esa-sceva/satcom-chunk-collection huggingface dataset:
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 3 Preliminaries and Definitions
### Communication Model  
In the proposed FL environment, FL is split into two major parts: workers and the central server. Our work aims to reduce communication overhead without sacrificing accuracy value during the training process. In this setting, the server coordinates a network of workers, controls the training progress of the model, broadcasts the original model to all participating workers, and then executes all the aggregation processes of the model updates. All workers are share model updates instead of sending their private data to a central server for global model aggregation. Figure 1 outlines the overall operations of the Federated Learning procedure.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Notation** & **Description** \\ \hline \(C=\{C_{1},\ldots,C_{k_{t}}\}\) & The clustering solution in round \(t\) \\ \hline \(\mathcal{M}\) & The global model \\ \hline \(\mathcal{M}^{*}\) & The optimal global model \\ \hline \(\mathcal{M}_{t}\) & The global model at \(t\)th round \\ \hline \(\mathcal{M}_{t}^{i}\) & The local model of worker \(w_{i}\) at round \(t\) \\ \hline \(F(.)\) & The objective function of the global model \\ \hline \(F_{i}(.)\) & The objective function of the local model of worker \(w_{i}\) \\ \hline \(T\) & Maximal number of communication rounds \\ \hline \(E\) & The number of local epochs \\ \hline \(\eta\) & Learning rate \\ \hline \(g_{t}^{i}\) & The gradients computed using back-propagation \\ \hline \(s(.)\) & Silhouette Index score \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: The general operations of the Federated Learning process.  
Data are protected, with private access for each worker. Thus, model training occurs locally on each worker's side. In this context, we assume that each worker agrees on the same learning task and the model parameters throughout the training process. In particular, the proposed FL model updates the global model only with local model parameters from a few workers that are considered representative. Such workers are selected at each training round by identifying the highest quality of the local model produced of the worker. The selection policy is assumed to be implemented in a server, i.e., a central node selects a representative of the cluster with the highest accuracy. Furthermore, we assume that the server is always reachable by the workers. Finally, our proposed technique works by following this iterative collaboration between the central server and the workers.
###### Abstract
In Central Asia, generally, WUE is negatively correlated with drought, and the response of WUE to drought changes in various areas and for different vegetation types [21]. [PERSON] [22] studied young forests in northern China and found that drought events increased autumn WUE and decreased summer WUE, and the effect of long-term drought impacts was greater than that of short-term drought. With a low latitude, Guangxi is located in southwestern China, having a meso-subropical monsoon climate [23]. As one of the areas with more concentrated karst landforms in China, the karst land areas in Guangxi total 83,500 km\({}^{2}\), accounting for about one-third of its total area. However, the soil in karst areas is relatively loose and weak in water retention capacity, coupled with the uneven spatial and temporal distribution of rainfall in the region, leading to a number of problems in the utilization of water resources. The Li River is located in the northeastern part of Guangxi, and it belongs to the upper reach of the Gui River in the Xijiang River system of the Pearl River Basin. The Li River Basin has a typical karst landform, which is one of the representative regions of global karst landforms [12]. In view of the special characteristics of the karst landform in the Li River Basin, combined with the background of the global climate anomalies and the frequent occurrence of extreme events, it is vital to analyze the spatial and temporal characteristics of the ecosystem WUE using meteorological factors (air temperature and precipitation) and remote sensing indices, for example, the NDVI, LAI, and SPEI.
## 2 Methodology  
Since compact polarimetric SAR systems have been gaining increasing attention, the aim of this study is to assess the performance of the DCP mode by comparing its information content with that of the FCP mode in the case of rice backscattering. For this task, it is required to derive and evaluate the compact versions of eigenvalue/eigenvector-based and model-based target decompositions. The relevant scattering decomposition methods for both the FCP and DCP modes are discussed in the following. The DLP version of eigenvalue/eigenvector-based decomposition is also explained to compare the CP-SAR performance with LP-SAR.
## 8 Future Work  
To address the limitations and challenges outlined in Section 6, future research should prioritize exploration in the following domains:  
* Broader Dataset Evaluation: Future investigations should involve testing the Transformer-CNN across a more diverse range of datasets, including KDDCup99 [36], NSL KDD [29], and newer datasets such as CSE-CIC-IDS2018 [34], and IoT23 [16]. This approach will provide insights into its robustness, generalization potential, and effectiveness in addressing emerging attack vectors.
* Data Preprocessing Refinement: The data preprocessing procedures should be meticulously refined and customized for each dataset to achieve optimal model performance. This entails experimenting with various preprocessing techniques and analyzing their effects on model results. Comprehensive discussions of these preprocessing strategies are extensively covered in Section 3.2 and 4.1 of the manuscript.
* Model Adaptation and Hyperparameter Optimization: Ongoing investigation into model adaptation techniques is essential, emphasizing the refinement of the hyperparameter optimization process tailored to various datasets. This process should undergo systematic analysis to uncover best practices for effectively adapting the model to different data environments. Detailed discussions of these aspects are presented in Section 3, specifically in Section 3.3.4.  
* Scalability and Computational Efficiency: It is imperative to enhance the model's computational efficiency and scalability, enabling it to effectively manage larger datasets and more intricate network traffic scenarios without sacrificing performance.  
Conceptualization, H.K. and M.M.; Methodology, H.K. and M.M.; Software, H.K. and M.M.; Validation, H.K. and M.M.; Writing--original draft, H.K. and M.M.; Supervision, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets used in our study, NF-UNSW-NB15-v2 and CICIDS2017, are publicly available. Below are the URLs for the datasets: NF-UNSW-NB15-v2: [[https://staff.itee.uq.edu.au/marius/NIDS_datasets/](https://staff.itee.uq.edu.au/marius/NIDS_datasets/)]([https://staff.itee.uq.edu.au/marius/NIDS_datasets/](https://staff.itee.uq.edu.au/marius/NIDS_datasets/)) (accessed on 15 December 2024); CICIDS2017: [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed on 15 December 2024).  
The authors declare no conflict of interest.
## 1 Introduction
### Challenges in Traditional Detection Methods  
The detection and prevention of stuck pipe incidents in oil and gas drilling have historically relied on conventional monitoring techniques that depend on real-time surface data, historical well records, and the expertise of drilling engineers [29; 30; 31]. These methods rely on the measurement of variations in weight on bit, torque, hook load, rate of penetration, and standpipe pressure to sense anomalies representative of pipe sticking. While such indicators can be valuable in providing insight, they are typically reactive and non-predictive in nature and, consequently, only identify occurrences of a sticking pipe once they have occurred or when mitigation becomes increasingly difficult. Manual observation also involves subjectivity and variability, as different engineers may have different interpretations of the same information and, consequently, response times may be delayed. Apart from this, conventional methods of sticking pipe detection are incapable of coping with the non-linearity and dynamics of the interactions between the drilling parameters, the formation characteristics, and the fluid dynamics, and consequently, early diagnosis becomes very challenging. Due to the fact that the conditions of drilling are very different in different well environments, surface-based observation by itself typically fails to capture downhole complexities, leading to sticking pipe occurrences.  
Traditional detection methods lack real-time predictive capability, and they lead to longer NPT and higher cost. Due to the reactive nature of these methods, they are inappropriate for demanding drilling, e.g., in directional and extended-reach wells. Historical data analysis is undermined by availability and quality and might not reflect the current conditions. Deeper formations also complicate detection with pressure, temperature, and geomechanical uncertainties. Furthermore, the vast, multidimensional drilling data are difficult for drilling engineers to interpret manually in real time.
## 3 Methods  
### MSS-YOLO Network Structure  
The YOLO framework has been widely used in the field of deep learning-based object detection, and its network structure includes three important components: Backbone, Neck, and Head. The Backbone uses a convolutional module to extract target features. The Neck component adopts a multi-scale feature fusion technique, which is capable of fusing different stages of feature maps from the Backbone to enhance the feature representation. The Head component is responsible for the final object detection and classification task, in which the detection head contains convolutional and deconvolutional layers to generate the detection results. YOLO's end-to-end detection and efficient inference speed enable it to adapt to most complex industrial scenarios.  
However, although YOLO is able to achieve high-precision object detection in different industrial fields, there are still some problems in the task of identifying miners in a fully mechanized mining face. First of all, the complex environment of underground coal mines, such as high water mist, high dust, and uneven lighting, causes motion blurring of personnel features in images, leading to problems such as omission and misdetection in the algorithm. At the same time, due to the influence of the harsh environment, personnel features at long distances are easily confused with background features, which further leads to the failure of the object detection algorithm under these conditions. In addition, the computational resources in underground coal mines are limited, and most of their devices are edge computing devices. Although the mainstream versions of the YOLO object detection framework have gradually included lightweight structure designs, when running on the equipment of a fully mechanized mining face, there are problems of computational overload, high latency, and high power consumption, making it difficult to effectively complete the detection task.  
Therefore, to address the above problems, this paper constructs the MSS-YOLO network based on the YOLOv8 network architecture, combined with the target characteristicsof the miners in the fully mechanized mining face of the coal mine. The structure of the MSS-YOLO network is shown in Figure 3. Among the features, the designs of the MSEE (M), SCDH (S), and SPSC (S) modules are elaborated in Section 3.1.1, Section 3.1.2, and Section 3.1.3, respectively.
## 2 Results and Discussion
### Volatile Compounds
#### 2.3.4 Hydrocarbons  
Although 12 alkanes were identified, the relative contents were very low. A series of \(n\)-alkanes (C\({}_{13}\)-C\({}_{16}\)) existed in all five pears. Alkenes account for 0.56-5.88% in total volatile compounds (Figure 4). A total of 30 alkenes were detected, comprising aliphatic alkenes (5), aromatic alkenes (6) and terpenes (19). Styrene was previously identified in Chinese white pear [5]. In addition to styrene, aromatic alkenes identified in this study comprise 1-propenylbenzene, 1-ethenyl-3-ethylbenzene, 1-ethenyl-4-ethylbenzene, 1,4-dethenyl benzene, 1,3-diethenylbenzene. Terpenes which play important role in fruit flavors have been identified in pears even if their contents were much lower than other compounds. Among these terpen, \(\beta\)-myrcene (Grassy, piney), (_Z_)-\(\beta\)-ocimene (floral, citrusy) and limonene (citrusy) are three monoterpenes [30]. Furthermore, \(\beta\)-myrcene has been previously identified in mango [31], apricot [16] and lychee [24]. In this study, four isomers of farnesene were detected for the first time, including (_E_)-\(\beta\)-farnesene, (_Z_,_E_)-\(\alpha\)-farnesene, \(\alpha\)-farnesene and (_Z_,_Z_)-\(\alpha\)-farnesene. \(\alpha\)-farnesene is the only alkenes found in five pear cultivars and accounted for the highest proportion in alkenes. (_E_)-\(\beta\)-farnesene, (_Z_,_E_)-\(\alpha\)-farnesene and (_Z_,_Z_)-\(\alpha\)-farnesene were also identified in four pear cultivars other than Packham. (_E_)-\(\gamma\)-bisabolene, (_Z_)-\(\gamma\)-bisabolene and \(\alpha\)-humulene are major volatile compounds in carrots [32; 33]. It is the first time that (_E_)-\(\gamma\)-bisabolene and (_Z_)-\(\gamma\)-bisabolene were found in pears. The two isomers existed in all pears apart from Packham. The \(\alpha\)-humulene contributing to the woody smell [32] was identified in Packham and Clapp. Other terpen such as \(\alpha\)-cubenene, copaene, \(\alpha\)-muvurolene, (+)-\(\delta\)-cadinene, _cis_-calamene, \(\alpha\)-calacorene also play important role in pear aroma.  
In contrast to other chemical classes of volatiles, arenes are minor components of total volatiles. A total of 19 arenes were identified in this study. In addition to benzene and benzene homologous compounds, polycyclic aromatic hydrocarbon 2-methylnaphthalene, 1-methylnaphthalene, naphthalene were also found (Table S2).
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 8 Simulation Results
### Simultaneous Relative Pose Measurement Assessment  
For CL, the number of relative measurements plays a significant role in lowering uncertainty. Therefore, we investigate how the error for a given vehicle (and across all vehicles) is affected by sequentially adding simultaneous measurements by other vehicles. We also assess what happens when vehicles make redundant measurements, such as two vehicles simultaneously taking relative measurements and exchanging information with each other. Hence, we consider two networks, one with all six vehicles, \(n=6\), for assessment of additional simultaneous measurements (Scenario 1), and another with only the first three vehicles, \(n=3\), for the assessment of redundant measurements (Scenario 2). In each scenario, we start with one vehicle taking the relative measurements of another vehicle. In subsequent cases, we continue to add more relative measurements to assess their contribution.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 9 Anticancer Drugs  
Anticancer drugs often derive from nature and typically have more rings and more stereogenic centers in their structures, with respect to the synthetic ones. The antibiotics produced from microbes, such as doxorubicin, bleomycin, actinomycin, and mitomycin C, have been demonstrated to possess good anticancer properties [117]. Moreover, plant-derived compounds are used widely as antitumor agents, namely, the _Vinca_ alkaloids, the campotthecins (**42**), the epipodophyllotoxins (**43**), and the taxanes [118]. Moreover, flavanoles and flavanones are natural compounds that present a stereogenic center and, specifically, naringenin (**44**), hesperetin (**45**) and hesperidin, the main bioactive polyphenols in vegetables and citrus fruits, have demonstrated antitumor activity [119; 120]. They may exist as both enantiomers, even though in nature they are mainly present as (\(S\))-enantiomers [121]. The major catechin found in green tea, (\(-\))-epigallocatechin-3-gallate (**46**), showed a potent antioxidant activity through its ability to scavene free radicals and chelate metal ions, and also showing antitumor effects [122; 123]. Other anticancer drugs with different mechanisms of action are chiral. In November 2018, (\(S\),\(R\))-enantiomer of larotectinib (**47**), a chiral small molecule acting as inhibitor of tropomyosin receptor kinase (TRK), received its first global approval in the USA: (\(i\)) for the treatment of adult and pediatric patients with solid tumors that have a neurotrophic receptor tyrosine kinase (\(NTRK\)) gene fusion without a known acquired resistance mutation; (\(ii\)) for metastatic solid cancers or where surgical resection is likely to result in severe morbidity, and (\(iii\)) for those patients that have no satisfactory alternative treatments or that have progressed following treatment [124; 125]. The chiral drug (\(S\))-niraparib (**48**) (Zejula(r)) is a poly (ADP-ribose) polymerase (PARP) inhibitor, which is approved for the maintenance treatment of adults with advanced ovarian, fallopian tube, or primary peritoneal cancer with complete or partial response to platinum-based chemotherapy [125; 126].
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 4 Conclusions  
In this paper, we propose a robust localization model for detecting and locating audio copy-move forgery by CNN-based spectral analysis. The localization model directly analyzes the extraction of Mel-spectrograms without pre-segmentation of audio. The Correlation Detection Module designed in the model enables the neural network to automatically learn and analyze similar regions in the feature map without relying on manual or heuristic thresholds to determine feature similarity. Moreover, the localization model can visually locate the forged area through the Mask Decoding Module.  
The experimental results show that the localization model can effectively locate audio copy-move forgery segments. Meanwhile, the model has suitable robustness against conventional signal processing attacks, such as adding noise, filtering, resampling, and MP3 compression.  
Although the model achieves suitable overall performance, it struggles to accurately identify forgeries when faced with noisy audio, relying solely on deep features extracted from the Mel-spectrogram. The subsequent work will attempt to analyze multiple features and explore detecting different types of tampering.  
Conceptualization, W.Z. and Y.Z.; methodology, W.Z. and Y.Z.; software, W.Z.; data curation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, Y.Z., Y.W. and S.Z.; visualization, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62072057, in part by the Natural Science Foundation of Shanghai under Grant No. 17 ZR1411900, in part by the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security under Grant No. AGK20190005, and in part by Innovation Fund for Industry-University-Research of Chinese Universities under Grant 2021 ZYB01003.  
Not applicable.  
The data presented in this study are available on request from thecorresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
###### Abstract  
This paper focuses on the design of a distributed algorithm for generalized eigenvalue problems (GEPs) in one-shot communication. Since existing distributed methods for eigenvalue decomposition cannot be applied to GEP, a general one-shot distributed GEP framework is proposed. The theoretical analysis of the approximation error reveals its relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers. If the symmetric data covariance has repeated eigenvalues in GEP, e.g., in canonical component analysis, we further modify the method for better convergence and prove the necessity experimentally. Numerical experiments validate the effectiveness of the proposed algorithms both on synthetic and real-world datasets.  
generalized eigenvalue problem; canonical correlation analysis; distributed algorithm +
Footnote †: journal: Applied Sciences  
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Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 5 Results and Discussion
### Training Time  
Figure 6 shows the association between the number of samples and the training time for all three algorithms. When the number of samples increases, all the algorithms' training times increase, where kNN shows the lowest training time while FFNN gives the highest training time. It was observed that from 200 samples to 1200 samples, the training time increased exponentially. Additionally, after 1200 samples, it showed stableness. When comparing the training time of FFNN, it was higher compared to the other algorithms tested. This could be due to the computational complexity of the neural network. To overcome this, the stochastic gradient descent can be proposed. Stochastic gradient descent works by randomly selecting a small number m, chosen haphazardly training inputs. The kNN showed less training time because it does not learn a discriminative function from the training data.
# Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution
## 2 Theoretical Background
### Homology and Persistent Homology  
Homology, another fundamental concept in algebraic topology, provides a means by which to count the number of holes in each dimension in a space. More precisely, it associates a sequence of abelian groups, known as homology groups, with each topological space. These groups offer an intuitive count of \(n\)-dimensional holes; for instance, the 0 th homology group counts connected components, the 1 st counts \"loops\", and the 2 nd counts voids.  
Persistent homology, a specialized form of homology used in TDA, introduces an additional dimension: scale, or persistence. Its main objective is to measure the persistence of topological features, such as connected components (0-dimensional holes), loops (1-dimensional holes), and voids (2-dimensional holes), across various scales [27]. This method helps in distinguishing significant features from noise by quantifying how long each topological feature persists as the scale changes.  
The process begins with a point cloud dataset, around each point of which balls of a certain radius are grown (as illustrated in Figure 1). As the radius increases, these balls intersect, forming shapes and allowing the tracking of the emergence and disappearance of homological features such as connected components or holes. These intersections form simplices: a 1-simplex (edge) is formed when the balls of two points overlap, a 2-simplex (triangle) forms from three overlapping points, and so forth. With the continued increase in radius, more simplices are added, creating a sequence of spaces known as a filtration. This filtration, alongside the corresponding homology groups, captures the evolving topological features of the data as the scale parameter (radius) changes.