esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 2 Materials and Methods
### MA Reduction Algorithms  
In general, MA reduction algorithms involve an identification step to test the presence of outliers and label MA windows, which will be corrected in a second correction step. Hence, MA reduction algorithms are time-variant (most often in on-off fashion) and non-linear due to thresholding methods employed in the identification step.  
Moreover, most algorithms separately label MAs in single channels (SCs). However, the possible correlation of MA occurrence among channels is an open question. Hence, we also tested multi-channel (MC) variants in which the SC labelling was propagated to all channels according to \"OR\" logical operator. In brief, MA labeling was extended to all time windows where at least one of the signals showed outlier features. Our interest in such variants was two-fold: (i) a large increase in labelled tracts passing from SC to MC would indicate that MA timing is sparse through channels, thus contrasting the hypothesis of common MA sources; (ii) the level of disruption in the useful HRF, which might be caused by an overcorrecting MC strategy.  
The correction algorithms of MA labelled windows were based on the reduction of outlier features by substitution of the raw signal with common-mode dynamics found by targeted PCA (tPCA, see Appendix A.3) [23] and spline interpolation [21]. Starting from the original version of the Movement Artifact Reduction Algorithm (MARA) strategy proposed by [PERSON] et al. [21], we also tested the hybrid version proposed by [PERSON] et al. [22] to simultaneously detect baseline shifts and spike artifacts. As illustrated in the Appendix A, the MA detection and the correction algorithms are found in the current literature. However, we tested several combinations of identification and correction methods, some of which not considered by the original works, and according to both the SC and the MC labeling. We also considered two additional algorithms, namely, the Temporal Derivative Distribution Repair (TDDR) [24] and a wavelet-based method [25], which conversely apply signal decomposition methods to the overall signal trend rather than acting only on specific MA windows. In conclusion, these MA algorithms paired to SC- vs. MC-labelling and correction methods led to eight different variants, successively considered pipelines, to discuss the results of HRF estimation. These algorithms are labelled as follows: SC-MARA-Spline, SC-MARA-tPCA, MC-MARA-Spline, MC-MARA-tPCA, HybridMARA-Spline SG, HybridMARA-tPCA SG, TDDR, and Wavelet.
## 5 Conclusions  
To address frequency regulation in microgrid systems, this paper proposes a mechanism of secondary frequency restoration through adjusting power reference values in primary-level droop control. The implementation uses SNNs to perform curve fitting between the common load power (set as inputs) and each DG system's power (set as outputs) under ideal conditions. Through the use of input enhancement, the model trains faster and performs more consistently. Subsequently, dynamic timing regularization is employed to achieve precise peak-to-peak correspondence between curves. This method continuously enhances frequency regulation capability, enabling the microgrid to effectively resist power fluctuations from common loads. The effectiveness of these methods has been validated through detailed simulations and comparative analysis.  
While this work explores how SNNs can enhance primary control and eliminate frequency deviation, modern DG systems face broader challenges that need further study, including transient instability during black-start events and misoperation under asymmetric grid conditions. Future research will investigate how artificial intelligence methods can detect faults and tune droop settings to prevent these failures, especially in industrial-scale DG systems. Furthermore, the proposed approaches could scale to larger microgrids with heterogeneous resources and loads. The SNNs only need to establish variable input-output relationships between common load and each DG, while frequency elimination does not hinder DG expansion due to the discrete nature of droop control. However, it is important to note that the proposed methods have limitations, particularly when dealing with large, real-time fluctuations in common load, which can challenge SNN training and may require high-performance training systems.  
Conceptualization, Z.L. and Y.S.; methodology, Z.L. and Y.S.; software, Y.S.; validation, Z.L. and Y.S.; writing--original draft preparation, Z.L. and Y.S.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Shanghai Explorer Program, China under Grant 24 TS1410100 and in part by the Fundamental Research Funds for the Central Universities, China.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the Shanghai Electric Digital Technology Co., Ltd. The authors declare that this study received partial funding from Grant 24 TS1410100. This paper only represents the opinions of the authors. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 1 Introduction
The \(n\) LSBs of the reference pixel and all bits of the target pixels are self-embedded into other parts of the image by a method of predictive error expansion (PEE). To avoid overflowing when embedding data, the \(n\) LSBs of the reference pixel are reset to zero before encryption. Then, the pixel values of the image are encrypted after encoded onto the points of the elliptic curve. The encrypted reference pixel replaces the encrypted target pixels surrounding it, thereby constructing the mirror central ciphertext (MCC). In a set of MCC, the data hider embeds the encrypted additional data into the \(n\) LSBs of the target pixels by homomorphic addition in ciphertexts, while the reference pixel remains unchanged. The receiver can achieve separation of additional data extraction from cover-image recovery, additional data extraction without any errors, and reversibility completely. The rest of this paper is organized as follows. Section 2 briefly describes homomorphic EC-EG cryptosystem, its properties and reviews predictive error expansion for reversible data hiding in the plain domain. Implementation of the proposed RDH-HEI scheme is elaborated in Section 3. Simulation experiment and theoretical analysis are presented in Section 4. Finally, the conclusion is drawn in Section 5. It is important to note that a preliminary version [24] of this paper is in proceedings of the \"11 th Intelligent Networking and Collaborative Systems (INCoS-2019)\".
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Performance Validation Metrics  
The evaluation of deep learning-based OFDM receivers for UWA communication includes several key performance metrics [43]. BER measures the accuracy of data transmission by comparing the number of bit errors to the total number of bits sent [44]. Lower BER values indicate better transmission accuracy. In addition, accuracy is the proportion of correctly predicted bits. Higher accuracy values reflect better receiver performance [45].  
In addition, Precision, recall, and F1 score are used to evaluate classification performance. These metrics assess model detection accuracy and classification balance [46, 47, 48]. Moreover, the Mathews correlation coefficient (MCC) evaluates binary classification performance. It takes into account true and false positives and negatives and gives a balanced score even if the classes are of different sizes, making it a reliable metric for performance evaluation. Higher MCC indicates better model accuracy [49, 50]. Also, the confusion matrix summarizes classification performance, showing true and false classifications across classes. The BER vs. SNR curve is used to assess receiver performance under varying noise levels. It helps identify performance thresholds for optimization in underwater conditions.
## 3 Target State Decision and Tracking
### Target State Decision for Approach Motion
\begin{array}{ll}\mathit{case}\;1:&0<t\leq t_{\mathit{prec}}+t_{\mathit{act} }\\ &v(t)=v_{\mathit{target}}\\ &\mathit{case}\;2:&t_{\mathit{proc}}+t_{\mathit{act}}<t\leq t_{\mathit{proc }}+t_{\mathit{act}}+t_{\mathit{slew}}\\ &v(t)=v_{\mathit{target}}-\frac{1}{2}\frac{t_{\mathit{min}}}{t_{\mathit{slew }}}\big{\{}t-(t_{\mathit{proc}}+t_{\mathit{act}})\big{\}}^{2}\\ &\mathit{case}\;3:&t_{\mathit{proc}}+t_{\mathit{act}}+t_{\mathit{slew}}<t \leq t_{\mathit{start}}\\ &v(t)=v_{\mathit{target}}-\frac{1}{2}a_{x,\min}t_{\mathit{slew}}-a_{x,\min} \big{\{}t-(t_{\mathit{proc}}+t_{\mathit{act}}+t_{\mathit{slew}})\big{\}}\\ \end{array}  \tag{3}\]  
As mentioned before, autonomous vehicle should be stopped at \(t_{\mathit{dart}}\). Therefore, \(v_{\mathit{target}}\) and \(d_{\mathit{brake}}\) are defined as follows:  
\[\begin{array}{ll}v_{\mathit{target}}&=\frac{1}{2}a_{x,\min}t_{\mathit{slew }}+a_{x,\min}\big{\{}t_{\mathit{dart}}-\big{(}t_{\mathit{proc}}+t_{\mathit{ act}}+t_{\mathit{slew}})\big{\}}\\ &d_{\mathit{brake}}=v_{\mathit{target}}t_{\mathit{dart}}-\frac{1}{2}a_{x,\min} t_{\mathit{slew}}t_{\mathit{dart}}\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\  
where \(p(k)\), \(v(k)\) and \(a(k)\) are the moving distance toward the intersection from the current position, longitudinal velocity and longitudinal acceleration, respectively. \(a_{dss}(k)\) is the desired acceleration of the autonomous vehicle. \(k\) is the prediction time index. The objective function for the MPC is defined as follows:  
\[ \begin{array}{l}J=\sum\limits_{k=1}^{N}\left[\tilde{x}(k)^{T}Q\tilde{x}(k)+u( k)^{T}Ru(k)\right]\\ where,\ \ \tilde{x}(k)=x_{guess}(k)-x(k)\end{array}  \tag{6}\]  
The constraints for this problem can be summarized into four categories, dynamic, state, input, and actuator constraints. The first-order model is used to define the dynamic constraint for the MPC problem. The vehicle model is defined with \(dt\), which is the sampling time of the MPC, as follows  
\[ \begin{bmatrix}p(k+1)\\ v(k+1)\\ a(k+1)\end{bmatrix} = \begin{bmatrix}1&dt&0\\ 0&1&dt\\ 0&0&1-dt/t_{act}\end{bmatrix}\begin{bmatrix}p(k)\\ v(k)\\ a(k)\end{bmatrix} + \begin{bmatrix}0\\ 0\\ dt/t_{act}\end{bmatrix} u(k) \tag{7}\]  
The state and input constraints are defined to satisfy the target states as follows:  
\[ \begin{array}{l}0\leq p(k)\leq d_{target}\\ 0\leq v(k)\leq v_{target}\end{array}  \tag{8}\]  
The actuator constraints are defined based on the actuator performance limit as follows:  
\[ \begin{array}{l}a_{\min}\leq u(k)\leq a_{\max}\\ |u(k+1)-u(k)|\leq a_{jerk,\max}\end{array}  \tag{9}\]  
where \(a_{min}\), and \(a_{max}\), are the minimum and maximum acceleration, respectively, and \(a_{jerk,max}\) is the maximum jerk. The parameters for the MPC-based target state tracker are summarized in Table 2. CVXGEN solver was used to implement the proposed MPC problem in simulation environments [45].
# Synthesis of Zeolitic Material with High Cation Exchange Capacity from Paper Sludge Ash Using EDTA
###### Abstract  
Paper sludge ash (PSA) typically has a low Si abundance and significant Ca content because of the presence of calcite fillers, which interfere with the zeolitic conversion of PSA. Ca-masking with ethylenediaminetetraacetic acid (EDTA) was used to reduce Ca interference during zeolite synthesis so that a zeolitic product with a high cation exchange capacity (CEC) could be synthesized. Hydroxysodalite, zeolite-P, hydroxycancrinite, tobermorie, and zeolite-A can be synthesized from PSA by an alkali reaction with EDTA. With the addition of EDTA, calcium ions in the solution were trapped by chelation, and the number of zeolitic crystals with low Si/Al (Si/Al = 1), zeolite-A, increased owing to the promotion of the synthesis reaction. A product with a high CEC that has a high zeolite-A content was obtained. The chelating agent can inhibit Ca interference for zeolite synthesis by Ca-masking, and a product with a high zeolite-A content can be obtained from PSA using EDTA.  
paper sludge ash; EDTA; zeolite-A; cation exchange capacity; Ca-masking +
Footnote †: journal: Applied Sciences  
13 October 2021  
**Keywords:** paper sludge ash; EDTA; zeolite-A; cation exchange capacity; Ca-masking
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Computation Algorithm
#### 3.1.1 Centralized Technique  
We have collected some articles based on the centralized technique that are classified into two subcategories, namely, estimation-based and prediction-based schemes.  
As for the estimation-based scheme, researchers in [24] propose two different types of algorithms. In [24], a Reverse Localization Scheme (RLS) that has a fast reaction to events is proposed and suitable for 3D mobile UWSNs. In this scheme, the ordinary sensor nodes have mobility with the water currents so that the height of the nodes is not fixed and can be used to detect a phenomenon in a 3D-UWSN. The 3D localization is transferred into 2D localization by equipping the pressure sensor in the ordinary sensor nodes to obtain the depth information (z-coordination). Surface anchors nodes are employed to help to estimate the position of an ordinary node. The sink or base station has a higher process capacity. Therefore, the data can be sent there, and the localization algorithm is performed at the station.  
Considering the prediction-based scheme, a collaborative localization algorithm that is self-organizing and autonomous is proposed in [25]. The nodes can collaborate to estimate their location information autonomously. The spatial variation of a process is sensed by the nodes and described as follows: starting at the surface, buoyancy is utilized to reach a deeper depth of the ocean and then float to the sea surface. Buoyancy repeats this process until a maximum desired depth is reached. The authors determine configurations of the swarm and use TOA measurements to obtain  
Figure 1: Taxonomy of the localization algorithms in UWSNs.  
distance estimation. Those distance estimates are used to periodically position the profile concerning other nodes. Then, using a moving reference frame, the authors can estimate the absolution position of the sensor nodes.
## 3 Result Comparison
### CNN Architecture  
The bar chart in Figure 13 compares the calculated results. In general, the G-Mean values achieved by the respective architectures are very high, and the distribution per class is low in most cases. It is also evident that the geometry class had the most deviations, which is due to the imbalance of the dataset. Furthermore, it is visible that the Inception-V3 derivative best classified the loads and geometry. For the plausibility cause mesh size, ResNet achieved the highest accuracy. The Vgg19 architecture also obtained very high results in the first two classes, though not for the geometric error reason. Here, MobileNet could score very high values, whereas it performed worse in comparison to the other two classes.  
Since the Inception network performed the best overall, another study with individual class weights was carried out. For each binary classifier, individual weights were determined depending on the class distribution. Afterwards, the networks were trained again with an adapted learning rate of 0.0005 and the results were compared, which are shown in the confusion matrix in Figure 14. The G-Mean values achieved are as follows: 0.9930 (Loads), 0.9866 (Mesh), and 0.9978 (geometry).  
Figure 12: Results for the test dataset with different evaluation metrics for the two classification approaches.  
The results show that accuracy could again be improved by weighted classes but on a smaller scale, which is inevitable due to the already very high values. Above all, the mesh class was further enhanced, whereas the other two plausibility causes marginally worsened.
## 4 Malatya Dominating Set Algorithm (MDSA)
### The Main Algorithm  
The dominating set determination is handled by the sequence of applications of centrality values computation. In the first step, the node with the maximum second Malatya centrality value is selected and removed from the graph. Its neighbors are designated as grey nodes, and the white neighbors of any node designate the active degree of the corresponding node. The grey nodes with active degrees equal to 1 are also removed from the graph. Algorithm 3 illustrates the pseudo-code of the dominating set algorithm. The function NodeSelection (A,D,DA,NodeColor,MC2,VD) selects a node with the maximum second Malatya centrality value for the dominating set, and then it removes this node from the graph. The neighbors of the selected node are marked as grey nodes. The grey nodes with active degree as 1 are also removed from the graph. If a grey node has maximum MC2 and its white neighbor's number is greater than the white neighbors of any white node with maximum MC2 amongst white nodes, then this grey node is selected for the dominating set. Otherwise, the white node with maximum MC2 is selected for the dominating set. **Theorem 1**. : _Assume that \(G=(V,E)\) is a graph where \(|V|=n\) and \(|E|=m\). Algorithm 1 has polynomial time and space complexities._  
**Proof. ** The proof of theorem can be given in two ways:  
* **First Way**: Adjacency matrix method. Space: The adjacency matrix of given graph is used and the proposed algorithm is an iterative algorithm not a recursive algorithm. Consequently, the space complexity is \(O(n\times d)\), where \(|V|=n\) and d is the average degree. Thus, the space complexity of Algorithm 1 is \(P_{\text{MC1}}(n)=\Theta(n\times d)\). Time: Assume that \(G=(V,E)\) and its adjacency matrix is \(A\) of size \(nxn\). To compute the first round \(\Psi_{1}(\ldots)\) values, an adjacency matrix \(A\) of \(G\) is used, and it requires\(O(n\times d)\) time complexity, since the adjacency matrix has size \(nxn\) and the average degree is d. That is why the time complexity of Algorithm 1 is \(T_{MC1}(n)=\Theta(n\times d)\). * **Second Way**: Linked list method using neighbors list. Space: The neighbors of any node are stored as a linked list data structure. Assume that the averaged degree in graph \(G\) is \(\Delta(G)\). If \(|V|=n\), the space complexity of Algorithm 1 is \(P_{MC1}(n)=\Theta(\text{n.}\Delta(G))\). Time: If linked list data structures are used to represent the given graph, the space requirement for storing graph is \(\Delta(G)\times n\). The time complexity of each node to compute \(\Psi_{1}(\ldots)\) is maximum \(\Delta(G)\). There are \(n\) nodes, and so, the time complexity of Algorithm 1 is \(O(\Delta(G)\times n)\).
## 1 Introduction
### Challenges and Research Objectives  
To understand the characteristics of internal waves in MODIS satellite imagery, it is important to consider their distinctive optical signatures. When examining internal waves in the ocean through MODIS satellite imagery, they are frequently observed to be more prevalent in the vicinity of optical glare areas. This phenomenon can be attributed to the fact that signals received in these regions primarily result from sunlight reflecting off the sea surface. The presence of internal waves induces alterations in the sea surface roughness, leading to variations in the gradient of the sea surface at various scales. These variations, in turn, modulate the intensity of reflected sunlight received by remote sensors. Consequently, internal waves manifest as stripes with varying degrees of brightness in optical images. In visible satellite images, the divergent regions within the glare area typically appear as dark stripes, while the convergent regions appear as bright stripes. Outside the glare area, the divergent areas are characterized by bright stripes, while the convergent areas exhibit dark stripes. For a visual representation of how internal waves are depicted in MODIS imagery, please consult Figure 1.  
The classification of remote sensing images depicting oceanic internal waves presents two significant challenges. Firstly, owing to the high resolution and abundant spatial and semantic information within remote sensing images, traditional machine learning methods like SVM, KNN, and decision trees struggle to effectively capture the intricate features inherent to these images. Secondly, the task of cropping and annotating remote sensing images is exceptionally labor-intensive, rendering it challenging for researchers to amass a substantial quantity of labeled image datasets for remote sensing data.  
This study aims to address these challenges by developing an innovative approach to automatically detect and classify internal waves in MODIS satellite imagery, leveraging advanced deep learning techniques and data augmentation methods. By doing so, we aim to contribute to the field of oceanography and remote sensing by providing a more efficient and accurate means of studying and monitoring internal waves in the ocean, ultimately enhancing our understanding of their role in oceanic and global climate processes.