esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Bioactive Properties in vitro
#### 2.3.3 Antimicrobial Activity Evaluation  
The following Gram (+) bacteria: _Staphylococcus aureus_ (ATCC 11632), _Bacillus cereus_ (food isolate), and _Microococcus flavus_ (ATCC 10240), as well as Gram (-) bacteria _Enterobacter cloacae_ (ATCC 35030), _Salmonella typhimurium_ (ATCC 13311), and _Escherichia coli_ (ATCC 25922) were selected to test the antibacterial activity of the extracts. Fungi _Aspergillus fumigatus_ (ATCC 9197), _Aspergillus versicolor_ (ATCC 11730), _Aspergillus niger_ (ATCC 6275), _Penicillium funciculus_ (ATCC 36839), _Penicillium verrucosum_ var. _cyclobium_ (food isolate), and _Trichoderma viride_ (IAM 5061) were selected to test the antifungal activity of the extracts. The microorganisms were obtained from the Mycological Laboratory, Department of Plant Physiology, Institute for Biological Research \"[PERSON]\", University of Belgrade, National Institute of Republic of Serbia.  
The extracts were redissolved in 5% dimethyl sulfoxide (DMSO) and the microdilution method was used as previously mentioned by the authors [45]. The results were presented as minimum inhibitory/bactericidal concentrations (MICs/MBCs) in case of bacteria and minimum inhibitory/fungicidal concentrations (MICs/MFCs) for fungi; E211 (sodium benzoate) and E224 (potassium metabisulphite) were used as positive controls and 5% DMSO was used as a negative control.
## 6 Evaluation Metrics of the Network Performance
### Throughput  
The quantity of successful transmissions via a communication channel in a given unit of time is known as throughput. Any network's goal is higher throughput. Average throughput is assessed over the network under the proposed architecture. In order to verify the consistency of the suggested technique, a throughput variation graph utilizing OPNET is also given [52]. Throughput \(Th\) refers to the data ratio of the received at the receiver node from the sender node during a given time [53].  
\[\text{Th}=\frac{D_{P}\times P_{s}\times 8}{T_{S}MP} \tag{7}\]  
where \(D_{P}\), \(P_{s}\), and \(T_{S}MP\) are delivered packets, packet size, and total simulation period, respectively.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
###### Abstract
At present, it is a challenge to integrate these observations and make them available for easy access and evaluation. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is located in Greenbelt, Maryland, USA [9]. As one of the 12 NASA data centers in Earth Sciences, GES DISC is a multi-disciplinary, satellite and model assimilation data center [9]. GES DISC hosts data products from several important NASA satellite missions for tropical meteorology and climatology, as well as data assimilation projects such as the Tropical Rainfall Measuring Mission (TRMM) [10, 11, 12, 13, 14], the Global Precipitation Measurement (GPM) Mission [15, 16, 17, 18] and the Modern-Era Retrospective analysis for Research and Applications (MERRA) [19]. Over the years, data services have been developed at GES DISC to facilitate data discovery, access, distribution, analysis and visualization like Giovanni [20,21], a powerful online analysis and visualization tool without the need to download data and software. Despite all the current services and tools being developed for improving data access, still quite a number of challenges remain in finding data and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. For example, finding the right datasets for tropical meteorology and climatology from over 1200 datasets at the GES DISC archive collection could be a daunting task. A collection of datasets, selected by data experts at GES DISC, can potentially help discovering and assessing the suitable datasets for research. In the future, more of such a kind of dataset collections, not only from GES DISC, but also cross multiple NASA data centers (also known as the Distributed Active Archive Centers or DAACs) [22,23] will also be made available to help research in other disciplines because interdisciplinary research often requires data from multiple sources such as NASA DAACs. In this article, we list and describe the major NASA satellite remote sensing and model reanalysis datasets and services at GES DISC for tropical meteorology and climatology along with examples of data and service usage, in hope that users can properly utilize the information to better facilitate their research and applications. The paper is organized as follows: Section 2 describes satellite missions, model projects and their data; Section 3 addresses data services; Section 4 provides examples; followed by summary and future plans in Sections 5 and 6, respectively.
## 3 Experiment Workflow
### Feature Engineering
Furthermore, with too many features, models may fit the noise in the training data rather than the underlying  
Figure 4: An example showing how significant and irrelevant features affect clustering. Feature a (**a**) and feature b (**b**) alone are not sufficient to cluster the data with satisfactory accuracy. The combination of these two features (**c**) makes the clusters visible. Adding an irrelevant feature feature c (**d**) also does not generate acceptable clusters. patterns, leading to poor generalization on unseen data. By filtering out these features, the model can focus on the most informative aspects of the data. As the name suggests, the software implements hypothesis tests to determine the features' significance. For our data, the Kendal rank hypothesis was applied. In the case of binary features or targets, the exact Fisher test of independence and the Kolmogorov-Smirnov test were used. The decision about the extracted features was made using the Benjamini-Yekutieli procedure [27]. To find the optimal feature subset, a feature selection method has to evaluate a total of \(2^{m}-1\) subsets, where \(m\) is the total number of features in the dataset. Such computations are infeasible even for a moderately large \(m\). Hence, multiple heuristic methods exist which can find a sufficiently good subset. These methods can be divided into three groups, as follows: filter, wrapper, and embedding methods. Filter methods assess the significance of features by examining their statistical characteristics, without relying on specific ML algorithms. Filtering ranks individual features or evaluates entire feature subsets. Wrapper methods evaluate subsets of features by training a model and assessing its performance. Embedded methods select features as part of the model training process. They combine the advantages of filter and wrapper methods. For our purposes, we selected a filter algorithm implemented in tsfresh. Compared to the wrapper and embedded techniques, filtering is independent of any ML model. The filtering algorithms have a straightforward design that typically utilizes a simple search strategy, resulting in high computational efficiency. From every network trace, we extracted a continuous subtrace that accounted for 70% of the whole trace and concatenated them. We submitted these traces to the filtering algorithm, which generated the most significant features. We used the remaining 30% of the data for testing the clustering performance.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
###### Abstract  
Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 ([EMAIL_ADDRESS]) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network's rank-1 performance by 3.86%.  
transmission tower re-identification; transmission tower detection; YOLO; triplet loss +
Footnote †: journal:  
0000-0001-9399-2233][PERSON]
## 4 Experiments
### Results and Analysis
#### 4.2.1 Overall Performance Comparison
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **RE (\%)** & **ART (s)** & **FPR (\%)** & **SSS** & **AI** & **RPI (\%)** \\ \hline TRB & \(72.3\pm 2.1\) & \(45.6\pm 3.2\) & \(8.9\pm 0.7\) & \(0.71\pm 0.03\) & \(0.65\pm 0.04\) & \(72.6\pm 1.8\) \\ MLER & \(78.5\pm 1.8\) & \(38.2\pm 2.9\) & \(6.7\pm 0.5\) & \(0.75\pm 0.02\) & \(0.72\pm 0.03\) & \(79.7\pm 3.4\) \\ DQN & \(81.2\pm 1.9\) & \(32.5\pm 2.7\) & \(5.8\pm 0.6\) & \(0.79\pm 0.02\) & \(0.76\pm 0.03\) & \(82.5\pm 2.7\) \\ A3C-Security & \(83.7\pm 1.7\) & \(29.8\pm 2.5\) & \(5.2\pm 0.4\) & \(0.82\pm 0.02\) & \(0.79\pm 0.03\) & \(74.5\pm 1.9\) \\ HRDL & \(85.4\pm 1.6\) & \(27.3\pm 2.4\) & \(4.7\pm 0.4\) & \(0.84\pm 0.02\) & \(0.81\pm 0.02\) & \(85.6\pm 3.7\) \\ ACNN & \(86.8\pm 1.7\) & \(26.4\pm 2.3\) & \(4.9\pm 0.4\) & \(0.83\pm 0.02\) & \(0.80\pm 0.03\) & \(88.1\pm 0.8\) \\ DRL-Sim & \(84.9\pm 1.8\) & \(28.1\pm 2.4\) & \(5.1\pm 0.5\) & \(0.81\pm 0.02\) & \(0.78\pm 0.03\) & \(88.3\pm 2.0\) \\ ARCS & \(89.2\pm 1.5\) & \(23.5\pm 2.2\) & \(3.9\pm 0.3\) & \(0.88\pm 0.02\) & \(0.85\pm 0.02\) & \(92.5\pm 2.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the different methods. effectively handles 92.5% of incidents within the predefined response time threshold, ensuring timely mitigation of threats. Compared to ACNN (88.1%), DRL-Sim (88.3%), and the other baselines' RPI values, ARCS's superior RPI underscores its capability to maintain high performance under realistic network conditions. This real-time efficiency, combined with its outstanding response effectiveness and low false positive rate, positions ARCS as a comprehensive solution for automated cybersecurity incident response--one that is capable of addressing both the speed and accuracy demands of modern cybersecurity challenges.
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
###### Abstract
Because of the different set selection, a sample of voice pathology, a vocal feature and classification method are applied in the experiments. This drives us to the following conclusions: most works analyse a single speech task, mainly the sustained phonation of the vowel /a/ (language-independent speech task)  
* The majority of studies focus only on analyzing a single voice segment. In particular the vocalization of the vowel /a/ in the independent language speech task. * The most analysis is conducted on limited acoustic pathologies from the SVD, AVPD and MEEI databases. * The conventional dysphonic feature is the feature most extracted to determine the voice aspect for a particular voice pathology. * Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM) are the most conventional machine learning methods employed for vocal-pathologies detection. In this paper, we attempt to make a comparative analysis with published results on the speech recordings of the vocalization of the vowel /a/. In spite of other studies, the comparison analysis will cover a bigger segment of SVD [21]. In order to widen the problem scope and maximize the generality of application of the proposed method, we will not limit the vocal pathologies in the database to the popular subtest that is commonly used in the literature. Therefore, a big number of voice pathologies with minimum voice recordings will be included in our dataset for this study. As far as we are concerned, no study presented in the literature [22], is based on a deep learning method for detection of vocal pathologies. In this study, a conventional voice pathology detection method is used and combined with a vocal feature selection method. We also will employ a gradient boosting method as a classifier. An investigation of anomaly detection methods usage is also conducted in this study to manage the wide distribution of vocal pathologies that are associated with a limited number of voice phytology recordings. In this study, we propose automatic rapid voice pathology detection based on a deep learning classifier, namely a DNN system for voice pathology detection. Our proposed methods are applied through four primary phases, including preparation of dataset following by learning process phase then a training and validation phase and finally an inference processing phase. This paper is organized as follows: An overview of currents studies and some related works are presented in Section 2. In Section 3, we describe our proposed voice pathology detection based on deep learning. We present the experimental results in Section 4. Finally, we present our conclusions and directions of future research in Section 5.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 6 Strategies for Optimal and Scalable Code Selection
### Performance Analysis of Surface Codes with Varying Rounds  
To begin with, we study the performance of the surface codes in relation to rounds (which denote the number of times an error correction code is executed and stabilizers are measured). Theoretically, the code becomes more efficient with an increasing number of rounds. In Figure 7, we apply two sets of physical noise levels, \(10^{-4}\) and \(10^{-3}\), and execute distance-3 and -5 rotated surface codes for the first set, and distance-7, -8, and -9 for the second set. We fit a logarithmic curve to the collected data to study its pattern and behavior. The choice to vary the physical noise levels between two different sets of distances is predicated upon the results from Figures 4 and 6. A physical noise of \(10^{-4}\) is  
Figure 6: Logical error rate vs. physical error rate in rotated surface codes for (**a**) code-capacity noise, (**b**) phenomenological noise, and (**c**) circuit-level noise models. These plots reveal the relationship between logical and physical error rates under different error models, with the surface code’s distance as a variable. (**d**) Logical error rate vs. code distance in rotated surface code depicting the superior performance of QECCs at lower physical noise levels. A threshold physical noise of \(10^{-2}\) is observed, beyond which, despite increasing the distance, QECC performance ceases to improve due to excessive physical noise. The black dotted line represents the typical error rate of modern processors.  
relatively low for a surface code distance \(\geq 7\), while a physical noise of \(10^{-3}\) would prove excessive for a \(distance\leq 5\).  
Figure 7a illustrates the behavior of the logical error rate as we vary the rounds of a distance-3 and -5 rotated surface code. As anticipated, the logical error rate decreases with each additional round. This is because the measure used in our plots is the logical error rate per round, meaning that as rounds increase, the effectiveness of error detection and correction also increases, thereby reducing the error rate per round. Likewise, in Figure 7b, we demonstrate that the logical error rate reduces with an increment in rounds for distance-7, -9, and -11 rotated surface codes, even though the noise level is higher than the previous set. Figure 7c-e display magnified plots for distances 5, 9, and 11 to provide a clear visualization of the line curvatures, reinforcing our understanding that higher distances of a surface code with more rounds perform better in a given scenario. However, this improvement comes with an overhead in terms of the number of qubits and gate count as shown in Table 1.
## 5 Experiments and Performance Evaluation
### Effect of End-to-End Delay Constraint  
In the optimal arrangement, some possible routes may not satisfy the end-to-end delay constraint, which results in low network reliability. The most effective way is adopting nodes with a large communication range. Assuming the end-to-end delay is mainly caused by multi-hopping and not accounting for queuing delay or channel collision, the latency incurred due to links is 1.2 ms [26]. Figure 8 shows the effect of end-to-end delay constraint when two nodes are deployed at each tower. Given a small deadline requirement to get a quick action of protection, it results in large-communication-range nodes being used. Notice in the graph that the communication range should cover two towers or above to achieve acceptable network reliability (over 0.9). For a given constraint, this graph can be used to choose the communication range of nodes and acquire acceptable reliability.
## 3 Results  
### XRD Analysis of the Untreated TCP and ACP Samples  
Figure 1a shows the XRD patterns of the untreated TCP. We observed that the raw material resembled the structure of CaP closely, indicating that TCP accounted for most of the TCP/HAP biphasic coral powder. According to the (0210) plane in the XRD pattern, the TCP sample can be identified as \(\beta\)-TCP. In addition, based on JCPDS card 00-009-0169, the result was a typical tricalcium phosphate structure, and its quality was indexed. Furthermore, no additional diffraction peaks were found, indicating that the untreated TCP had a single tricalcium phosphate structure. Figure 1b shows the XRD patterns of the untreated ACP. The results show a broadband of ACP presence in the XRD pattern. Moreover, a weak peak of Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\) was also found, with the characteristic peak of the (211) plane as a reference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **Brand** & **Active Ingredients** & **Inactive Ingredients** \\ \hline iBrite & & & saccharin sodium, FD\&C blue \\ Nano-Bio & Pac-Dent Inc., & sodium fluoride, & \#1, FD\&C green \#3, glycerin, hydrated silica, polyethylene glycol 400, water, sodium \\ Activation & USA & potassium nitrate & \#1, lauryl sulfate, sodium \\ Toothpaste & & & pyrophosphate, sodium citrate, \\  & & & sodium phosphate, tribasic, \\  & & & carboxy methylcellulose \\  & & & sodium \\ Advanced & Colgate- & & & vinyl ether/maleir anhydride \\ Whitening & Palmolive & sodium fluoride, & copolymer, sodium lauryl \\ Toothpaste & Company, USA & triclosan & sulfate, sodium hydroxide, \\  & & & propylene glycol, saccharin \\  & & & sodium, carboxy \\  & & & methylcellulose sodium, \\  & & & titanium dioxide, carrageenan \\ \hline  & & & water, butylene glycol, panax \\  & & & ginseng root extract, vinegar, \\  & & & pruns murine fruit extract, \\  & & & allium sativum (garlic) bulb \\  & & & extract, platycondon \\  & & & grandilformu root extract, \\  & & & codonopsis lancolata root \\  & & & extract, rubus fruitucscus \\  & & & (blackberry) fruit extract, \\  & & & acamothapax sentiscous \\  & & & (eleuthero) root extract, glycine \\  & & & soja (soybean) seed extract, \\  & & & oryza sativa (rice) extract, \\  & & & sesamum indicum (sesame) \\  & & & seed extract \\ \hline BioMin F & BioMin & & glycerin, silica, polyethylene \\ Toothpaste & Technologies & fluoro calcium & glycol 400, sodium lauryl \\  & & & sulphate, titanium dioxide, \\  & & & aroma, carbomer, potassium \\  & & & acesulfame. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ingredients of the investigated toothpastes.
## 6 Conclusions  
In this paper, we use stochastic geometry approach to determine the energy efficiency of picocell BS density and transmit power for a two-tier HCN. We formulate and derive the expression of the network EE in terms of the MUE density, BS density, target SIR, and the transmit powers of BSs. We propose an alternating optimization scheme to achieve joint power and density optimization. The scheme can achieve optimal pairing of transmit power and density. Simulation results validated the analysis and proved the effectiveness of the EE joint optimization scheme. We found that compared with unilateral optimization of density or transmit power, joint optimization can improve system EE more effectively. This work can offer theoretical references for the design and deployment of dense HCNs.  
Conceptualization, J.Y. and Z.P.; methodology, J.Y.; simulation, H.H. and J.Y.; formal analysis, J.Y. and H.X.; writing--original draft preparation, J.Y. and Z.P.; writing--review and editing, H.X. This work was supported by the National Natural Science Foundation of China No. 61701221, 61901211, the Natural Science Foundation of Jiangsu Province (China) No. BK20160781, Jiangsu Higher Education Institutions Natural Science Foundation No. 18 KJB510017, Nantong Science and Technology Project No. JC2018127, and Research Initiation Fund Project of Nanjing Institute of Technology No. YKJ201869. The authors declare no conflict of interest.  
Figure 4: Network EE with fixed picocell BS density and optimal picocell BS density. Note: SIR = signal-to-interference ratio.