esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems  
Table 1 presents the review summary, specifying the classification and distribution of all the articles based on the power system application and the PHC technology. The summary also organizes the applications based on the number of references, leading to the present power flow analysis as the application where researchers have parallelized most of the power flow algorithm using heterogeneous computing. Figure 1 shows how PHC technologies have been used during the last three decades. In the beginning, CPU clusters, FPGAs, and fog and cloud computing were the most popular PHC alternatives. During the last ten years, vector processors, such as GPU, have had significant growth in power system applications since GPU represents 80% of all the articles included in the review and published during the last five years.
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## 3 Implementation of the QC-LDPC Decoder Based on the Token Ring Architecture with Clock Gating
This processing scheme allows for significant reduction of the energy consumption due to activating the clock signal delivered to computation logic only in the needed time slot. Table 1 presents the analysis of the operating time of individual decoder elements and the number of Adaptive Logic Module (ALM) blocks [37; 42] utilized by the designed QC-LDPC implementation. These results have been obtained for a single-iteration-decoding of a QC-LDPC regular matrix with dimensions \(512\times 1024\) (\(M\times N\)), \(P=64\) and \(Z=3072\), where \(Z\) is the number of nonzero elements of the parity check matrix \(\mathbf{H}\). During normal operation, in the first iteration the decoder performs initialization once and in the last iteration it communicates the results module. For all other iterations, both decoder  
elements are not active. The communication support module was not taken into account during the analysis because it is an element necessary for the proper operation of the decoder but it strictly depends on the design assumptions and can be implemented in any form (serial, parallel or partially-parallel), so it should not be included in Table 1. In Table 1, it is worth noting that for the proposed architecture, a single decoder element works no more than 34% of the total time and no more than 50% of ALM blocks have an active clock signal at any time. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Element** & **Time [\%]** & **ALM [\%]** \\ \hline Initialization module & 11.2676 & 10.4995 \\ Control node computing unit & 26.7606 & 36.1635 \\ Bit node computing unit & 33.8028 & 49.5803 \\ Verification of control equations module & 16.9014 & 3.3714 \\ Communicating the results module & 11.2676 & 0.3853 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of the operation time of individual elements and the number of active Adaptive Logic Module (ALM) blocks of the QC-LDPC decoder based on the Token Ring architecture with the following QC matrix parameters: \(512\times 1024\) (\(M\times N\)), \(P=64\) and \(Z=3072\). Figure 5: The block diagram of clock gating and activation of a distributed control element using a token in an architecture based on a Token Ring.
## 2 Results and Discussion
### General workflow
#### 2.1.7 Pathway Analysis  
**Figure 2a-c**. Network interactions among feature components of the _Pinus taeda_ L. HILIC (**a**), RP (**b**) LC-ESI-MS, and GC-TOF-MS (**c**) data. The line thickness represents the dependency strength relative to the population of relations shown. Top components were color-coded: the most frequent ones were black, then red, green, blue, cyan, pink, yellow, and gray. Predictors are described as mass to charge ratio which is unique characteristic of metabolite or chemical name for annotated metabolite.  
**c**)  
A major difference between GA and other machine learning approaches is its ability to determine relationships among feature components, providing valuable information about metabolite interactionsand metabolic pathways. The result of the evolutionary process of GA is a large pool of subsets of feature components. The models built on these subsets can be used for classification. In addition, associations can be identified through the most important feature components. The dependency of top-ranked GA selected feature components with each other was illustrated in Figures 2a-2c. The line thickness represents the dependency strength relative to the population of relations shown. By default, only the two most important dependencies per feature were shown. The authors understand that in order to get more complete information of biological pathways, these most important feature components have to be imported into specific pathway analysis software such as Cytoscape ([[http://www.cytoscape.org/](http://www.cytoscape.org/)]([http://www.cytoscape.org/](http://www.cytoscape.org/))), GeneGo ([[http://www.genego.com/](http://www.genego.com/)]([http://www.genego.com/](http://www.genego.com/))) and/or Ingenuity ([[http://www.ingenuity.com/](http://www.ingenuity.com/)]([http://www.ingenuity.com/](http://www.ingenuity.com/))) Pathway Analysis tool. Here we demonstrate that even with the current correlation pathway analysis, preliminary hypothesis can be generated based on GA selected features/metabolites. Moreover, we believe that proposed pathways were the most affected ones. These proposed pathways are subjects for further targeted metabolomics study applying SRM/MRM techniques which provide much higher level of sensitivity and confidence.
## 6 Conclusions  
In this paper, we illustrated the MLO functionality of the upcoming IEEE 802.11 be standard and described how the channel access schemes of IEEE 802.11 be facilitates multi-link utilization. We demonstrated the coexistence challenge presented by the enhanced synchronous channel access scheme, and we proposed solutions to mitigate this challenge. Through comprehensive simulations, we evaluated the throughput and latency gains of MLO and the proposed solutions relative to legacy systems in various network environments. At the same time, we investigated the coexistence performance of MLO with and without the STR capability and demonstrated that the proposed solutions mitigate the coexistence problem of the enhanced synchronous channel access scheme to different degrees. A metric for evaluating both the throughput and latency gains and the coexistence performance of a multi-link channel access scheme using a single value was also proposed. The evaluation results showed that, among the proposed solutions, compensating the backoff count achieves the best coexistence performance with a marginal throughput decrease of MLDs. However, there may arise a backoff count overflow problem that the backoff count of an MLD accumulated by repeated compensations gets excessive, thus not giving a transmission opportunity to the MLD once an overflow occurs. We leave an extended design of the compensation feature to handle this problem as future work. As machine learning has been applied to a wide range of communication and networking problems [27], developing a machine learning-based channel access scheme for MLO is also a subject for future research.  
This work was realized through the collaboration of all authors. [PERSON] contributed to the main results and simulation. [PERSON] organized the work, provided the funding, supervised the research, and reviewed the draft of the paper. All authors discussed the results, approved the final version, and contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute of Information Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00650, Biosignal-based Predictive Content Creation for Cloud VR).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 24: Values of the new metric \(\gamma_{t}\) for 80 devices in total in the indoor random deployment scenario. (**a**) With STR capability, (**b**) Without STR capability.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets
### Coastal Altimetry Data Sets
#### 3.1.2 Waveform Retrackers  
Waveform retrackers can be classified into two categories: physically-based retrackers and empirical retrackers [12]. The model-free retracker is purely based on the statistics of the waveforms and does not require any echo model. Among the multiple model-free retrackers available, the improved threshold retracker [13] is usually considered as the best one. It combines the advantages of the OCOG (Offset Center of Gravity, [14]) and simple threshold [15] algorithms.  
Figure 3: Bathymetry and shoreline distance at the Hong-Kong (HK) coast along 3 segments of the Jason-2 pass identified as cases 1, 2, and 3 (see location in Figure 2).  
Over the last decade, a new approach called \"sub-waveform\" has been developed. It consists of fitting only the portion of the waveform that contains the leading edge but excludes the trailing edge where most artifacts appear (e.g., [3; 8; 16; 17; 18]).  
The retrackers that are available in the different L2 products are summarized in Table 1. The standard GDRs include two solutions: MLE3 and MLE4. The rationale of these two retrackers is similar: fitting the waveform to a Brown model based on the MLE (essentially non-linear least squares) techniques. The main difference between them is the number of parameters included in the model. MLE3 estimates three parameters: epoch (i.e., altimetric range), Significant Wave Height (SWH) and amplitude (i.e., backscatter coefficient-sigma-0), while MLE4 also retrieves the square of off-nadir angle.  
The PISTACH products provide four retrackers: OCE3, RED3, ICE1, and ICE3 [8]. OCE3 is essentially the same as the MLE3 parameters in the GDRs. ICE 1 is a modified threshold retracker. RED3 and ICE3 are the counterparts of OCE3 and ICE1 respectively, where the retracker is executed in a sub-waveform version instead of on the entire waveform.  
ALES is based on an advanced retracker called sub-waveform retracker [3]. It is an improved version of RED3, and the difference between them is the estimation of the sub-waveform. In RED3, the sub-waveform has always 31 bins (t\({}_{0}\) + [\(-\)10:20], where t\({}_{0}\) is the 32 nd bin of the entire waveform), while in ALES, the sub-waveform length can vary from 39 bins (for SWH = 1 m) to 104 bins (i.e., the entire waveform, for SWH \(\geq\)17 m). In practice, the content of the ALES product is the same as the standard SGDR product (Sensor GDR) plus seven additional parameters specific to to the ALES retracker.
# Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
## 3 Development of the Models
### Holt-Winters Exponential Smoothing  
The Holt-Winters seasonal method comprises the forecast equation and three smoothing equations--one for the level \(\ell_{t}\), one for the trend \(b_{t}\), and one for the seasonal component \(s_{t}\), with corresponding smoothing parameters \(\alpha,\ \beta^{*}\) and \(\gamma\).  
The parameter \(m\) is used to denote the seasonality period [42]. Two variations of this method differ from the seasonal component--the additive and multiplicative methods.  
The component form for the additive method is:  
\[\begin{split}&\hat{y}_{t+h|t}=\ell_{t}+hb_{t}+s_{t+h-m(k+1)}\\ &\ell_{t}=\alpha(y_{t}-s_{t-m})+(1-\alpha)(\ell_{t-1}+b_{t-1})\\ & b_{t}=\beta^{*}(\ell_{t}-\ell_{t-1})+(1-\beta^{*})b_{t-1}\\ & s_{t}=\gamma(y_{t}-\ell_{t-1}-b_{t-1})+(1-\gamma)s_{t-m}\;.\end{split} \tag{3}\]  
The component form for the multiplicative method is:  
\[\begin{split}&\hat{y}_{t+h|t}=\ell_{t}+hb_{t}+s_{t+h-m(k+1)}\\ &\ell_{t}=\alpha(y_{t}-s_{t-m})+(1-\alpha)(\ell_{t-1}+b_{t-1})\\ & b_{t}=\beta^{*}(\ell_{t}-\ell_{t-1})+(1-\beta^{*})b_{t-1}\\ & s_{t}=\gamma(y_{t}-\ell_{t-1}-b_{t-1})+(1-\gamma)s_{t-m}\;. \end{split} \tag{4}\]  
The \(k\) in Equations (3) and (4) is the integer part of \((h-1)/m\). The level equation \(\ell_{t}\) shows a weighted average between the seasonally adjusted observation \((y_{t}-s_{t-m})\) and the non-seasonal forecast \((\ell_{t}-\ell_{t-1})\) for time \(t\). The trend equation \(b_{t}\) is identical to [PERSON]'s linear method. The seasonal equation shows a weighted average between the current seasonal index, \((y_{t}-\ell_{t-1}-b_{t-1})\), and the seasonal index of the same season \(m\) time periods ago. The \(s_{t}\) is an equation for the seasonal component. To create a predictive model based on the Holt-Winters Exponential Smoothing method, the function _Exponential Smoothing_ of the statmodels library [40] is used. The function takes three parameters as input: the data set for training, the type of seasonal component add/mul, and the value of the parameter m from Equations (2) and (3)--the value of the seasonality period of the sample. The result of forecasting with the Holt-Winters model is shown in Figure A2 (see Appendix A).
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 1 Introduction
The \(l_{2}\) norm and the \(l_{1}\) norm are, respectively, denoted by the symbols \(\left\|\cdot\right\|_{2}\) and \(\left\|\cdot\right\|_{1}\), and the Frobenius norm is represented by the symbol \(\left\|\cdot\right\|_{\mathsf{F}}\). \(\mathbf{I}_{N}\) stands for the identity matrix of size \(N\times N\). Finally, we use \(\text{Re}\{x\}\) and \(\text{Im}\{x\}\) to denote the real and imaginary part of \(x\); \(\mathbb{E}(\cdot)\) means the expectation operator; and the symbol \(\otimes\) stands for Kronecker product.
# An Intrusion Detection System for RPL-Based IoT Networks
## 4 IDS Design
### Central IDS and IDS Detector Communication  
In order to detect the attacks described above, it is necessary to provide a hybrid placement IDS module throughout the entire network. As explained before, the IDS design is composed of a central IDS and multiple IDS detectors. The central IDS is focused on detecting specific attacks, but one of its core functions is to handle the alerts received by the IDS detectors. If the central IDS is not capable of \"hearing\" those alerts, some attacks will go unnoticed. For this reason, ensuring appropriate communication between the central IDS and IDS detectors is crucial.  
Every \(T_{c}\) (where \(T_{c}\) represents an interval of time in seconds), the central IDS sends a message to all IDS detectors in the network. These messages are sent to ensure that the communication between the two parties is working correctly. Hence, if the central IDS does not receive a reply, the link between this specific IDS detector and the central IDS will be marked as down, and further actions from the network administrators will be required. At this point, the design will have two variants, which were evaluated in Section 5. One of the variants is that, in addition to the security message, the IDS detector will make use of the packet sent to raise an alert, if required, so that the central IDS can be made aware of possible attacks outside its range. The other variant consists of sending an alert as soon as the IDS detector is aware of it. The first variant reduces the messages exchanged inside the network, but the IDS will detect attacks every \(T_{c}\) seconds (e.g., 30 or 60 s). On the other hand, if the IDS detector needs to send the alert as soon as the attack is detected, resource usage is increased as well as the number of exchanged messages when an attack takes place. Figure 4 illustrates the communication protocol for the aforementioned variants. IDS Detector 1 uses the variant of sending only relevant information every time the central IDS asks for this information. On the contrary, IDS Detector 2 sends the attack information as soon as it detects the suspicious behaviour.  
Figure 4: Diagram of central IDS and IDS detector communication variants.
# Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement
## 4 Conclusions  
In conclusion, we have proposed and demonstrated a novel approach to improve the measurement resolution of photonic-assisted scanning receiver systems in which the spectrum of input microwave signals is recovered using a matrix pencil based deconvolution method. As a proof of concept, signal reconstruction of both amplitude and frequency information has been demonstrated. A two-tone microwave signal measurement with a resolution reduced from 1 GHz to 0.4 GHz has also been  
Figure 6: (**a**) Measured output signal and (**b**) reconstructed spectrum with input RF tones at 12 GHz and 12.4 GHz. (**c**) Measured output signal and (**d**) reconstructed spectrum with input RF tones at 12 GHz and 13 GHz.  
experimentally demonstrated in a scanning receiver where an optical filter with a bandwidth as wide as 1.325 GHz has been employed. The presented deconvolution theorem offers an effective and widely applicable solution for improving the resolution and accuracy performance of various scanning receiver systems and is highly promising for meeting the increasingly stringent requirements in applications such as radar and electronic warfare.  
Conceptualization--S.S. and X.Y.; data curation--S.S. and W.Y.; methodology--S.S. and L.G.; supervision--X.Y. and L.N.; writing--review and editing--S.X.C., L.L. and R.M.  
This research was supported by the Australian Department of Defence.  
The authors would like to extend their thanks to [PERSON] from the University of Sydney for his valuable contributions to this work.  
The authors declare no conflict of interest.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion
### Other Underwater Text Data Set  
To further verify the robustness of the proposed method, we dealt with a real text data set in underwater scenes [24]. The data set contains 101 frames, and the size of each frame is \(512\times 512\). The result is also compared with other registration algorithms [20; 21]. As shown in Figure 7, the proposed method still outperforms other registration strategies, and the boundary between letters is successfully restored without blurring.  
Figure 6: The results of the proposed method with different combinations of \(\varepsilon\) and \(r\). The blurred regions are marked with red rectangles.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 2 Materials and Methods
### Data Preprocessing  
Each point in the Pheno4D dataset is labeled as \"soil\", \"stem\", or \"leaf\", with individual leaves assigned unique instance labels to distinguish them from others on the same plant. As this study focuses on structural understanding and segmentation of tomato plants, all points labeled as \"soil\" were removed during preprocessing to eliminate background noise and concentrate on the plant body. In addition to lowering computational complexity, this step increased the precision of semantic and instance segmentation by focusing on pertinent categories (\"stem\" and \"leaf\"). Moreover, a significant class imbalance exists in the raw data: most points belong to the \"soil\" and \"leaf\" categories, while the \"stem\" class is underrepresented. Such imbalance can bias deep learning models toward dominant classes and impair learning for minority classes like \"stem\". By removing soil points, we mitigated this issue and ensured the model focused on essential plant structures. The final preprocessed dataset contains only \"stem\" and \"leaf\" points, which serve as input for subsequent model training and evaluation.  
Since the original dataset only provides instance labels without explicit semantic labels, we derived semantic annotations from the instance information. Specifically, the semantic labels distinguish only between \"stem\" and \"leaf\", without differentiating between individual leaves of the same plant. Figure 2 illustrates examples of raw and annotated point clouds.  
To improve the deep neural network's capacity for feature learning and prevent overfitting, we further partitioned and augmented the dataset. The 77 annotated point cloud samples were randomly divided into training and testing sets at an 8:2 ratio, ensuring that both subsets covered tomato plants from all growth stages. This strategy prevents bias in the training set toward mature plants or specific developmental stages and enables a more comprehensive evaluation of the model's generalization ability across different stages of tomato growth. For each point cloud, we applied Farthest Point Sampling (FPS) [24] 20 times, using random initialization to both restrict the point count and augment the dataset. Each augmented point cloud was constrained to 4096 points, reducing data density while improving computational efficiency and accuracy. Although the augmented samples originate from the same raw point cloud, they exhibit significant differences in local point distributions. Table 1 summarizes the data distribution before and after augmentation.  
Figure 2: Examples of raw and annotated tomato plant point clouds from Pheno4D. (**a**) Raw 3D point cloud of a tomato seedling. (**b**) Instance-level annotation distinguishing individual leaves using unique colors. (**c**) Annotated point cloud showing semantic labels (stem in red, leaf in blue).  
Finally, as the original Pheno4D dataset is stored in TXT format with low I/O efficiency, we converted the data into HDF5 format to accelerate loading during model training.
###### Abstract  
Transdermal drug delivery offers a non-invasive route for the systemic and localized administration of therapeutics; however, the skin's barrier function limits its efficiency. This study investigates the application of various electromagnetic field (EMF) configurations to enhance the transdermal delivery of salicylic acid, a model compound with moderate lipophililicity and ionizability. Samples were exposed to pulsed, oscillating, static, and rotating magnetic fields, and their effects on physicochemical properties, thermal stability, skin permeation, and accumulation were evaluated. Structural analyses (FTIR, XRD) and thermal assessments (TGA, DSC) confirmed that EMF exposure did not alter the chemical structure or stability of salicylic acid. In vitro transdermal studies using porcine skin and Franz diffusion cells revealed that pulsed magnetic fields--especially with a 5 s on/5 s off cycle--and rotating magnetic fields at 30-50 Hz significantly enhanced drug permeation compared to controls. In contrast, static fields of negative polarity increased skin retention, suggesting their potential for controlled, localized delivery. These findings demonstrate that EMFs can be used as tunable, non-destructive tools to modulate drug transport across the skin and support their integration into transdermal delivery systems aimed at optimizing therapeutic profiles.  
electronic field; transdermal drug delivery; salicylic acid; skin permeability; drug accumulation; controlled release Article  
* 2025157606
## 2 Materials and Methods
### Virtual Inertia Optimization Allocation Model
#### 2.2.1 Frequency Stability Constraints
Considering the rate of frequency change and the constraint of the lowest frequency point comprehensively, the minimum inertia requirement of the system can be derived as follows:  
\[H_{\min}=max(H_{RoCoF},H_{nadir}) \tag{12}\]where \(RoCoF_{max}\) represents the maximum frequency variation rate constraint, \(f_{N}\) represents the system's rated frequency, \(t_{db}\) represents the fast power response action time, \(R_{sys}\) represents the system's power response rate, \(\Delta P_{max}\) represents the maximum power disturbance, \(D\) represents the load frequency response coefficient, and \(f_{min}\) represents the lower limit of frequency.