esa-sceva/satcom-chunk-collection huggingface dataset:
# Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems
## 2 Establishing the Problem Model
### Basic Model and Problem Hypothesis
\begin{array}{rl}\dot{V}_{\overline{\epsilon}_{ij}}&=-\frac{\dot{i}_{i}}{l_{ i}}(2\Delta P+DP+PD)\overline{\epsilon}_{ij}^{2}-l_{i}\overline{\epsilon}_{ ij}^{2}+\big{(}\overline{\epsilon}_{ij}\big{)}^{T}\big{(}F^{T}P+PF\big{)} \overline{\epsilon}_{ij}\\ &+2l_{i}^{-\Delta}L_{i}^{-1}\overline{\epsilon}_{ij}b_{i}\Big{(}T-\frac{TH}{h^ {2}+\sigma_{\infty}^{2}}\Big{)}\end{array} . \tag{25}\]  
**Remark 3**. : _Compared to the methods described in [28; 29], the quadratic form employed in this study not only exhibits fundamental quadratic characteristics but also offers a simpler form and more favorable mathematical properties. The convex nature of quadratic functions facilitates easier analysis and the derivation of system stability and convergence properties, as well as controller design._  
**Proposition 5**. : _There exists a smooth non-negative function \(\zeta(y_{i})\) that fulfills \(\|\dot{r}_{i}(y_{i})\|\leq\zeta(y_{i})\). The dynamic gain is subjected to the flattening function whose word is \(1.6y_{i}^{2}+0.1y_{i}+0.15\) according to us._  
The compensation component and hysteresis effect can be reduced as follows by combining Lemma 1, Lemma 2, Proposition 5, and the aforementioned theorems:  
\[T-\frac{TH}{h^{2}+\sigma_{\infty}^{2}}=\frac{Th^{2}+T\sigma_{\infty}{}^{2}-TH} {h^{2}+\sigma_{\infty}{}^{2}}=\frac{T\big{(}h^{2}-H\big{)}+T\sigma_{\infty}{} ^{2}}{h^{2}+\sigma_{\infty}{}^{2}}\leq H+\frac{H\big{(}\sigma_{\infty}{}^{2}- H\big{)}}{h} \tag{26}\]  
\[-\frac{\dot{l}_{i}}{l_{i}}(2\Delta P+DP+PD)\overline{\epsilon}_{ij}^{2}\leq- \sigma_{i}\frac{\dot{l}_{i}}{l_{i}}\|\overline{\epsilon}_{ij}\|^{2} \tag{27}\]  
\[F^{T}P+PF\leq 2\|\zeta(y_{i})\|\|P\|. \tag{28}\]  
**Remark 4**. : _The primary purpose of introducing \(T\) is to compensate for the self-interference term in the traditional high-gain observer, thereby reducing the impact of interference terms and consequently decreasing observational errors._  
On the other hand, \(M=\bigg{(}H+\frac{H\big{(}\sigma_{\infty}{}^{2}-H\big{)}}{h}\bigg{)}2l_{i}^{ -\Delta}L_{i}^{-1}\overline{\epsilon}_{ij}\), where \(M\) represents a finite parameter. Finally, we can obtain the following:  
\[\dot{V}_{\overline{\epsilon}_{ij}}\leq-\bigg{(}\frac{\dot{l}_{i}}{l_{i}}\sigma _{1}+l_{i}-2\|P\|\|\zeta(y_{i})\|\bigg{)}\overline{\epsilon}_{ij}{}^{2}+M. \tag{29}\]  
**Remark 5**. : _The value of \(M\) is very small due to the influence of observer compensation, dynamic gains, and related parameters, which are consistently greater than 1. This fact corresponds with the stability proof content, ensuring the validity of the stability analysis._  
**Remark 6**. : The selection criterion for \(\sigma_{\infty}\) is to ensure that the value of this expression is minimized, ideally approaching the value of \(\sqrt{H}\). The purpose of this design is to ensure that the value of \(M\) is sufficiently small, which will be further explained in the stability analysis section.
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 5 Recursive Least-Squares Adaptive Channel Estimation
Repeat:  
```
1:\(\alpha_{i}=\frac{1}{(1+\lambda^{-1}\mathbf{u}_{i}\mathbf{F}_{i-1}\mathbf{u}_{ i}^{T})}\)
2:\(\mathbf{l}_{i}=\lambda^{-1}\mathbf{F}_{i-1}\mathbf{u}_{i}\alpha_{i}\);
3:\(e_{i}=d_{i}-\mathbf{u}_{i}\mathbf{w}_{i-1}\); % minimizing the error
4:\(\mathbf{w}_{i}=\mathbf{w}_{i-1}+\mathbf{l}_{i}e_{i}\); % update the filter value
5:\(\mathbf{P}_{i}=\lambda^{-1}\mathbf{P}_{i-1}-\lambda^{-1}\mathbf{l}_{i}\mathbf{ P}_{i-1}\); % updating the \(\mathbf{P}\) value End
```  
**Algorithm 1** Recursive Least-Squares  
Here is the derivative of the Recursive Least-Squares,  
\[\boldsymbol{\phi}_{i}=\lambda\boldsymbol{\phi}_{i-1}+\mathbf{u}_{i}\mathbf{u}_ {i}^{T}. \tag{26}\]By applying the matrix inverse formula Equation (26) can be written as:  
\[\mathbf{\phi}_{i}^{-1}=\lambda^{-1}\mathbf{\phi}_{i}^{-1}-\frac{\lambda^{-2}\mathbf{\phi}_{i- 1}^{-1}\mathbf{u}_{i}\mathbf{u}_{i}^{T}\mathbf{\phi}_{i-1}^{-1}}{1+\lambda^{-1} \mathbf{u}_{i}^{T}\mathbf{\phi}_{i-1}^{-1}\mathbf{u}_{i}}. \tag{27}\]  
Replacing \(\mathbf{P}_{i}=\mathbf{\phi}_{i}^{-1}\), the above equation can be expressed as:  
\[\mathbf{P}_{i}=\lambda^{-1}\mathbf{P}_{i-1}-\frac{\lambda^{-2}\mathbf{P}_{i-1} \mathbf{u}_{i}\mathbf{u}_{i}^{T}\mathbf{P}_{i-1}}{1+\lambda^{-1}\mathbf{u}_{i} ^{T}\mathbf{P}_{i-1}\mathbf{u}_{i}}. \tag{28}\]  
By simplifying the above equation, we get:  
\[\mathbf{P}_{i}=\lambda^{-1}\mathbf{P}_{i-1}-\lambda^{-1}\mathbf{l}_{i}\mathbf{ P}_{i-1}. \tag{29}\]  
where \(\mathbf{l}_{i}=\frac{\lambda^{-1}\mathbf{P}_{i-1}\mathbf{u}_{i}}{1+\lambda^{-1 }\mathbf{u}_{i}^{T}\mathbf{P}_{i-1}\mathbf{u}_{i}}\), after further simplification we get:  
\[\mathbf{l}_{i}=\lambda^{-1}\mathbf{P}_{i-1}\mathbf{u}_{i}\mathbf{u}_{i}, \tag{30}\]  
where \(\alpha_{i}=\frac{1}{1+\lambda^{-1}\mathbf{u}_{i}\mathbf{P}_{i-1}\mathbf{u}_{i }^{T}}\), the main equation of the weighting factor \(\mathbf{w}_{i}\) is derived as:  
\[ \begin{array}{l}\mathbf{w}_{i}=\mathbf{P}_{i}\lambda\mathbf{\phi}_{i-1}\mathbf{ w}_{i-1}+\mathbf{u}_{i}d_{i}=\mathbf{P}_{i}((\mathbf{\phi}_{i}-\mathbf{u}_{i} \mathbf{u}_{i}^{T})\mathbf{w}_{i-1}+\mathbf{u}_{i}d_{i})=\mathbf{w}_{i-1}- \mathbf{P}_{i}\mathbf{u}_{i}\mathbf{u}_{i}^{T}\mathbf{w}_{i-1}+\mathbf{P}_{i} \mathbf{u}_{i}d_{i}\\ =\mathbf{w}_{i-1}+\mathbf{P}_{i}\mathbf{u}_{i}d_{i}-\mathbf{u}_{i}^{T}\mathbf{ w}_{i-1}=\mathbf{w}_{i-1}+\mathbf{P}_{i}\mathbf{u}_{i}e_{i}=\mathbf{w}_{i-1}+ \mathbf{l}_{i}e_{i},\end{array}  \tag{31}\]  
where  
\[e_{i}=d_{i}-\mathbf{u}_{i}\mathbf{w}_{i-1}. \tag{32}\]
# A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach
## 4 Results
### Graphs  
Construction of graphs and variation calculation takes about 42 minutes for Case 1 (Mozambique, 7 timesteps) with an Intel(R) Core(TM) i7-4770 CPU @ 3.40 GHz, 3401 Mhz, 4 Core(s), 8 Logical Processor(s). It took 6h24 min for the second case. This is because of the dispersed nature of the India flood, forming a multitude of graphs (32505 graphs in Case 2 versus 2192 graphs in Case 1). Because of  
Figure 4: Comparison between maximum flood extent map and global variation map for the Mozambique flood.  
Figure 5: Comparison between maximum flood extent map and global variation map for the India flood.  
the object-based approach, an upscaling in area does not necessarily imply an increase of calculation time. However, the calculation time can be linked to the degree of dispersion. A dispersed flood will result in more entities, meaning more graphs and a higher calculation time. The Mozambique flood reaches a very large extent at the third timestep, combining previous separate entities in one big entity (one graph).  
In Figure 6, the temporal profile of one entity is depicted (an additional example can be found in the Supplementary Materials). Each node in the graph represents a (flood) water polygon, while an edge depicts a spatial overlap between the connecting nodes. In this way, a water body is described by the evolution of the number of polygons and their backscatter over the timesteps. In Figure 7 the last timestep of Figure 6 is shown in detail. Five polygons are present at this timestep (TS8) with backscatter values ranging between \(-19\) and \(-15\) dB. These polygons form the nodes of the last timestep in the graph in Figure 6. Summing the timestep variations produces the global variation, depicted in Figure 5b. The timestep variation, depicted in the upper part of Figure 6, is assigned to the polygons prior to the transition. These timestep variations are available in the Supplementary Materials.  
Figure 6: Variation (top) between consecutive timesteps (TS), temporal profile (graph in the middle) and polygon evolution (bottom) of one single entity in the Indian region of interest (Case 2). Blue circles in the graph represent the polygons on the maps in the bottom of the figure.  
Figure 7: Detail for timestep 8 of entity depicted in Figure 6 showing the 5 different polygons and their respective mean backscatter.
## 3 Experiment and Results
### Experimental Details  
The experimental hardware platform was composed by an AMD EPYC 7542 (2.90 GHz) computer with 256 GB of memory and a Geforce RTX 4090 24 GB GPU. The system operated on Ubuntu 22.04, with Visual Studio Code (version: 1.99.2) as the development software, Python 3.10 as the programming language, and PyTorch 2.3.0 as the deep learning framework. In the experiment, the training image size was set to 1024 pixels \(\times\) 1024 pixels, the dataset was divided into training and validation sets at an 8:2 ratio, the number of epochs was set to 300, the batch size was set to 8, the learning rate was 0.001, the patience was 50, the automatic mixed precision (amp) was true, the weight decay was 0.0005, and the momentum factor was 0.937.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Network Layer  
Furthermore, in the network layer, novel routing protocols contribute significantly to reduce the energy [6]. Clustering can bring an important energy saving, whether it is with a periodic variation of the cluster head. It is chosen according to a Round-Robin management policy or the definition of a threshold beyond which the node responsible for sending useful information will switch to balance the node energy consumption [17; 18]. Parameters such as residual energy, centrality, and density are to be considered in this case [19; 20].
## 2 Data Description  
Due to the unavailability of any image dataset of tobacco plants, we developed an extensive image dataset, TobSet, from the actual fields in Swabi, Khyber Pakhtunkhwa, Pakistan (34\({}^{\circ}\)09\({}^{\prime}\)07.3\({}^{\prime\prime}\) N 72\({}^{\circ}\)21\({}^{\prime}\)36.2\({}^{\prime\prime}\) E). The main objective of building this dataset is to provide real-field data for training and evaluating the performance of state-of-the-art algorithms for tobacco crop and weed detection. TobSet comprises (a) 7000 images of tobacco plants and (b) 1000 images of bare soil and weeds (that grow up in tobacco fields), with a resolution of 640 \(\times\) 480. The images are captured using a 13-megapixels color digital camera possessing a CMOS-image sensor (IMX258 Exmor RS by Sony, Japan), 28 mm focal length, 65.4\({}^{\circ}\) horizontal FOV, and 51.4\({}^{\circ}\) vertical FOV. A comprehensive dataset was built over a period of 2 months, i.e., starting from the first week of tobacco seedling transplantation from seedbeds to the time when plants gain an approximate height of 1.25 m. All images in the dataset were captured manually by human scouts in the months of June and July 2020. No artificial shading and sources of lightning were used while collecting the images. During image acquisition, the camera's height was adjusted between 1 and 1.5 m. In order to maintain diversity in the dataset, all images in TobSet are captured under several factors of variations: different growth stages, different day timings, varying lighting and weather conditions (i.e., on normal, bright sunny, and cloudy days), and visual occlusions of crop leaves by weeds, etc. The existing literature on vision-based detection of crops and weeds lacks experimental validation on hard real-world datasets such as TobSet. Some sample images from the publicly available TobSet are presented in Figure 2. After data acquisition, the main step involved in crop/weed detection is the annotation of images for ground truth data. All images in the TobSet are manually labeled with the LabelImg tool.  
Figure 2: Illustration of factors of variation in the actual tobacco fields.  
TobSet is publicly available and offers multi-faceted utilities:  
1. It comprises labelled images of tobacco plants and weeds that can be utilized by computer scientists for performance evaluation of their developed computer vision algorithms;
2. Scientists working on agricultural robotics can use it to train their robots for variable rate-spray applications, plant or weed detection, and detection of plant diseases;
3. It can also be used by agriculturists and researchers for studying various aspects of tobacco plant growth, weed management, yield enhancement, leaf diseases, and pest prevention, etc.
## 3 Methodology
### Spatial Attention Encoder  
As shown in Figure 2, the Spatial Attention Encoder is designed to process multi-scale InSAR imagery data, extract spatial patterns, and encode relationships between railway segments through a combination of cross-attention mechanisms. The encoder is composed of three primary stages: multi-scale patch embedding, channel cross-attention, and spatial cross-attention.  
In the first stage, multi-scale patch embeddings are generated from raw InSAR data using average pooling layers followed by projection and reshaping operations. This ensures that spatial information at multiple resolutions is captured effectively. Each token \(t_{i}\) represents a specific patch of the image and serves as the input to the subsequent attention mechanisms. In the second stage, channel cross-attention is applied to enhance feature interactions across different channels. Given input tokens \(\{t_{1},t_{2},\ldots,t_{n}\}\), the attention mechanism computes the relevance between tokens using queries (Q), keys (\(K\)), and values (\(V\)):  
\[\text{Attention}(Q,K,V)=\text{Softmax}\left(\frac{QK^{\top}}{\sqrt{d_{k}}} \right)V, \tag{7}\]  
where \(Q=\text{Proj}_{Q}(X)\), \(K=\text{Proj}_{K}(X)\), and \(V=\text{Proj}_{V}(X)\) are linear projections of the input features \(X\), and \(d_{k}\) is the dimensionality of the key vectors. The output from this stage undergoes layer normalization and concatenation to prepare for spatial cross-attention.  
In the third stage, spatial cross-attention is employed to model spatial relationships between patches. This mechanism captures dependencies between neighboring segments, enabling the encoder to focus on high-risk areas dynamically. For a given token \(t_{i}\), the importance of a neighboring token \(t_{j}\) is calculated as \[\alpha_{ij}=\frac{\exp\bigl{(}\phi(t_{i})^{\top}\psi(t_{j})\bigr{)}}{\sum_{k=1}^{n} \exp\bigl{(}\phi(t_{i})^{\top}\psi(t_{k})\bigr{)}}, \tag{8}\]  
where \(\phi(\cdot)\) and \(\psi(\cdot)\) are learnable transformations. The enriched spatial representation \(\mathbf{z}_{i}^{s}\) is then computed by aggregating context information:  
\[\mathbf{z}_{i}^{s}=\sum_{j=1}^{n}\alpha_{ij}t_{j}. \tag{9}\]  
Finally, the combined output of the channel and spatial cross-attention modules is passed through a layer normalization and activation function (GeLU), producing the refined spatial features. These features are critical for highlighting deformation risks and downstream prediction tasks. This encoder enables the framework to efficiently process spatial dependencies, refine features, and adaptively focus on relevant high-risk regions.
## 4 Discussion  
To our knowledge, this is the first experimental study about the outcome of HI LF-PEMFs stimulation on bone tissue. On the contrary, the effects of low-intensity PEMFs stimulation on bone have been extensively studied, even though the mechanisms of action of this procedure still remain partially unknown and the quantification of the effects on bone is difficult to evaluate. Nevertheless, the positive outcome of PEMFs on osteoblast activity and differentiation have been demonstrated in vivo on animal models and in human clinical studies [6]. Owing to this, many LF-PEMFs devices for clinical use have been put on the market in recent years. However, clinical protocols for the use of these devices in the treatment of different bone conditions are still based more on anecdotal experience than on sound experimental data. Preclinical studies on simple animal models, as in this case, might contribute to screen and identify more effective protocols to be applied in the human clinical setting. Our results showed a positive, significant anabolic effect on regenerating zebrafish fin. The effect is minimal for the area but more evident in the regenerated fin length. The application of a strong magnetic field should increase the movement of intra- and extra-cellular liquids, facilitating transmembrane molecules and ions transport, enzyme-substrates interactions, and cellular metabolism as a whole. These actions are expected to promote trophic and beneficial effects in tissues. By literature, the modulation of Wnt/\(\beta\)-catenin, ALP, osterix, osteoclastcin, and BMP signalling pathways may play a crucial role in physical stimulation of bone regeneration in zebrafish fin [20] and in other animal models [5; 6]. In addition, it has been reported that signal transduction through A2A and A3 adenosine receptors stimulate the synthesis of ECM components [21]. Since these molecules and pathways are well conserved in zebrafish, we hypothesize that PEMF-mediated fin regeneration could be stimulated troughs the same mechanisms. The rationale for analyzing only the three longest regenerated fin rays' is due to the attempt to reduce the fish-to-fish anatomic variability. Although this is a preliminary report, we can observe positive results deriving from the diamagnetic effect while interesting speculations can be made also from the technical point of view. If we do a comparison with other experimental studies on cells, our experience shows that the magnetic field values of 2.9 mT  
Figure 6: Daily measurements of regenerated fin length in untreated (CTR, 7 fish) and treated with HI LF-PEMFs (HLP, 7 fish) adult zebrafish.  
at the target, with an exposure time of 64 min/2 times each day, have allowed to reach a significative biological effect respect to other conditions that required 9 h of stimulation at 1.5 mT [22] or an exposure time of 24 h at 1.8-3 mT of Magnetic Field values [23].  
The major limitation of this study resides in the relatively small sample size. Our purpose is to enlarge it during new future studies conducted on a larger fish population.
## 4 Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{6}{c}{**First Order and Dominant Resonant Frequencies**} \\ \cline{2-7}  & \multicolumn{3}{c}{**First Order Resonant Frequency**} & \multicolumn{3}{c}{**Dominant Resonant Frequency**} \\ \cline{2-7}  & **(Hz)** & **\(\Delta\)(\#-\#1) (Hz)** & **\(\Delta\)(\#1-\#) (\%)** & **(Hz)** & **\(\Delta\)(\#-\#1) (Hz)** & **\(\Delta\)(\#1-\#) (\%)** \\ \hline \#1 & 6 & 0 & 0 & 142 & 0 & 0 \\ \hline \#2 & 5 & 1 & 16.67 & 130 & 12 & 8.45 \\ \hline \#3 & 5 & 1 & 16.67 & 118 & 24 & 16.90 \\ \hline \#4 & 5 & 1 & 16.67 & 110 & 32 & 22.53 \\ \hline \#5 & 5 & 1 & 16.67 & 107 & 35 & 24.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The first order and dominant resonant frequencies comparison for five scenarios in the dynamic flexibility amplitude of the rail. Figure 8: Wheel-rail contact force under \(-\)40, \(-\)20, 0, 20, and 40 \({}^{\circ}\)C.  
Table 5 illustrates the peak values and the dominant frequencies from the vertical acceleration of the wheelset for the five scenarios. Both the peak value and the dominant frequency changed greatly for the five scenarios in comparison to the scenario #1. Figures 11-14 illustrate the fastener's force, accelerations of the rail, slab, and viaduct, respectively. From Figures 11-14, the temperature dependent Young's modulus of the fastener rubber pad had similar influence to the acceleration responses of the rail, slab and viaduct. Taking the acceleration response of the viaduct as example, in frequency range below 30 Hz, the vertical accelerations of the viaduct for all five scenarios had little difference, which suggests that  
From the above discussions, when the temperature increased, the stiffness of the fastener rubber pad decreased more and more slowly, and all the response peaks for carriage, rail, and viaduct decreased; this is because at high temperature, the stiffness of the fastener rubber pad changes very little, this further enables the peaks and dominant frequencies of the accelerations decrease and tend to be constant as the temperature increases; the dominant frequency is mainly affected by the resonant frequency of the coupled wheel rail system.
# A Distributed Indexing Method for Timeline Similarity Query
## 5 Experimental Evaluation
To test the impact of the label types in the process of similarity query, we change the label types in DS1 from 5 to 25. If the query input timeline is same as Figure 15, that is the query input timeline's label types is fixed, the results of DTI-Tree and R-Tree are shown in Figure 16. The similarity query performance will slightly improve, because the RDD filter function can filtrate more intervals by label type equation to reduce the similarity computation in the later stage. However, if the label types of the input timeline for query increase with the DS1's label types, then the similarity query performance will not change or decrease slightly. In the other words, the impact of the label types is slight because the impact is mainly in memory computation. When comparing to the disk IO and network IO, the memory computation time is a little part of them. To test the influence of the partitions in the process of similarity query, we change the partitions from 4 to 32. The result is shown in Figure 17. It shows that the similarity query performance will increase with the increment of the partitions. Whether the dataset is DS1 or DS2, the conclusion is right. However, the communication costs among the slaves increase as the number of slaves increases. It results in the slower performance growth as the slaves increase, as shown in Figure 17.  
In summary, the experimental results shown in the figures from Figures 13-17 present that our proposed index structure, DTI-Tree, can efficiently process timeline similarity queries in distributed  
Figure 16: Similarity query performance (label types). Figure 17: Similarity query performance (partitions). platforms and has higher performance than R-Tree in GeoSpark. The more partitions may improve the index performances and the index has scalability.