esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
A PLC-based fractional-order controller design was proposed for input delayed systems, and the non-integer-order controller was compared against integer-order PID controls on an industrial-oriented water tank volume control application [22]. A PLC- and Simulink-based fractional-order controller was presented for an industrial-oriented water tank level  
Figure 1: A typical networked control monitoring system using programmable logic controllers (PLCs.). control system and its desired phase/gain margin is validated through hardware-in-the-loop simulations and experimental measurements [23]. An innovative self-tuning nonlinear control algorithm was proposed for programmable logic controllers, and the performance was experimented with an industrial PLC platform [24]. New concepts and technologies have also been introduced to PLC-based control systems in the past years. For example, the concept of embedded PLCs was presented with the customization of hardware structure and real-time compilation for motion control [25]. The design and implementation of a real-time wireless control and monitoring system was discussed with the potential of utilizing ZigBee protocol [26]. However, no previous research has been done to optimize data packet-based communication for PLC-based NCSs' visual monitoring specifically. In PLC-based NCS, it is important for supervisory engineers to obtain real-time information from remote PLCs, especially each logic sequence in a field PLC is individually coded to sub-networks. This paper aims to investigate a typical visual monitoring scenario between supervisory computers and field PLCs, and then optimize the data packet size of communication dynamically in order to meet the real-time requirement of human visual response. The rest of the paper is organized as follows: In Section 2, the typical visual monitoring scenario is introduced, and its data packet-based communication is briefly described, before a dynamic optimization solution is fully developed to determine the optimal data packet size with the consideration of system settings. In Section 3, a range of experimental results is provided to illustrate the effectiveness and efficiency of the proposed dynamic optimization method. The paper is concluded in Section 4.
## 2 Instrumentation and Methods
### Instruments and Measurements  
Solar global radiation (Q) and direct radiation (D) were measured at a frequency of 1 Hz using radiometers (model TBQ-4-1 and TBS-2, 322, Institute of Jinzhou, Jinzhou, China) at Qianyanzhou Station, 800 m away from the flux tower [35]. Diffuse radiation (S) is derived from Q-D \(\times\) cosZ, where Z is the solar zenith angle. PAR was measured using a LI-1908A Quantum Sensor (LI-COR, Inc., Lincoln, NE, USA, relative error < \(\pm\)5%), except from 1 January to 21 May 2013, when Q and PAR were obtained from the weather station at Qianyanzhou Station. All solar radiation sensors were cleaned every morning and when needed, such as after rain. Detailed explanations of the solar radiation system are reported in [58]. Meteorological parameters (temperature and relative humidity) were measured using a HOBO weather station (Model H21, Onset Company, Bourne, MA, USA) [35]. The measurement frequency was 1 Hz for solar radiation and meteorological parameters.  
CO\({}_{2}\) fluxes, together with another set of solar radiation and meteorological parameters, were measured at a 42-m tower starting in 2003 [59]. The eddy covariance technique has been extensively used for the direct measurement of net carbon exchange between the atmosphere and ecosystems [60]. The eddy covariance system included a 3-D sonic anemometer (Model CSAT3, Campbell Scientific Inc., Logan, UT, USA), open-path CO\({}_{2}\)/H\({}_{2}\)O analyzer (Model LI-7500, Li-cor Inc., Lincoln, NE, USA) and a CR5000 data logger (Campbell Scientific Inc., USA) located 23.6 m above the ground to measure CO\({}_{2}\) flux at the Qianyanzhou subtropical evergreen conifer plantation [59], and observational data (NEE, GPP) measured at 23.6 m from 1 January 2013 to 31 December 2016 were used in this study.  
Additionally, BVOC emission fluxes were measured using a relaxed eddy accumulation (REA) system, which was installed on a platform 23 m off the ground in the flux tower [35]. The REA system was composed of a 3-dimensional sonic anemometer ([PERSON], Traverse City, MI, USA, Model 81000), a data logger (Campbell Scientific, Logan, UT, USA, Model CR1000) and a data acquisition and control unit and was used to collect air samples in stainless steel cartridges filled with Tenax GR and Carbograph 5 TD (Markes International Inc., Gold River, CA, USA). The cartridges were analyzed in the laboratories at the National Center for Atmospheric Research (NCAR) in Boulder, CO, USA and at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP, CAS) in Beijing, China. The procedures for sample analysis by gas chromatograph equipped with flame ionization detector (GC-FID) at the Beijing laboratory (IAP, CAS) were similar to those described by [PERSON] et al. [32; 35]. BVOC emission fluxes were measured from 22 May 2013 to 4 January 2016: air samples of BVOCs were collected around noon or five times with 3 h interval per day from sunrise to sunset, and a more detailed introduction to BVOC studies is reported by [PERSON] et al. [35]. The measurement frequency was 10 Hz over 30 min for CO\({}_{2}\) flux, NEE and BVOC data collection.
## 2 Full DC Microgrid System Modeling  
The DC microgrid system assumes PV sources as renewable energy sources. Due to the characteristic of the intermitted PV power generation, a BS system is added. The DC microgrid system also assumes the power exchange with the public grid to sell or buy the renewable power. If the BS and the public grid cannot deal with the problem of deficient PV power generation, load shedding will occur, and the worst condition is the critical load to be shed. Thus, the backup source DG is necessarily assumed to connect with the proposed DC microgrid system. The DG starts operating thanks to the SC power that compensates for the DC bus-required power. The load is assumed to be controllable by both the user and the microgrid operator. Moreover, the optimization real-time algorithm based on the optimal load combination considering the load characteristics will achieve load optimization. A power management strategy is assumed to manage the power flow aiming to achieve the power balance between power generation and power consumption considering the physical constraints of each component.  
The physical components of a DC microgrid are shown in Figure 1. It consists of PV sources, a BS system, a public grid connection, a DG, an SC, and a DC load. All parts are connected to the common DC bus of the microgrid through their power converters. In order to simplify the DC microgrid system, the converters' efficiency is not considered in the power management strategy.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 2 Materials and Methods
### Computational Platform  
Most of the analyses were run on the Illinois Campus Cluster, with the exception of the TreeMerge analyses on the Cox1-HET dataset as well as the RNASim10k and RNASim50k datasets, which were run on NCSA's Blue Waters. All of the analyses were allowed between 54 Gb and 64 Gb of memory, an amount that is sufficient for all alignments of the sizes that we study, with the exception of one model conditions which involves 50,000 sequences. Unless otherwise specified, no method came close to being bottlenecked by the limitations on allocated memory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **\# Seqs** & **\# Reps** & **Description** \\ \hline \multirow{4}{*}{1000M1-HF} & \multirow{4}{*}{1000} & \multirow{4}{*}{5} & Introduced in [16]. Created by making the 1000M1 model condition from [30] fragmentary (half the sequences with length 25% of the original median sequence length). Sequence evolution under this model uses the standard GTR+G substitution model, enhanced with indels. \\ \hline \multirow{4}{*}{RNASim1000} & \multirow{4}{*}{1000} & \multirow{4}{*}{5} & Randomly sampled for this study from the RNASim million-sequence simulation [28]. The RNASim sequence evolution model is non-standard, including selection to maintain the RNA structure. \\ \hline \multirow{4}{*}{Cox1-HET} & \multirow{4}{*}{2341} & \multirow{4}{*}{10} & Created for this study by evolving sequences down a model tree with different evolutionary parameters for different areas of the tree. This model condition involves substantial heterogeneity across the tree, including heterotachy. \\ \hline \multirow{4}{*}{RNASim10k} & \multirow{4}{*}{10,000} & \multirow{4}{*}{10} & Randomly sampled for this study from the RNASim million-sequence simulation. See comments above for RNASim1000. \\ \hline \multirow{4}{*}{RNASim50k} & \multirow{4}{*}{50,000} & \multirow{4}{*}{10} & Randomly sampled for this study from the RNASim million-sequence simulation. See comments above for RNASim1000. \\ \cline{1-1} \cline{3-3}  & & & Randomly sampled for this study from the RNASim million-sequence simulation. See comments above for RNASim1000. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset overview.  
Most of the multi-threaded methods were allowed between 10 and 16 cores, with few exceptions. Each constraint tree estimation for the 1000M1-HF and RNASim1000 datasets was allowed to run with up to four cores, with IQ-TREE deciding the appropriate number of cores. For the Cox1-HET dataset, constraint trees estimated with IQ-TREE were allowed to run up to 16 cores, and we let IQ-TREE select the appropriate number of cores; most of the constraint tree calculations only used 1-3 cores.  
Constrained-INC and GTM were used in their currently available implementations, taken from the developers' Github repositories, and are single-threaded. TreeMerge was modified to take in pre-estimated RAxML branch lengths (that were computed with two cores), and is otherwise run single-threaded.
## 3 Attacks on AES
### AES T-Table Implementation  
The steps of the round transformation include a non-linear S-box substitution and a matrix multiplication. To reduce the number of operations performed during each round and to improve performance, AES software implementations without special assembly instructions use tables (T-tables) with precalculated values for each of the possible 256 values of an input element. In this work, we evaluate the T-table-based implementation available in OpenSSL version 1.0.1f or newer versions when compiled with the no-asm flag.  
In the aforementioned implementation, the state is represented by four 32-bit variables starting with \(s\) or \(t\), one for each column of the state. The name of the variable starts with \(s\) for odd rounds and with \(t\) for even rounds, considering the first round to be 0. The Most Significant Byte (MSB) of the variable represents the element in Row 0, while the Least Significant Byte (LSB) represents the element in Row 3. Encryption and decryption algorithms use four tables, each containing the result of S-box substitution and part of the matrix multiplication. The tables start with Te for encryption and with Td for decryption. Generically, we denote each of the four tables T\({}_{i}\) (Te\({}_{i}\) and Td\({}_{i}\)), where \(i\) is the column of the multiplication matrix considered in the table. Table T\({}_{i}\) contains the contribution of the element in row \(i\) to each row of the resulting column state. That is, each of the 256 positions of the table (2\({}^{8}\)) contains a 32-bit value, where each byte is the contribution to each row, aligned to perform the addition of the different contributions. The size of each table is 1024 bytes. The column state is calculated by adding (bitwise XOR in GF(2)) the contributions from the different rows and the round key. Therefore, the four tables are accessed for each output column state. An appropriate selection of the input variable s\({}_{j}\) for the different rows is used to perform the ShiftRows step; for the destination variable t\({}_{j}\), variable s\({}_{i+j}\) is used for row \(i\) (index of table T\({}_{j}\)). In this example, and for the entire work, the addition in subindexes \(i\) and \(j\) is done modulus 4. For example, in Round 1, we can obtain t\({}_{0}\) as follows:  
\(\texttt{t}_{0}=\texttt{Te}_{0}[\texttt{s}_{0}\gg 24]\oplus\texttt{Te}_{1}[ (\texttt{s}_{1}\gg 16)\&0 xff]\oplus\texttt{Te}_{2}[(\texttt{s}_{2}\gg 8)\&0 xff] \oplus\texttt{Te}_{3}[\texttt{s}_{3}\&0 xff]\oplus\texttt{rk}[4]\);  
The last round does not include the matrix multiplication. A table for \(S_{RD}^{-1}\) (only InvSubBytes step) is required for decryption algorithm (table Td\({}_{4}\)). As there are coefficients with value 1 in the multiplication matrix of step MixColumns, the table is not needed for the encryption algorithm. It is implicitly available in the Te tables. In this case, Te\({}_{(i+2)mod4}\) is used for row \(i\), because it has coefficient 1 for that row.
## 4 Experimental Verification
### Analysis of Model Noise Resistance Results
Although the ANN + FFT algorithm model retains high diagnostic accuracy even in the high-noise environment, the average diagnostic accuracy in the \(SNR\) range of \(-8\) dB to \(10\) dB is still lower than that of the Propose + FFT model. In addition, the standard deviation error range of the ANN + FFT model is significantly higher than that of Propose + FFT as shown in Table 6, which also reflects the instability of the ANN + FFT model. After the previous analysis, it can be known that the average diagnostic accuracy and error range embodied by different algorithmic models in the three operating conditions are very different, mainly due to the differences in the bearing datasets in the three operating conditions and the influence of the randomly grabbed network input sample data in the experiment. After comparison with the other five models, it can be concluded that both the average accuracy and error range of the FFT signal processing combined with the network model proposed in this paper (Propose + FFT) are better than those of the other five models under the three working conditions.
## 3 Results and Discussion
### Characterization of Gypsum/RT18 HC@AC Composite Boards
#### 3.2.3 Mechanical Properties of GB/RT18 HC@AC Composite Boards  
The results of the mechanical properties from the bending and impact resistance tests for the gypsum board and composite GB/RT18 HC@AC board with varying RT18 HC@AC contents are shown in Figures 9 and 10, respectively. The load deformation data reveal a complex relationship between the percentage of additive and the mechanical behavior of the composite boards. At lower concentrations, a positive correlation is evident, suggesting that the incorporation of RT18 HC@AC enhances the material's strength, which is possibly due to improved intermolecular interactions and reinforcement effects. However, as the RT18 HC@AC content increases, a decline in the mechanical strength is observed. This inverse relationship could be attributed to the potential disruption of the gypsum matrix by higher concentrations of RT18 HC@AC particles. As it shown in Figure 9b, the optimal content of the additive onto gypsum boards is 10% w.t., showing that the modulus of rupture is equal to 4.68 MPa, which is much higher than that of the reference gypsum board (2.64 MPa).
## 2 Calculation Methods  
First-principles calculations were performed with the CASTEP code on the basis of the density functional theory (DFT). The stable ground structure of quaternary Heusler alloy TiZrCoIn was obtained by optimizing the structure. On the basis of the stable ground structure, one, two, three, or four Ge atoms were used to replace In atoms in a unit cell. Doping concentrations of 25%, 50%, 75%, and 100% were obtained. For the simulation of small doping concentrations of 12.5%, 6.25%, and 3.125%, TiZrCoIn supercells with 32, 64, and 128 atoms, respectively, were produced. The In atom was replaced by one Ge atom. Then, the correlation property calculations, such as the single point energy, density of states (DOS), and band-structure, were performed on the minimized doped cases obtained from the geometry optimization. In our calculations, all the electronic structure calculations were performed with the spin polarization. Generalized gradient approximation parameterized by [PERSON] [40; 41] were used for the handling of the exchange and correlation term. Electron-ion interactions were disposed with ultrasoft pseudopotentials [42]. In the SCF calculation, a refined \(11\times 10^{-6}\) eV/atom was used as a convergence criterion. For the energy cutoff of the planewave, the basis was set to 310 eV. The calculation was considered converged when the largest gradient was less than 0.002 eV/A. In the property calculations, the 7\(\times\)7\(\times\)7 special k-mesh points in Brillouin zone were applied. The electrons of Ti 3d\({}^{2}\)4s\({}^{2}\), Zr 4d\({}^{2}\)5s\({}^{2}\), Co 3d\({}^{7}\)4s\({}^{2}\), In 5s\({}^{2}\)5p\({}^{1}\), and Ge 4s\({}^{2}\)4p\({}^{2}\) were regarded as valence electrons.
# Decision Support Using Machine Learning Indication for Financial Investment
## 5 Results  
This section will present the results obtained with the algorithms implemented in this work. First, a comparison will be presented regarding the accuracy obtained with each algorithm using the 1998-2019 database, as shown in Figure 9.  
In the graph shown in Figure 9, the Decision Tree has the highest accuracy, with 77%. This is followed by 69% accuracy for the ANNs and 66% for the Logistic Regression. However, to analyze the results in a deeper and more adequate way, it is necessary to analyze the classification metrics, as presented in Figure 10.  
Figure 8: Confusion Matrix of the Logistic Regression.  
Figure 9: Comparison of algorithm Accuracy.  
In the graph shown in Figure 10, the metrics for the three algorithms are presented. The ANN has an accuracy of 0.72, recall of 0.69 and F1-score of 0.71. The Decision Tree has an accuracy of 0.75, recall of 0.80 and F1-score of 0.78. Finally, the Logistic Regression has an accuracy of 0.69, recall of 0.67 and F1-score of 0.68. Decision Tree obtained higher values for its classification metrics compared to the other algorithms. It is worth noting that the Recall of the Decision Tree was higher than the Precision and F1-score. This indicates that the algorithm obtained a low number of false negatives for Class \"1\". Figure 11 shows the metrics obtained with the three algorithms for Class \"0\".  
In the graph shown in Figure 11, the metrics of the three algorithms for Classes \"0\" are presented. The ANN has an accuracy of 0.66, recall of 0.68, and F1-score of 0.67. The Decision Tree has an accuracy of 0.79, recall of 0.73 and F1-score of 0.76. Finally, the Logistic Regression has an accuracy of 0.62, recall of 0.64 and F1-score of 0.63.  
Through the numbers presented in each metric of the graphs presented, it is noted that the Decision Tree presented higher values for all metrics both for Class \"1\" and for Class \"0\". It is worth noting that in the application of this work, bad stocks (Class \"0\") classified as good (Class \"1\") generate worse consequences for the investor than good stocks (Class \"1\") classified as bad (Class \"0\").  
Making an investment in a bad stock generates losses for the investor. However, not making an investment in a good stock does not generate profit, but it also does not generate loss. Therefore, to select the best algorithm for the application of this work, it is necessary to analyze the metrics of the Class \"0\" classifications (stocks considered bad for investment).  
Figure 11: Comparison of Metrics for Class “0” Classification.  
Figure 10: Comparison of Metrics for Class “1” Classification.  
Thus, Decision Tree was really the algorithm that presented the best values for precision, recall and F1-score for the application of this work.
## 4 Discussion
In many non-medical settings where CPR experts are not available, non-expert responders face significant challenges in determining whether defibrillation is required or in recognizing ROSC during cardiac arrest; in this context, the development of AI-based rhythm classification is critically important. Our study's 1D-CNN model demonstrates the potential to provide context-aware guidance to non-experts by automatically classifying cardiac arrest rhythms. Such an approach could be integrated into emergency response tools, including automated external defibrillators, to support rapid and accurate decision-making in critical situations where expert interpretation is unavailable. Despite the promising results, our study has several limitations that should be addressed in future research. Firstly, the data were collected from a single hospital, which may limit the model's generalizability to different populations or healthcare settings. It should be noted that a previous study used 1889 cases from 172 participants in another hospital, applied a 1D CNN and achieved an F1 score of 73.2% for shockable vs. non-shockable rhythm classification [37]. The F1 performance in the current study, 90.8%, is higher than that in this previous study, 73.2%. More examination is needed for greater generalization in this direction. Secondly, the relatively small sample size of 508 ECG segments is modest in the context of deep learning applications, potentially affecting the robustness of our model when applied to more diverse datasets such as independent test sets. Thirdly, hyper-parameter tuning and regularization were beyond the scope of this study. Fourthly, our study did not account for the potential impact of different CPR quality levels on rhythm classification, which could be an important factor in real-world applications. To address these limitations, future research should focus on multi-center studies involving diverse patient populations. This approach would not only validate the robustness of these models across different clinical environments but also provide larger and more varied datasets. Fourthly, incorporating CPR quality metrics (e.g., compression depth, rate and fraction) into the model could yield significant benefits. This integration would not only improve classification accuracy but also provide valuable insights into how CPR quality affects ECG signal characteristics and pulse-generating rhythm prediction. Sixthly, explainable artificial intelligence such as a gradient-weighted class activation map (Grad-CAM) is expected to further the performance of deep learning and expand the boundaries of knowledge on this topic [38]. By considering these factors, future models could offer more comprehensive and accurate assessments during resuscitation efforts, potentially leading to improved patient outcomes.