esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Resource Exhaustion Attacks against Wireless Mobile Devices
## 1 Introduction
;
* specific recommendations and particular solutions that can be used to protect against a certain type of such attacks, such as isolation of segments and layers of sensor networks to protect the nodes (victims of ERE attacks) [2; 20], packet filtering on some intermediate nodes and comparison with passing traffic patterns [17; 19], in particular by using machine learning [21]. In contrast to the above works, this paper concentrates on the classification, comparison and evaluation of various types of ERE attacks on cyber-physical devices, ranging from impacts performed at the physical level to various combined attacks involving social and cyber-physical aspects. The goal of this paper is, first, to model ERE attacks analytically, focusing on various types of attacking influences and their conditions and, second, to simulate some of the attacks in physically implemented cyber-physical scenarios to verify the feasibility of such attacks experimentally and draw some conclusions on their effectiveness. The main contribution of the paper is a general framework for modeling and analyzing various types of ERE attacks, including the evaluation of their performance indicators. The novelty of the work is expressed by a suggested unified approach to representing heterogeneous types of ERE attacks on cyber-physical devices, including both purely software and information-based influences through an intruder's manipulation by data packets, code, etc. and complex actions, including the physical and electromagnetic effects on devices. The experimentally obtained data on evaluating the effectiveness of denial-of-sleep attacks on examples of cyber-physical devices has a certain practically oriented novelty as well. The general framework for modeling and analyzing ERE attacks includes:  
* analysis of possible kinds of ERE attacks on cyber-physical devices;
* an ERE intruder model representing knowledge required for simulation and analysis of ERE influences; and
* experimental studies on the modeling and evaluation of ERE attacks on two developed use cases--a network of end-user mobile devices and a self-organizing mesh network designed for operational management and emergency response. The rest of the paper is structured as follows. Section 2 provides an overview of the related work. Section 3 presents the proposed model of the intruder. Section 4 encompasses description and analysis of two developed use cases. Section 5 contains experimental studies on the use cases. Section 6 provides the analysis of results. Conclusions and directions of further research are presented in Section 7.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 4 Discussion
### Comparision of TD-CDE and CQWO-TD-CDE  
As analyzed in Section 3, for TD-CDE, the calculation amount of real number multiplication and the addition calculation amount of each equalized sample output depends on the number of filters taps \(N\). In the CQWO-TD-CDE, the calculation amount of real number multiplication is only related to the quantization stages M, and the degree of correlation between the calculation amount of real number addition and the filter taps \(N\) is also lower than that of TD-CDE. Considering different quantization stages M, the quantitative analysis of the changing trend of \(\eta_{\text{mul}}\) and \(\eta_{\text{add}}\) is shown in Table 2.  
From the analysis in Table 2, we find that when the fiber transmission distance is fixed, as the quantization stages increase, the computational complexity of the optimized algorithm will increase slightly. Still, the computation complexity of the real number multiplication \(\eta_{\text{mul}}\) can also reduce by more than 90%. From Section 3, we can see that the increase in the quantization order can significantly improve the algorithm's performance. When the quantization stage M is fixed, as the transmission distance increase, the computational complexity of the optimized algorithm decreases. In summary, the proposed optimization algorithm can greatly reduce the computational workload under long-distance and large CD conditions. Even considering the performance requirements to compromise and the quantization stages are set to M = 16, the optimization indexes \(\eta_{\text{mul}}\) and \(\eta_{\text{add}}\) also show efficient optimization.
## 3 Simulation and Calculation  
By changing the instructions parameters of the control subsystem, the integrated modeling method can predict the impact of micro-vibration under a variety of imaging modes, including push-broom, gaze, large side swing, and others. In this paper, the simulation imaging condition is push-broom imaging after adjusting attitude, which is widely employed in optical imaging satellites. The relevant parameters set for the proposed integrated model are shown in Table 4.  
As an example of simulation results, the response curve of the XYZ-direction rotation angle and the Y-direction displacement of the secondary mirror node and flywheel speed are shown in Figures 7 and 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Object Name** & **Simulation Parameter** \\ \hline orbit height & 500 km \\ initial attitude angle & [0 0 0]\({}^{\circ}\) \\ target attitude angle & [1 2 3]\({}^{\circ}\) \\ flywheel’s moment of inertia & 0.00321 kg\({}^{\circ}\)m\({}^{2}\) \\ star sensor error & [5 5 10]\({}^{\prime\prime}\) \\ gyro constant drift & 0.0008\({}^{\circ}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation instructions parameters.  
Figure 6: Schematic diagram of optical subsystem.  
Figure 7: Time-domain response of secondary mirror node.  
The satellite adjusts its attitude angle from the initial attitude angle to the target attitude angle, and after about 30 s, it is stable and turns into the imaging stage. The focus of this paper is the micro-vibration response during imaging in the last 20 s. The response of micro-vibration could be observed when curves were amplified. Flywheel rotation speeds in the stage of push-broom imaging are not at a fixed value due to the addition of star sensor error, gyro constant drift, and micro-vibration feedback. Figure 9 shows the movement of image points in the XY-plane. Figure 10 shows the frequency domain response diagram of the Y-direction which is charge coupled device camera (CCD) push-broom direction.  
Figure 8: Flywheel rotation speed curve.  
Figure 9: The movement of image point in the XY-plane.
## 2 Graphene and Its Derivatives: Graphene Oxide and Reduced Graphene Oxide
### Optical Properties of Graphene, GO, and rGO  
The optical properties of graphene vary with the layer number. It only absorbs 2.3% of light for single-layer graphene; hence, 97.7% of light passes through a single-layer with around 0.1% reflected from its initial trajectory. Figure 4a represents the optical image of a 50-\(\upmu\)m aperture partially covered by graphene and its bilayer. The line scan profile shows the intensity of transmitted white light along the yellow line. Inset shows the sample design--a 20-\(\upmu\)m thick metal support structure has apertures 20 \(\upmu\)m, 30 \(\upmu\)m, and 50 \(\upmu\)m in diameter with graphene flakes deposited over them. However, with increasing graphene layers that are stacked on top of each other, the greater the light absorption becomes and the  
Figure 3: (**a**) Contour plot of the energy in the first Brillouin zone of graphene [42] and (**b**) electronic energy dispersion in the honeycomb lattice. Left: energy spectrum for finite values of t and t’ with t = 2.7 eV and t’ = \(-\)0.2t. Right: zoom-in of the energy bands close to one of the Dirac points (reprinted permission from [23]).  
Figure 2: (**a**) Pristine graphene (pure-arranged carbon atoms) with sp\({}^{2}\)-hybridized carbon atoms and (**b**) graphene oxide (GO) and (**c**) reduced graphene oxide (rGO) of the chemically modified graphene [35].  
lower the optical transparency becomes. However, because the relationship between light absorption and the graphene layers is linear, a graphene sample composed of five layers would have absorption of 11.5% and optical transparency of around 88-88.5% [43; 44; 45] (Figure 4b).  
For GO (rGO), the properties of these materials are dependent upon the thickness of the graphene and the number of graphene layers. Similarly, the optical transmittance of these materials can be tuned by varying the thickness of the material. GO has an optical absorption of 225-275 nm, whereas the absorption in rGO materials is usually around 270 nm. The optical absorption values in these materials are due to the \(\pi\) electrons in the graphene sheet and the \(\pi\)-\(\pi\) transitions. GO can also absorb wavelengths in the ultraviolet (UV) and near-infrared (NIR) regions [46].
## 1 Introduction
Although the upstream of a potassium transporter gene were discovered first, many of these riboswitches have been predicted to be located in front of a variety of genes across Gram-positive bacteria. Whereas in Bacillus and Clostridia over 50% of these genes seem to encode various ions, osmoprotectants or amino acid transporters, almost all of the _ydaO_-preceded genes in Actinobacteria seem to be cell wall related [26]. This has also been further investigated in _Streptomyces coelicolor_, where a c-di-AMP-dependent _ydaO_-type riboswitch in front of the resuscitation-promoting factor gene _rpfA_ contributes to a complex multilevel regulatory mechanism, also involving other nucleotides, such as cAMP and ppGpp [28]. However, in contrast to Firmicutes, profound knowledge about the role of c-di-AMP in Actinobacteria is still limited, i.e., to several reports about _Streptococcus_ and _Mycobacterium_ species, e.g., [24; 28; 29; 30]. Whereas in Streptococcus, c-di-AMP seems to play a role in cell wall maintenance and in ion homeostasis, reports about Mycobacteria suggest an implication in ensuring DNA integrity [24; 28; 31]. The Gram-positive _Corynebacterium glutamicum_ is a non-pathogenic Actinobacteria, known for its wide usage in industrial biotechnology, i.e., amino acid production (for reviews, see [32; 33]). In this context, not only have its metabolic pathways been extensively studied but its specific and global regulatory pathways are also well investigated (see reviews [34; 35; 36; 37]). Thus, it represents a well-characterized model organism for its close relatives, such as the human pathogenic _Corynebacterium diphtheiae_ and _Mycobacterium tuberculosis_. In their study, [PERSON] et al. [26] predicted two putative _ydaO_-type riboswitches in the genome of _C. glutamicum_, one of them located upstream of the _rpfI_ gene, which is similar as for _rpfA_ in _S. coelicolor_ and the second upstream of the _cg2402/nlpC_ gene. This gene encodes for one of four putative NlpC/P60-type cell wall peptidases in _C. glutamicum_[38; 39; 40] and is the first gene of an operon comprising, among others, a second _nlpC_-type gene (_cg2401_). The operon seems to be strongly regulated, i.e., by the global master regulator GlxR, the two-component system MtrAB and the three-component system EsrISR [39; 41; 42] and thus represents an interesting topic for investigation. In our study, we initially characterized c-di-AMP metabolism in _C. glutamicum_ and describe a non-canonical c-di-AMP riboswitch as the first regulatory target of c-di-AMP in this organism.
## 4 Discussion  
### General Discussion  
This study, including hives exposed to extreme loads of varroa mites, helps to understand the progression of viral infections and sugar concentrations in hemolymph throughout the season in correlation with the degree of infestation.  
Figure 5: Viral loads in in honeybees of highly infested and control hives over time: (**a**) ABPV loads (copies/actin); (**b**) IAPV loads (copies/actin). Infested hives Nos. 522 and 505 showed a high ratio of viral copies in September for both ABPV and IAPV. A significant difference (*, \(p<0.05\), \(t\)-test) between studied groups was seen for IAPV.  
During the summertime (June and July), honeybees were able to maintain their homeostasis despite the parasitism, which was reflected by the stability of sugar concentrations in hemolymph. In the fall (late September), viral loads increased significantly, and the combination of a high parasitism and viral infections led to an alteration of their metabolism, demonstrated by the decreased sugar concentrations in hemolymph. This cohort study was initially planned to continue throughout overwintering. However, three weeks after the last time point (late September), four highly infested hives were dead, and the two others were very weak, containing only a handful of live bees.  
The particularity of this study is its use of sugar concentrations in hemolymph as a predictive indicator of honeybee winter loss. However, further studies need to be performed to confirm this hypothesis.
# Active Control of Broadband Noise Inside a Car Using a Causal Optimal Controller
## 3 Formulation of the Causal Optimal Controller
For modelling the \(k\)th (\(k>1\)) primary path, however, we need to remove the components related to the former \(k-1\) reference signals from the signal \(x_{k}\) and undesired noise \(d\). Similarly, using residual power spectral density and residual cross-spectral density functions \(S_{x_{k}x_{k}x_{1}-x_{k-1}}\big{(}e^{j2\pi f}\big{)}\) and \(S_{ddx_{k}x_{k-1}-x_{k-1}}\big{(}e^{j2\pi f}\big{)}\), and \(S_{dx_{k}x_{k}1-x_{k-1}}\big{(}e^{j2\pi f}\big{)}\) and \(S_{x_{k}dx_{1}-x_{k-1}}\big{(}e^{j2\pi f}\big{)}\) derived from Equation (9), the \(k\)th primary path \(P_{k}\big{(}e^{j2\pi f}\big{)}\) can be identified as follows:  
\[P_{k}(e^{j2\pi f})=\frac{S_{ddx_{k_{1}-x_{k-1}}(e^{j2\pi f})-S_{x_{k}x_{k}x_{k }-1}(e^{j2\pi f})+\sqrt{\big{[}S_{ddx_{k_{1}-x_{k-1}}(e^{j2\pi f})-S_{x_{k}x_{k }\cdot x_{k}-x_{k-1}}(e^{j2\pi f})\big{]}^{2}+4\big{|}S_{x_{k}d_{k1}-x_{k-1}}( e^{j2\pi f})\big{|}^{2}}}}{2S_{dx_{k}x_{1}-x_{k-1}}(e^{j2\pi f})}. \tag{13}\]  
Transforming Equation (13) into \(z\) domain and substituting the result and Equation (7) into Equation (1), the optimal controller can be expressed as follows:  
\[W_{opt,k}(z)=P_{k}(z)\big{(}\sum\big{(}X_{q}^{r}(z)+X_{q}^{c}(z)\big{)}\big{)}. \tag{14}\]  
As is explained in Section 2.1 and Figure 4, the future reference signals of an ANC system cannot be acquired. Thus, the noncausal part of the optimal controller has to be discarded, and the causal optimal controller (COC) is derived as follows:  
\[W_{cmu,k}(z)=\big{\{}P_{k}(z)\big{(}\sum\big{(}X_{q}^{r}(z)+X_{q}^{c}(z)\big{)} \big{)}\big{\}}_{+}, \tag{15}\]  
where the subscript \(+\) denotes the causal part of the optimal controller, that is, the terms with non-positive exponential index of \(z\). Equation (15) is a causal optimal controller for a physical system, and the best noise-reduction result can be achieved with it. Meanwhile, Equation (15) can also be used to predict the achievable noise-reduction performance by transforming it into frequency domain and substituting it for the following expression:  
\[NR_{cmu}(e^{j2\pi f})=-10\lg(1-\gamma_{x_{1}-x_{R}d}^{2}(e^{j2\pi f})\big{|} W_{cmu}(e^{j2\pi f})\big{|}^{2}/\bigg{(}\big{|}W_{cmu}(e^{j2\pi f})\big{|}^{ 2}+\big{|}W_{nocmu}(e^{j2\pi f})\big{|}^{2}\bigg{)}. \tag{16}\]
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
###### Abstract  
In this work, a new Time Difference of Arrival (TDoA) scheme for distance measurement based on Optical Camera Communication (OCC) systems is proposed. It relies on the use of optical pulses instead of radio-frequency signals as the time reference triggers, and the introduction of a rolling shutter camera, whose characteristics allows substituting the timer modules used in conventional TDoA techniques by image processing of the illuminated area in the picture. This processing on the camera's images provides time measurements and implies and specific analysis, which is presented in this work. The system performance and properties, such as resolution and range, mainly depends on the camera characteristics, such as the frames capture rate and the image quality. This new technique is suitable to be implemented in smartphones or other Commercial Off-The-Shelf (COTS) devices equipped with a camera and speakers.  
Optical Camera Communications; ranging technique; statistical pulse detection Article  
A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival  
1
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### The Algorithms
#### 4.2.2 Early Pruning Optimizations
This lemma is also a pruning condition. If itemset \(x\) satisfies it, then there is no need to count it in the old \(\mathcal{D}\). Observation 2 (Absolute Count Delta):  
\[xeFI(\Delta,minsup*(1+1/inc))\to\text{ }{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{} }{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}  
Note, that when the exact count is known, then _mincount_ equals to _maxcount_. If \(\text{maxcount}<minSup*|\mathcal{D}^{+}|\), then \(x\) is pruned (by the original IMRApriori algorithm). If \(\text{mincount}\geq minSup*|\mathcal{D}^{+}|_{\epsilon}\) then \(x\) is globally frequent, and should not need to be recounted in the missed Splits. Previously defined lemmas are applied in the algorithm during the Join in Step 8. In this step, we already know the sizes of \(\mathcal{D}\) and \(\Delta\), and therefore we know \(n\) and _inc_. So, we compare potential counts of itemsets directly to sizes of databases. The map phase of Join extracts the potential counts for some DB part (old \(\mathcal{D}\) or \(\Delta\)) from the input and outputs it immediately together with the DB \"marker\" (variable that determines if it is \(\mathcal{D}\) or \(\Delta\)). Algorithm 6 determines the total potential counts and makes the decision about itemset future processing. Split size information and total \(\mathcal{D}\) size is being passed as \"previous knowledge\" (_PK_) input into the FIM incremental algorithm in \"General Scheme\" at Step 3, and in our implementation of IMRApriori in phase 1. ```
Input:\(x\), List<DBPartMarker, MinCount, MaxCount>, \(|\mathcal{D}|\), \(|\Delta|\), minSup
1: MinDB = 0
2: MaxDB = \(|\mathcal{D}|\)
3: MinDelta = 0
4: MaxDelta = \(|\Delta|\)
5:foreach T in List<DBPartMarker, MinCount, MaxCount>
6:if DBPartMarker = \(\mathcal{D}\)then
7: MinDB=MinCount
8: MaxDB=MaxCount
9:else // DBPartMarker = \(\Delta\)
10: MinDelta = MinCount
11: MaxDelta = MaxCount
12:endif
13:endforeach
14: Min = MinDB + MinDelta
15: Max = MaxDB + MaxDelta
16:if Min >=\(minSup*(|\mathcal{D}|+|\Delta|)\)then //Lemma 1 or 3
17: Output <\(x\), Min, Max> in frequent itemset file and end
18:if Max <\(minSup*(|\mathcal{D}|+|\Delta|)\)then //by Lemma 2
19:endif/No need to output not frequent itemset
20:if MinDB<MaxDB then
21: Output+<\(x\), MinDelta, MaxDelta> in \"Count in \(\mathcal{D}\)\" file
22:endif
23:if MinDelta<>MaxDelta then
24: Output+\(x\), MinDB, MaxDB> in \"Count in \(\Delta\)\" file
25:endif
26:endif
```  
**Algorithm 6**The Reducer phase of Join with Lemmas Applied.
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 4 DWT Reconstruction and Analysis
### DWT Analysis Process and Theory
For a time series \(\{X_{t}\}\), the autocorrelation coefficient \(r_{k}\) is defined at lag \(k\) is defined as  
\[r_{k}=\frac{\sum\limits_{t=k+1}^{n}\left(X_{t}-\mu\right)\left(X_{t-k}-\mu \right)}{\sum\limits_{t=1}^{n}\left(X_{t}-\mu\right)^{2}} \tag{10}\]  
where \(\mu\) is the mean of the time series and \(n\) is the length of the time series. The autocorrelation coefficient ranges from \(-1\) to \(1\). A value closer to \(1\) indicates a stronger positive correlation, while a value closer to \(-1\) indicates a stronger negative correlation. A value near 0 suggests a weak correlation. The autocorrelation function (ACF) plot illustrates the variation in the autocorrelation coefficient as the lag (k) increases. By examining the ACF plot, one can assess whether the time series exhibits a periodic pattern or trend. For time series with periodicity, the ACF typically displays prominent peaks at multiples of the period. The partial autocorrelation function (PACF) test is a statistical tool for time series analysis, which measures the degree of direct correlation between two time points after excluding the influence of the intermediate lag term. PACF helps to identify and quantify net correlations between different time points in a time series. For the time series \(\{Xt\}\), the partial autocorrelation coefficient at lag k, denoted as \(\varphi_{kk}\), can be understood as the strength of the linear relationship between \(X_{t}\) and \(X_{t\text{-}k}\), while controlling for the intermediate lags \(X_{t-1}\), \(X_{t-2}\), \(\ldots\), \(X_{t-(k-1)}\). \[X_{t}=\varphi_{1}X_{t-1}+\varphi_{2}X_{t-2}+\cdots+\varphi_{p}X_{t-\text{k}}+ \epsilon_{t} \tag{11}\]  
\(\epsilon_{t}\) is white noise. \(\varphi_{kk}\) is calculated by solving the following Yule-Walker equations. \[ \begin{pmatrix}r_{1}\\ r_{2}\\ \vdots\\ r_{k}\end{pmatrix} = \begin{pmatrix}1&r_{1}&\cdots&r_{k-1}\\ r_{1}&1&\cdots&r_{k-2}\\ \vdots&\vdots&\ddots&\vdots\\ r_{k-1}&r_{k-2}&\cdots&1\end{pmatrix}\begin{pmatrix}\varphi_{1}\\ \varphi_{2}\\ \vdots\\ \varphi_{k}\end{pmatrix}  \tag{12}\]  
In this context, \(r_{i}\) denotes the autocorrelation coefficient at lag \(i\). The partial autocorrelation function (PACF) plot is useful for assessing the strength of long-term relationships while controlling for short-term fluctuations. If the PACF approaches 0 at multiple lags, the direct linear correlation between time points in the time series is weak. In contrast to the ACF, the PACF emphasizes direct correlations over indirect ones. Moreover, in the analysis of pulsating wind characteristics, the gust duration (\(t_{g}\)) is typically 3 s; thus, the 3-s time series results are primarily considered in ACF and PACF analysis.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 2 SoC Overview and RF Architecture  
### SoC Overview  
An overview of the CCNV1-A1 chip is shown in Figure 1. The chip consists of a three-core microcontroller featuring a rich set of peripherals, a 512 kiB SRAM (static random-access memory) and a 768 kiB eFlash.  
The peripherals associated with the GNSS receiver include two 256-point FFT (fast Fourier transform) cores, as well as a dedicated module supporting signal acquisition (see Figure 1; GNSS Controller). The microcontroller peripherals include a number of communications interfaces (UARTs, SPIs, I2C, CAN, 1 WIRE) and GPIO (general-purpose input/output), as well as timers, a watchdog and a battery backed-up RTC (real-time clock) domain. Two out of the three cores support the instruction set extension (GNSS-ISE [21]) for GNSS signal processing. GNSS-ISE forms two autonomous 16-channel GNSS baseband coprocessors. The third core is intended for user application. The required external components are a passive antenna, a crystal oscillator, a few capacitances which decouple supply and optionally external SAW (surface acoustic wave) filters.
## 2 Background and Related Work
### Signature Verification by Single Reference Sample  
Signature verification based on only a single reference sample is a big challenge, because it is difficult to extract useful features from a sample of limited size. That is the reason that there has thus far been relatively little research in this area. [PERSON] et al. [16] proposed one of the state of the art practices nowadays of considering only one signature for training, but their work was focused on online (dynamic) signature verification. As for offline signature verification, [PERSON] and [PERSON] [17] used sampling techniques to acquire the feature vector from a one-pixel-wide signature. However, their study only dealt with random forgery samples (by using other author's genuine signatures as forgeries), rather than skilled forgeries (forgers imitate the genuine signature) that are used in our work.