esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Testbed, Data Acquisition, and Framework
While the system can function autonomously, human oversight is vital as certain decisions can only be partially entrusted to AI. For example, AI can detect anomalous activity on a critical system, such as radar or health monitoring equipment. However, only a human can be responsible for making the call to shut down that device. In hierarchical approaches, federated models could greatly enhance the system's capabilities. Individual models could operate per device, or IoT gateway, overseen by a hierarchical AI that could supervise the models' outputs, making predictions over many AI model outputs. This would enable security teams to make more informed decisions for individual devices or even families of devices, thereby facilitating the identification of individual devices or device groups under attack, predicting potential targets, or tracing the propagation vector of an attack through a network. Such insights empower security teams to make more informed decisions during an attack, swiftly isolating affected devices without shutting down an entire network topology. This framework could extend beyond IoT devices to encompass every level of computing. Any system exhibiting a baseline normal operation could benefit from this framework, including internal servers, workstations, high-performance computers, manufacturing devices, robotics, and supervisory control and data acquisition (SCADA) systems. Attacks can be very intensive on a system; even if the normal information is very noisy, training an AI framework, such as the CAE featured in this study, should make it simple to detect large-amplitude anomalies. For example, denial of service (DOS) and distributed DOS (DDOS) attacks could be easily detected using AI trained on the server's, router's, or gateway's normal baseline power consumption. This type of attack has maximum detectability since it exhibits such large power consumption signatures. This detection capability is enhanced by the increased availability of resources at these levels, which can allow more complex models to be deployed.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### Cocoid Formation  
_H. pylori_ can live in two forms: spiral actively dividing forms and nonculturable but viable coccoid forms. _H. pylori_ cells can transform into coccoid forms within biofilms. The coccoid form is a viable but nonculturable state and less active metabolically, which can revive and cause the recurrence of the infection [79]. The coccoid forms reported in the study were detected using Gram staining and light microscopy, and their morphological modifications were validated [80], which can result in recurrent infections and relapses, even after attempts at eradication [81]. The term relapse is also called recrudescence, defined as the re-emergence of infection due to the survival of the original infecting strain. This differs from reinfection, where different strains infect an individual [80]. The existence of coccoid _H. pylori_ within biofilms can potentially worsen chronic infections and impede the successful eradication of the bacteria. Cocooid variants of _H. pylori_ develop in response to unfavorable environmental conditions, including food scarcity, antibiotic exposure, and pH and oxygen levels [82; 83]. This activity is essential as it may result in varied levels of inflammation, which can sustain gastritis and induce relapses post-treatment [84; 85].  
It has been reported that exposure to metronidazole or clarithromycin in sub-MIC doses increases the change of cells into coccoid form. [86]. The coccoid type of bacteria possesses peptidoglycan modification and a mucoid covering that hinders the penetration of antibiotics. The molecular synthesis and metabolism are also reduced. The antibiotic works best for the bacteria in the lag and log phases [87]. Hence, the presence of \"dormant\" cells is a significant cause of the ineffectiveness of antibiotics against bacterial biofilms [88].
## 3 Results and Discussion  
This section details the results and discussion of the following investigations: the thermal expansion behaviour of PTFE at ablation temperatures for both annealed and non-annealed antennas; the implications of annealing for antenna performance, as assessed through return loss measurements conducted before, during, and after ablation; and a comparative analysis of ablation lesion ellipticity between annealed and non-annealed cases.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 4 Simulation and Experiment
### Simulation  
The main performance parameters of the simulation server are as follows: the operating system is Linux, version 18.04, the programming language is Python3.7, and the Pytorch 1.9.1 framework was used. The CPU processor is i7-10700K, the memory is 32 GB, and the GPU is NVIDIA Geforce RTX3080 with 10 GB of video memory.  
ILSVRC2015_VID was selected as the training set, and the UAV123 [25] and VOT2018 [26] datasets were used as the test evaluation set, respectively. We set up a comparison algorithm including ablation experiments and introduced evaluation metrics such as accuracy, tracking precision and tracking speed. A quantitative analysis of the algorithm performance was carried out based on the experimental data.
## 4 Experiments  
In the experiments, we used a subset of the VCC2018 [35] dataset used in many existing voice conversion studies to show that the proposed method can be effective, even with a small amount of data. Two female speakers (SF3, TF1) and two male speakers (SM3, TM1) were used as both source speakers and target speakers. We denote these speakers as F1, F2, M1, and M2, respectively. We used 81 training sentences and 35 test sentences for each speaker. A preliminary experiment was conducted to decide the optimal values of the hyperparameters \(\lambda_{\text{id}}\), \(\lambda_{\text{cyc}}\), and \(\lambda_{\text{fm}}\), which were found to be 30, 45, and 0.5, respectively. We used MaskCycleGAN [11] as a baseline for comparison since it was the state-of-the-art among the CycleGAN-based voice conversion methods. For fair comparison, we trained a MelGAN to synthesize the waveforms from the output of MaskCycleGAN using the two speakers, i.e., the source and target speakers, for each conversion direction.
## 4 Simulation Results  
Semi-analytical simulation techniques [16] were adopted to show the performance of the proposed dual antenna loop, compared to the two independent loops. Since the phase errors in a single loop have been researched in many references, the performance simulation in this paper mainly includes the phase jitters in the slave loop and the differential phase as the results of the dynamic, thermal noise, and clock phase noise.
# Space Habitat Data Centers--For Future Computing
## 4 Proposed Solution
### SHDC Network Architecture  
The proposed solution reduces data centre demand on Earth's water resources. Instead of realizing the data centre by integrating the storage and computing capabilities aboard small satellites, the use of a manned SHDC is proposed. The use of small satellites and space vehicles in a distributed architecture is suited for realizing edge nodes. However, it is challenging to upgrade computing payload after launch. Manned SHDCs support the upgrade of data centre computing payload or replace of damaged payload components.  
The proposed SHDC comprises the cooling system (CLS), the server and computing system (SCS), and the communication system (CCS). The CCS enables communications with space edge-computing nodes, other SHDCs, and ground stations. The CLS's coolant is asteroid water. This is feasible as meteorites and asteroids host water reservoirs [46; 47]. The relation between the CLS, SCS, CCS, and the Earth segment is in Figure 1. The SCS receives workload from the terrestrial ground station gateway (TSGW) via the CCS. In Figure 1, the SHDC in low Earth orbit (LEO) executes the workload received from space and terrestrial assets.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 5 Conclusions  
This research is based on the CIC-IoT-2023 dataset and conducts an in-depth discussion and analysis of IoT network intrusion detection. We apply deep learning methods to improve the detection performance of abnormal behaviors and intrusions. Compared with other papers, we further use the Transformer model and further use multi-class classification. The experimental results show that in binary classification, DNN and CNN + LSTM have the highest accuracy, while in multi-class classification, the Transformer model has the highest accuracy. This proves the potential application value of deep learning methods in IoT network intrusion detection. In the future, the dataset can be reconstructed and balanced to avoid the unpredictable situation of minority category attacks, so that these 34 categories can be directly used for classification to improve the generalization ability of the model and remove some features that have no impact on model classification to improve classification efficiency.  
The method used in this study brings new possibilities to the field of IoT network intrusion detection. It is hoped that the results of this study can provide a valuable reference for the development of the field of IoT security.  
Conceptualization, S.-M.T. and Y.-C.W.; methodology, S.-M.T. and Y.-C.W.; software and data curation, Y.-Q.W.; funding acquisition, S.-M.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council, Taiwan grant number NSTC 112-2221-E-027-079-MY2.  
The data can be shared up on request. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 5 Result and Discussions
### BER Estimation  
In this paper, we have developed comparative experiments to evaluate the system's performance. The term BER refers to the proportion of bit errors that occur within a given period of time expressed as a ratio between the number of bit errors and the total number of bits transmitted. A bit error occurs when a data payload received via a transmission channel experiences changes in its bits as a result of noise, interference, distortion, or transmission errors caused by movement.  
Figure 10 displays the successful OCC experimental results based on our proposed method with no bit error. In this implementation, we adopted the Logitech Brio camera with a resolution of 1920 \(\times\) 1080 pixels and a frame rate of 60 fps. Our system is capable of achieving data transmission speeds of up to 3.84 kbps when using an 8 \(\times\) 8 LED array configuration, and this data rate can increase up to 15.36 kbps when utilizing a 16 \(\times\) 16 LED array configuration. However, in our implementation, we focused on employing an 8 \(\times\) 8 LED array.  
Figure 11 displays the BER performance at varying communication distances with changes in camera velocity at (a) 2 m; (b) 5 m; (c) 8 m; and (d) 10 m. In our implementation, we systematically move the camera and LED array congruently in the same directions (up, down, left, and right) at velocities ranging between 0-10 m/s. This approach is adopted to comprehensively assess the system's performance. The alignment of the camera and LEDs in terms of direction and velocity ensures that communication distances remain consistent even during dynamic detector movement, facilitating the calculation of BER under stable conditions. At all distances, when the transmission system is stationary, the bit error rate (BER) remains at zero, permitting the system to be deployed. At 2 m, Figure 11a demonstrates that the transmitted motion has a minimal effect on the BER. Even at 10 m/s, the BER does not exceed 10\({}^{-2}\). In this scenario, the system demonstrates outstanding mobility support, and its performance remains consistent despite a variety of mobile circumstances. Therefore, faultless transmission is possible within a 2 m range, regardless of system motion. Figure 11b shows that between 5 m and 8 m, the system's mobility begins to affect transmission. The BER of 10\({}^{-1}\) indicates that the system's performance is unaffected by its mobility, even at 10 m/s, as demonstrated by the system's performance. Regarding Figure 11c,d, it can be seen that the mobility of the system has a substantial impact on the BER. Therefore, ensuring the system's optimal performance over extended distances and under conditions of high mobility becomes impossible. Improving the performance of OCC systems over extended distances and in scenarios with high mobility represents a significant challenge for the future.  
Figure 10: The received interface is displayed on the screen.
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 3 Results
### Instance Balancing  
During our experimentation, we observed that for the training set, only a tiny portion of the resulting datasets contained events. For this reason, we decided to use an instance-balancing process for the training set. Instance balancing is a typical process used in machine learning to level the occurrence of classes in the instances so that all classes have the same impact on the algorithms, thus allowing them to perform better [35; 36].  
In the literature, diverse authors have used instance balancing to improve the results of their proposals, as in the case of [PERSON] et al. [37] and [PERSON] et al. [38]. In this sense, we considered an exciting alternative to adopting this concept in time series, to corroborate if this could help the LSTM network accurately learn how to identify its correct behavior in the presence of events.  
The balancing process consisted of duplicating the datasets containing events in the instances until we balanced them with those without events in the training set.  
We designed Formula (1) to balance the instances. In this formula, \(R\) represents the number of times the balancing process will duplicate the event-containing datasets, \(S_{w0}\) represents the number of datasets that end in a week without an event, and \(S_{w}\) represents the number of datasets that end in a week with an event:  
\[R=\frac{S_{\text{wo}}-S_{\text{w}}}{S_{\text{w}}} \tag{1}\]  
After standardizing the instances, we tested the best configuration found, Configuration C, with the balanced and standardized instances. The objective was to evaluate the potential of this balancing approach and determine its relevance for training the LSTM network to improve forecast accuracy.  
Figure 7 presents the experimental results. For clarity, we compared the outcomes of the balanced instances with those of the unbalanced:  
The C configuration test results, combined with instance balancing, showed a considerable improvement compared to those obtained without balancing. This approach enabled the LSTM network to generalize more effectively for sales forecasting of lumberyard products.
# Dynamic Interactions between Parallel Grid-Forming Inverters in a Microgrid
## 4 Stability Analysis
### Eigenvalue Analysis  
The eigenvalue analysis is performed using \(Y(s)\) of the system. MATLAB command tfzero can lead to all zeros of \(Y(s)\).  
First, a single VSC connected to a load (the other VSC is disconnected) is considered. Figure 7a shows the eigenvalues of a single VSC connected to a load. As can be seen, the system is stable. Figure 7b shows the eigenvalues when the two VSCs are operating in parallel. Two pairs of the eigenvalue are introduced in the right half-plane (RHP). The system becomes unstable. Therefore, although a stable operation of a single inverter is observed, the interaction between the parallel-inverter could lead to instability.  
Figure 8a presents the eigenvalues of the parallel VSCs with different distribution line impedance. It is evident that increasing the line impedance improves the system's stability. This observation indicates that the undesired resonance can be prevented by adjusting the impedances between the parallel VSCs. This can be done through changing inverter controls.  
Based on (9), (12) and (17), there are two approaches that can be applied to change the VSC output impedance. They are:  
1. Adding the virtual impedance;
2. Applying the feed-forward loop in the inner control.  
The two approaches are investigated. Figure 8b shows the eigenvalues of the parallel VSCs when the virtual impedance loop is used. Different values are examined. Based on the results, it is clear that the virtual impedance has a slight influence on the resonant frequency. This indicates that the virtual impedance does not affect the stability of the system.  
Figure 8c depicts the eigenvalues of the parallel VSCs when the feed-forward loop is added to the inner control. As can be seen from the results, it is obvious that the system becomes stable with no eigenvalue on the RHP. Therefore, adding the voltage feed-forward loop stabilizes the system.  
Figure 7: **(a) Eigenvalues of a single VSC connected to a load. The system is stable. (b) Eigenvalues of the paralleled VSCs system. Two pairs of the eigenvalue are introduced at the RHP. The system is unstable. The four modes are: \(\lambda_{12}\) at \(1130\) Hz, \(\lambda_{3,4}\) at \(57\) Hz, \(\lambda_{5,6}\) at \(60\) Hz, \(\lambda_{7,8}\) at \(1750\) Hz.**
## 3 Discussion  
All the secondary metabolites were isolated from _A. mono_ Maxim. (**1\(-\)8**) and evaluated to determine their \(\beta\)-glucuronidase inhibitory activity. Compound **1** exhibited a \(\beta\)-glucuronidase inhibitory effect, with an IC\({}_{50}\) value of 58.83 uM, whereas the remaining compounds demonstrated no inhibitory activity against \(\beta\)-glucuronidase. The results suggest that the different positions of an oxygen atom and carbonyl group in the lactone ring of the isocoumarin structure, as well as the presence of a double bond between C-3 and C-4, could contribute to \(\beta\)-glucuronidase inhibition. Kinetic analysis indicated that active compound **1** displayed non-competitive inhibition. Thus, molecular docking studies were employed to determine the binding position and critical amino acid interactions of compound **1** with the allosteric binding site of the \(\beta\)-glucuronidase protein. The docking results indicated that compound **1** could tightly bind to the allosteric binding site of \(\beta\)-glucuronidase, with a binding energy of \(-8.35\) kcal/mol. The lactone ring of compound **1** displays hydrogen bonds with Thr191 and Thr438, \(\pi-\sigma\) interactions with Val190, \(\pi-\)cation interactions with Arg439, and \(\pi-\)bone pair interactions with Val189, which might explain their inhibitory activity against \(\beta\)-glucuronidase. In addition, the conformational stability of compound **1** complexed with the \(\beta\)-glucuronidase protein and its variations were investigated by performing MD simulation trajectories (100 ns). According to the MD results, all ligand properties fluctuated during the initial simulation period, gradually reaching equilibrium and a steady state as the simulation was completed. This indicated that compound **1** was stable in the allosteric binding site of \(\beta\)-glucuronidase. Based on the predicted pharmacokinetic properties (Supplementary Material), compound **1** exhibited no blood-brain barrier penetration and behaved as a safe drug candidate, given that it adhered to [PERSON]'s rule of five.
###### Abstract  
On 5 September 2022 (BJT), Luding, located in southwestern Sichuan Province, China, experienced an \(M_{S}\) 6.8 earthquake. This earthquake occurred within the historical rupture zone of the 1786 \(M_{S}\) 7.75 event, part of the southern section of the Xianshui He Fault belt. Given the average 155-year recurrence interval for strong earthquakes in this area, the 236 years since the last event made this earthquake somewhat expected. However, prior to this event, we did not detect any anomalies indicating low surface \(b\)-values, which are often indicative of a high-stress state in the source area before strong earthquakes, as highlighted by numerous studies. Our research focused on the northern section of the eastern boundary of the Sichuan-Yunnan sub-block, encompassing the Xianshui He, Anning He, Zemu He, and Daliang Shan fault belts. We meticulously located earthquakes of \(M_{\rm L}\geq 1.5\) from 2009 to May 2022. The catalog was divided into two periods: 2009-2014 and 2015-May 2022. Using an AIC-constraint method, we analyzed the changes in \(b\)-values (\(\Delta\)b) in the latter period compared to the former. Our findings revealed a significant abnormal \(\Delta\)b zone (\(\Delta\)b \(<-0.3\)), with a radius of approximately 50 km, when \(\Delta\)AIC \(\geq 2\) was selected. Intriguingly, the epicenter of the recent Luding \(M_{S}\) 6.8 earthquake fell within this abnormal zone. Furthermore, we calculated the \(b\)-value cross-section for the southern section of the Xianshui He fault belt using a directory of precisely located small earthquakes. This revealed that the location, scale, and shape of the abnormally low-\(b\)-value area corresponded with the large displacement co-seismic area of the main earthquake, affirming the \(b\)-value's effectiveness in identifying asperities. The \(b\)-value's temporal evolution prior to the mainshock exhibited a nearly decade-long continuous decrease, signifying a long-term stress-loading process akin to that observed before many strong earthquakes. The \(b\)-value anomalies observed from different profiles before the Luding earthquake underline the necessity of a comprehensive, multi-dimensional analysis of such anomalies. Finally, our analysis indicates that nine earthquakes with \(M_{S}\geq 6.5\), including the Luding \(M_{S}\) 6.8 event, have contributed to increased Coulomb Failure Stress change (\(\Delta\)CFS) in the Daofu (DF)-Kangding (KD) section of the Xianshui He fault belt and the northern section of the Anning He fault belt south of Shimian (SM), with amplitudes surpassing the 0.01 MPa threshold. This suggests the potential for strong earthquakes in these zones.  
Luding earthquake; \(b\)-value; stress state; rupture zone; AI recognition +
Footnote †: journal: _Sci._**2024**, 14, 4345. [[https://doi.org/10.3390/app14114345](https://doi.org/10.3390/app14114345)]([https://doi.org/10.3390/app14114345](https://doi.org/10.3390/app14114345))