esa-sceva/satcom-chunk-collection huggingface dataset:
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 4 Federated Optimisation (FedOpt)
### Sparse Compression Algorithm (SCA)  
In the existing literature, sparse top-k, a compression algorithm prove the significant performance in distributed training of data [16; 17; 18]. Therefore, we use this observation as a starting point to construct a communication efficient protocol in FL. To this end, we design a Sparse Compression Algorithm (SCA) for FedOpt, to reduce the number of communication bits during the models training. In particular, in SCA algorithm, we introduce temporal sparsity into DSGD, which is inspired by [6] to reduce the communication delay. SCA allow each user to perform multiple epochs of SGD, to compute more informative updates. These updates are given by  
\[\Delta\
u=SGD_{n}(\
u,G_{\rm II})-\
u \tag{5}\]  
where \(SGD_{n}(\
u,G_{\rm II})\) refers to the set of gradient updates after \(n\) epochs of SGD on DNN parameters \(\
u\) during the sampling of mini-batches from local data \(G_{\rm II}\). Based on the experiments in Sections 5.1-5.3, we conclude that communication delay reduces drastically, with marginal degradation of accuracy. For details about the impact of existing compression techniques on communication delay, we refer the reader to [18].  
SCA Technique  
We use the proportion of each user gradient into a full gradient update. To implement this, we set the biggest and smallest fraction \(q\) of gradient updates to zero. Then, we compute the mean \(\Psi\) of all the remaining negative and positive gradient updates, separately. Afterwards, if the absolute negative mean \(\Psi^{-}\) is smaller than the positive mean \(\Psi^{+}\), then we set all the positive values to the positive mean \(\Psi^{+}\) and all the negative values to zero. Otherwise, if the absolute negative mean \(\Psi^{-}\) is bigger than positive mean \(\Psi^{+}\), then we set all the positive values to zero and all the negative values to the negative mean \(\Psi^{-}\). The detailed technique is formalised in Algorithm 1. In order to find the values of biggestand smallest fraction \(q\) in a parameter vector \(\
u\), SCA requires the number \(\Theta(|\
u_{n}|)\) operations, where \(\
u_{n}\) refers as the total number of parameters in \(\
u\). Following the above technique, SCA reduces the required number of bits \(\xi_{num}\) from 32 to 0 through computing the non-zero values of sparse gradient update to the mean \(\Psi\). This results in the reduction of communication cost of up to \(\times 3\).  
```
Input : temporal vector \(\Delta\
u\), Sparsity Fraction \(q\) Output : sparse temporal \(\Delta\
u^{*}\) Initialisation: \(num^{+}\gets top_{q\%}\left(\Delta\
u\right)\); \(num^{-}\gets top_{q\%}\left(-\Delta\
u\right)\) \(\Psi^{+}\leftarrow\) mean \((num^{+})\); \(\Psi^{-}\leftarrow\) mean \((num^{-})\) if\(\Psi^{+}\geq\Psi^{-}\)then return \((\Delta\
u^{*}\leftarrow\Psi^{+}\left(\
u\geq\min(num^{+})\right))\) else return \((\Delta\
u^{*}\leftarrow-\Psi^{-}\left(\
u\leq\min(-num^{-})\right))\)  end if
```  
**Algorithm 1**SCA: Communication Efficiency in FedOpt
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
###### Abstract  
Buffer Overflow (BOF) has been a ubiquitous security vulnerability for more than three decades, potentially compromising any software application or system. This vulnerability occurs primarily when someone attempts to write more bytes of data (shellcode) than a buffer can handle. To date, this primitive attack has been used to attack many different software systems, resulting in numerous buffer overflows. The most common type of buffer overflow is the stack overflow vulnerability, through which an adversary can gain admin privileges remotely, which can then be used to execute shellcode. Numerous mitigation techniques have been developed and deployed to reduce the likelihood of BOF attacks, but attackers still manage to bypass these techniques. A variety of mitigation techniques have been proposed and implemented on the hardware, operating system, and compiler levels. These techniques include No-Execute (NX) and Address Space Layout Randomization (ASLR). The NX bit prevents the execution of malicious code by making various portions of the address space of a process inoperable. The ASLR algorithm randomly assigns addresses to various parts of the logical address space of a process as it is loaded in memory for execution. Position Independent Executable (PIE) and ASLR provide more robust protection by randomly generating binary segments. Read-only relocation (RELO) protects the Global Offset Table (GOT) from overwriting attacks. StackGuard protects the stack by placing the canary before the return address in order to prevent stack smashing attacks. Despite all the mitigation techniques in place, hackers continue to be successful in bypassing them, making buffer overflow a persistent vulnerability. The current work aims to describe the stack-based buffer overflow vulnerability and review in detail the mitigation techniques reported in the literature as well as how hackers attempt to bypass them.  
b 20 April 2022  
Accepted: 23 June 2022  
Published: 1 July 2022
## 2 Materials and Methods
### Workflow and Model Implementation
#### 2.4.1 Data Preprocessing  
The calibration of multispectral satellite imagery was completed by DigitalGlobe before delivery, including correcting for radiometric and geometric sensor distortions [28]. Radiometric corrections include relative radiometric response between detectors, non-responsive detector fill, and conversion for absolute radiometry. Sensor corrections include corrections for internal detector geometry, optical distortion, scan distortion, line-rate variations, and misregistration of the multispectral bands. Each image had also been projected to UTM zone 28N using the WGS84 datum.  
The eight bands of Worldview-2 imagery are Coastal, Blue, Green, Yellow, Red, Red Edge, NIR1, NIR2 (details in Table 1). Each pixel is geo-referenced and encoded as INT2U data type (hence, information is stored as digital numbers spanning 0-65,534). Before making label masks, we split each image mosaic into 16 sub-images of size 256 \(\times\) 256 \(\times\) 8, which was chosen as the input size for the U-Net model.
## 1 Introduction
It allows for the creation of constraint matrices by considering displacement fields and various mechanical criteria. Applying these constraint matrices to the eigenvalue problem of linear buckling in the SAFSM leads to strain fields that meet the considered criteria and isolate specific modes. By carefully selecting mechanical criteria, the resulting strain fields can be limited to specific buckling modes. This is known as modal decomposition. Structural elements made of thin-walled profiles often also feature perforation. This perforation is not used to reduce the weight of the element as is often the case with hot-rolled profiles, but is necessary for technological reasons, such as accommodating essential installations. The presence of perforations is another factor that complicates the accurate estimation of the load-bearing capacity of such elements. The use of computational methods like the GBT, SAFSM, or cFSM in the case of perforated elements can be challenging or may come with certain limitations. Although there are studies showing that such limitations can be overcome [18], in the case of perforated thin-walled elements, it is particularly important to accurately consider local weaknesses and stress concentrations around the holes. Methods like the finite element method (FEM) are often more suitable for such analyses, as they allow for more detailed modeling of local effects and are undoubtedly more popular and widespread in the engineering community. It is also important to take into account in the calculations the fact of the cooperation of the analyzed element with neighboring elements, the susceptibility of bridges connecting cooperating elements, and the influence of the strength of connectors (connecting elements) on the behavior of the entire structural system, and consequently the influence of all these factors on the load capacity of a single structural element. Including all these effects is possible thanks to the use of a model based on the finite element method. Such a model, to be credible, must be validated by laboratory tests. This approach was used in this work to estimate the load capacity of a thin-walled perforated beam working in a structural system with a steel grating.
## Appendix A Green's Function
\begin{array}{c}\pi\Big{[}e^{-\kappa_{2}z_{0}}+e^{-\kappa_{z}(l-z_{0})}-2 \Big{]}J_{v}(\kappa_{2}r_{0})A_{v}J_{b}^{a}J_{v}(\kappa_{2}r)J_{v}(\kappa_{2} r)rdr\\ =\int_{b}^{a}\delta(r-r_{0})J_{v}(\kappa_{2}r)dr\\ =J_{v}(k_{2}r_{0}).\end{array}  \tag{113}\]  
Applying the normalization and the orthogonality of Bessel function,  
\[\int_{0}^{c}J_{p}\Big{(}\alpha\frac{r}{c}\Big{)}J_{q}\Big{(}\alpha\frac{r}{c} \Big{)}rdr=\frac{c^{2}}{2}\big{[}J_{p+1}(\alpha)\big{]}^{2}\delta_{pq},\]  
to the integration of the Bessel function in Equation (113) gives the following:  
\[ \begin{array}{c}\int_{b}^{a}J_{v}(\kappa_{2}r)J_{v}(\kappa_{2}r)rdr\\ =\int_{0}^{a}J_{v}(\kappa_{2}r)J_{v}(\kappa_{2}r)rdr-\int_{0}^{b}J_{v}(\kappa_ {2}r)J_{v}(\kappa_{2}r)rdr\\ =\frac{\alpha^{2}}{2}[J_{p+1}(\kappa_{2}a)]^{2}-\frac{\
u}{2}[J_{p+1}(\kappa_ {2}b)]^{2}.\end{array}  \tag{114}\]  
Substituting Equation (114) into Equation (112) gives the following:  
\[A_{v}=\frac{1}{\pi\big{[}e^{-k_{2}z_{0}}+e^{-k_{z}(l-z_{0})}-2\big{]}}\times \frac{2}{\Big{\{}a^{2}\big{[}J_{p+1}(\kappa_{2}a)\big{]}^{2}-b^{2}\big{[}J_{p+ 1}(\kappa_{2}b)\big{]}^{2}\Big{\}}}.\]  
Finally, the [PERSON]'s function can be obtained as follows:  
\[g(\mathbf{r},\mathbf{\theta},\mathbf{z};\mathbf{r}_{0},\mathbf{\theta}_{0},z_{0})=G_{v}J_{v}( \kappa_{2}r)\cos[v(\theta-\theta_{0})], \tag{115}\]  
\[G_{v}=-\frac{1}{2\kappa_{2}}A_{v}J_{v}(\kappa_{2}r_{0})\times \begin{cases}e^{- \kappa_{z}(z_{0}-z)}0<z<z_{0}<l\\ e^{-\kappa_{z}(z-z_{0})}0<z_{0}<z<l^{\prime}\end{cases}  \tag{116}\]  
In addition, replacing the \((r,\theta,z)\) coordinates by relative coordinates, defined as \(\xi=r-r_{0}\), \(\theta=\theta-\theta_{0}\) and \(\eta=z-z_{0}\), sets the location of the PS as the new origin at \(\xi_{0}=0,\vartheta_{0}=0\) and \(\eta_{0}=0\) (Figure 11). In the \((\xi,\vartheta,\eta;0)\) coordinates, Equations (A15)-(A17) are rewritten as follows:  
\[g(\xi,\vartheta,\eta;0)=G_{v}(\eta)J_{v}(\kappa_{\eta}\xi)\cos v\vartheta,\] (A18)  
\[G_{v}=-\frac{1}{2\kappa_{z}}A_{v}J_{v}\big{(}\kappa_{\eta}\xi_{0}\big{)}\times \begin{cases}e^{\kappa_{v}\eta}\ (0\leq\eta\leq l-z_{0})\\ e^{-\kappa_{v}\eta}\ (-z_{0}\leq\eta\leq 0)\end{cases} ,\] (A19)  
\[A_{v}=\frac{1}{\pi\big{[}e^{k_{\pi}z_{0}}+e^{-k_{v}(l-z_{0})}-2\big{]}}\times \frac{2}{\Big{\{}a^{2}\big{[}J_{v+1}\big{(}\kappa_{\eta}a\big{)}\big{]}^{2}-b^{ 2}\big{[}J_{v+1}\big{(}\kappa_{\eta}b\big{)}\big{]}^{2}\big{\}}}.\] (A20)
## 3 Aircraft Low Altitude Operation Scene
### Design Requirements of Avionics System for Low-Altitude Operation Mode of Aircraft
#### 4.1.3 Design Requirements for Precise Navigation  
Usually, through the navigation function of the avionics system, the aircraft can achieve autopilot-based navigation, which greatly reduces the burden on the aircrew. Low-altitude aircraft are rich in variety, generally small in size, large in number, slow in speed, and strong in arbitrariness when flying in low-altitude airspace, which often makes the management of low-altitude aircraft out of control. In this regard, the characteristics of radio navigation, inertial navigation, and satellite navigation should be considered comprehensively when designing and planning the precise navigation function of aircraft in low-altitude airspace. Accurate, real-time, and dynamic navigation design are required on the ground. When several navigation modes are used synthetically, the functional requirements of avionics systems on low-altitude aircraft should be taken into account [11]. For example, the combination of satellite navigation and inertial navigation can be used in urban areas because of the interference of radio signals.
## 4 Performance  
Two different experiments were used to evaluate the performance of the JSATS cabled system--one in a controlled laboratory environment and the second in near-dam field environments at the Bonneville Dam spillway and John Day Dam. This article briefly describes the decoding efficiency in the laboratory environment and at the Bonneville Dam spillway. Part 2 [22] describes in detail its performance at John Day Dam.
## 2 Data and Methods
### Strain Rate  
We employed strain rate analysis to investigate active tectonics in the Eastern Java back--arc thrust region. This approach is particularly effective because it operates independently of any specific reference frame, making it well--suited for discussions related to tectonic activity and deformation [29]. By examining the strain rates, we can gain valuable insights into the tectonic processes at play in this geologically dynamic area. We calculated the principal strain using velocity data that had been transformed into the Sunda block reference frame. These data were calculated using the Velocity Interpolation for Strain Rate (VISR) algorithm. The method represents an optimal horizontal strain rate field based on the least squares method and spatial weighting [30]. The study used distance--dependent weighting, which can be optimally achieved by applying a Gaussian weighting scheme to  
Figure 4: (**a**) Scatter plot illustrates the relationship between velocity components in the North-South (NS) and East-West (EW) directions. (**b**) Scatter plot illustrates the relationship between uncertainty components in the NS and EW directions. The red linear trendlines in both subplots provide a visual representation of the data’s underlying trends.  
estimate the principal strain. A weighting scheme, especially for the geodetic--derived model with relative weight, would give a better fit and explain the observation [31]. The equations for the VISR method are presented below.  
\[d=am+e \tag{2}\]  
\[\varepsilon_{princ}=\frac{\varepsilon_{xx}+\varepsilon_{yy}}{2}\ \pm\sqrt{\frac{ \varepsilon_{xx}+\varepsilon_{yy}}{2}+\varepsilon_{xy^{2}}} \tag{3}\]  
\[\theta_{princ}=\frac{tan^{-1}\frac{\left(2\ x\ \varepsilon_{xy}\right)}{ \varepsilon_{xx}-\varepsilon_{yy}}}{2} \tag{4}\]  
Equation (2) defines the relationship between observed displacement (\(d\)), the design matrix (\(a\)), the model parameters (\(m\)), and the residuals (\(e\)). Equations (3) and (4) calculates the principal strain (\(\varepsilon_{princ}\)). The normal strain component (\(\varepsilon_{xx^{2}}\), \(\varepsilon_{yy}\)) represents the relative change in length along the x--axis and y--axis due to deformation. The shear strain component (\(\varepsilon_{xy}\)) captures the distortion or angular deformation within the xy--plane due to shear forces. The dilatation strain represents the total volumetric deformation by summing the principal strain components. The shear strain quantifies the maximum deformation due to shear forces. The difference between the principal strains determines the magnitude of the shear strain. Principal strain shows the fractional length change along the principal strain axis. The dilatation rate value indicates the presence of dip--slip faults (thrust fault or normal fault), while maximum shear strain indicates the presence of strike--slip faults [32].
# Adaptive Piecewise Poly-Sinc Methods for Ordinary Differential Equations
## 3 Piecewise Collocation Method  
We discuss the piecewise collocation method, in which the domain \(I=[a,b]\) is discretized into \(K\in\mathbb{N}\) non-overlapping partitions \(I_{n}=[x^{n-1},x^{n}),n=1,2,\ldots,K-1\), \(x^{0}=a\), \(x^{K}=b\) with \(I_{K}=[x^{K-1},b]\) and \(\cup_{n=1}^{K}I_{n}=I=[a,b]\). The space of piecewise discontinuous polynomials can be defined as  
\[\mathfrak{D}_{k}(I)=\{v:v|_{I_{n}}\in\mathbb{P}_{k}(I_{n}),\;\;n=1,2,\ldots,K\},\]  
where \(\mathbb{P}_{k}(I_{n})\) denotes the space of polynomials of degree at most \(k\) on \(I_{n}\). The piecewise collocation method solves the collocation method in Section 2.4 over partitions. The approximate solution in the global partition \([a,b]\) can be written as  
\[y_{h}(x)=\sum_{k=1}^{K}y_{h,k}(x)\mathbb{1}_{x\in I_{k}}=\sum_{k=1}^{K}\mathbb{ 1}_{x\in I_{k}}\sum_{j=1}^{m_{k}}y_{j,k}u_{j,k}(x), \tag{9}\]  
where \(y_{h,k}(x)=\sum_{j=1}^{m_{k}}y_{j,k}u_{j,k}(x)\) is the Lagrange interpolation in the \(k-\)th partition. The basis functions  
\[u_{j,k}(x)=\frac{g_{k}(x)}{(x-x_{j,k})g_{k}^{\prime}(x_{j,k})},\quad j=1,2, \ldots,m_{k},\;k=1,2\ldots,K,\]  
where \(\{x_{j,k}\}_{j=1}^{m_{k}}\) are the interpolation points in the \(k-\)th partition, \(g_{k}(x)=\prod_{l=1}^{m_{k}}(x-x_{l,k})\), and \(m_{k}\) is the number of points in the \(k-\)th partition. The function \(\mathbb{1}_{\mathcal{C}}\) is an indicator function which outputs \(1\) if the condition \(\mathcal{C}\) is satisfied and otherwise \(0\). If the coefficients \(y_{j,k}\) are unknown, then we replace \(y_{j,k}\) with \(c_{j,k}\), and Equation (9) becomes  
\[y_{\mathrm{c}}(x)=\sum_{k=1}^{K}y_{\mathrm{c},k}(x)\mathbb{1}_{x\in I_{k}}= \sum_{k=1}^{K}\mathbb{1}_{x\in I_{k}}\sum_{j=1}^{m_{k}}c_{j,k}u_{j,k}(x). \tag{10}\]  
The residual for the \(k-\)th partition can be written as  
\[F\Big{(}x,y_{\mathrm{c},k},(y_{\mathrm{c},k})^{\prime},(y_{\mathrm{c},k})^{ \prime\prime}\Big{)}=R_{k}(x),\quad x\in I_{k},\;k=1,2,\ldots,K.\]  
The collocation method solves for the unknowns \(c_{j,k}\) by setting \(R_{k}(x_{j,k})=0\), \(j=1,2,\ldots,m_{k},k=1,2,\ldots,K\), which we discuss next for IVPs and BVPs.
## 3 Methodology
### Controlled Source Audio-Frequency Magnetotellurics for Goaf Detection
#### 3.1.2 Data Collection and Processing  
The surveying work was implemented using a V8 multi-function receiver system produced by Canadian Phoenix Corporation. The transmitter used was a TXU-30 high-power transmitter also produced by this corporation. The schematic of the measuring device is shown in Figure 5. The supply voltage and supply current were selected as 600 V and 14 A, respectively. The transmitting-receiving distance was the key to achieving high-quality data. The presently developed CSAMT 2D inversion software such as SCS2D used in this study took the receiving far-field data of EMF as the precondition. If the transmitting-receiving distance was too small, near-field data were received, which cannot reflect the characteristics of frequency sounding. Conversely, if the transmitting-receiving distance was too large, the signal was too weak to suppress local noise and was accompanied by a degradation in data quality. The transmitting-receiving distance was taken as R = 7.8 km according to the range of exploration area and actual objective conditions, and this distance  
Figure 4: Sketch Plan of the CSAMT-based Measuring Device.  
Figure 3: EMF Components Measured by the CSAMT and Coordinate System.  
satisfied the estimated far-field conditions. To improve data quality, multiple-stacking technology was used in the surveying process, and repeated observation was conducted in the seriously interfered zone.  
Through data preprocessing, the various noises in the CSAMT data such as instrument noise, natural electromagnetic noise, human noise, geological noise, and non-plane-wave triggered curvilinear distortion were eliminated, suppressed, or calibrated as the study area is in the vicinity of residential areas, where human noise has the strongest potential influence on measurement errors. The processing methods include data editing, filtering, static-displacement calibration, and transition-zone calibration. In formal processing, data with large deviations and evident distortion at the measuring point were firstly smoothened by multipoint smooth filtering. Subsequently, the near-field influence was analyzed, followed by the near-field calibration of the curves with near-field additional effects. Afterward, static-displacement calibration, modeling, and inversion were performed. In the end, the 2D inversion results were interpreted and inferred by combining the geological data.
## 3 Results  
### Fatigue Protocol  
After the fatigue protocol, the mean values of HR, BL, and RPE were greater than 185 beats/min, 14 mmol/L, and a score of 18, respectively. Results of repeated measurement indicated that HR, BL, and RPE were all affected by the fatigue protocol (partial \(\eta^{2}\): 0.99, 0.876, and 0.994 respectively), and the \(p\) values were all less than 0.001 (Table 1).  
Figure 1 illustrates the HR-time curve (mean) of four participants throughout the fatigue protocol. To divide the fatigue protocol into stages, time was normalized, the slope of the HR-time curve was calculated, and four stages were distinguished, namely, P1 (0-10% duration of fatiguing process (D)), P2 (10-30%D), P3 (30-60%D) and P4 (60-100%D).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean \(\pm\) SD**} & \multicolumn{3}{c}{**95\% CI of the Difference**} & \\ \hline  & **Pre** & **Post** & \({\eta_{p}}^{2}\) & **Lower** & **Upper** & \(t\) & \(p\) \\ \hline HR (beats/min) & 78.2 \(\pm\) 9.4 & 185.8 \(\pm\) 9.1 & 0.99 & \(-\)115.013 & \(-\)100.32 & \(-\)32.256 & \(<\)0.001 \\ BL (mmol/L) & 3 \(\pm\) 2.4 & 14.3 \(\pm\) 3.1 & 0.876 & \(-\)14.241 & \(-\)8.543 & \(-\)8.801 & \(<\)0.001 \\ RPE & 6 \(\pm\) 0 & 18.4 \(\pm\) 1 & 0.994 & \(-\)13.05 & \(-\)11.784 & \(-\)43.176 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of heart rate (HR), blood lactate acid (BL), and rating of perceived exertion (RPE) pre- and post-fatigue protocol (mean \(\pm\) SD).  
Figure 1: Heart rate (HR) of four participants during the fatiguing process (mean). According to the slope of the HR–time curve, the overall duration of the fatiguing process was divided into four stages, that is, P1, P2, P3, and P4. Duration of fatiguing process (%) vs. heart rate (beats/min).