esa-sceva/satcom-chunk-collection huggingface dataset:
# MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-\(k\) Elephant Flows
## 3 Top-\(k\) Measurement Scheme Based on MSLCFinder
### Multi-Sampling Lightweight Counting Finder
#### 3.3.4 The Flow Label Recording Module  
Figure 6 shows that the flow label recording module is designed to record and find top-\(k\) elephant flows. The MSLCFinder achieves this goal based on a min-heap data structure.  
Algorithm 1 shows the flow label recording algorithm. For each packet \(Pac_{T}\) belonging to flow \(f_{\varphi}\), which is counted by MSLCFinder, the module first inserts it into itself. Suppose that MSLCFinder reports the size of \(f_{\varphi}\) as \(\widetilde{n-pac}_{\varphi}\). If \(f_{\varphi}\) is already in the module, it will update the estimated flow size with \(\max(\widetilde{n-pac}_{\varphi},min\_heap[f_{\varphi}])\), where \(min\_heap[f_{\varphi}]\) is the record of the size of \(f_{\varphi}\) in the module. Alternatively, determine whether the module's number of elements is fewer than \(k\) of the top-\(k\). If the number of elements is fewer than \(k\) of the top-\(k\), insert it into the module. Otherwise, judge whether the new count of the flow is greater than the current minimum in the module. If so, eliminate the minimum in the module, and insert the ID information of the new flow and its estimated count. To query top-\(k\) flows, we simply report the \(\hat{k}\) IDs of the \(k\) flows in the module. If the measurement task wants to obtain the estimated flow size of these \(k\) flows, we let the module record and output this information; otherwise, it is not necessary to know. The FlowID of top-\(k\) flows is the top target of MSLCFinder.  
**Analysis**: (1) The time complexity: when a new element comes, it needs \(O(\log k)\) time to query or update the min-heap in the flow label recording module of MSLCFinder. Therefore, the total time complexity is \(O(n\log k)\). (2) The space complexity: the flow label recording module requires \(O(k)\) space.  
Figure 6: The structure of the flow label recording module in MSLCFinder.
## 4 Results
### Electric Propulsion
In the latter case, drag comes into, play further reducing the propellant mass need (Figure 29b). Figure 28: Deorbiting with an electric propulsion system that is activated for a fraction of the orbit: (**a**) total thrusting time; (**b**) activation time vs. orbital fraction. Figure 27: Deorbiting with an electric propulsion system that is activated for a fraction of the orbit: (**a**) thrust; (**b**) decay time. The effect of drag is much more prominent for continuous thrusting as all the orbit is spiraling down at low altitudes so the high drag can act along all the orbit and not only near the perigee. The total mass of the system is the sum of the thruster mass and the propellant-related mass. The first is decreases with the activation time, while the other does the opposite. Comparing Figure 29 it is clear that it makes little sense to narrow too much the thrusting time near the apogee (let us say below 20%) as the propellant savings are little while the mass of the thruster soars. In Figure 30 the total mass of the propulsion system has been calculated adjusting the thrust exactly to obtain the same total decay time (so something slightly different than Figure 27). Different decay times from 1 month to 12 months have been considered. It is possible to see that the total mass reduces as the decay time is increased because the thruster mass is reduced. Moreover, an optimum point is present at a specific activation time that minimizes the total mass as a compromise between propellant mass and thruster mass. The optimum point shifts toward the left as the decay time is increased because of the lower impact of the thruster mass. A continuous thrusting strategy is not that far from the optimum mass, particularly for short total decay times. The results depend on the selected thruster parameters: \(k=0.25\), \(lsp=1000\) s and \(\eta=0.65\). A lower thruster efficiency or a higher \(lsp\) will shift the optimal point toward a continuous firing strategy. Figure 30: Deorbiting with an electric propulsion system that is activated for a fraction of the orbit. Total mass as a function of activation time, and parametric with total deorbit time (1–12 months). Figure 20:0 Deorbiting with an electric propulsion system that is activated for a fraction of the orbit: (a) thruster mass; (b) propellant-related mass.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Experiments
#### 4.2.1 Route Update Time and Throughput  
We evaluated GCS-routing route update time and throughput. We configured a network comprising 1-4 drones, then added a new drone to the network and measured the time until all drones updated the optimal route to each other. Since the HELLO interval for PRPs significantly affects throughput and route update time, we used different HELLO intervals for evaluation. _Babeld_ based on DSDV dynamically adjusts the HELLO interval during data transmission, whereas dynamic adjustment of HELLO interval is not mentioned in the OLSR protocol RFC, and _OLSRd_ does not dynamically adjust the interval during data transmission. Thus, we compared GCS-routing with DSDV with different HELLO intervals, and did not consider OLSR for these evaluations.  
Figure 9(a) shows that DSDV route update times decrease as the HELLO interval shortens. However, they were always greatly larger than that of GCS-routing, i.e., GCS-routing immediately reacted to topology changes. As discussed in Section 3.5, GCS-routing complexity is significantly lower than DSDV because GCS-routing does not issue HELLO messages and hence routing table management has much lower overhead.  
In addition to the route update time, we also measured UDP data throughput between two drones using _iperf_. Figure 9(b) shows that average DSDV throughput significantly decreases as HELLO interval shortens, but is always significantly lower than that of GCS-routing. Thus, throughput stability is significantly affected by shorter HELLO interval. Consequently, HELLO messages with shorter intervals reduce route update time, but degrade network throughput. Therefore, it is more appropriate for drone networks to use GCS-routing, which outperforms other protocols in terms of both throughput and route update time.  
Figure 9: Drone prototype and network module.
# Transmission Removal from a Single OSN-Shared Glass Mixture Image
## 3 Proposed Method
### Two-Stage Reflection Extraction Network  
(1) Network architecture  
In the last section, we progressively regenerated the enhanced features of the glass mixture image after the platform processing and simultaneously output the associated gradient profile information as the input for the next step in implementing the network. Since the transformer performs image vision operations by downscaling the input tensor, this results in the unfolded tensor occupying different spaces depending on the input size; in other words, it is easy to for the system capacity to be insufficient when the image size is too large.  
Therefore, in response to the above analysis, we instead use the adversarial generative network as our main framework to implement the subsequent operations concerning transmission removal. We reconstruct the two-stage network to extend more prior knowledge as guidance. Figure 7 illustrates the process of transmission layer removal after using the acquired enhanced features. We initially use the residual network for the prediction of the transmission layer, and then link the three features together again to form the feature map input for the second-stage generation network.  
(2) Loss function  
For the output results, we construct a discriminator network to discriminate the authenticity of the final results. Based on this, we determine the loss of the first-stage predicted transmission layer \(L_{\text{T}}\) and the loss of the second-stage predicted reflection layer \(L_{\text{S}}\), respectively.  
\(L_{\text{T}}\) is defined as  
\[L_{\text{T}}=\lambda_{1}L_{\text{P}}+\lambda_{2}L_{1}, \tag{6}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are the parameters, which are set as 5 and 2, respectively.  
In addition, for the content of the image reflection layer, we use texture loss \(L_{\text{texture}}\)[26] instead of pixel-level loss and weaken the correlation with the transmission layer using the exclusion loss \(L_{\text{excl}}\)[9]. The second-stage predicted reflection layer loss is constructed as  
\[L_{\text{S}}=\lambda_{11}L_{\text{excl}}+\lambda_{21}L_{\text{texture}}+\lambda_{31}L_{\text{TV}}, \tag{7}\]  
where \(\lambda_{11}\), \(\lambda_{21}\), and \(\lambda_{31}\) are the parameters set as 2, 2, and 5, respectively.  
Ultimately, the specific loss function \(L_{\text{B}}\) is composed of \(L_{\text{T}}\) and \(L_{\text{S}}\) together as  
\[L_{\text{B}}=L_{\text{T}}+L_{\text{S}}. \tag{8}\]
## 2 Materials and Methods
### Statistical Analyses  
Descriptive analyses are presented as mean \(\pm\) standard deviation (SD). The normality of data distribution was checked using the Shapiro-Wilk test. As the data met the normality assumption, parametric tests were conducted. All statistical analyses have been conducted using the statistical software R version 4.2.3. Specifically, the reliability analyses were performed using the R package \"SimplyAgree\" (2018). The intra-session and inter-session reliability were explored using the following coefficients: 2-way fixed effects interclass correlation coefficient (ICC3,1), standard error of measurement (SEM), and coefficient of variation (CV). The thresholds for interpreting ICC results were: 0.20-0.49 low, 0.50-0.74 moderate, 0.75-0.89 high, 0.90-0.98 very high, and \(\geq\)0.99 extremely high ([PERSON] et al., 2018). A CV of \(\leq\)10% was considered small ([PERSON] et al., 2018; [PERSON] et al., 2018). The average reliability of each measure was interpreted as acceptable for an ICC \(\geq\) 0.75 and a CV \(\leq\) 10%, moderate when ICC \(<\) 0.75 or CV \(>\) 10%, and unacceptable/poor when ICC \(<\) 0.75 and CV \(>\) 10% ([PERSON] et al., 2018). A \(t\)-test for related samples was used to explore the differences between sessions 1 and 2. In this analysis, the effect sizes and confidence intervals (95% Cis) were calculated using [PERSON]'s d. Based on [PERSON] et al. (2018), [PERSON]'s d is interpreted as trivial (d \(<\) 0.2), small (0.2 \(\leq\) d \(<\) 0.6), moderate (0.6 \(\leq\) d \(<\) 1.2), large (1.2 \(\leq\) d \(<\) 2.0), and very large (\(\geq\)2.0). In addition, a repeated measures analysis of variance (ANOVA) was applied to compare the same-day (intra-session) CS performance for both sides. For repeated measured ANOVA, the partial eta squared (\(\eta^{2}\)) was used. The statistical significance was set at \(p<\) 0.05 for all statistical analyses.  
Figure 1: The 3-point line curve sprint test.
## 5 Conclusions  
The research work in this paper elaborates on the theoretical effectiveness of the proposed method based on the smooth local subspace projection and PE. The new weight matrix also proves to be better than the existing weight matrices after being verified by repeated simulation researches. Through numerical simulations, the effectiveness of the smooth local projection denoising method can be verified, also the effectiveness of applying PE into the fault classification of mechanical signals. Through the applications to gear signals of the Drivetrain Diagnostics Simulator and the processing of _Case Western Reserve University Bearing Data_, multiple groups gear and bearing signals of different conditions are analyzed, in order to make the previous aliasing PE values into regular different PE mean value and ranges, and different fault types can be clearly classified. The results of numerical simulations and application researches demonstrate the significance of the proposed method in the field of fault classification. Hence, it can be concluded that gears and rolling bearings can be classified by the proposed novel smooth local subspace projection method, using the logarithmic weighting function and PE in this paper. The proposed method can effectively classify the fault modes, which is very beneficial to fault diagnosis of rotary machinery. From all above, it clearly indicates that the proposed method is a successful exploration, and it is very promising in the field of fault classification of rotary machinery.  
Figure 35: The permutation entropies of the proposed approach.  
**Author Contributions:** [PERSON] conceived the study and providing valid criticism. [PERSON] performed the statistical analysis, discussed results and wrote the manuscript. [PERSON] contributed to data collection and analysis. All authors read and approved the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China under Grant No. 51875416 and No. 51475339, and Natural Science Foundation of Hubei Province under Grant No. 2016 CFA042.  
**Acknowledgments:** The authors appreciate the free download of the original bearing failure data and one photo picture provided by Case Western Reserve University Bearing Data Center Website.  
**Conflicts of Interest:** None of the authors have conflicts of interest in relation to this study.
# Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function
## 3 Definitions and Notations
### Amortized Local Competitive Analysis  
The objective considered is (\(G\)) scaled importance-based flow time plus energy. Let \(\mathbf{G_{A}}(\mathbf{t})\) and \(\mathbf{G_{o}}(\mathbf{t})\) be the increase in the objective in the schedule for any algorithm A and offline adversary Opt, respectively at time \(t\). Opt optimizes \(G\). At any time \(t\), for algorithm \(A\), \(\mathbf{G_{A}}(\mathbf{t})\) is \(\mathbf{P_{A}^{t}}\left(\mathbf{s_{A}^{t}}\right)+\mathbf{p_{A}^{t}}\), where \(\mathbf{s_{A}^{t}}\), \(\mathbf{P_{A}^{t}}\left(\mathbf{s_{A}^{t}}\right)\) and \(\mathbf{p_{A}^{t}}\) are speed of processor, power at speed \(\mathbf{s_{a}^{t}}\) and scaled importance of all active jobs, respectively. To prove that A is c-competitive a potential function \(\mathbf{\Phi}(\mathbf{t})\) is required which follows the following conditions: Boundary Condition: Initially, when no job is released and at the end, after all jobs are completed \(\Phi=0\). Job Arrival and Completion Condition: There is no increment in \(\Phi\), when any job arrives or completes. Running Condition: At any other time when no job arrives or completes, \(\mathbf{G_{A}}(\mathbf{t})\) plus the rate of change of \(\Phi\) is no more than c times of \(\mathbf{G_{o}}(\mathbf{t})\): \(\mathbf{G_{A}}(\mathbf{t})+\ \frac{d\mathbf{\Phi}(\mathbf{t})}{d\mathbf{t}}\leq\mathbf{c}\cdot\mathbf{G_{o}}(\mathbf{t})\).  
**Lemma 1**.: _(Young's Inequality [34]) Let \(\mathbf{f}\) be any real-valued, continuous and strictly increasing function such that \(\mathbf{f}(\mathbf{0})=\mathbf{0}\). Then \(\forall\ \mathbf{m}\), \(\mathbf{n}\geq\mathbf{0}\)_  
\[\int_{0}^{m}f(x)\ dx+\ \ \ \int_{0}^{n}f^{-1}(y)\ dy\geq\ m\cdot n \tag{1}\]  
_where, \(f^{-1}\) is the inverse function of \(f\)._
# Extraction of Creation-Time for Recovered Files on Windows FAT32 File System
## 2 File Allocation Behavior of Windows FAT32 File System  
Figure 2 shows the structure of the FAT32 file system, where the storage device is managed with three separate areas: reserved area, file allocation table(FAT) area and data area [28; 29]. In the reserved area, the basic information for the file system is stored such as boot code, partition information, size of the addressable minimum block in the data area, etc. In the file allocation table(FAT) area, the offsets allocated to each file are stored as a linked list. In the data area, the contents of each file and meta information of files are stored. The data area is divided into multiple addressable minimum units,  
called clusters. Contents of each file are recorded in one or more clusters. A cluster stores the contents of at most one file, even when the cluster size is larger than the size of recorded contents.  
Some cluster stores the meta information of stored files in a form of directory entry, instead of file contents. Figure 3 shows the structure of a directory entry that contains the meta information of a single file. The directory entry includes file name, attributes, creation-time, last access time, write time, the first cluster allocated to storing of file contents, and the number of clusters allocated to storing file contents. The directory entry of a deleted file remains with a deletion mark '0 xE5' in the first byte of the file name field until it is overwritten by recording the meta information of another new file. The size of the directory entry is 32 bytes for a file name with 8 bytes or less. For a long file name with larger than 8 bytes, the size of the directory entry is multiple of 32 bytes, depending on the size of the file name. In this paper, the cluster storing file contents is referred to as content cluster and the cluster storing directory entries is referred to as directory entry cluster.  
Because there is no public reference for the Windows FAT32 implementation [26], it is imperative to empirically examine the file allocation pattern on Windows-family operating systems. We examine the file allocation pattern in detail upon Lumia 630 smartphone with Windows 10 Mobile and personal computers with Windows XP SP3, Windows 7 Home Basic, Windows 7 Professional, Windows 7 Ultimate 64 bit Edition, Windows 8.1, Windows 8.1 Pro, Windows 10 Home, or Windows 10 Pro. We dump the binary image of external storages using FTK Imager tool after storing new files or deleting files, and interpret all binary codes of the dumped image using WinHex or HxD tool. Elaborate interpretation of their file allocation patterns results in some common behaviors, which are summarized in the following subsections.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
###### Abstract  
In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and lower linearity requirements (e.g., compared to orthogonal-frequency division multiplexing radars), as well as its high velocity unambiguity and multiple-input operation capability, all of which are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.  
Doppler shift; periodic autocorrelation function; phase-modulated continuous wave; pseudorandom sequence; radar +
Footnote †: journal: Sensors
## 1 Introduction
Although studies using ILs as catalysts have been rapidly increasing in number, the cationic structures described in these studies mainly focus on carrying sp3-hybridized carbon atoms as substituents at both nitrogen heterocyclic atoms [13; 49; 50]. Very recently, a unique series of tunable aryl-imidazolium iron-containing ILs with dual Brensted-Lewis acidity (abbreviated as B-L MILs) were reported by our group [51]. These B-L MILs have a combination of one proton and sp2 aryl substituents at the nitrogen atoms of the imidazolium core. The physical-chemical properties of B-L MILs are tuned by specific electronic effects (the \(\sigma\)- and \(\pi\)-system) and even by the substituent effect in the aryl moiety [51]. Even though the molar ratio of FeCl\({}_{3}\) to [HPhImX][Cl] (X = H, CH\({}_{3}\), OCH\({}_{3}\), and NO\({}_{2}\)) is only 0.5 in these B-L MILs systems, they have been shown to have good Lewis acidity [51]. In this study, we demonstrate the utility of the B-L MILs as both solvents and catalysts for the Friedel-Crafts alkylation reaction of p-xylene with benzyl chloride. The influence of various reaction parameters, such as the molar ratio of the reactants, catalyst dosage, reaction time, temperature, and the catalyst recycle are investigated. When compared to the conventional catalysts reported for the Friedel-Crafts alkylation reaction [11; 15; 16; 17; 18; 19; 29; 30; 47; 48; 52; 53; 54], the dual solvent-catalyst B-L MIL function was found to exhibit high catalytic activity and could be reused after several recycles, thus making the synthesis process greener.
# Characterization of Nanoparticle Batch-To-Batch Variability
## Materials and Methods
### Physicochemical Characterisation of NM Batches
#### 2.6.1 Representative TEM Pictures and Derived Data  
For all investigated NMs batches of the different materials (SiO\({}_{2}\), ZnO, TiO\({}_{2}\), PS-NH\({}_{2}\) and CeO\({}_{2}\)), TEM samples of the NMs were prepared by nebulizing a thereof produced dispersion onto a Cu grid (400 mesh) covered with a combined holey and ultrathin (about 3 nm) carbon film. This was done by means of an ultrasonic apparatus. Using the ultrathin C film helps obtaining a larger area-deposition density in comparison to simple holey carbon films. Furthermore, the 3 nm thick supporting carbon does not drastically disturb the TEM inspection of the NMs because of low additional electron scattering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Property** & **Analytical Method** & **References** &
\begin{tabular}{c} **Supplementary** \\ **Materials** \\ \end{tabular} \\ \hline \multirow{4}{*}{Trace impurities} & Optical Emission Spectrometry with & & \\  & Inductively Coupled Plasma (ICP-OES), & & \\  & Mass Spectrometry with Inductively Coupled & & S1-1 \\  & Plasma (ICP-MS) & & \\ \hline Water Solubility & Concentration of free ions in solution & [28] & S1-2 \\ \hline \multirow{2}{*}{Crystalline phase} & Electron Microscopy and Recussed Ion Beam & & \\  & Nanostructuring & - & S1-3 \\ \hline Crystallite size & X-ray Diffraction (XRD) & - & S1-4 \\ \hline Primary particle size & Transmission Electron Microscopy (TEM) & - & S1-6a,b \\ \hline Agglomeration/aggregation state & Transmission Electron Microscopy (TEM) & - & S1-6a,b \\ \hline Morphology & Transmission Electron Microscopy (TEM) & - & S1-6a,b \\ \hline \multirow{2}{*}{Diameter of aerosolized NMs} & Electrospray aerosol generator coupled with & [29] & S1-7 \\  & Scanning Mobility Particle Size (SMPS) & & \\ \hline Hydrodynamic diameter & Dynamic Light Scattering (DLS) & [30; 31] & S1-8a,b \\ \hline Porosity and surface area & Gas sorption analysis (BET method) & - & S1-9 \\ \hline Surface charge & Zeta potential & - & S1-10 \\ \hline Photocatalytic activity & UV-Vis spectrophotometer & - & S1-11 \\ \hline \multirow{2}{*}{Octanol-water partition coefficient} & Mass Spectrometry with Inductively Coupled & - & S1-12 \\  & Plasma (ICP-MS) & - & S1-13 \\ \hline Radical formation potential & Dichlorofluorescein (DCF) assay & [32; 33] & S1-13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical methods chosen to characterize the particle properties.  
Crystallite size and the primary particle size distribution were observed. Typical TEM findings are briefly summarized and compared to the results from characterization according to other methods. Particular emphasis is put on determining, or in the case of apparent problems due to agglomeration/aggregation, at least estimating, average NM sizes.