esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Based on the aforesaid facts, performance improvements can be observed when classification is done using proposed PNN with MCUB in contrast to the standard PNN with MCUB. By considering the improved PNN model with MCUB introduced in the study, 66.37%, 67.02% and 70.12% accuracies can be observed in forecasting the direction of AORD, GSPC and ASPI indices respectively. It is generally accepted that these percentages are high enough to make profit from investments in financial markets. To understand the generalization ability of the proposed PNN model, performance was evaluated for an updated data set which contains three months' daily data just after the ending date of the data set used in the study. Table 7 illustrates that the proposed model (proposed PNN with MCUB) is capable of forecasting the direction of all considered stock indices with similar accuracy for the novel data set. All types of misclassification percentages exhibit similar values when comparing corresponding values of the final proposed model. Therefore, it is evident that the proposed dynamic model is proficient to forecast the direction of a given stock market index for any given time period with higher accuracy. Finally, this study identified an improved PNN model to forecast the direction of a given stock market index by incorporating a suitable non-Gaussian multivariate distribution for calculating probabilities and multi-class undersampling based bagging approach to overcome the class imbalanced problem. Based on past studies, influences from few inter-market, other financial markets and world major commodities were considered in identifying potential inputs to the probabilistic neural network models. Other stock market indices, other financial markets and other commodities may have significant influence on the considered three stock indices. Even though the gross domestic product (GDP), interest rates, Treasury bill rates, etc., affect the stock market indices, they were not considered as potential inputs to the PNN models because of the non-availability of daily data corresponding to those variables. These facts can be identified as limitations of the study. As a future study, the proposed dynamic model with multivariate scaled t distribution and multi-class undersampling based bagging technique can be used to build a computer intensive tool, an attractive, user friendly platform to support stakeholders in decision making. The proposed probabilistic neural network model can be used to forecast the direction of other financial indices like exchange rates, interest rates, Treasury bill rates, etc., with higher accuracy. Moreover, the proposed PNN can be used for classification purposes in other applications such as weather, biology, agriculture, medicine, image processing, natural language processing, marketing research, etc.
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 4 BDS-2/BDS-3 Clock Stabilities and Detum Discrepancy
### BDS-2/BDS-3 Clock Stabilities
Meanwhile, the STD values of broadcast clock offsets for all BDS, GPS and Galileo satellites were calculated corresponding to the GFZ clock products as reference values. Since the update frequency are different for different constellations, the arcs used for statistical accuracy for all constellations were uniform at 1 h. Eventually, the values were averaged over MJD 58815-59214 for all satellites in the same system and listed in Table 2. Owing to the better stability performance, the STD value of broadcast clock offsets for BDS-3 is significantly less than that of GPS and BDS-2 [22].
## 5 Robustness Analysis  
### Analysis of Positioning Error under Different MSR  
According to Equation (32), we set MSR to 0.10, 0.55, and 0.95, respectively. The results are shown in Figure 10.  
Figure 10 shows that when MSR = 0.15, the error of the algorithm under the same orbit type is significantly smaller than MSR = 0.55 and MSR = 0.95. That is to say, the interference in rural or suburban areas is significantly smaller than in cities or canyons and harsh, challenging environments, which is consistent with what is known. Error statistics for these situations are shown in Figure 11.  
Figure 11 shows that even in harsh and challenging environments, we can use the algorithm based on adjacent orbits, and achieve an accuracy of 2.4177 \(\times\) 10\({}^{-4\circ}\), 1.7854 \(\times\) 10\({}^{-4\circ}\) and 4.7087 m in longitude, latitude and altitude respectively, which can also meet the location service needs of most users.
## 4 Conclusions  
In the current work, Cd-I-MA-co-AN was synthesized using the precipitation method. Cd(II) was used as a template ion; acrylonitrile and maleic acid were chosen as functional groups. The surface and composition of Cd-I-MA-co-AN were characterized with UV-vis, SEM and FTIR, which demonstrated that Cd-I-MA-co-AN was successfully prepared. The effect of parameters such as pH, flow rate and the type and concentration of eluent were investigated. The results showed that the adsorption capacity of Cd-I-MA-co-AN under optimal experimental conditions was 20.46 mg g\({}^{-1}\). The adsorption behavior of Cd-I-MA-co-AN obeyed the Freundlich isotherm model. Cd-I-MA-co-AN was shown to be promising for the determination of cadmium ions in water samples. In conclusion, the Cd-I-MA-co-AN exhibited a good adsorption capacity and specific selectivity for Cd(II).  
Conceptualization, A.M.; methodology, A.M.; investigation, A.M.; data curation, L.W.; writing--original draft preparation, A.M.; writing--review and editing, S.A. and A.Y.; visualization, L.W.; supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 52163030.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In addition to these papers, other works discuss the integration of MEMS inertial sensors with a GPS receiver for various applications (see e.g., [12, 13]). Within this context, we propose in this paper a GPS/INS tightly coupled assisted PLL (TCAPLL) architecture aiming to reveal more precisely the advantages, limitations and drawbacks that can be encountered when using MEMS inertial sensors for land navigation. The architecture is implemented using an extended Kalman filter (EKF) that combines pseudoranges and Doppler measurements from a very simple GPS receiver with the INS measurements to obtain a position, velocity and attitude solution, and later send a feed-forward aiding component to the PLL tracking loops to compensate for the dynamics and GPS receiver clock frequency error. A monitoring system is also implemented to check the quality of the aiding information to ensure robust tracking especially when few satellites are being tracked. The performance of the architecture is evaluated by simulations under different scenarios assuming a commercial off-the-shelf low-quality MEMS inertial sensor. The assessment is made in terms of navigation accuracy, tracking robustness, sensitivity and re-acquisition time. Finally, the new architecture is tested in a measurement campaign consisting of a test vehicle driven in an urban canyon environment and mounted with a GNSS antenna connected to a front-end, a MEMS IMU, and a navigation grade INS for the reference trajectory. Measurements from these three sensors are gathered and post-processed to highlight the advantages of the TCAPLL architecture over a stand-alone GPS solution.
## 5 Results and Validation
### Cutter-Workpiece Engagement Angle  
The relationship between engagement angle and toolpath parameters is investigated here. The cutter-workpiece engagement angles with different toolpath parameters are calculated by using the proposed method in Section 3. The toolpath parameters are listed in Table 3. The corresponding start and exit angles are plotted in Figure 11a-f.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Semi-Major Axis** & **Semi-Minor Axis** & **Compression** & **Cutter Radius** & **Trochoidal Step** \\  & **(mm)** & **(mm)** & **Ratio** & **(mm)** & **(mm)** \\ \hline
1 & 15 & 15 & 100\% & 5 & 0.6 \\
2 & 15 & 11.25 & 75\% & 5 & 0.6 \\
3 & 15 & 7.5 & 50\% & 5 & 0.6 \\
4 & 15 & 3.75 & 25\% & 5 & 0.6 \\
5 & 12.5 & 6.25 & 50\% & 7.5 & 0.6 \\
6 & 10 & 5 & 50\% & 10 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ellipse-based trochoidal toolpath parameters.  
Figure 10: Generated ellipse-based trochoidal toolpaths. The slot width is 40 mm. The slot length is 30 mm. \(a=15\) mm. \(s=0.6\) mm. \(R_{c}=5\) mm. (**a**) \(\rho=100\%\), \(b=15\) mm; (**b**) \(\rho=75\%\), \(b=11.25\) mm; (**c**) \(\rho=50\%\), \(b=7.5\) mm; (**d**) \(\rho=25\%\), \(b=3.75\) mm.  
Figure 11a-d show that as the compression ratio decreases, the start and exit angles gradually increase in the initial stage, gradually decrease in the final stage, and change less in the middle stage. Figure 12 shows the variation of the engagement angle with the revolution angle for different compression ratios. It can be found that as the compression ratio decreases from 100% to 50%, the engagement angle becomes more balanced. This is beneficial for balancing the cutting load. As the compression ratio decreases from 50% to 25%, the engagement angle increases significantly in both the initial and final stages, especially in the initial stage. While in the middle stage, it decreases just slightly. This leads to the fact that the trochoidal toolpath cannot be compressed infinitely in the step direction in order to improve the machining efficiency. For the trochoidal toolpath in this slotting example, a compression ratio of 50% is optimal.  
Figure 13 shows the variation of the engagement angle with the revolution angle for different cutter radii. It can be found that when the compression ratio is set to 50%, the engagement angle tends to sink slightly in the middle and final stages as the cutter radius increases. While the overall variation is not significant. More specifically, as the cutter radius becomes larger, the exit angle remains essentially unchanged, while the start angle sinks slightly in the middle and final stages of the effective revolution interval. In summary, the effect of the cutter radius on the engagement angle is not significant.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 3 N-Modular Redundancy and M-Partitions Approach
### Background
#### 3.1.2 Partitioning  
Mixed-criticality systems use partitioning to increase the reliability of embedded systems [24]. To achieve fault tolerance by partitioning, the behavior of one partition must not be affected by other partitions. Hence, time and spatial partitioning must be guaranteed.  
* Time partitioning: The use of resources is exclusively assigned to a partition during a scheduled period of time. In a general manner, this is associated with the Central Process Unit (CPU) time assigned with each partition.
* Space partitioning: A function cannot change the code or data of another partition. This is associated with the memory space assigned to each partition.  
Robust partitioning is a traditional concept used by Federal Aviation Administration (FAA) and EASA to certify safety-critical avionic applications. It is defined in the standard RTCA/DO -297 [36]. Space partitioning can be easily carried out by multi-core and many-core processors since the system can prevent invalid memory accesses while achieving spatial partitioning in such devices, which is not a trivial issue. The work in [21] gives some directions concerning temporal partitioning on multi-core processors used for avionics.
# Single-Shot Global and Local Context Refinement Neural Network for Head Detection
## 4 Experiments and Analysis
### Performance Evaluation  
According to the ablation studies above, we adopt GRM on the outputs of all stages and a multi-scale dilated convolution with dilatation rates of \(\{6,12,18,24\}\). Finally, we evaluate the performance of our detector against several other representative detectors, including SSD, Faster-RCNN, TINY [42], and the existing methods for head detection, such as HeadNet [13] and SANM [32]. Table 4 offers a summary of the results from the Brainwash dataset. We provide some examples in Figure 6. These examples indicate that our model has good performance in densely populated scenes and small target detection.  
Figure 5: Input images and the CAMs of the \(M_{5}\), \(M_{4}\), \(M_{3}\), and \(M_{2}\) are displayed in order from left to right.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configurations of Dilation Rates** & **AP (\%)** \\ \hline \hline \(\{6\}\) & 93.46 \\ \hline \(\{6,12\}\) & 93.67 \\ \hline \(\{6,12\,.18\}\) & 93.84 \\ \hline \(\{6,12,18,24\}\) & 93.89 \\ \hline \hline \(\{4,8,12\,16\}\) & 93.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of different configurations of dilation rates on the Brainwash test dataset.  
Figure 7 compares CRNet with other head detectors that show good performance by plotting the PR curves at IoU 0.5 on the Brainwash dataset. Our method shows a better performance than other detectors, which proves the superiority of our proposed model. A competitive performance is obtained when we test our model on HollywoodHeads as well. In Table 5, the outcomes for HollywoodHeads are presented. Figure 8 presents the qualitative results of our method on the HollywoodHeads dataset.  
In terms of running time, we use a Ubuntu platform with one NVIDIA 3060 GPU and an Intel Xeon CPU E5-2697 v2 @ 2.70 GHz to test the running speeds of different algorithms. At the same time, we also test the effect of LRM and GRM on the running speed of our model. As shown in Table 6, our model achieves a good balance between precision and speed. Furthermore, as expected, LRM and GRM do not have a very large negative impact on the operating speed.  
Figure 8: Examples of CRNet’s head detection results on the HollywoodHeads dataset.  
Figure 7: The PR curves of our model and other methods at IoU 0.5 on the Brainwash dataset.
# Volatile Organic Compound from _Trichoderma asperelloides_ TSUI: Impact on Plant Pathogenic Fungi
###### Abstract  
Soil microorganisms are well studied for their beneficial effects on plant growth and their impact on biocontrol agents. The production of volatile antifungal compounds emitted from soil fungi is considered to be an effective ability that can be applied in biofumigants in the control of plant diseases. A soil fungus, _Trichoderma asperelloides_ TSU1, was isolated from flamingo flower cultivated soil and identified on the basis of the morphology and molecular analysis of the internal transcribed spacer (ITS), _rpb2_, and _telf-\(\alpha\)_ genes. To test _T. asperelloides_ TSUI-produced volatile organic compounds (VOCs) with antifungal activity, the sealed plate method was used. The VOCs of _T. asperelloides_ TSUI inhibited the mycelial growth of fungal pathogens that were recently reported as emerging diseases in Thailand, namely, _Cornespora cassicola_, _Fusarium incarnatum_, _Neepestalotiopsis clavispora_, _N. cubana_, and _Sclerotium rolfsii_, with a percentage inhibition range of 38.88-68.33%. Solid-phase microextraction (SPME) was applied to trap VOCs from _T. asperelloides_ TSU1 and tentatively identify them through gas chromatography-mass spectrometry (GC/MS). A total of 17 compounds were detected in the VOCs of _T. asperelloides_ TSUI, and the dominant compounds were identified as fluoro(trinitro)methane (18.192% peak area) and 2-phenylethylhanol (9.803% peak area). Interestingly, the commercial 2-phenylethylanol showed antifungal activity against fungal pathogens that were similar to the VOCs of _T. asperelloides_ TSU1 by bioassay. On the basis of our study's results, _T. asperelloides_ TSU1 isolated from soil displayed antifungal abilities via the production of VOCs responsible for restricting pathogen growth.  
soil fungi; _Trichoderma_; VOCs; plant pathogen; antifungal activity
## 5 Performance Evaluation
### Numerical Results  
Figures 7 and 8 show the numerical results of exact link level capacity for \(M=N=2\). Figure 7 shows that MN SM provides better capacity than SN SM for \(\eta=0.8\), regardless of the SNR. Comparing MN SM and SN OSTBC, MN SM outperforms SN OSTBC after the crossing point, \(\gamma_{\text{OSTBC-SM}}\), because the capacity of SM increases more rapidly with parallel data transmission [35]. Note that the crossing point decreases as the path gain ratio increases, because MN SM results in better performance for the higher path gain ratio. Figure 8 shows the capacity with low spatial correlation when \(\eta\) is 0.3. In this case, the capacities of SN SM and MN SM are almost same, while the SNR crossing point between SN SM and SN OSTBC appears in relatively low SNR region compared to the high spatial correlation case. The SNR crossing points are easily calculated from the upper bound result Equation (23). In Figures 7 and 8,  
Figure 6: MIMO mode classification for the \(2\times 2\) case.  
the SNR crossing points, where the MIMO mode switching occurs, are indicated by arrows. Note that the MIMO mode switching points in the proposed scheme are different from the exact analysis obtained from Equations (11) and (13), but the error is marginal, as illustrated in Figures 7 and 8. Figures 9 and 10 show the ergodic capacity of the \(4\times 4\) case where OSTBC with a code rate (RC) of 3/4 is employed. For MN SM, the antenna set is chosen to maximize the capacity. The performance tendency is similar to the \(2\times 2\) case; however, the capacity gain of MN SM increases and OSTBC has less benefit, because of the increased number of antennas. These capacity gains can be achieved when a sink node moves slowly. If a sink node moves rapidly and, consequently, the MIMO channel between the sink node and a sensor node changes more shortly than
## 5 Conclusions  
A new space-time block code asynchronous cooperative scheme is proposed. The rotated constellation is introduced to select half of transmitted signals at the source. The Jafarkhani structure combined with time-reversion is then extended to constitute a virtual array with maximum rate as well as full diversity. By doing that, the fast symbol-pair-wise ML decoder converges to the global optimum rapidly, accompanied by a lower SER with no observable gain in complexity. Moreover, if we assume that if the relays construct space-time block transmitted signals matrix in manner of the Tirkkonen, Boariu, and Hottinen structure [12], the Papadias-Foschini structure [13] in a two-hop cooperative communication system, we can generalize this system to universal RQSTBC systems with maximum rate. Furthermore, if we let \(S_{l\,m}\big{(}s_{1},\cdots,s_{m}\big{)}\) denote a \(m\times m\) orthogonal matrix in complex symbols \(s_{1},s_{2},\cdots,s_{m}\), we can extend the quasi-orthogonality matrix \(\mathbf{S}_{l}\) in (5) by \(\mathbf{S}_{l2m}\) of size \(2m\times 2m\) in complex symbols \(s_{1},s_{2},\cdots,s_{2m}\), expressed as [10]:  
\[S_{l\,2m}=\begin{bmatrix}S_{l\,A}&S_{l\,B}\\ -S_{l\,B}^{*}&S_{l\,d}^{*}\end{bmatrix} \tag{9}\]  
where \(S_{l\,A}=S_{l\,m}\big{(}s_{1},s_{2},\cdots,s_{m}\big{)}\) and \(S_{l\,B}=S_{l\,m}\big{(}s_{m+1},s_{m+2},\cdots,s_{2m}\big{)}\). Then, with the same method similar to (4), we can generalize all RQSTBCs to be used in asynchronous cooperative systems. Meanwhile, the propose scheme can be relatively straightforwardly applied to analyze and discuss those systems.
## 4 Exploratory Data Analysis
### Breach Types  
As observed in Figure 8, the most prevalent breach types are PORT (139 events) and HACK (118), collectively accounting for 50.79% of the incidents within the dataset. This understanding reinforces the significance of these breach types in the overall landscape of data breaches. Cases with unknown causes (UNKN) may indicate either an ineffective forensic investigation or a lack of transparency.  
Determining whether the problem of data breaches is worsening and identifying the prevailing trends are critical concerns. It is important to emphasize that these trends are not always immediately apparent. Thus, there is a need for rigorous data analyses to ascertain whether any discernible trends exist. Moreover, when possible, such studies can help make predictions of the trajectory of data breaches.  
To improve the understanding of the Tactics, Techniques, and Procedures (TTPs) employed by attackers in data breaches and their evolving trends over time, Figure 9a provides a visualization of the total breaches reported by year for each breach type. When correlating Figures 8 and 9a, it becomes evident that while PORT breaches are the most frequent category of disclosures, HACK attacks have grown significantly since 2012.  
Figure 8: Count of breaches by type.  
Figure 7: Accumulated number of records breached per announcement type.  
The disclosure of data as a consequence of PORT TTPs was more prominent during the early years of the dataset, with 2006 standing out, especially with 43 breaches in this category. This number was nearly four times greater than the second-most-relevant type, DISC, which had 11 breaches. These trends offer relevant information about attackers' shifting tactics and priorities over time.  
The statement that humans are often considered the weakest link in cybersecurity is well-documented. Building upon this, [PERSON] et al. [9] concluded that breaches originating from human factors were on the decline, potentially due to increased awareness among personnel. A similar analysis was carried out on companies listed on the NYSE and NASDAQ to validate this assertion in this scenario.  
In this context, we focused on breach types INSD, PHYS, PORT, STAT, and DISC, which are associated with human factors. Figure 8(b) displays the cumulative count of breaches related to these types over the years. This analysis led us to conclude that human involvement in data breaches is diminishing in publicly listed companies. However, it is important to note that this reduction in cases may also be influenced by the decline in PORT cases, as previously discussed, which were particularly numerous in 2006.  
In Section 5.2, we further explore potential causes for the declining trend in human-related data breaches. Additionally, we present a more comprehensive discussion of mitigation strategies specific to each breach type. This analysis provides a deeper understanding of the factors influencing the reduction in such breaches and reviews best practices on how organizations can effectively address these vulnerabilities.