\leq v_{\max} \tag{26}\]  
Acceleration constraint: To ensure a smooth and dynamically feasible trajectory, it is necessary to limit the acceleration of all the vehicles at all times within the range defined by minimum acceleration (\(a_{\min}\)) and maximum acceleration (\(a_{\max}\)). Thus, for a specific vehicle '\(i\)' at time step \(k\) (\(>\)1), the acceleration constraints can be expressed as follows:  
\[a_{\min}\leq a_{i}^{t}\leq a_{\max} \tag{27}\]  
Jerk constraint: Limiting a specific vehicle's jerk (\(j_{i}\)) is essential. Jerk can be defined as the first derivative of the acceleration to maintain smooth acceleration in the quadcopter [2; 3]. Jerk must be bounded for smooth and feasible trajectories. For simplicity, the jerk can be constrained in each axis individually.  
\[j_{\min}\leq j_{i}^{t}\leq j_{\max} \tag{28}\]The equation below expresses the jerk of any agent at any time \(j_{i}^{t}\) in discrete time.  
\[j_{i}^{t}=\frac{a_{i}^{t}-a_{i}^{t-1}}{\Delta t} \tag{29}\]  
By combining all these constraints, we derive the following inequality:  
\[A_{feasibility}X\leq b_{feasibility} \tag{30}\]  
where \(A_{feasibility}\in\mathbb{R}^{6N(2T+1)\times 3N(T+1)}\) and \(b_{feasibility}\in\mathbb{R}^{6N(2T+1)}\).  
The inequality constraints established so far constitute a convex feasible set. However, the collision avoidance constraint is formulated as a non-convex constraint. We employed the SCP algorithm to obtain an affine approximation of this constraint. Subsequently, we constructed the total inequality matrices for the true optimization variable \(\mathbf{X}\).
# New Step Size Control Algorithm for Semi-Implicit Composition ODE Solvers
## 4 Conclusions  
The reported study is dedicated to variable step size control algorithms for composition integration methods. The general form of composition methods was considered, and the problem of constructing an efficient local error estimator was outlined. Four algorithms for automatic step size control were compared, including a novel technique that combines a semi-explicit modification of the midpoint method with two adjoint D methods in a composition scheme. This allows the construction of an efficient local error estimator. Performance tests on nonlinear systems including the Rossler system, the Van der Pol system, and the two-body problem have shown that the new approach has the greatest CPU time to truncation error ratio than other known error estimation techniques for composition schemes described in the literature. The investigated techniques included using one composition scheme for different methods, different composition schemes for similar methods, and (as recently reported) Blanes' embedded local error estimator. The experimental part of the study confirmed that the proposed step size control algorithm for semi-implicit composition ODE solvers outperforms the known solutions. The obtained results can be used to develop simulation software based on composition ODE solvers, with increased performance. Our further studies will be dedicated to developing a general theory of embedded composition methods based on the proposed method and finding other efficient and versatile local error estimation algorithms.  
Conceptualization, D.B. and A.K.; data curation, D.P.; formal analysis, D.P.; funding acquisition, D.B.; investigation, P.F. and D.P.; methodology, D.B. and A.K.; project administration, D.B. and A.K.; software, D.B., P.F. and A.K.; supervision, D.B.; validation, D.P. and D.B.; visualization, P.F.; writing--original draft, D.B. and P.F.; writing--review and editing, D.P. and A.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the grant of the Russian Science Foundation (RSF), project 22-19-00573.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.