esa-sceva/satcom-chunk-collection huggingface dataset:
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 2 Related Work and State of the Art
### Existing Distributed Network Emulation Systems Compared with Megalos  
Some network emulators allow to distribute the workload among different hosts.  
ClickOS [18] is a system that aims to provide middleboxes (e.g., firewall, load balancer, NAT) running on minimal virtual machines. However, it cannot emulate arbitrary network devices.  
GLANF [19] allows to run middleboxes using containers and to interconnect them with a SDN approach. It cannot emulate arbitrary network devices and requires a SDN architecture.  
DVCL [20] is a project, based on NetKit, that allows the emulation of network topologies distributed across multiple hosts. The scalability of the system is not discussed, so it is unclear if it allows massive virtualization activities. Moreover, being based on NetKit, it leverages on UML virtual machines which are slower and heavier than container-based solutions.  
Kollaps [21] is a distributed emulation system which leverages on the Kubernetes container orchestrator. The main novelty of the system is that it can precisely emulate data-plane metrics (e.g., latency, bandwidth). However, network devices do not behave like real ones (e.g., they cannot run a routing daemon). Additionally, all the tested data-plane properties and actions are pre-computed in a graph. This means that the user cannot interact with the devices during the experiment and when all the events are executed, the network scenario is shut down.  
CrystalNet [22] is an emulation system proposed by Microsoft, which is able to emulate an entire Azure data center. CrystalNet is mainly used by Microsoft operators to validate network configurations changes before applying them in the production environment. However, CrystalNet is not open-source, and uses a proprietary orchestrator. Also, it is unclear if the system is only able to emulate Azure networks or it implements a description language to specify arbitrary topologies.  
The present paper describes the Megalos architecture and is an extended version of [1]. With respect to [1], we generalized the model from one restricted to the NFV standard to a more versatile one that allows us the emulation of general networks and VDs. Further, we provide a detailed description of the Megalos networking stack, which is a critical component of the architecture. Finally, we extend the experimental evaluation adding tests aimed at assessing the scalability of Megalos with respect to latency and startup times.
## 2 Adaptive Sensor Arrays
### Beamforming  
The objective of beamforming is to adjust the weight vectors in order to obtain the maximum SINR in the output of the array. This can be achieved by minimizing the total interference in the array output, while maintaining a constant gain for the signal of interest [5, 29, 30]. Taking the array output given in Equation (2), the average total output power (in W) for the \(i\)-th user can be written as:  
\[P_{i}=E[y_{i}(n)^{2}]=E[\mathbf{w}_{i,K}^{H}(n)\mathbf{x}(n)\mathbf{x}^{H}(n)\mathbf{w}_{i,K}( n)] \tag{3}\]  
where \(E\) is the mean operator. Defining:  
\[R=E[\mathbf{x}(n)\mathbf{x}(n)^{H}] \tag{4}\]  
as the autocorrelation matrix of the input signal, the average total output power of the array is given by:  
Figure 1: Functional diagram of an adaptive array.  
\[P_{i}=\mathbf{w}_{i,K}^{H}(n)R\mathbf{w}_{i,K}(n) \tag{5}\]  
The weight vectors that maximize the SINR of the array output can then be found using the following minimization problem:  
\[\begin{split}\underset{(\mathbf{w}_{i,1},\ldots,\mathbf{w}_{i,K},\mathbf{P}_{1 },\ldots,\mathbf{P}_{k})}{min}&\left(\sum_{i=1}^{K}P_{i}\right)\\ \text{subject to }SINR_{i}\geq\delta_{i}\end{split} \tag{6}\]  
where \(\delta_{i},\,P_{i},\,\) and \(\mathbf{w}_{i,K}\,\)are, respectively, the smallest allowed SINR value in dB (the pre-established threshold level), the transmission power, and the beamforming vector for the \(i\)-th user. The aim is to obtain an optimum pair of the weight and transmission power vectors, with minimization of the total transmission power, maintaining the SINR above a pre-established threshold (\(\delta_{i}\)).
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction
### Small-Scale Channel State Prediction
#### 4.2.2 Residual Series Prediction  
Then, after decomposition of the original channel state series, the auto-regression (AR) model is used to predict the residual series due to the low computational complexity. The AR prediction model is presented as  
\[\hat{h}_{t+1}^{re}=\sum_{i=1}^{L_{AR}}a_{i}h_{t-L_{AR}+i}^{re}+b_{t+1}. \tag{21}\]  
\(\hat{h}_{t+1}^{re}\) represents the predicted value of residual series in slot \(t+1\), and \(h_{t}^{re}\) is the value of the residual series in slot \(t\). \(L_{AR}\) denotes the prediction order. \(a_{i}\) represents the \(i\)-thcoefficient of AR prediction model and and \(b_{t+1}\) is a noise term. The coefficients of the AR model can be calculated by the least squares algorithm [5; 11; 25].  
Finally, given the predicted large-scale channel state values \(\hat{h}_{t+1}^{L}\) in Section 4.1.2, and the prediction value of residual series \(\hat{h}_{t+1}^{re}\), the predicted channel state of the next slot is  
\[\hat{h}_{t+1}=\hat{h}_{t+1}^{L}+\hat{h}_{t+1}^{re}. \tag{22}\]
# Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval
## 3 Experimental Results and Discussion
### Experimental Results
As described above, the binary coding methods, OCH [1], Top-RSBC [25], and KMH [9], have shown efficacy for ANN search tasks, but they consider the importance of each binary bit equally. Therefore, many data with different binary codes would share the same Hamming distance to a query sample, and the tied ranking orders that exist in their retrieval results would result in inferior performance. To avoid the above ambiguous situation, TORBW, WhRank [23], and QRank [24] assign different weight values \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SIFT1M** & **GIST1M** & **Cifar10** \\ \hline \(p\) value & \(6.57\times 10^{-8}\) & \(1.76\times 10^{-5}\) & \(1.9\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA tests on three widely benchmark datasets. Figure 4: The _recall_ curves of the comparative experiments on the SIFT1M dataset mapped into 32-bit (**a**), 64-bit (**b**), and 128-bit (**c**) binary codes. The numbers of the true nearest neighbors in the first row and the second row are 10 and 100, respectively. to each binary bit, and they use the weighted Hamming distance to resort the retrieval results. As a result, bitwise weight methods can effectively improve the ANN search performances of binary coding methods. The bitwise weights in TORBW and QRank [24] are particularly sensitive to query data points, and they have a better performance. The aforementioned methods including WhRank [23], QRank [24], OCH [1], KMH [9], ITQ [10], and LSH [7] treat all data points equally. However, evaluation criterions of ANN search tasks pay more attention at the top position of the retrieval results. To satisfy this requirement, TORBW and Top-RSBC [25] penalize mistakes at the top position more than those at the bottom. Therefore, TORBW and Top-RSBC can well preserve a similarity relationship among data points at the top position. As described above, TORBW can effectively preserve ordinal relations and utilizes query-sensitive bitwise weights to reduce the tied ranking orders occurring at the top position. Finally, TORBW achieves an excellent ANN search performance.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 4 Materials and Methods
### Methods  
This section presents the methods used in the development of the proposed hybrid control system. The signal capture uses EMG sensors to read myoelectric signals and gyroscopes to know the position of the user's joints. ESP32 performs the admittance control system, while the Raspberry Pi 3 runs the impedance control system. The PC microcomputer monitors system operation in real-time for analysis.
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 4 Simulation Experiments and Results
### Results from Simulation Experiments
#### 4.2.4 Performance of Look Downstream Control Rule (LKDN)  
The LKDN rule loads the wafer lots even with a smaller batch size than MBS level when the downstream bottleneck workload is less than the predefined target workload, 35 wafers in this case. The target workload is obtained from preliminary experiments. For the benchmark scenario, the MBS6 rule is used. Figure 9a,b show the throughput rate and lead time under the LKDN control rule and the MBS6, under the CONWIP job release environment. It is seen that the LKDN rule gives better performance than the MBS6 with more throughput rate and less lead time, especially with lower BP utilization levels. Since most wafer fabs keep the BP utilization less than 80%, the LKDN rule may be a good alternative to the MBS6. The high performance of LKDN can be realized by utilizing the bottleneck station (BOT) with less starvation downtime. Figure 9c compares the performance of LKDN and MBS6 in terms of the BOT utilization over different BP utilizations. The figure shows that the LKDN provides higher BOT capacity utilization than the MBS6, especially when the BP utilization is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**BP Utilization**} & \multicolumn{3}{c}{**Waiting Time at BP Station (h)**} & \multicolumn{3}{c}{**System Lead Time (h)**} \\ \cline{2-7}  & **MBS1** & **LKUP** & **\% Difference** & **MBS1** & **LKUP** & **\% Difference** \\ \hline
60\% & 7.08 & 6.79 & \(-\)4.1\% & 55.30 & 55.61 & 0.6\% \\ \hline
65\% & 7.82 & 7.60 & \(-\)2.8\% & 57.17 & 57.08 & \(-\)0.2\% \\ \hline
70\% & 8.69 & 8.40 & \(-\)3.3\% & 58.78 & 58.69 & \(-\)0.2\% \\ \hline
75\% & 9.61 & 9.24 & \(-\)3.9\% & 61.71 & 60.23 & \(-\)2.4\% \\ \hline
80\% & 10.72 & 10.39 & \(-\)3.1\% & 63.87 & 62.58 & \(-\)2.0\% \\ \hline
85\% & 12.30 & 11.74 & \(-\)4.6\% & 65.89 & 64.78 & \(-\)1.7\% \\ \hline
90\% & 14.37 & 14.16 & \(-\)1.5\% & 69.82 & 68.94 & \(-\)1.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the Performance of LKUP (Look-Upstream) and MBS1 (Minimum Batch Size 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Flow Type**} & \multicolumn{3}{c}{**Waiting Time at BP Station (h)**} & \multicolumn{3}{c}{**System Lead Time (h)**} \\ \cline{2-7}  & **MBS1** & **LKUP** & **\% Difference** & **MBS1** & **LKUP** & **\% Difference** \\ \hline serial flow & 10.4 & 10.2 & \(-\)2.4\% & 60.3 & 59.6 & \(-\)1.2\% \\ \hline reentrant flow & 8.7 & 8.4 & \(-\)3.3\% & 58.8 & 58.7 & \(-\)0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of LKUP (Look-Upstream) for the Serial and Reentrant Product Flows.  
not too high. It should be noted that an increase in BOT capacity utilization directly leads to in greater throughput, which means a great deal in the capital-intensive wafer fab.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 3 Results  
### Marshall Parameters  
Figure 3 shows Marshall parameters of the asphalt mixture containing different dosages of basalt fiber, including the optimum asphalt content (OAC), the percent air voids in bituminous mixtures (VV), bulk specific gravity and Marshall stability.  
Figure 3a displays the OAC of asphalt mixture containing different dosages of basalt fiber.When the dosage of basalt fiber in the asphalt mixture is 0.1%, 0.2%, 0.3%, 0.4% and 0.5%, the OAC of asphalt mixture is 4.87%,4.98%, 5.00%, 5.20% and 5.30%, respectively. As the basalt fiber dosage increases, the value of OAC increases significantly.Basalt fiber has large specific surface area, which results in absorbing more asphalt. Therefore, the OAC increases after adding the basalt fiber.  
Figure 3b displays the bulk specific gravity of asphalt mixtures containing different dosages of basalt fiber. The values of the bulk-specific gravity were between 2.383 and 2.368.As the dosage of basalt fiber increases, the value of bulk-specific gravity decreases slightly. The basalt fiber density is small and occupies a certain space. For the same volume of specimens, the mass of the asphalt mixture with the fiber is lower, which results in the decrease of the bulk-specific gravity.  
Figure 3: Marshall parameters of asphalt mixture containing various dosages of basalt fiber (**a**) optimum asphalt content (OAC), (**b**) bulk-specific gravity, (**c**) air voids (VV) and (**d**) Marshall stability (MS).  
Figure 3c shows that the value of VV increases with the increase of basalt fiber dosage. When the dosage of basalt fiber reaches 0.5%, the value of VV increases by 4.53%.The basalt fiber was mixed in the mixture, the gross bulk density of the mixture decreased and the elastic modulus of the basalt fiber is large. Therefore, the structure of the mixture is not as dense as that of the ordinary asphalt mixture under the same compaction condition.  
Figure 3d displays the value of Marshall Stability (MS) of asphalt mixtures containing various basalt fiber dosages. As the amount of basalt fiber dosage increases, the value of MS increases first and then decreases. The reason may be that excessive fiber dosage leads to uneven fiber dispersion in asphalt mixture. These defects inside the mixture will lead to a decrease of MS. When the fiber dosage was 0.3%, the maximum MS value was obtained. Compare with the control sample, the MS value of asphalt mixture containing 0.3% basalt dosage was increased by 19.6%. [PERSON] found that asphalt mixture with higher values of MS always have a better anti-crack resistance property [19].
# Response of Mangrove Carbon Fluxes to Drought Stress Detected by Photochemical Reflectance Index
## 2 Materials and Methods
### Flux Data and LUE Calculation  
Four-year continuous 10-Hz time series raw data were measured using an EC system and recorded in a CR3000 datalogger (Campbell Scientific, Inc., Logan, UT, USA). The EC system included a three-axis sonic anemometer (CSAT-3, Campbell Scientific, Inc., Logan, UT, USA) and an open path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). Net ecosystem exchange (NEE) was calculated through flux corrections and quality control procedures [44] (including axis rotation, ultrasonic correction, frequency response correction, steady-state test, turbulent conditions test, statistical test, absolute limits test, and rain test) mainly using the EddyPro6.1 software (Li-COR Inc., Lincoln, NE, USA). Daytime Re (ecosystem respiration) was estimated from daytime temperature based on the fitted nighttime temperature-respiration exponential regression model [44] and then GPP was calculated as the value of daytime Re minus NEE (Equation (1)). LUE was computed as the ratio of GPP and APAR (Equation (2)), where APAR was the product of PAR and \(f_{APAR}\) (fraction of absorbed PAR) (Equation (3)). \(f_{APAR}\) was derived from \(SW_{in}\) (incoming shortwave radiation) and \(SW_{out}\) (outgoing shortwave radiation) (Equation (4)). The calculation of LUE was based on half-hour data of GPP and APAR and then converted to daily mean values. In this study, the downward (from the atmosphere to mangroves) and upward carbon fluxes were represented by positive and negative values, respectively.  
\[GPP=R_{e}-NEE \tag{1}\]  
\[LUE=GPP/APAR \tag{2}\]  
\[APAR=PAR\times f_{APAR} \tag{3}\]  
\[f_{APAR}=1-SW_{out}/SW_{in} \tag{4}\]
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 7 Conclusions and Future Work  
This article proposes a novel method for managing usage counters within a system designed to ensure user anonymity. The core concept revolves around establishing a predetermined maximum number of uses at the time of counter creation, with explicit agreement from the user. Each subsequent use also needs user consent, ensuring ongoing transparency and control.  
To facilitate this secure and auditable process, all checks and validations are handled by a Smart Contract deployed on the Ethereum blockchain. This decentralised storage mechanism guarantees the immutability and public visibility of transaction records, fostering trust and fairness within the system.  
To validate the practicality and efficiency of the proposed approach, a prototype implementation has been conducted on a private Ethereum blockchain. Preliminary experimental results denote that the proposed method can be executed with reasonable computational costs, paving the way for its potential deployment in real-world scenarios requiring both anonymity and usage tracking, which will be the focus of the next issue.  
For the purpose of this study, the scenario has been simplified by assuming that the service provider (\(\mathcal{SP}\)) deploys the Smart Contract initialised with the attribute provider's (\(\mathcal{AP}\)) public key. This assumption has been made because \(\mathcal{SP}\) is the entity that directly uses the Smart Contract for authentication. However, it is important to note that this is not mandatory. In principle, any actor could deploy the Smart Contract. If a different party were to take on this role, an additional mechanism would be required to publicly disseminate the contract address. This may be explored in future research endeavours.  
Another issue to be addressed in the future is the introduction of temporal restrictions in the use of ZKP to permit sequenced uses without user intervention, targeting periodic renewals, for example.  
Replacing the usage of EIP-196 and EIP-197, which work with the elliptic curve alt_bn128, with EIP-2537 [27], which works with BLS12-381 curve, thus offering more security [28] and less gas consumption could also be explored in the future. At the time of writing this paper, EIP-2537 is in the peer review phase and not available on Ethereum Main Net.  
Investigation, F.G.-G.; Writing--original draft, F.G.-G.; Writing--review & editing, F.G.-G., J.H.-J. and A.D.J.; Supervision, [PERSON] and A.D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 LFC Modeling in the Hybrid AC/DC System with Virtual Inertia
### LFC for AC/HVDC Interconnected System  
Next, we consider the scenario where the controlled areas now are interconnected by AC/HVDC transmission lines. There are usually two converters in the HVDC system: one converter controls the active power flow, and the other one would be responsible to control the level of DC link voltage [27]. Here, we introduce the concept of Supplementary Power Modulation Controller (SPMC) to model the effects of HVDC link on the dynamic performance of the overall LFC loop. Note that the dynamics of fast transient HVDC power electronic parts is neglected when we analyze the dynamic effects of the HVDC link on LFC. This is because of the fact that the time constant of electronic parts is much smaller than that of mechanical parts in the analysis of dynamic behavior of the power system.  
As a high-level supervisory control loop, the SPMC is able to improve the performance of the power system when there are load changes. To construct the SPMC, one needs the frequency deviations in each area, i.e., \(\omega_{i}\) and \(\omega_{j}\), and the power flow variations in the AC line, i.e., \(\Delta P_{tie,AC}\). Then, the HVDC link generates the desired DC power based on the output of SPMC, by changing the duty cycles of converters. The SPMC strategy as a damping controller can be expressed as  
\[\Delta P_{DC_{ref}}=K_{i}\Delta\omega_{i}+K_{j}\Delta\omega_{j}+K_{AC}\Delta P _{tie,AC}\, \tag{7}\]\[\Delta P_{DC}(s)=\frac{1}{1+sT_{DC}}\Delta P_{DC_{ref}}\,, \tag{8}\]  
where \(\Delta P_{DC_{ref}}\) denotes the reference of the DC power; \(K_{i}\), \(K_{j}\), and \(K_{AC}\) represent control gains; and \(T_{DC}\) denotes the time constant of the HVDC link. According to the work in [27], the proper time response of this kind of supervisory controller could range from 100 ms to 500 ms. Here, we assume that \(T_{DC}\) is 100 ms.  
In (8), \(\Delta P_{DC}\) is the generated power by the HVDC link. Then, the deviations of total tie-line power flows on both AC and HVDC transmission lines become  
\[\Delta P_{tie_{ij}}=\Delta P_{tie,AC}+\Delta P_{DC}\,. \tag{9}\]  
Note the difference between Equations (9) and (2) which is for the normal AC system. Then, considering the new added DC power in the total tie-line power flow variation, the ACE signal of each area now needs to be adjusted to  
\[ACE_{i}=\beta_{i}\Delta\omega_{i}+\Delta P_{tie,AC}+\Delta P_{DC}\,. \tag{10}\]
## 1 Introduction
A third identified methodology to arrange the sensors in IMU starts from the classical arrangement but uses many detectors on the same axis, measuring the same quantity; it is called redundant linear configuration [69; 73; 79]. The work presented in the paper refers to a new method to increase the quality of the signals provided by the miniaturized IMUs by using low-cost sensors arranged in redundant linear configurations. The direct application of the here proposed methodology is the estimation of small satellites' attitude-based gyro measurements. The main objective of the paper is to provide a method for the accuracy improvement of the three-axis attitude solution for small satellites, in terms of roll, pitch, and yaw angles, through the development of a small-size, low-cost gyro IMU, with a high degree of redundancy, equipping the detection unit of the ADCS, whose data are synergized on each detection axis by using a data fusion algorithm, which reduces the noise level, but also the bias values of the fused signals, which are finally used in a strap-down inertial attitude algorithm based on the quaternionic parameterization. Therefore, the exposure is focused on the development of the attitude system based on strap-down inertial techniques and quaternionic parameterization, which uses a redundant IMU with three linear clusters of miniaturized gyros (Figure 1), with the gyro data fused by using a mechanism based on the maximal ratio combining method. In the next sections are successively shown the mathematics in the back side of the attitude algorithm and of the data fusion algorithm, the software implementation and the results obtained through numerical simulation, but also the results achieved by testing the integrated redundant system with experimental data.