esa-sceva/satcom-chunk-collection huggingface dataset:
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
## Appendix A
### Offset Analysis  
The random offset of the proposed amplifier is mostly due to the mismatches of the devices in the input stage. The sub-threshold current equation can be rewritten as  
\[I_{a}=I_{0_{a}}\exp\bigg{(}\frac{V_{ox_{a}}}{n_{x}U_{T}}\bigg{)}\] (A8)  
where  
\[V_{ox_{a}}=\begin{cases}V_{N}+V_{DD}-V_{th_{N}}\bigg{(}V_{B_{N}}+V_{DD}\bigg{)} &a=N_{1},N_{2}&x=n\\ V_{DD}-V_{P}-V_{th_{P}}\bigg{(}V_{DD}-V_{B_{P}}\bigg{)}&a=P_{1},P_{2}&x=p\end{cases}\] (A9)  
and the effect of the drain-source voltage is neglected. We can assume the difference in the threshold voltages (hence in the overdrive voltages) as the main source of mismatch [13]. Considering the pair of NMOS (PMOS) devices, their overdrive voltages can be written as  
\[V_{ox_{a}}=V_{ox_{b}}+\Delta_{x}/2\] (A10)  
where x = n,p. Under the hypothesys of infinite CMRR (given by (10)), The input offset voltage can be calculated as the differential output voltage due to the overdrive mismatch divided by Ad1; since mismatches for NMOS and PMOS devices are uncorrelated, we get  
\[V_{os}= \frac{\sqrt{\bigg{(}\Delta_{n}/n_{n}\bigg{)}^{2}+\bigg{(}\Delta_ {p}/n_{p}\bigg{)}^{2}}}{\frac{n_{n}-1}{n_{n}}+\frac{n_{p}-1}{n_{p}}}=\] (A11) \[=\frac{\sqrt{n_{p}^{2}\Delta_{n}^{2}+n_{p}^{2}\Delta_{p}^{2}}}{2 n_{n}n_{p}-n_{n}-n_{p}}\]
# Review on the Recent Numerical Studies of Liquid Atomization
## 5 Hybrid of Interface Capturing and Particle Tracking
### Basic Idea  
The atmosphere is always in Eulerian manner while the liquid phase is conditionally treated as Eulerian phase or Lagrangian phase for interface capturing and particle tracking methods, respectively. In the hybrid method, the liquid in the primary atomization is mostly Eulerian phase, while the liquid in the secondary atomization is usually Lagrangian phase. This classification is not absolute since in practice there are two ways of mixing the interface capturing and particle tracking. The first way is to transform any liquid masses that have detached from the liquid core in Eulerian phase into Lagrangian phase as long as they meet the criteria such as size and sphericity. As shown in Figure 4a, the transformation to DPM (discrete particle method) particles is able to occur anywhere in the flow field. Several levels of grid refinement must be employed to sufficiently resolve the interfaces and after the removal of qualified liquid parcels from the Eulerian field, the grid would be reverted. Small time steps must be applied throughout the whole calculation domain to guarantee the small Courant number, thus wasting computer resources for the Lagrangian region. As a substitute shown in Figure 4b, the transformed DPM particle can be sampled on either a cut plane or the outlet boundary of the domain, and the information for every particle including location and speed be saved into a file, after which the injection file can be used for another particle tracking simulation. By doing so, a larger time step can be used on the Lagrangian phase, thus greatly saving computer cost.
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 1 Introduction
According to these experimental data, some statistical models are built to characterize the echo number, delay, power and Doppler [14; 15]. These statistical models are also studied in different environments and applications [16]. The achievements are included in the ITU-R report [17]. A satellite-to-indoor channel model is proposed for testing and validating range estimation algorithms [18]. To study the characteristics of the real signal, the GNSS group at Calgary University investigated the urban multipath channel characteristics, and their results showed the relationship among the code delay, fading frequency and carrier noise ratio [19]. The antenna is an important factor to consider in multipath interference, so a statistical model of multipath affection for antenna is proposed [20]. In conclusion, the statistical method is often used to study the multipath characteristics in a large-scale environment. However, the challenge of this method is to precisely obtain raw data about the multipath characteristics. Although many achievements have been made in previous research, further research efforts in multipath channels are still required, especially for the urban environment. First, as new constellations are deployed, different orbit satellites, such as geosynchronous orbit (GEO), inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO), are put to work. There are not enough studies on the statistical multipath channels of different orbits, especially for the BeiDou Navigation Satellite System (BDS). Second, most researchers have focused on kinematic multipath channel models. Research on static multipath channel models is seldom seen in the literature. Third, the models developed by research groups such as DLR are based on data obtained from emulated signals that are emitted from a Zeppelin instead of a real GNSS signal. A research method based directly on real GNSS signals has not been widely used. Fourth, no empirical formulas have been proposed to describe the statistical characteristics of multipath parameters based on the experimental data. Targeting these goals, a signal sampling device is used to collect BDS BII and GPS L1 CA signals in a static urban canyon environment. Statistical models about multipath core parameters, including the time delay, the power attenuation and the fading frequency, are developed by analyzing and fitting those experimental data. The paper is organized as follows. First, the tools that are used to extract raw multipath parameters from the real signal are introduced, and the performance is tested by a GNSS simulator. Next, the urban canyon environment, where the real multipath data are collected, is described, and the method used to process multipath raw data is illustrated. Then, the statistical analysis of multipath time delay, multipath power attenuation, and multipath fading frequency is described. Finally, some conclusions are summarized based on the experimental results.
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
###### Abstract  
With the continuous growth of network traffic scale, traditional software-based intrusion detection systems (IDS) constrained by CPU-processing capabilities struggle to meet the requirements of 100 Gbps high-speed network environments. While existing heterogeneous acceleration solutions enhance detection efficiency through hardware acceleration, they still exhibit technical limitations including insufficient throughput, simplistic task offloading mechanisms, and poor compatibility in rule compilation. This paper is based on the collaborative design concept of \"hardware-accelerated preprocessing + software-based precise detection\", fully leveraging FPGA's parallel processing capabilities and CPU's flexible computation advantages. We construct an FPGA + CPU heterogeneous detection system featuring a five-tuple segmented matching architecture, which integrates hash bitmap and shift-or algorithms to achieve fast-pattern matching. A hardware compiler supporting 10,000+ detection rules is developed, enhancing hardware adaptability through packet optimization and mask compilation. Experimental results demonstrate that the system maintains 100 Gbps throughput with 2000-10,000 rule sets, achieves over 97% detection accuracy, and consumes only 33% hardware logic resources. Compared with Snort software implementation on equivalent configurations, it delivers 10.5-27.1 times throughput improvement, providing an efficient and reliable solution for real-time intrusion detection in high-speed networks.  
Keywords:network intrusion detection system (NIDS); heterogeneity; rule compilation; pre-filtering +
Footnote †: journal: Applied
## 3 Results and Discussion
### AXT Accumulation and Cell Viability during 10 mA Electrotreatment for 12 h  
Figure 3 shows the quantification of cellular AXT content determined by HPLC analysis. Untreated microalgal cells maintained AXT levels (7.6 \(\pm\) 0.2 mg/g DCW) consistent with their initial content (7.6 \(\pm\) 0.7 mg/g DCW) after 12 h. In contrast, microalgal cells treated with 10 mA for 4 h reached a maximum AXT content of 10.2 \(\pm\) 0.4 mg/g DCW, showing a significant increase of 34.9 \(\pm\) 4.3% over that of the untreated controls. However, similar to the microscopic observations, a progressive decrease in AXT content was observed in _H. lacustris_ after 6 and 12 h of prolonged electrotreatment, declining to 7.1 \(\pm\) 0.3 and 4.1 \(\pm\) 0.7 mg/g DCW, respectively.  
To evaluate the effect of electrochemical stress on _H. lacustris_ during 10 mA electric treatment for 12 h, cell viability was assessed by trypan blue dye staining [18; 31]. Within the range of 2-4 h, the electrotreated microalgal cells consistently maintained cell viability rates close to 100%, similar to those of the untreated controls (Supplementary Figure S1). However, after 6 and 12 h of electrotreatment, the cell viability gradually decreased to 96.9 \(\pm\) 0.7% and 89.2 \(\pm\) 1.1%, respectively. This decline may be associated with the mor  
Figure 3: Time-course profiles of astaxanthin content in _Haematococcus lacustris_ electrotreated for 12 h at 10 mA. The error bars represent the standard deviations of four samples. * \(p\) < 0.05. DCW, dry cell weight.  
Figure 2: Time-course profiles of microscopic features in _Haematococcus lacustris_ electrotreated for 12 h at 10 mA. Red arrows in the 12 h image show the separation of the cytoplasm from the cell wall. Scale bar: 20 \(\upmu\)m.  
phological perturbations observed during these treatment durations (Figure 2). Overall, the combined results from HPLC and cell viability assessments suggested that very mild electrical stimulation, particularly at 10 mA for 4 h, effectively triggered AXT biosynthesis in _H. lacustris_ without detrimental effects on cell viability.
## 6 Conclusions  
This work investigated the performance of AN-assisted location-based beamforming in Rician wiretap channels. Assuming that the location information of the legitimate user and the eavesdropper is available at the base station, AN is used to interfere with eavesdroppers. The influence of the AN has been considered for both the legitimate user and the eavesdropper. Closed-form PDF approximations of the SINRs are derived. Moreover, the expressions of the expectations of the SINRs and the BERs are deduced. A secure beamforming scheme is proposed to ensure a minimum SINR requirement for the legitimate user and minimize the SINR of the eavesdropper. Numerical results show that the proposed beamforming scheme can effectively degrade the performance of nearby eavesdroppers even with a small number of antennas. When the base station has eight antennas, the BLER of the eavesdropper reaches 1 when the eavesdropper is located \(5^{\circ}\) away from the legitimate user, while the BLER of the legitimate user remains 0. In the case where the location of the eavesdropper is unknown to the base station, the proposed beamforming scheme can still be used to minimize the area near the legitimate user with eavesdropping risk. When more antennas are used at the base station, the area with eavesdropping risk can be further reduced.  
Figure 11: The simulated BLERs of [PERSON] and [PERSON] in the function of the location of [PERSON] \(\theta_{e}\).  
**Author Contributions:** H.F.: Conceptualization, methodology, software, validation, formal analysis, resources, writing--original draft preparation; X.Z.: software, validation, investigation, resources, writing--review and editing; L.P.: writing--review and editing, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China under Grant 2022 YFB4300300, the National Natural Science Foundation of China under Grants 62001106 and 62171120, the Guangdong Key Research and Development Program under Grant 2020B0303010001, the Jiangsu Provincial Key Laboratory of Network and Information Security No. BM2003201, and the Purple Mountain Laboratories for Network and Communication Security.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
In this method, the key factors which affect the test results are the multi-beam pointing errors (consistency errors within each tube in the array) and the array pointing errors (overall pointing deviation of the tube array at different positions during the scanning process). In [PERSON]'s method, the pointing consistency between the 3 \(\times\) 3 beams needs to be calibrated in advance. After obtaining all the measured array data, a correction quantity is applied to the beam pointing consistencies based on the assumption of small aberrations of the full-aperture optical system. However, when the samples of measurement data are limited, this method will still introduce significant testing errors. To address this issue, this paper proposes an iterative calculation algorithm that can simultaneously obtain the multi-beam pointing errors, the array pointing errors, and wavefront errors with high accuracy. The basic principles of the algorithm are described and verified through simulation calculations firstly. Based on this, a testing system is developed, and experimental comparisons are conducted to validate the feasibility of the proposed algorithm.
## 5 Conclusions  
* In HTS tapes subjected to multiple test current pulses (corresponding to the values of the prospective short-circuit current), the value of the critical current decreases depending on the number of transitions from the superconducting state.
* The value of the critical current of the HTS tapes decreases with the increase in the value of the test impulse.
* Measurements of the energy released in the sample during the operation of the test impulse allow us to determine the permissible range of energy and voltage drops that practically do not change the value of the critical current or do not exceed the permissible values from the point of view of cooperation between the SFCL and the system protection.
* HTS tape with a silver layer of 4 \(\upmu\)m thickness degrades very quickly above a certain energy value; therefore, a safe voltage drop range of 0.55 V/cm can be assumed, at which the drop in the critical current value does not exceed 3%.
* Decrease in the value of the critical current of the HTS tape due to multiple occurrences of a short circuit in the SFCL system should be considered at the design stage. It should be controlled during the operation of the SFCL.
* Morphological studies reveal that HTS tapes subjected to test current impulses (corresponding to the values of the prospective short-circuit current), causing their exit from the superconducting state, and show microdamage occurring in the superconducting layer and the boundary layer between the superconductor and silver in the form of bubbles, which may cause degradation.
* The observed microstructural changes in HTS 2G tapes due to the effect of test currents on the parameters of HTS tapes require further research and analysis. Future research will include an extension of microstructural studies and statistical studies.  
Conceptualization, S.H. and A.K.; methodology, S.H., A.K., and M.M.; investigation, S.H. and J.R.; writing--original draft preparation, S.H., K.S., and M.M.; writing--review and editing, A.K. and P.S.; visualization, S.H. and M.M.; supervision, A.K. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 22: AFM image of the HTS tape sample subjected to surge currents along with the cross-section in the place marked with red lines 1 and 2.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] for the discussion and substantive support. The authors thank [PERSON] for making the measurements possible.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Results
Nevertheless, it is shown that a spike current happens when \(S_{2}\) is turned on, meaning that \(S_{2}\) does not achieve ZVS operation. At the output side of the developed circuit, the ZCS performance has been accomplished by inspecting the relationship between diode voltage waveforms and diode current waveforms on both \(D_{1}\) and \(D_{2}\). Figure 18 obviously shows that the current of the output diodes naturally fall to zeros prior to the moment that the transformer secondary windings switch. It means that diodes \(D_{1}\) and \(D_{2}\) can operate at ZCS, resulting in a zero-recovery current and low turning-off losses. Figure 19 shows the waveforms of the output voltage and current, and as displayed, there are both high-frequency and low-frequency ripple components in the voltage and current. The measured average voltage and average current are 95 V and 0.42 A, respectively. The circuit efficiency is 92.2%. The measured values are consistent with the theoretical prediction, and the peak-to-peak voltage ripple is 560 mV, while the peak-to-peak current ripple is 46 mA. The ripple factors of the voltage and current are 0.6% and 10.9%, respectively. Figure 16: Waveforms of the input line voltage and current. (\(v_{in}\): 100 V/div, \(i_{in}\): 0.5 A/div, time: 5 ms/div). Figure 17: Voltage and current waveforms of the power switches. (\(v_{DS1}\), \(v_{DS2}\): 200 V/div, \(i_{S1}\), \(i_{S2}\): 1.0 A/div, time: 10 \(\upmu\)s/div).
## 3 Results and Discussion
### Two Revised DNNs Applied to the CGL Data Sets  
Figure 3 shows the results of applying our revised U-Net DNN to the CGL data sets. The training set was used to train the model independently. The batch size is set at 12. The training process was focused on minimizing the loss function. In each of the training iterations, we obtained a temporary calibration model. We then conducted a random sampling to pick 12 samples from the training set and the validation set, respectively. We applied the temporary calibration model to the selected samples to obtain the calculated values of the four components and then to calculate the \(R^{2}\) values from the reference values of the components. When the minimum values of \(R^{2}\) for both the training set and the validation set reached the threshold of \(R^{2}\geq 0.999\), we stopped the training process and obtained an acceptable calibration model.  
The \(R^{2}\) and \(RMSE\) values shown in Figure 3 were calculated using the whole set of samples from the three data sets and not the 12 randomly picked samples. In this work, we continuously used this strategy in training our network and presenting our results. Compared with our previous work [13], the results shown in Figure 3 are adequate.  
Figure 4 shows the results of applying our revised attention mechanism DNN to the CGL data sets. The results were still acceptable compared with the results shown in Figure 3 and our previous results [13]. The more important thing is that we still fulfill the goal of establishing a single calibration model for four species. So far, to our knowledge, no other researchers have reached that goal.  
We did not try the transformer model architecture [15] because it is too complicated for us to find a reasonable way to use it. We did try multiple stacking of the attention module but failed to achieve better results in what was then a more complex model. Thus, we only used one attention module followed by the MPR and the APR modules. It might be that convolutional layers play the major role. However, these results mean that there may still room for further improvements.  
Figure 4: Predicted versus measured plots of the training set, validation set and test set using revised attention mechanism DNN.  
Figure 3: Predicted versus measured plots of the training set, validation set and test set using revised U-Net DNN.
## 4 Experiment and Analysis
### QoS Evaluation Metrics  
Various organizations, such as the International Telecommunication Union (ITU) [48], develop standards and guidelines for QoS to ensure consistent service quality globally. QoS in EFL is a critical concept that governs the performance and reliability of the learning process within a decentralized network of edge devices. EFL leverages QoS to ensure that the transmission, aggregation, and processing of ML/DL models occur efficiently. By optimizing metrics, such as latency and reliability (e.g., model delivery ratios), QoS ensures that industrial applications and services meet predefined standards, resulting in enhanced user experiences and efficient operations across diverse industries [49].  
In our study, we prioritize the minimization of delay (see (4)) and optimization of reliability metrics (FL model drop and delivery ratios). By leveraging MLSTM-CP on predictive capabilities, the system anticipates congestion points and allocates resources proactively. Reduced latency guarantees real-time interactions and timely model updates, thus enhancing the final averaged learning model. Equation (13) presents the total latency \(T_{TOTAL}\) of E2E IIoT. The total latency is the sum of radio access delay \(t_{RAN}\), edge aggregation delay \(t_{EDGE}\), and orchestration control delay \(t_{CONTROL}\). The edge latency encompasses the transmission time between gateways in the fronthaul networks. The control latency refers to the model updating time between edge aggregators and federated averaging entities in the control plane. The latencies associated with resource adjustment and scheduling, subsequent to MLSTM-CEFL orchestration, are considered by \(t_{CONTROL}\).  
\[T_{TOTAL}=t_{RAN}+t_{EDGE}+t_{CONTROL} \tag{13}\]  
FL model drop ratios indicate the proportion of models that were discarded during the FL round communication process due to congestion or resource limitations. It reflects the effectiveness of the controller as VIM and policy orchestration. The drop ratios highlight the potential challenges faced in maintaining a stable connection between participants, edge aggregators, and the central server. In contrast, FL model delivery ratios quantify the success rate of delivering the model updates in IIoT-EFL systems.