esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 1 Introduction
Given that the BGP has been successfully shown to exhibit the characteristics of a nonlinear dynamical system [16], we posit that capturing and investigating the interactions and group dynamics among groups of ASs, be it a public or private collection and monitoring infrastructure, can facilitate advanced group-level anomaly detection techniques. While both time series anomaly detection, generally, and BGP anomaly detection, specifically, have been previously surveyed [6; 11; 17], there exists no survey of the techniques capable of MVP BGP anomaly detection nor of the detection of advanced BGP attacks. Previous criteria for next-generation BGP anomaly detection are insufficient to detect some advanced BGP attacks, such as those that evade public collector monitors. This survey directly addresses security weaknesses in the BGP by identifying techniques that can enhance the resilience of BGP infrastructure against advanced attacks. This survey presents an evaluation of different techniques as evidence of their MVP capability and this next-generation anomaly detection requirement.Our contributions are that we:  
* Establish the demand and conditions for an AS-group-level multi-viewpoint approach to the detection of advanced BGP attacks. * Conduct a systematic survey of 178 unique anomaly detection techniques for the benefit of researchers. * Identify possible MVP detection candidates for the detection of advanced BGP attacks that target route collector visibility limitations. * Perform early exploratory analysis of, and report preliminary results from, experiments conducted using some of the identified candidates that have never before been applied to BGP anomaly detection. The remainder of this paper is organized as follows. In Section 2, we summarize the functionality of the BGP. Section 3 describes the different types of BGP attacks. Sections 4 and 5 frame the need for computationally efficient AS-group MVP anomaly detection. Section 6 evaluates all known attack categories based on their complexity and requirement for group-level MVP anomaly detection. Section 7 is a survey of 178 unique anomaly detection techniques, conducted for the purpose of identifying advanced MVP BGP anomaly detection candidates. Note that some techniques are identified and reside in more than one category. In Section 8, we briefly describe likely candidate approaches and evaluate two approaches in some preliminary detail. Section 9 discusses some future research opportunities and the paper concludes in Section 10.
## 4 Results
### Contour Profile Inspection  
The results of the contour profile inspection were conducted by generating the signature spectrum evaluation, involving the examination of 60 signatures for each profile, encompassing four distinct materials. This compares the spectrum, \(M_{d,\beta}(k)\), of the calibration piece versus the spectrum, \(M_{\beta,\beta}(k)\), of the reference. The subtraction of the two spectrum signals generates a third spectrum signal \(M_{\Delta,\beta}(k)\), which defines the profile variation.  
The analysis of an aluminum sample is renowned for its superior dimensional precision, making it an ideal material for evaluating profile accuracy. The examination of signature profiles \(I_{d,C}(x,y)^{SG}\), \(I_{d,L}(x,y)^{SG}\), and \(I_{d,S}(x,y)^{SG}\) generated the spectrum signals, \(M_{\Delta,\beta}(k)\), shown in Figures 21-23.  
The values depicted in Figures 24-26 reveal the result of the third signal spectrums for each profile signature. The maximum peaks: \(M_{\Delta,C}(k)=0.623\) for \(I_{d,C}(x,y)^{SG}\), \(M_{\Delta,L}(k)=0.556\) for \(I_{d,L}(x,y)^{SG3}\), and \(M_{\Delta,S}(k)=0.489\) for \(I_{d,S}(x,y)^{SG4}\) indicates significant form deviations within each signature. However, they fall within the tolerance zone limits. The \(M_{\Delta,\beta}(k)\) numerical representations serve as a quantitative indicator, offering a precise contour profile inspection of the machined parts in each signature \(I_{d,\beta}(x,y)^{SG}\). The significance of \(M_{\Delta,\beta}(k)\) values lies in the ability of the NIIM to quantitatively assess the line profile tolerance and characterize the contour profile quality, giving an impact of machining on the accuracy of each profile type, ensuring a thorough understanding of the impact of the NIIM on profile inspection accuracy.  
Figure 20: CNN architecture used for \(I(x,y)^{G}\) analysis.  
Figure 23: \(M_{g,S}(k)\) and \(M_{d,S}(k)\) FSS for \(l_{d,S}(x,y)^{SCA}\).  
The form deviation results are presented in a probability plot, demonstrating a systematic approach that guarantees close correspondence between the derived inspection and the genuine physical attributes of the profile. The results obtained from the analysis of each contour based on the study of its shape deviation are shown in the following subsection.
# Examining the Efficiency of Electric-Assisted Mountain Biking across Different Types of Terrain
## 3 Results
This indicates that the cyclist's respiratory rate was lower, suggesting less stress on the respiratory system. The study also shows that the differences in physiological responses between electric-assisted and conventional mountain bikes were less pronounced during the not-uphill sections (downhill, flat, technical demanding terrain). Based on the results (Table 4) for power calculations between the electric-assisted and conventional mountain bikes for each section of the off-road course, the findings reveal distinct differences in power outputs. The most noticeable differences can be observed in the uphill section, where the electric-assisted mountain bike shows significant differences compared to the conventional mountain bike. When riding uphill, it can be seen that the cyclist applies less pedaling power (Pp) with less effort, while the estimated engine power (Pe) generates a significantly higher output power due to the higher speed. At the same time, the power required to overcome gravity (Pg), rolling resistance (Pr) and air drag (Pd) also increases due to the higher speed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Uphill**} & \multicolumn{2}{c}{**Downhill**} & \multicolumn{2}{c}{**Flat**} & \multicolumn{2}{c}{**Technical**} \\ \cline{2-9}  & **e-MTB** & **MTB** & **e-MTB** & **MTB** & **e-MTB** & **MTB** & **e-MTB** & **MTB** \\ \hline Pp (W) & 220 \(\pm\) 25 * & 274 \(\pm\) 34 * & 14.7 \(\pm\) 10 & 20.3\(\pm\) 18 & 147.6 \(\pm\) 44 * & 219.3 \(\pm\) 25 * & 135.7 \(\pm\) 47 * & 230 \(\pm\) 26 * \\ Pr (W) & 3.71 \(\pm\) 0.2 * & 1.54 \(\pm\) 0.3 * & 5.89 \(\pm\) 0.8 & 5.4 \(\pm\) 0.7 & 0.42 \(\pm\) 0.2 & 0.49 \(\pm\)0.2 & 1.15 \(\pm\) 0.2 * & 0.9 \(\pm\) 0.2 * \\ Pg (W) & 530 \(\pm\) 43 * & 219 \(\pm\) 44 * & \(-\)839 \(\pm\) 123 & \(-\)780 \(\pm\) 99 & \(-\)2.7 \(\pm\) 13 & 11.5 \(\pm\) 22 & \(-\)25.3 \(\pm\) 34 & \(-\)4.7 \(\pm\) 12 \\ Pd (W) & 30.8 \(\pm\) 4.3 * & 5.7 \(\pm\) 3.9 * & 137 \(\pm\) 40 & 173 \(\pm\) 29 & 62.4 \(\pm\) 25 & 65.3 \(\pm\) 31 & 63 \(\pm\) 16 & 50.6 \(\pm\) 9 \\ Pa (W) & \(-\)1.2\(\pm\) 1.6 & \(-\)0.6 \(\pm\) 1.1 & \(-\)0.5 \(\pm\) 2.5 & 2.9 \(\pm\) 4.8 & \(-\)7.1 \(\pm\) 3.1 & \(-\)5.3 \(\pm\) 4.9 & 0.2 \(\pm\) 3.4 & 0.7 \(\pm\) 3.4 * \\ Pe (W) & 346 \(\pm\) 40 * & \(-\)47 \(\pm\) 17 * & \(-\)710 \(\pm\) 94 & \(-\)624 \(\pm\) 87 & \(-\)66 \(\pm\) 30 * & \(-\)151 \(\pm\) 36 * & \(-\)97 \(\pm\) 34 * & \(-\)184 \(\pm\) 20 * \\ \hline \hline \end{tabular}  
* The values presented are means \(\pm\) standard deviations. Pp—pedaling power; Pr—rolling resistance power; Pg—power needed to overcome gravity; Pd—air drag power; Pa—power needed for acceleration; Pe—estimated engine power;*’ Indicates statistical differences at \(p<0.05\) between the electric-assisted and conventional mountain bikes.  
\end{table} 
Table 4: Mean (\(\pm\)SD) data of power values in varying terrain with electric-assisted (e-MTB) and conventional (MTB) mountain bike.
## 3 Discussion
### Axial Compression Without Lateral Pressure  
The process of rock fracture comprises several stages [1,2] (Figure 2). This section focuses on two stages of the fracture process, distinguished by the differing tensile and shear strengths of rocks. The examples presented (Figure 11) demonstrate that significant damage to the surface layer of rock pillars under axial compression does not immediately lead to failure but elevates the risk of final failure to critical levels [33]. A comparison of the fracture patterns in Figures 4 and 11 suggests that the initial stage of the fracture process involves the nucleation and growth of radial cracks--a necessary but not sufficient condition for spalling.  
Damage to the surface layer and subsequent spalling considerably reduce the cross-sectional area (and consequently the radius) at the middle height of the rock pillar (Figure 11). The circumferential tensile stress inducing radial cracks is proportional to the radius and is described by Equation (8), rewritten below as (12) for the case where s = 0:  
\[\sigma_{t}=n\sigma_{z}\left(\frac{R}{t}-1\right);\ 0<t\leq R. \tag{12}\]  
An increase in axial load amplifies the destruction process, resulting in a greater thickness of the damaged layer and a reduction in the cross-sectional radius. This leads to a decrease in circumferential tensile stress, as described by Equation (12), down to a threshold value equivalent to the tensile strength of the rock. Consequently, when the tensile stress reaches this threshold, the propagation of radial cracks ceases, marking the completion of the first of the two aforementioned stages of destruction. At the conclusion of this first stage, the initial geometry of the rock column (Figure 11) and cylindrical rock specimen (Figure 4) transforms into an hourglass-like shape.  
The second stage of the destruction process becomes more pronounced with a further increase in axial load. During this phase, in the mid-height region of the partially damaged column or specimen, circumferential stresses are lower than their peak values. However, the reduction in radius--and thus the cross-sectional area--causes an increase in shear stress, which in turn triggers shear failure (Figure 4).  
It should also be noted that, according to Equations (6) and (12), an increase in Poisson's ratio results in a proportional increase in circumferential stress during axial compression without lateral pressure.
## 3 Results  
### Statistics of Soil Physical and Chemical Properties  
The soil physicochemical analysis in Table 1 provides the general information on soil saline and alkaline properties in the study area. The pH values of all samples ranged from 5.34 to 10.86, with an average of 8.43 and median of 9.48. The EC values ranged from 0.05 dS/m to 153.00 dS/m, with an average of 5.22 dS/m and a median of 0.78 dS/m. According to [PERSON] [42] and [PERSON] [36], soil with a pH above 7.5 and EC above 4 dS/m is considered alkaline and saline, respectively. The high pH values in Table 1 reveal that soils are predominantly alkaline in the study area. The low median of EC and its large discrepancy with the mean indicates that saline soils are rare. Moreover, saline soils are also alkaline soils, which are typical characteristics of saline-alkali soils in the Songnen Plain.  
The high contents of CO32- and HCO3- in Table 1 may reveal further information on the soil saline and alkaline properties in the study area. The CO32- ion content was only found in samples with a pH above 8.7, which was close to the findings of [PERSON] and [PERSON] [43]. Owing to the alkalinity of the soil solution, the combination from Na+ and the two ions were NaHCO3 and Na2 CO3 in the study area. NaHCO3 and Na2 CO3 resulted in soil alkalinity. This result was also validated by the high correlation between pH and the two ions. The correlation coefficients are 0.87 between the pH and CO32- ion and 0.85 between the EC and CO32- ion (Table 2). Thus, Na2 CO3 was the primary source of high pH level and salt content in the study area. Moreover, the relationship between EC and pH is high, reaching a correlation coefficient of 0.74 (Table 2). Thus, soil salinity was highly correlated with alkalinity in the study area. The TOC content values range from 0.25 to 5.71% with an average of 1.82% and median of 1.44% (Table 1). The relationships between the TOC content and EC and pH are negative (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Mean** & **Maximum** & **Minimum** & **Standard Deviation** & **Median** \\ \hline pH & 8.43 & 10.86 & 5.34 & 1.91 & 9.48 \\ EC (dS/m) & 5.22 & 153.00 & 0.05 & 19.64 & 0.78 \\ TOC (\%) & 1.82 & 5.71 & 0.25 & 1.40 & 1.44 \\ HCO3 (mg/L) & 1247.95 & 4515.00 & 55.57 & 1408.92 & 788.14 \\ CO32- (mg/L) & 1017.96 & 12,436.00 & 0 & 2406.44 & 224.55 \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of soil physical and chemical measurements.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.1 KRN Sensor
In the present study, displacement measurements were only made at L = 300 mm. Effects of changes in L on the integrated intensity were examined using two KRN sensors (without FET) at different L values from 200 to 600 mm. Results are given in Figure 9 using the average of spectral values over equivalent signal ranges; i.e., 27-133 \(\upmu\)s for L = 200 mm to 120-400 \(\upmu\)s for L = 600 mm. The changes were small, mostly less than 0.5 dB. That is, displacement signals attenuated little except by dispersion (signal spreading) effect, especially below 1 MHz. Practical consequence of the present finding is that one needs to avoid using conventional face-to-face calibration results in selecting sensors for detecting guided waves. This aspect has been noted from the early days of AE testing [1]. However, the surface wave calibration for most commercial AE sensors has generally been unavailable, resulting in inadequate recognition of the aperture effect and of the needs of guided wave calibration data.
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 4 Methods and Algorithms for Finding Critical Path
### Parallel Dijkstra  
We implemented parallel Delta-stepping for \(delta=1\). This algorithm is equivalent to the Dijkstra algorithm but processes all nodes and edges in parallel in a distributed memory and exchanges only a small number of nodes that need to be processed in the current iteration.  
The algorithm works as follows.  
1. We start with the nodes and edges distributed across the MPI processes: each process stores only the nodes that correspond to the MPI calls that were made by this process and all incoming edges of these nodes. The process that contains a particular node is determined using its global identifier and simple division.
2. Each process pushes the source node to its own per-process queue and the main loop begins.
3. For each iteration, each process extracts the next node from the queue and finds all incoming edges of this node. Then, we group the edges by the rank of the process that stores the source node of each edge. After that, each process sends each non-empty group to the corresponding rank.
4. After the communication, each process concatenates all of the groups of edges that were received from other processes. The resulting edges are scanned, and, if the new distance is smaller, then the distance is updated, and the source node of the edge is pushed into the queue with the new distance (distances are stored in the local hash table).
5. The iterations continue until all per-process queues are empty. If the queue becomes empty, then the corresponding process takes part in the collective operations but does not process nodes from the queue.
6. After the last iteration, the hash table that stores the path that was followed by the algorithm is gathered in the first MPI process. Then, the algorithm terminates.  
The parallel Dijkstra algorithm does not give any improvements in the execution time, but it consumes a smaller amount of memory because we no longer need to gather all graph nodes and edges in a single MPI process.
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
###### Abstract  
Massive MIMO deployments have been traditionally based on dedicated links in the front-haul, i.e., between the central processing units and the Access Points (APs). Recently, cell-free massive Multiple-Input Multiple-Output (MIMO) systems based on serial front-haul links have been discussed to simplify the deployments, among other reasons. However, the power consumption models currently used for cell-free massive MIMO deployments typically assume dedicated front-haul links. This paper highlights the inaccuracy of these models when applied to serial front-hauls and proposes simple adaptations to achieve more realistic results. The results obtained for an exemplary scenario show that the front-haul power would represent 61.73% of the total consumed power with the original models. In contrast, with the proposed adaptations, it could be as low as 1.59% of the total consumed power for some serial front-haul configurations. Additionally, the impact of considering APs with lower power consumption is studied, in which case, the percentages above would become 93.15% and 11.96%, respectively. Hence the importance of having power models that fit the front-haul topology.  
cell-free massive MIMO; power consumption; serial front-haul 2024 5, 1021-1030. [[https://doi.org/10.3390/telecom5040052](https://doi.org/10.3390/telecom5040052)]([https://doi.org/10.3390/telecom5040052](https://doi.org/10.3390/telecom5040052))
## 3 Experimental Results and Discussion  
### Mechanical Properties  
The strength test results are presented in Figure 3 and the failure mode is shown in Figure 3c. The 28-day compressive strengths of sprayed UHPC at the foot (S-F), haunch (S-H), and vault (S-V) were 111.2 MPa, 110.9 MPa, and 110.6 MPa, respectively (the subsequent description follows the same order), while the compressive strength of cast UHPC was measured at 108.2 MPa. The 28-day flexural strengths of sprayed UHPC at S-F, S-H, and S-V were 11.8 MPa, 12.0 MPa, and 11.7 MPa, respectively, while the 28-day tensile strengths were 4.4 MPa, 4.3 MPa, and 4.3 MPa, respectively. The cast UHPC yielded flexural and tensile strengths of 12.3 MPa and 4.6 MPa, respectively. The mechanical properties of sprayed UHPC displayed minimal variation relative to those of the cast UHPC. The compressive strengths of sprayed UHPC at S-F, S-H, and S-V increased slightly as the curing age increased from 28 days to 90 days, with increases of 6.9 MPa, 6.1 MPa, and 5.3 MPa, respectively. These increases were lower than the 12.8 MPa recorded for cast UHPC.  
This modest enhancement in mechanical properties may be attributed to the unique characteristics of the manufactured sand that was used in the sprayed UHPC mixture. The surface of manufactured sand particles is rough and angular, which significantly enhances the mechanical interlocking that occurs between the aggregate and the mortar, thereby effectively improving the interfacial bond performance [33]. Under the action of high-pressure air, the interlocking effect between the aggregate and the mortar becomes more pronounced, further promoting the densification of the interfacial transition zone (ITZ). This results in a more compact microstructure, thereby maintaining the outstanding mechanical properties of sprayed UHPC. Additionally, the incorporation of trace stone powder with a particle size of less than 75 \(\upmu\)m filled voids within the concrete, thereby increasing the density of the matrix and enhancing the structural integrity of the sprayed UHPC [34; 35]. Moreover, the sprayed UHPC experienced water loss over extended pumping distances and during atomization at the nozzle, leading to a decrease in the water-to-binder ratio. This temporarily enhanced the short-term mechanical properties. Concurrently, the use of an alkali free accelerator accelerated the hydration process and consumed a large amount of mixing water in the early stage of hydration [36], which was not conducive to the late hydration of the sprayed UHPC with a low water-binder ratio, and this was one of the reasons for the slow strength gain in the later stage [37; 38; 39].  
Figure 3: \(Cont.\)
## 2 Methodology
### Processing Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Correction** & **L-Band Model** & **Ka-Band Model** \\ \hline Observation type & Code and carrier observations of B1 and & \\  & B3 & Code observations \\ \hline Measurements & & Geometry-free combination and \\ combination & Ionosphere-free combination & clock-free combination \\ \hline Elevation cutoff & 10\({}^{\circ}\) & - \\ \hline Sampling rate & 300 s & 300 s \\ \hline Weight & Elevation-dependent weight factor with a & 0.5 m for the ISL geometry-free and \\  & standard deviation of 0.002 m and 0.2 m & clock-free measurements and \\  & for raw phase and code, respectively & independent of satellite nadir angle \\ \hline  & Saastamoinen model [22], global mapping & Same as L-band model when \\  & function [23], and two-hourly Zenith & considering the anchor stations in \\  & Tropospheric Delays (ZTD) without & ISL-only POD, and no correction is \\  & gradients. & needed for satellite-to-satellite link \\ \hline Phase wind up & Corrected [24] & - \\ \hline Satellite antenna & Published on BDS official website & \\  & ([[http://en.beidou.gov/cn/SYSTEMS/](http://en.beidou.gov/cn/SYSTEMS/)]([http://en.beidou.gov/cn/SYSTEMS/](http://en.beidou.gov/cn/SYSTEMS/)) & Provided by manufacturer \\  & Officialdocument) & \\ \hline  & Solid Earth tide, pole tide, and ocean tide & Same as L-band model when \\ Tide displacement & loading; according to International Earth & considering the anchor stations in \\  & Rotation Service (IERS) Conventions 2010 & ISL-only POD \\  & model [25] & \\ \hline Relativity effect & IERS Conventions 2010 model [25] & \\ \hline Geopotential & EIGEN\_GL04C up to 12 \(\times\) 12 degrees and orders [26] & \\ \hline N-body gravitation & Jet Propulsion Laboratory (JPL) DE405 ephemeris model & \\ \hline Solar radiation & ECOM five-parameter model [27] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Processing strategy for the POD. least squares to estimate the orbital and clock parameters together with L-band measurements. After a few iterations of least squares adjustment, the orbital parameters along with satellite clock and other estimated parameters can be obtained. The general method to evaluate the internal consistency of satellite orbits is the direct comparison of positions from different orbit solutions, e.g., the last two days of a three-day arc with the first two days of the next three-day arc. The residuals are usually given in a satellite-centered orbit system in along-track, radial, and cross-track directions. Besides, the Satellite Laser Ranging (SLR) data can be used as an independent tool to evaluate orbit accuracy.