esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### Statistical Tests  
In this paper, we applied the test \"analysis of variance\" (ANOVA), a statistical technique to verify potential significant difference among means and if these factors can influence the outcome variable.  
ANOVA allows the comparison of several groups using continuous variables, and the means of outcome variables are computed within an acceptable margin of error. This error is referred to as \(\alpha\) probability, and we selected the following values, 1%, 5%, or 10%.  
In our performance evaluation, we used a significance level of 5%, \(\alpha\) = 0.05, so we could verify the hypothesis H\({}_{0}\): \(\mu 1=\mu 2=\ldots=\mu c\) and H\({}_{1}\), where the means are statistically different. So, the hypotheses applied in this paper were:  
H\({}_{0}\)--Null Hypothesis, where the means do not differ.  
H\({}_{1}\)--Alternative Hypothesis, where the means differ, i.e., at least one mean value is different from others. To evaluate the variance of an object, we applied Equation (5).  
\[SQTotal=\sum_{i=1}^{I}\sum_{j=1}^{J}y^{2}ij-C,\ where\ C=\frac{\left(\sum_{i=1}^{I}\sum_{j=1}^{J}yij \right)^{2}}{IJ} \tag{5}\]  
Furthermore, the sum of the squares of the residuals is obtained from the difference:  
\[SQRes=SQTotal-SQTrt \tag{6}\]  
where \(SQRes\), the variance within groups, is a function of differences between the same treatment repetitions. The \(SQTrt\), variation between groups is the variance between different treatments. The mean squares are calculated as follows:  
\[QMTreat=\frac{SQTreat}{(I-1)},\ QMRes=\frac{SQRes}{(I(J-1))} \tag{7}\]  
When applied to more than two treatments, we cannot designate the best treatment for the variable outcome in case of a significant difference among samples. We also applied a statistical test to complement the results found with ANOVA.  
Tukey test is based on minimum significant difference (HSD), defined by Equation (8):  
\[\Delta=q_{k,g[t,\alpha}\sqrt{\frac{QMRes}{r}},\ hsd=q\cdot\sqrt{\frac{QMRes}{ r}} \tag{8}\]  
where \(q\) is the total studentized amplitude given by the division of amplitude and standard deviation \(s\) for \(k\) means (number of treatments), and \(gl\) is the degree of freedom at the level \(\alpha\) of the residue's significance.  
_QMRes_ is the mean square of the residue, and \(r\) is the number of repetitions. If the contrast is greater than \(\Delta\) (_lstd_), the means differ at the significance level or \(\Delta<\alpha\).
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
## 2 Hot-IP Finding Methods
### Comparison of Hot-IP Finding Methods
#### 2.3.1 Complexity of Algorithms  
Count-Sketch  
The space required for this algorithm is \(O(d\times w+2d)\) because the counter matrix has the size of \(d\times w\) and there are two hash functions for each row. Thus, the space required for the algorithm is \(O\left(d\times w+2d\right)\ =\ O(\ln\left(\frac{1}{\delta}\right)\times\frac{ \epsilon}{\epsilon^{2}})\). The processing time for each element is \(O\left(d\right)\ =\ O(\ln\frac{1}{\delta})\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **IP Addresses** & **Frequency (Count-Sketch)** & **Frequency (Count-Min)** \\ \hline
1 & 1.1.17.29 & 29,670 & 29,670 \\
2 & 1.1.6.6 & 17,243 & 17,243 \\
3 & 1.1.35.39 & 16,345 & 16,350 \\
4 & 1.1.17.17 & 12,068 & 12,071 \\
5 & 1.1.4.4 & 8996 & 8996 \\
6 & 15.111.6.94 & 6573 & 6556 \\
7 & 10.82.164.13 & 4295 & 4298 \\
8 & 14.150.78.191 & 3895 & 3900 \\
9 & 1.1.19.94 & 3727 & 3727 \\
10 & 7.210.22.183 & 3432 & 3438 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result of finding 10 source IP addresses having the highest occurrence frequency using Count-Sketch and Count-Min.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No** & **Hot-IPs** \\ \hline
1 & 1.1.17.29 \\
2 & 1.1.6.6 \\
3 & 1.1.35.39 \\
4 & 1.1.17.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of finding Hot-IPs using Group Testing.  
and the average processing time for \(d\) elements is linear; therefore, the processing time required is \(O\left(d\right)\ =\ O(\ln\frac{1}{\delta})\)[12].  
Count-Min  
Count-Min uses a counter matrix sized \(d\times w\) and d hash functions, thus the space required for the algorithm is \(O\left(d\times w\right)=O(\ln\frac{1}{\delta}\times\frac{\varepsilon}{ \varepsilon})\). The processing time for each element is \(O\left(d\right)=O(\ln\frac{1}{\delta})\) and the average processing time for \(d\) elements is linear; therefore, the processing time required is \(O\left(d\right)=O(\ln\frac{1}{\delta})\)[6].  
Group Testing  
The polynomial space required for each counter is \(O(\log N+\log M)\) bit, which means the space required for the \(t\) counter is (\(O(\log N+\log M)\ \times t\)) bit [1]. Given \(t=O(d^{2}\log N)\) and \(d=O(\log N)\), the space required is \(O\left(\log^{3}N\ (\log N+\log M)\right)\)[1]. The \(d\)-disjunct matrix created using a linear code concatenation (Reed-Solomon code, \(RS[n,k]_{q}\)) requires a polynomial time of \(O(q^{2}\times\mathrm{poly(log\emph{)}})\). Thus, the processing time required is \(O(q^{2}\times\mathrm{polylog\emph{)}}\)[1]. The time required to find the Hot-IP is \(\mathrm{poly(\emph{)}}(d)\times\mathrm{log^{2}}t+O(t^{2})\)[1].  
Table 3 presents the complexity of the Count-Sketch, Count-Min and Group Testing methods.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Routing Approaches Considered for the Analysis
#### 3.5.2 Routing Approaches Considered for the Analysis  
The counterpart of the reactive routing approach is the proactive routing approach, which generates route discovery and maintenance processes regardless of whether nodes have information to send. In this approach, the most representative routing protocol is the well-known \"Optimized Link State Routing Protocol\" or OLSR.  
In OLSR, nodes periodically exchange one-hop broadcast packets called \"Hello Packets\" to build their one-hop neighborhood. In addition, OLSR categorizes specific neighbors as \"Multipoint Relays\" or MPRs. This MPR must reach all nodes within the 2-hop neighborhood of the node. In addition, nodes periodically send \"Topology Control\" or TC packets, which are used to know the network topology. TC packets are flooding through the MPR nodes.
## 3 Methodology
### Temporal Convolutional Network Model with Channel Attention
#### 3.1.2 Scale Dimension Attention Compression Module  
In this module, the multidimensional time-frequency signal X, processed by the PPG signal preprocessing module, serves as the input for channel attention, which primarily consists of an adaptive average pooling layer, an adaptive maximum pooling layer, and a linear layer. Firstly, \(X\) enters the adaptive average pooling layer and adaptive maximum pooling layer, respectively, to calculate their respective pooling features \(F_{c\text{-}avg}\) and \(F_{c\text{-}max}\), and then perform feature calculation through an MLP (multi-layer perceptron). Subsequently, the calculated features from both layers are fed into an activation function to calculate the corresponding weight value \(\mathbf{W_{c}}\). Finally, the features are weighted to obtain the weighted attention feature \(F_{c}\). Channel attention can directly discern meaningful features by weighting them before network training. The higher the feature weight, the greater the significance of the features for subsequent emotion prediction. This method allows the subsequent neural network to better learn important features [30]. The specific calculation method is shown in Equations (7)-(9):  
\[\mathbf{W_{c}}=\sigma\big{(}\text{MLP}\big{(}\text{AvgPool}\big{(}F_{c-avg} \big{)}\big{)}+\text{MLP}(\text{MaxPool}(F_{c-max}))\big{)} \tag{7}\]  
\[F_{c}=X*\mathbf{W_{c}} \tag{8}\]  
\[F=\text{Squeeze}(F_{c}) \tag{9}\]  
Before data compression, some regions may contain no values, resulting in blank data. Consequently, following the completion of the attention weighting operation on the scale dimension, it is necessary to compress the attention features to condense the second dimension of the data. This process emphasizes regions with higher data values. The outcome of this is the refined input data \(F\) for the backbone network.
## 2 Materials and Methods
### Methods
#### 2.3.5 Selection and Classification of Driving Factors  
Owing to the comprehensive impacts of natural and anthropogenic factors, CI shows spatial characteristics in the Sanjiang Plain. In this study, we considered the main driving factors which have an important influence on the spatial differentiation of CI based on previous studies and the vegetation coverage in the Sanjiang Plain. Eight natural factors--the vegetation type (Veget), soil type (Soilt), geomorphic type (Geomt), average annual temperature (Temp), annual precipitation (Prec), elevation (Elev), slope (Slop), and aspect (Aspe)--and three anthropogenic factors--the land use type (Landt), gross domestic product (GDP), and population density (POP)--were selected as the driving factors. The spatial resolutions and the data sources of the CAS CI, natural factor data, and anthropogenic factor data differed. Therefore, this study took the GDM's computing power and accuracy into consideration, resampling the resolution to 1 km based on the nearest neighbour method [60]. Moreover, all of the data were uniformly projected as Albers equal area conic projections.  
When GDM is applied to analyse the driving mechanism, all of the driving factors must be qualitative data. Therefore, the numerical driving factors must be discretized into several strata. Discretization methods, such as the standard deviation, natural breaks, equal interval, geometrical interval, and quantile methods, are often used to discretize the numerical data. Natural breaks [61] were proposed to divide the numerical data into different intervals, with the minimum variance within intervals and the maximum variance between intervals. In this study, the natural breaks method was applied to reclassify the elevation,  
Figure 3: Types of Interactions.  
slope, average annual temperature, annual precipitation, population density, and gross domestic product into six classes. The aspect was reclassified into eight classes according to the eight geographical directions. The vegetation type, land use type, geographic type, and soil type were reclassified into seven, five, five, and six classes, respectively, according to their major types.
## 4 Proposed Methodology
For this, the procedure \(inpolygon()\) in Lines 6, 11-13 checks if the center of a \(mb\) is within of the OFoV of a visual sensor \(vs\), and within of one of its quality level sub-regions \(FoV_{vs}^{H}\), \(FoV_{vs}^{M}\) or \(FoV_{vs}^{L}\), respectively. This procedure implements a Ray casting algorithm [28, 29]. Notice that we first verify if a \(mb\) is within of the OFoV of \(vs\) (Line 6). This is important to not consider monitoring blocks in occluded areas. According to how a sensor \(vs\) covers a monitoring block \(mb\), the functions \(w_{l}\left(mb,vs\right)\) and \(cover\left(mb,vs\right)\) are updated (Lines 14-25). This information is used to proper compute the overall coverage area \(CA_{mb}\left(VS^{\prime}\right)\) and QoM \(AQM\left(VS^{\prime}\right)\) provided by a subset of visual sensor \(VS^{\prime}\subseteq VS\) (Lines 30-32). Finally, the NFC is updated if the sensors in this subset are able to fulfill the application requirements together, _thatis_, if they cover an area greater or equal to \(CA_{min}\) with QoM greater or equal to \(AQM_{min}\) (Lines 33-34). Figure 8: A sensor’s field of view in different processing steps: (**a**) Original coverage, (**b**) Field of View (FoV) under occlusion, (**c**) Quality of Monitoring (QoM)-aware FoV and (**d**) Quality-Occluded FoV (QOFoV) for both occlusion and QoM-aware conditions. ```
Data:nfc = NFC(\(CA_{min}\), \(AQM_{min}\), VS, Obs); Input: Min. Coverage Area, Min. QoM, set of Visual Sensors, set of Obstacles. Output: Network Failure Condition. 1 foreach\(vs\in VS\)do
2\(vs\_OFoV\_Vertices=\text{getOFeV(}vs\_\text{Obs)}\);// [11]
3 foreachMonitoring Block \(mb\)do// Coordinates of the center of \(mb\) (See Figure 2). \(xc=mb.x1s+(mb.x4s-mb.x1s)/2\); \(yc=mb.y1s+(mb.y4s-mb.y1s)/2\); \(isInsideOFoV=\text{inpolygon}((xc,yc),vs.OFoV\_Vertices);// Ray casting algorithm. 4 if\(isInsideOFoV\)then// Coordinates of the vertices of each quality level: // High, Medium and Low (See Equation (1)) vertices_FoV\({}_{ns}^{H}=[(A_{x},A_{y}),(F_{x},F_{y}),(G_{x},G_{y})]\); vertices_FoV\({}_{ns}^{M}=[(F_{x},F_{y}),(D_{x},D_{y}),(E_{x},E_{y}),(G_{x},G_{y})]\); vertices_FoV\({}_{ns}^{U}=[(D_{x},D_{y}),(B_{x},B_{y}),(C_{x},C_{y}),(E_{x},E_{y})]\); \(isInside\_FoV_{ns}^{H}=\text{inpolygon}((xc,yc),vertices_FoV_{ns}^{H})\); \(isInside\_FoV_{ns}^{H}=\text{inpolygon}((xc,yc),vertices_FoV_{ns})\); \(isInside\_FoV_{ns}^{H}=\text{inpolygon}((xc,yc),vertices_FoV_{ns}^{L})\); if\(isInside\_FoV_{ns}^{H}\)then \(w_{l}\,(mb,vs)=w_{1}\); \(cover\,(mb,vs)=1\);  end if elseif\(isInside\_FoV_{ns}^{M}\)then \(w_{l}\,(mb,vs)=w_{2}\); \(cover\,(mb,vs)=1\);  end if elseif\(isInside\_FoV_{ns}^{L}\)then \(w_{l}\,(mb,vs)=w_{3}\); \(cover\,(mb,vs)=1\);  end if  end if  end if  end if  end if
```  
**Algorithm 1**NFC. Following all these steps, the dependability of any wireless visual sensor networks can be evaluated, potentially supporting in the designing, deployment and management of those networks. At this point, what is required is the proper setting of the evaluation parameters, which will be discussed in next section.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 4 Full-Attitude Determination Performance
The sensor calibration was beneficial for the full-attitude determination functions, improving the estimation accuracy both in sunlight and in eclipse. The on-board autonomous calibration is continuously running, being able to calibrate the drifts in the sensor errors and to recalibrate the whole system, if the primary sensor fails and a redundant one is set in use. This is possible thanks to the relatively fast dynamics of the calibration algorithms. Figures 15 and 16, respectively, report the residual error in the calibrated angular velocities and in the calibrated magnetic field. In both cases, the calibration functions, initialized from null initial conditions, were capable of canceling the measurement errors in less than 30 min, allowing a complete system calibration in less than one orbital period, which is a time scale relevant for the spacecraft operations and for the error drifts. Moreover, it is noted that the initial cold start trend, leading to the slower convergence of the calibration filters, was not present when the attitude determination functions were already up and running. The orbit determination functions are fundamental in satisfying the imposed mission requirements, but they also guarantee the correct on-board environmental modeling to carry out the attitude determination calculations. In fact, the wrong position estimation may impact the computation of the Sun direction and of the geomagnetic field vector in the inertial reference frame, with a direct negative consequence on the attitude determination accuracy. Figure 17 shows the absolute position error of the on-board determination functions, with performance fully in line with the imposed requirements. In fact, the three-axis orbit determination error was smaller than 10 m at all times the GNSS receiver was operational. The presence of the accelerometers and of the on-board orbit propagation functions did not significantly improve the GNSS measurements, but it made the overall orbit determination block robust with respect to GNSS failures and more reliable in the case of orbital control maneuvers. In fact, in this last case, the accelerometers provided a faster and more reliable convergence to the correct new orbital states. Figure 16: Magnetic field calibration error. Figure 15: Angular velocity calibration error.
###### Abstract  
The Cuk power factor correction (PFC) converter with an input inductor operating discontinuous conduction mode (DCM) is widely utilized for its advantages of continuous input and output currents, low output voltage ripple, and simple control. However, the conventional Cuk PFC converter encounters issues such as the inability to achieve high power factor (PF) because of input current distortion and high intermediate capacitor voltage, especially at high input voltage. To achieve high PF, high efficiency, and low intermediate capacitor voltage simultaneously, by operating the output inductor at critical conduction mode (CRM) and adjusting input inductance from 170 uH to 930 uH within the half-line cycle dynamically with the transient rectified input voltage, a DCM-CRM Cuk PFC converter utilizing variable inductor control is proposed in this paper. The topology operational principle, control strategy, and key characteristics of the proposed converter have been studied. A 108 W experimental prototype was built and tested to validate the proposed converter. According to the comparative experimental results between the conventional converter and the proposed converter, it can be concluded that the proposed converter utilizing variable inductor control can enhance the PF and efficiency and reduce the intermediate capacitor voltage and total harmonic distortion (THD) of input current with universal 90-240 Vac input voltage range.  
power factor correction converter; Cuk converter; DCM-CRM; variable inductor control
## 2 System Model
Since zero-forcing precoding is employed, the inter-user interference is eliminated. Then, the signal obtained by the scheduled user \(i\) can be written as  
\[y_{i}=\mathbf{h}_{i}\mathbf{w}_{i}x_{i}+n_{i},\;i=1,2,\ldots,K. \tag{2}\]  
The SNR at the scheduled user \(i\) is  
\[\gamma_{i}(\mathcal{A},\mathcal{S})=\frac{p_{i}\,\mathbf{h}_{i}\mathbf{w}_{i} \,|^{2}}{\sigma_{i}^{2}}, \tag{3}\]  
where \(p_{i}\) denotes the user's allocated power and \(\sigma_{i}^{2}\) is the noise power. The highest achievable rate for the scheduled user \(i\) is  
\[R_{i}(\mathcal{A},\mathcal{S})=\log_{2}(1+\gamma_{i}(\mathcal{A},\mathcal{S})). \tag{4}\]  
Further the total throughput expression can be calculated as [25]  
\[R_{sum}(\mathcal{A},\mathcal{S})=\sum\limits_{i=1}^{K}R_{i}(\mathcal{A}, \mathcal{S})=\sum\limits_{i=1}^{K}\log_{2}\Bigg{(}1+\frac{p_{i}\,\mathbf{h}_{ i}\mathbf{w}_{i}\,|^{2}}{\sigma_{i}^{2}}\Bigg{)}. \tag{5}\]
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 6 Conclusions  
Physical covert channels are capable of subverting the established security policy of a device by transmitting data from a secure compute element to an insecure compute element. Physical covert channels accomplish this unauthorized data transmission through side-channel signals (such as temperature, power consumption, and electromagnetic waves). As such, they do not require physically shared resources between the compute elements, unlike host-based covert channels, such as caches, data path units, and memory controllers. In this paper, we first provided a background on methods to establish a covert channel, and then presented an extensive survey on, and perspective of, state-of-the-art physical covert channels in 2D ICs, and relevant countermeasures, including run time detection techniques. Additionally, the potential of covert channels in 2.5D/3D ICs, due to the increased coupling between chiplets, was discussed. We summarized existing recent works on covert channel attacks in 2.5D/3D ICs. Finally, we proposed power covert channel attack models for 2.5D ICs and discussed design-time techniques to mitigate covert channels in these emerging advanced packaging technologies.  
Conceptualization, I.M. and E.S.; methodology, I.M., K.D. and E.S.; investigation, I.M., K.D. and E.S.; resources, I.M. and E.S.; writing--original draft preparation, I.M.; writing--review and editing, I.M. and E.S.; visualization, I.M. and E.S.; supervision, E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Extended Modified Lambert-Beer Law  
The Extended Modified Lambert-Beer law (EMLB) was introduced by [PERSON] et al. in [20] to address the deficiencies in the ability of regression models, such as the standard Lambert-Beer law and the Modified Lambert-Beer law, introduced by [PERSON] [24], to accurately estimate vital parameters including tissue oxygenation (SO2) and percent blood carboxyhemoglobin (COHb). The EMLB has previously been expressed (e.g., [22; 25; 26]) as:  
\[A(\lambda)=G_{0}+\mu_{a}(\lambda)d_{0}+G_{1}\lambda+\lambda e^{-\mu_{a}( \lambda)d_{1}} \tag{1}\]  
In this equation, the second term represents the standard Lambert-Beer law, where \(\mu_{a}(\lambda)\) is the wavelength-dependent absorption coefficient of blood and \(d_{0}\) is the optical path length through the skin. The remaining terms then model artifacts in the absorption spectrum caused by extraneous phenomena. The first term, \(G_{0}\), provides a constant offset value for the absorbance fit. The combination of these first two terms forms the Modified Lambert-Beer law. The third term is used to model both the scattering effects and absorbance due to melanin in the epidermis, assuming both remain approximately linear over the spectral region of interest. Finally, the last term is used to model the nonlinear effects that arise from complex light scattering and absorption in the dermis.  
To better describe how the EMLB is solved to estimate SO2 for a given absorbance spectrum, we rewrite the EMLB as:  
\[A(\lambda)=\beta_{0}G_{0}+\beta_{1}\mu_{a}(\lambda)d_{0}+\beta_{2}G_{1}\lambda+ \beta_{3}\lambda e^{-\mu_{a}(\lambda)d_{1}} \tag{2}\]  
Note that the form of the EMLB is modified slightly from its expression in Equation (1) through the inclusion of the \(\beta_{3}\) parameter, which adds an extra degree of freedom. The solution is then conducted in an iterative two-step manner. In the first step, the MATLAB function _fminsearch_ is invoked to generate candidate values for the parameters \(G_{0}\), \(SO_{2}\), \(d_{0}\), \(G_{1}\), and \(d_{1}\). These values are then passed to the function that _fminsearch_ has been tasked to minimize. Within this function, the values for the five parameters are set as constants and a linear regression is performed to estimate the values for the regression coefficients \(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\). Finally, these regression coefficient values are inserted into Equation (2), and the Euclidian distance between the original spectrum and the EMLB reconstruction is computed and returned to _fminsearch_. This two-step process continues until either the number of _fminsearch_ iterations exceeds 10,000 or the return value changes by no more than \(1\times 10^{-20}\). Bounds for the SO2 estimates were set at 0.0 and 1.0 to prevent physically unrealistic estimates.