esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The results from the polymerization kinetics analysis are indicated in Figure 2. Figure 2A shows the results of DC versus time, evidencing the differences in the conversion process among the experimental adhesives. The groups with the ionic liquid started converting carbon-carbon double bonds (C=C) into single carbon-carbon single bonds (C-C) before the G\({}_{\text{CTRL}}\). In Figure 3A, it is possible to observe the mean and standard deviation values of DC for each group. The values ranged from 65.16 (\(\pm\)0.83)% for G\({}_{\text{1\%}}\) to 69.75 (\(\pm\)0.09)% for G\({}_{\text{5\%}}\) (\(p\) < 0.05). There was no statistical difference from the G\({}_{\text{CTRL}}\) to G\({}_{\text{2.5\%}}\) (\(p\) > 0.05). However, the addition of 5 wt.% of ionic liquid increased the DC compared to G\({}_{\text{CTRL}}\) and G\({}_{\text{1\%}}\) (\(p\) < 0.05), without statistical difference for G\({}_{\text{2.5\%}}\) (\(p\) > 0.05).  
Figure 3: Graphs evidencing the results of degree of conversion (image **(A)**), Knoop hardness (image **(B)**), Knoop hardness variation (image **(C)**), and ultimate tensile strength (image **(D)**). The values are expressed as mean and standard deviation values. Different capital letters indicate statistically significant differences among groups in the same test (\(p<0.05\)). Different lowercase letters indicate statistically significant differences within the same group before and after the alcoholic solution immersion (\(p<0.05\)).  
Figure 2: Degree of conversion as a function of time (image **(A)**); polymerization rate as a function of time (image **(B)**); polymerization rate as a function of the degree of conversion (image **(C)**) of the experimental dental adhesives.  
Still, in Figure 2B, we observed that the ionic liquid groups reached higher maximum polymerization rates than G\({}_{\text{CTRL}}\). In addition, Figure 2B,C evidence that groups with the ionic liquid started the self-acceleration process before G\({}_{\text{CTRL}}\).  
Figure 3 also shows the experimental adhesive resin's initial and final Knoop hardness (Figure 3B) and Knoop hardness variation (\(\Delta\)KHN%, Figure 3C). The groups showed different initial Knoop hardness (\(p<0.05\)), but all of them decreased the hardness after immersion in the alcoholic solution (\(p<0.05\)). The \(\Delta\)KHN% ranged from 49.47 (\(\pm\)9.94)% for G\({}_{\text{CTRL}}\) to 68.04 (\(\pm\)3.08)% for G\({}_{\text{5\%}}\) (\(p<0.05\)). From G\({}_{\text{CTRL}}\) to G\({}_{\text{2.5\%}}\), there were no differences in the softening in solvent (\(p>0.05\)), with higher softening in solvent values for G\({}_{\text{5\%}}\) (\(p<0.05\)). However, there were no statistical differences among the ionic liquid groups (\(p>0.05\)).  
Figure 3D shows the ultimate tensile strength (UTS) mean and standard deviation values. The values ranged from 28.71 (\(\pm\)12.37) MPa for G\({}_{\text{CTRL}}\) to 49.99 (\(\pm\)8.88) MPa for G\({}_{\text{2.5\%}}\). There was no statistically significant difference among groups for this mechanical test (\(p>0.05\)).
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 4 Methods for Controlling Inter-Head Diversity
### Orthogonality Regularizer  
We also revisit the orthogonality loss [7] which adds disagreement term on between heads' representations. The disagreement term can be interpreted as a weak orthogonal constraint term since it is computed by cosine similarity between \(V_{li}\) and \(V_{lj}\), where \(V_{li}\) is the \(l\)-th vector in the \(i\)-th head. Therefore, the disagreement term orthogonalizes an orientation through minimizing the cosine similarity. We apply the disagreement term to \(q\), \(K\), and \(V\) in our model, assuming that it can give variation to inter-head diversity with SVCCA and CKA.  
In line with orthogonality regularization, [PERSON] et al. [25] have suggested Spectral Restricted Isometry Property (SRIP) regularization as a stricter orthogonal constraint. SRIP regularizer minimizes a spectral norm of orthogonality to its target matrix more strictly because the spectral norm requires all singular values of its target matrix to be close to 1. Thus, by utilizing both SRIP regularizer and the disagreement regularizer, we suggest an orthogonality regularizer for multi-head attention as a tool for controlling inter-head diversity. Our orthogonality term \(L_{\text{ortho}}\) is computed as follows. We first build \(V_{all}\) by collecting every \(l\)-th vector of _value_ matrix \(V\) in every \(i\)-th head, \(V_{all}=[V_{0},...,V_{i},...,V_{H}]\). Then, we take SRIP of \(V_{all}\);  
\[L_{\text{ortho}}=\lambda_{ortho}\cdot\sigma(V_{all}^{T}V_{all}-I), \tag{8}\]  
where \(\sigma(W)\) is the spectral norm of \(W\).  
Surprisingly, although [PERSON] et al. [7] has claimed that the disagreement regularizer encourages inter-head diversity, we find it slightly decreases inter-head diversity measured with SVCCA and CKA. However, instead of encouraging inter-head diversity, we observe that the orthogonality regularizer increases top-few SVCCA coefficients that can be regarded as core representations. We report detailed results and discussion in Section 6.
# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 2 Environment Identification Method and Model Establishment of Tunnel Section
### The Cutting Head Positioning Model  
The relationship between the ordinate of the cutting head in the section and the stroke of the lifting cylinder \(l_{1}\) is shown in Formula (1).  
\[Z=(L+\Delta l)\sin\Biggl{\{}\cos^{-1}\left[\frac{L_{1}^{2}+L_{2}^{2}-(L_{0}+l_{1} )^{2}}{2L_{1}L_{2}}\right]-\phi_{0}+\theta_{0}\Biggr{\}} \tag{1}\]  
where \(Z\) is the ordinate of the cutting head in the section; \(\Delta l\) is the elongation of the telescopic cylinder of the cutting head; \(L\) is the length of the cutting arm; \(\theta_{0}\) is the angle between the cutting arm and the connect of cutting arm vertical swing center and the hinge point of lifting cylinder and the cutting arm; \(L_{2}\) is the distance between the cutting arm vertical swing center and the hinge point of lifting cylinder and the cutting arm; \(L_{1}\) is the distance between the cutting arm vertical swing center and the hinge point of lifting cylinder and the frame; and \(\phi_{0}\) is the angle between the horizontal line and the connect of the cutting arm vertical swing center and the hinge point of lifting cylinder and the frame.  
In Formula (1), only \(l_{1}\) is a variable. According to the \(l_{1}\) detected by the cylinder displacement sensor, the ordinate of the cutting head in the section \(Z\) can be determined.  
The relationship between the abscissa of the cutting head in the section and the stroke of the rotation cylinder \(l_{2}\) is shown in Formula (2).  
\[y=\left\{e+(L+\Delta L)\cos\left[\cos^{-1}\left[\frac{L_{1}^{2}+L_{2}^{2}-(L_{0 }+l_{1})^{2}}{2L_{1}L_{2}}\right]-\phi_{0}+\theta_{0}\right]\right\}\cdot\sin \left\{\cos^{-1}\left[\frac{n^{2}+r^{2}-(L_{0}^{2}+l_{2})^{2}}{2 nr}\right]- \theta\right\} \tag{2}\]  
where \(y\) is the abscissa of the cutting head in the section; \(e\) is the distance between the vertical swing center point of the cutting arm and the center of the horizontal rotary table; \(r\) is the radius of the rotary table; \(n\) is the distance between the center of the horizontal rotary table and the hinge point of horizontal rotation cylinder and the roadheader body; \(L_{0}^{\prime}\) is the distance between the hinge point of rotation cylinder and the roadheader body and the hinge point of rotation cylinder and the horizontal rotary table when the cutting head is in the horizontal middle position; and \(\theta\) is the angle between \(n\) and \(r\) when the cutting head is in the horizontal middle position.  
In Formula (2), only \(l_{2}\) is a variable. According to the \(l_{2}\) detected by the cylinder displacement sensor, the abscissa of the cutting head in the section can be determined.  
Therefore, as long as the stroke of the vertical lifting cylinder \(l_{1}\) and the stroke of the horizontal rotation cylinder \(l_{2}\) are detected, the specific position of the cutting head in the tunnel section can be determined, and the positioning model of the cutting head is constructed.
# Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites
## 2 Experimental Procedure  
### Materials  
CFRP specimens in the SLS configuration were used in this study. The CFRP prepreg laminates were prepared by impregnating high-strength carbon fibers in the epoxy matrix (SATTI CIT CC206 ER 460). The carbon fibers were woven in the epoxy matrix in a stitched configuration. The laminate piles, which had a nominal thickness of 0.244 mm, were cured using the autoclave method. The upper and lower adherends for the SLS configuration were prepared using the autoclave method. Then, a high-strength epoxy adhesive EA9395, with a shear strength of 25 MPa and a peel strength of 65 MPa, was used for bonding the upper and lower adherends in the SLS configuration. The dimensions of the specimens, the laminate layup configuration, and other dimensional details are presented in Table 1 and Figure 1: A total of three specimens were tested for this research work and were named SLS 1, SLS 2, and SLS 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Upper Adhered**} \\ \hline Length \(l_{u}\) (mm) & Width \(h_{u}\) (mm) & Thickness \(h_{u}\) (mm) & No. of Plies & Stacking Sequence \\ \hline
101.6 \(\pm\) 0.11 & 25.33 \(\pm\) 0.12 & 1.3 \(\pm\) 0.05 & 5 & +45/+45/ \(-\)45/ +45 \\ \hline  & \multicolumn{4}{c}{**Lower Adhered**} \\ \hline Length \(l_{l}\) (mm) & Width \(l_{l}\) (mm) & Thickness \(h_{l}\) (mm) & No. of Plies & Stacking Sequence \\ \hline
101.6 \(\pm\) 0.09 & 25.33 \(\pm\) 0.14 & 6.4 \(\pm\) 0.12 & 26 & +45/[+45/ \(-\)45]12/ +45 \\ \hline  & \multicolumn{4}{c}{**Overlapping Region**} \\ \hline Length \(l_{u}\) (mm) & Width \(h_{u}\) (mm) & Thickness \(h_{u}\) (mm) \\ \hline
26 \(\pm\) 0.12 & 25.33 \(\pm\) 0.25 & & 8.5 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SLS specimen nomenclature, geometry, and configurations.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Results and Analyses
#### 5.2.1 Case 1: Whole Day  
In the whole day simulation, this study simulated the traffic information (e.g., traffic flow and vehicle speed) in Cell\({}_{6}\) in the whole day shown as Figure 18. In experiments, this study analyzed the relation of traffic flow and the number of Handover In (\(I_{i}\)) in each cell. This study observed the CBP (\(P_{6}\)) in Cell\({}_{6}\) during the traffic peak hour (i.e., 8:00 a.m.) and the traffic off-peak hour (i.e., 4:00 a.m.). In Figure 18, this study can discover that the minimum number of traffic flow is off-peak at 4:00 a.m. because the time is deep into the light in Taiwan. Figure 19 shows the number of Handover In (\(I_{6}\)) in Cell\({}_{6}\) for the whole day. In this situation, the number of Handover In (\(I_{6}\)) is lower with the lower traffic flow. When the time is going into the morning, this study can discover that the maximum amount of traffic flow at 8:00 a.m. reaches the peak of the whole day. The number of Handover In (\(I_{6}\)) is higher with the higher traffic flow during the traffic peak hour. These simulation results can prove that the number of handovers increase with the amount of traffic flow increase in Case 1. According to the simulation results, the proposed mechanism can dynamically allocate the channels to use the source of channels effectively.
# Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion
## 1 Introduction
Most existing symbolic execution techniques are used in the stage of initial filtering suspicious code with ACVs. However, applying symbolic execution to the payload generation stage also has certain limitations, such as lack the ability to support complex data types. A call-chain-based and manually assisted payload generation method is proposed, which can more efficiently accelerate the vulnerability verification and payload generation. To demonstrate the ability of our proposed method to detect ACVs in open-source repositories, we conducted an analysis on Maven, the most popular third-party component repository in Java. During the experiment, we identified a large number of vulnerability call chains with algorithmic complexity risks and conducted manual verification. We constructed corresponding payloads and provided vulnerability reports to the project maintainers. Among them, eight were officially recognized and assigned CVE numbers. In addition, other vulnerability call chains have been manually confirmed to affect the reliable operation of the algorithm, but have not yet been submitted to the maintainers. Compared to the State-of-the-Art (SOTA), it has also achieved significant improvements. In summary, the main contributions of our work are listed as follows:  
* A static analysis method for filtering program call chain. We provide optional static analysis methods, including sensitive call chain filtering based on the Root framework and graph based analysis, supporting abstract understanding of program algorithm and logic, and supporting the construction and application of vulnerability models. * A new model for detecting algorithm complexity vulnerabilities. An ACV model related to Java deep recursion and exception handling mechanisms has been proposed, and a call chain filtering and analysis method has also been proposed. This model have been overlooked in existing research, but still remain an important cause of spatial algorithm complexity vulnerabilities. * Payload generation guided by vulnerable call chains. Applying symbolic execution and constraint solving to payload generation instead of path solving still has certain shortcomings. A vulnerable call chains guided payload generation method is proposed to achieve faster vulnerability verification, and improve the efficiency of vulnerability exploitation. * New algorithm complexity vulnerabilities. Based on the above work, detailed testing was conducted on third-party components in the Maven Repository, discovering many new 0-day vulnerabilities and obtaining eight CVE numbers. The remaining parts of our paper are organized as follows: Section 2 summarizes the work in related fields, Section 3 introduces the background and threat model of this research, Section 4 introduces the main innovations of this paper, Section 5 describes and introduces the experimental results, and Section 6 summarizes and prospects for the future.
## 5 Bonded Anchor Tests
### Short-Term Tests and Loading Rate Sensitivity
As a result, the loading rate is generally neither controlled, nor consistent with the rate used for the determination of the short-term capacity. Consequently, the resulting inconsistencies between the loading rates used for the determination of the short-term capacity (100%) and later for sustained load (long-term) tests will lead to inaccurate definitions of relative load levels with respect to the short-term tests. Clearly, as a result, the observed failure times for a given load level would be unreliable. Considering the intrinsically large experimental scatter that is typically observed in time-to-failure tests, the avoidance of any such additional and especially systematic error is detrimental for a successful experimental campaign. Therefore, in this study the same loading rate was used both for the peak identification and sustained load tests. Furthermore, the short-term capacities (100% load level, \(N_{100\%}\)) were not only determined in slabs, but also for the same specimen geometry that is later used for the time-to-failure tests. Based on the quasi-static short-term results, the load levels for the sustained load tests were selected, taking into account the observed scatter and the probability of failure already during loading given the relatively high target load levels. Table 5 summarizes the experimentally obtained mean peak values for both products including the corresponding coefficients of variation (CoV). Each peak value represents a mean value of four and six tests for Product 1 and Product 2, respectively. As it can be seen from Table 5, for the same embedment depth (\(h_{ef}\)) and anchor size (M16) tests for Product 1 resulted in a higher pull-out capacity and lower scatter in terms of CoV. Based on the uniform bond model [56], the bond strength of Product 1 is \(\tau_{1}~{}=~{}31.3\) MPa, while for Product 2 \(\tau_{2}~{}=~{}22.2\) MPa is obtained.
## 3 Three-step WLS Localization Algorithm for Hybrid Positioning
### First-Step WLS  
To calculate the initial position, we assume that the distances from ANTs to UE are equal to the ratio \(k\). Rewriting the weighting matrix \(W\) as \(W^{\prime}\):  
\[W^{\prime}=\left[\begin{array}{ccccc}1&0&0&\ldots&0\\ 0&1&0&\ldots&0\\ 0&0&1&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&1\end{array}\right]. \tag{19}\]  
We could obtain \(w^{\prime}=\left(x^{\prime},y^{\prime},{r_{1}}^{\prime}\right)^{T}\) using Equation (16) as  
\[w^{\prime}=\left(G^{T}W^{\prime}G\right)^{-1}G^{T}W^{\prime}h. \tag{20}\]  
It is an ordinary least squares. When the UE positions are different, the deviation of the weighting matrix and the initial positioning error would change. The second-step WLS could mitigate this error and can be repeated again to give an even better estimate.
## 2 Related Works  
Studies on CS can be traced back to the era of homogeneous networks, such as the work by [PERSON] [24], [PERSON] and [PERSON] [25], [PERSON] _et al._[26], _etc._ The issue becomes much more complicated when it is in a heterogeneous environment due to the multi-attribute feature of various access technologies and cells. For a heterogeneous wireless network environment composed of WiFi, cellular access technology, personal area networks, _etc._, NS has been widely studied [17]. Optimization techniques, such as multi-attribute decision making, fuzzy logic and utility theory, have been used for deciding the best network to access. Game theory was also used to study the competition between users for accessing these networks [18]. However, the above two research directions, _i.e._, CS in homogeneous networks and NS in heterogeneous networks, did not cover the studies on the CS in HCNs, which attracted much attention in recent years. Cells in HCNs not only have a multi-attribute feature, but also suffer from ICI, which brings in new challenges for a CS decision. Existing studies can be divided into two categories, _i.e._, optimization solutions, as summarized in Table 1, and game theory solutions, as summarized in Table 2. CS optimization was studied from the system-level point of view, _i.e._, the performance optimization of the whole heterogeneous environment. By contrast, the CS game was studied from the user-level point of view, where each game player rationally aims to improve his/her own benefits. For the two tables, we summarize mainly the mathematical methodologies used in these papers. For simulation studies based on some widely-used parameters, such as reference signal receiving power (RSRP), reference signal receiving quality (RSRQ) and signal-to-interference-plus-noise ratio (SINR), we also point out the key parameters used in their studies. For theoretical studies combining CS with other techniques, such as ICI coordination and resource allocation (RA), we point out that they are joint optimizations or games combining multiple techniques. Considering the scope of this study, we further describe in detail the related studies on CS games in HCNs as follows. [PERSON] _et al._[59] provided a general form of the CS game and proved the existence of PSNE. [PERSON] _et al._[60] studied the case where a femtocell changes from closed access to open access, so that nearby users could access it instead of accessing the macrocell, which decreased uplink ICI from these
## 5 Results and Analysis  
In this section, we evaluate the performance of SURAA against seven well-known and widely used SpMV storage and computation techniques. It comprises seven subsections. The details of the experimental testbed including the storage schemes used are given in Section 5.1. The details of the dataset comprising 26 matrices for comparatively benchmarking SURAA and other schemes are provided in Section 5.2. Section 5.3 analyzes in detail the comparative performance of SURAA against the seven other schemes using three performance metrics, namely throughput, speedup and effective memory bandwidth. Section 5.4 provides a comparative analysis of SURAA with other schemes with respect to \(npr\) variance of all the 26 matrices in our dataset. Section 5.6 compares the setup cost or overhead of SURAA with other SpMV computation techniques. Section 5.5 discusses the parametric configuration for SURAA's performance in terms of the number of maximum child threads (_max_child_threads_) that can be invoked by the dynamic kernel. Finally, Section 5.7 relates SURAA with our broader work on developing sparse iterative solvers.
## 4 Challenges and Future Research Areas
### Measurement Campaigns  
The presented case studies highlighted the need for further measurements for both A2A and A2S channels regarding various parameters that affect their communication performance. These include the carrier frequency, the underlying terrain, the flight altitude and dynamics, mobility scenarios, etc. Therefore, it is necessary to carry out measurements in new frequency bands, such as Ka and Ku bands, millimeter-wave, and sub-THz, which remain unexplored. In addition to built-up areas (dense urban, urban, and suburban environments), a variety of propagation terrains have to be taken into account, especially highly reflective surfaces, such as lakes and seas. These are highly likely to produce severe ground reflections, which entail increased multipath components and enhanced dispersive characteristics. Different velocities for one or more UAVs should also be tested, along with mobility randomness and trajectory scenarios. Current channel models rely on low flyingaltitudes; therefore, additional campaigns should be focused on unmanned vehicles at much higher altitudes. Excess loss can be induced by the atmosphere and rain, which can degrade the channel performance, especially if new proposed frequency segments are adopted (see examples above). Additional campaigns have to be undertaken in order to assess these effects. Despite the inherent difficulties, measurements during rain events would constitute optimal scenarios, which would provide significant information about the propagation characteristics in cases of precipitation, especially for long-duration UAV flights. Finally, in future 6G networks, THz and FSO frequency bands will be considered. This will be a novel and challenging field for future research, since channel measurements in these bands have still not been addressed.