esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Bituminous Road Surfacing Rehabilitations Based on Optimization of Road Asset Value
## 2 Road Asset Value Calculation
### Value of Road Asset Technical Condition  
The technical condition of a road asset consists of structural condition value, i.e., material, human, time, energy, and financial resources used for the construction of the road and the operational capacity of the pavement, i.e., value of the remaining pavement serviceability.  
The structural condition value is financially expressed current valued acquisition price of the pavement depreciated by the pavement degradation. The degradation can be financially expressed as the residual service life of the pavement and its ratio to the required service life.  
\[SCV=AAP_{\text{PC}}*\frac{RLE}{DLE} \tag{3}\]  
where:  
_SCV_: structural condition value [6],  
_AAP\({}_{\text{PC}}\)_: acquisition price of the asset in a pristine condition [6],  
_RLE_: residual life expectancy [year],  
_DLE_: designed life expectancy [year].  
The proposed calculation of the current design value of an asset in relation to the optimal value, i.e., projected life can be expressed by the Equation:  
\[CVR=1-\frac{\sum_{q=1}^{n_{\text{agents}}}\sum_{n=1}^{n_{\text{agents}}}\left( AAP_{\text{PC}}-AV_{\text{CC}}\right)_{a,n}}{\sum_{q=1}^{n_{\text{agents}}}\sum_{n=1}^{n_{ \text{agents}}}AAP_{\text{PC}}\;n,q} \tag{4}\]  
where:_CVR_: is the ratio of the current value of the asset to the current acquisition price of the asset in pristine condition [%].  
Operation Capacity Value financially expresses pavement serviceability in relation to users, i.e., traffic quality impacting road user costs. The value can be increased by improving the surface characteristics of the pavement, which results in road user cost savings, i.e., vehicle operating cost savings and travel time cost savings. From the road user point of view, the increased value of the asset is a net present value of road user benefits achieved by pavement rehabilitation achieved during the whole asset life cycle.  
The benefits are expressed as the difference between the user costs of the desired pristine condition and its present state as seen in Equation:  
\[\text{OCV}=\sum_{t=1}[(UC_{DN}-UN_{AR})*k_{DEG}*k_{GAADT}]^{t} \tag{5}\]  
where:  
OCV: operation capacity value [\(\mathbf{\xi}\)],  
_UCDN_: total road user costs for the asset in present state (Do nothing scenario) [\(\mathbf{\xi}\)],  
_UNAR_: total road user costs for the asset after rehabilitation (Do something scenario) [\(\mathbf{\xi}\)],  
_kDEC_: degradation coefficient,  
_kGAADT_: growth coefficient of annual average daily traffic.
## 3 Results and Discussion
### Pore Orientation  
As noted in the previous subsection, pores are not perfectly spherical. Therefore, it is possible to determine a principal axis and a principal length and, consequently, an orientation of this principal axis.  
For each pore it is possible to determine the angles \(\alpha\), \(\beta\) and \(\gamma\) (Figure 6). With the information from all the pores, histograms of orientation distribution can be obtained (Figure 25).  
In this particular case, the pores show an approximately uniform orientation. These histograms can provide very useful information because they can detect a certain bias in pore orientation, which could indicate the presence of potential planes of structural weakness.  
From the data provided by the histograms it is possible to determine the efficiency index, which can be defined according to the following expressions [1] (Equations (9)-(11)):  
\[ei_{x}=\sum_{i=1}^{n}f_{i}\cdot\cos^{2}\overline{\alpha_{i}} \tag{9}\]  
\[ei_{y}=\sum_{i=1}^{n}f_{i}\cdot\cos^{2}\overline{\beta_{i}} \tag{10}\]  
\[ei_{z}=\sum_{i=1}^{n}f_{i}\cdot\cos^{2}\overline{\gamma_{i}} \tag{11}\]  
where \(\overline{\alpha_{i}}\), \(\overline{\beta_{i}}\) and \(\overline{\gamma_{i}}\) are the mean values of each angular range, \(f_{i}\) is the frequency associated with each angular range and \(n\) is the number of bars in the histogram.  
The efficiency index takes values between zero and one, being one when the pores are totally oriented with the corresponding axis and zero when the pores are arranged  
Figure 25: Histograms of pore orientation of the angles \(\alpha\), (**a**) \(\beta\) (**b**) and \(\gamma\) (**c**).  
perpendicularly to it. The values of the efficiency indexes of the specimens in this study are shown below (Table 4):  
These indices are especially interesting in fiber-reinforced concretes, since it is possible to study if there is a correlation between pore orientation and fiber orientation.
# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 4 Experiment
### Ablation Experiments
#### 4.5.1 Ablation Experiments of Rotation Position Encoding RoPE  
To prove the effectiveness of the rotation position encoding RoPE, this study removed the RoPE from the GlobalPointer module and conducted two comparative experiments.  
In the comparison between BERT-ATT-BILTAR-GlobalPointer and BERT-ATT-BILTAR-GlobalPointer (without RoPE), the \(F1\) value of the model decreased by 0.841% on the CLUENER2020 dataset, and the \(F1\) value of the model decreased by 4.972% on the Weibo dataset.  
In the comparison between BERT-GlobalPointer and BERT-GlobalPointer (without RoPE), the \(F1\) value of the model decreased by 0.460% on the CLUENER2020 dataset, and the \(F1\) value of the model decreased by 2.338% on the Weibo dataset.  
Therefore, it is shown that the rotational position encoding RoPE can extract the position information of the sequence, thereby improving the predictive ability of the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CLUENER2020**} & \multicolumn{3}{c}{**Weibo**} \\ \hline
**Compare Models** & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) & \(\mathbf{P\%}\) & \(\mathbf{R\%}\) & \(\mathbf{F1\%}\) \\ \hline BERT-ATT-BILTAR-GlobalPointer & **80.485** & 81.270 & **80.848** & **79.227** & 72.720 & **75.751** \\ BERT-BILTAR-GlobalPointer & 80.203 & 79.611 & 79.845 & 70.034 & 60.886 & 65.019 \\ BERT-GlobalPointer & 78.683 & 80.839 & 79.697 & 71.691 & 67.838 & 69.644 \\ BERT-ATT-GlobalPointer & 76.746 & 81.047 & 78.801 & 76.533 & 67.004 & 71.116 \\ BERT-BILSTM-GlobalPointer & 76.638 & 80.233 & 78.370 & 71.625 & 68.167 & 69.767 \\ BERT-ATT-BILSTM-ATT-GlobalPointer & 78.689 & 81.778 & 80.162 & 67.894 & 70.224 & 68.882 \\ BERT-ATT-BILSTM-PNATT-GlobalPointer & 79.858 & 79.775 & 79.773 & 75.468 & 68.006 & 71.451 \\ BERT-BILSTM-PNTMER-GlobalPointer & 75.394 & 81.580 & 78.337 & 72.701 & 70.223 & 71.306 \\ BERT-BiLSTM-PN-GlobalPointer & 75.602 & 81.716 & 78.515 & 69.242 & 68.885 & 69.012 \\ BERT-BiLSTM-TIMER-GlobalPointer & 76.715 & 79.956 & 78.274 & 70.780 & 64.475 & 67.352 \\ BERT-BiLSTM-ATT-TIMER-GlobalPointer & 78.752 & 80.751 & 79.731 & 72.603 & **73.487** & 72.749 \\ BERT-ATT-BILSTM-ATT-TIMER-GlobalPointer & 79.608 & **81.856** & 80.683 & 76.927 & 67.653 & 71.939 \\ BERT-BILTAR-CRF & 73.064 & 80.175 & 76.455 & 62.272 & 67.156 & 64.622 \\ BERT-CRF & 72.337 & 80.273 & 76.099 & 65.446 & 70.098 & 67.692 \\ BERT-ATT-BILTAR-CRF & 74.182 & 79.785 & 76.882 & 62.962 & 66.666 & 64.761 \\ BERT-ATT-CRF & 74.133 & 67.653 & 71.939 & 62.831 & 69.607 & 66.046 \\ BERT-ATT-BILTAR-GlobalPointer(without RoPE) & 79.628 & 80.330 & 79.948 & 76.342 & 65.979 & 70.778 \\ BERT-GlobalPointer(without RoPE) & 77.421 & 80.850 & 79.237 & 66.719 & 68.568 & 67.306 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Eighteen ablation experiments of the present model.  
Putting rotation position encoding into the GlobalPointer module has significant performance on Twitter datasets.
# Study on Mechanical Properties of Fractured Sandy Mudstone Based on Triaxial Compression Experiment
## 3 Analysis of Triaxial Test Results
### Influence of Confining Pressure on Shear Strength of Specimens  
The triaxial compression failure of rock results in a high number of tensile and shear fractures. Shear failure occurs when the shear stress on the shear plane is greater than the shear strength, resulting in the shear fractures. When assessing the mechanical characteristics of rock, shear strength is a crucial metric to consider. The molar Coulomb criterion, which represents shear strength \(\tau\), is used to calculate the shear strength of both whole rock and broken sandy mudstone:  
\[\tau=\text{m}\sigma_{3}+\sigma_{0} \tag{1}\]  
The uniaxial compressive strength of the rock specimen, expressed in MPa, is denoted by \(\sigma\)0 in Formula (1), and m is the effect coefficient of confining pressure on sandy mudstone. The following is the connection between the cohesive force C, the internal friction angle (\(\varphi\)) of the rock, and the m value:  
\[\varphi=\arcsin\frac{m-1}{m+1} \tag{2}\]  
\[C=\sigma_{0}\frac{1-\sin\varphi}{2\cos\varphi} \tag{3}\]  
When Formulas (2) and (3) are combined, it is evident that the bigger the rock's uniaxial compressive strength, the higher its cohesion C, and the greater the value of m, the greater the internal friction angle and the rock's shear strength. A statistical analysis of the m values of both whole and cracked rock samples at various confining pressures is shown in Figure 9.  
Figure 9 shows that, while the range of variations between the two is limited, the m value of intact sandy mudstone progressively falls with an increase in confining pressure, whereas that of cracked sandy mudstone gradually rises. It can be seen that the sensitivity of the m value to confining pressure is very low, much lower than the sensitivity of peak strength to confining pressure, as the m value change degree of the intact rock  
Figure 8: Yield stress values of rock specimens under different confining pressures.  
specimen under different confining pressures is 5.7%, while the m value change degree of the fractured rock specimen under different confining pressures is only 3.1%. Under various confining pressures, the shear strength of both whole and fractured rock is computed and averaged. The average cohesive force C1 is 12.4 MPa, the average internal friction angle \(\varphi\)1 is 35\({}^{\circ}\), and the shear strength of complete rock is \(\tau\)1 = 3.84\(\sigma\)3 + 49.38, according to the data. The average cohesion force C2 is 3.9 MPa, the average internal friction angle \(\varphi\)2 is 6\({}^{\circ}\), and the shear strength \(\tau\)2 = 1.26\(\sigma\)3 + 8.52. According to the findings, with the same confining pressure, the shear strength of cracked sandy mudstone is only 20% that of unbroken rock.
## 6 Challenges and Future Research Directions
### Lack of Real-Time Datasets  
This research area suffers from a lack of real-time datasets for attack scenarios. It is unfortunate that access to real-time data is constrained by a number of ethical issues. The only real-world datasets still in existence are Kyoto, UNIBS, Kent2016, and ISCX2012, which are outdated. Existing datasets are obviously too obsolete to capture the latest attack features, as novel attack types are constantly appearing. The commonly used datasets for DDoS attack detection research, such as CICIDS2017 and CISDDOS2019, are generated in a simulated environment or synthesized. Although the simulated data are appropriate for research purposes, in most cases, a simplified simulation of an internet-enabled environment is typically utilized, which does not accurately depict the heterogeneous nature of contemporary internet-enabled smart devices. Additionally, they might not account for the evolution of new DDoS attack types on real networks. Furthermore, the issue of attack detection in the internet of flying things and drone networks is a recent one. As a result, the dataset in this field is very sparse. Some research studies [146, 147, 176, 179, 14] have adopted the regularly used datasets in other internet-enabled networks; unfortunately, this may not accurately represent genuine attack scenarios in drone networks. This hinders the advancement of research in this field. In addition, real-time datasets to be used for machine and deep learning for attack detection in RPL and NDN networks are unavailable. Research studies in this domain make use of simulated data, which do not accurately reflect the dynamism of current internet traffic. There is a dire need for studies to be conducted in this area. It is necessary to create realistic and traffic-dynamic datasets which are capable of simulating the heterogeneous traffic of a DDoS attack in an internet-enabled environment. Moreover, the datasets should be balanced, with reduced noise and redundancy.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 3 Sensor Selection
### GNSS Receiver  
The presence of an on-board GNSS receiver allows implementing autonomous orbit determination functions, which are fundamental to have an accurate full-attitude determination subsystem. Moreover, in the HERMES nano-satellites, the position estimation requirements are anyway very stringent. In the proposed architecture, the selected GNSS module was the Novatel OEM719 because of its proven flight heritage and its compatibility with some existing nano-satellite interface electronic boards. The module can be associated with different antennae, but the Tallysman TW1322 was used in this case. Both elements are shown in Figure 12.  
The GNSS receiver testing is not really representative of the space operation conditions, because of the different altitudes and velocities in place. Indeed, the module shall be applied  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Specification** & **Value** & **Units** & **Notes** \\ \hline Noise Level & 144 & nT & Worst Axis Value \\ Bias & 3491 & nT & Worst Axis Value \\ Scale Factor Error & 7765 & ppm & Worst Axis Value \\ \hline \hline \end{tabular}
\end{table}
Table 6: Memsense MS3025M magnetometer specifications.  
Figure 12: Novatel OEM719 GNSS module and Tallysman TW1322 antenna.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Specification** & **Value** & **Units** & **Notes** \\ \hline Noise Level & 17 & nT & Worst Axis Value \\ Bias & 440 & nT & Worst Axis Value \\ Scale Factor Error & 4885 & ppm & Worst Axis Value \\ Mass & 7.9 & g & \\ Sampling Power & 8.5 & mW & \\ \hline \hline \end{tabular}
\end{table}
Table 5: PNI RM3100 magnetometer specifications.  
with the CoCom limits removed for in-orbit applications. Thus, the testing activities were limited to verify the correct functionality of the antenna and of the receiver, solving for a ground-based position and velocity GNSS fix. Yet, the orbit determination performance was not assessed. Table 7 lists the main specifications of the GNSS sensor.
# Computational Complexity and ILP Models for Pattern Problems in the Logical Analysis of Data
## 2 Basic Definitions  
A _binary string_ (or, simply, a _string_) \(s\) is a sequence of symbols where each symbol can be either 0 or 1. With _\(n\)-binary string_ we denote a binary string of length \(n\). An _\(n\)-pattern_ (or simply a _pattern_\(p\) is sequence of symbols where each symbol can take the values 0, 1 or -, i.e.,  
\[s_{i}\in\{0,1\},\qquad p_{i}\in\{0,1,-\},\qquad i=1,\ldots n.\]  
We call the symbol - a _gap_. With _\(n\)-pattern_ we denote a pattern of length \(n\). Notice that a string is in fact a particular pattern, i.e., a pattern without gaps. A pattern \(p\)_covers_, or _generates_, a string \(s=(s_{1}\cdots s_{n})\) if \(s_{k}=p_{k}\) for each \(k\) such that \(p_{k}\in\{0,1\}\). The _span_ of \(p\) is the set of all binary strings generated by the pattern \(p\), i.e.,  
\[S(p)=\big{\{}s\in\{0,1\}^{n}:s_{i}=p_{i}\text{ if }p_{i}\in\{0,1\}\big{\}}\]  
Given a set \(P\) of patterns the span \(S(P)\) of \(P\) is the set  
\[S(P)=\bigcup_{p\in P}S(p)\]  
Two sets \(P\) and \(Q\) of patterns are _equivalent_ if \(S(P)=S(Q)\). A set of patterns \(P\) is a _minimum_ set if \(|P|\leq|Q|\) for each set of patterns \(Q\) equivalent to \(P\).  
We say that a pattern \(p\) is _compatible_ with a set of strings \(S\) if \(S(p)\subseteq S\). Similarly we say that a string \(s\) is compatible for a pattern \(p\) if \(s\in S(p)\). We denote by \(P(S)\) the set of all compatible patterns for \(S\). Let \(p\) be a pattern compatible with a given set \(S\) of strings. If there is no compatible pattern \(p^{\prime}\) such that \(S(p)\subset S(p^{\prime})\), we say that \(p\) is _maximal_ (for \(S\)). We denote by \(P^{*}(S)\) the subset of maximal patterns in \(P(S)\). Given a set \(S\) of strings and a set \(P\) of patterns we say that \(P\) is compatible for \(S\) if each \(p\in P\) is compatible for \(S\) and so \(P\subseteq P(S)\) and \(S(P)\subseteq S\).  
Moreover, a set of patterns \(P\) is called a _cover_ of \(S\) if \(S(P)=S\). Notice that all covers of \(S\) are equivalent to each other and \(S\), viewed as a set of patterns, is equivalent to each of its covers.  
A set \(P\) of patterns is said to be _complete_ if \(S(P)=\{0,1\}^{n}\), i.e., if \(P\) generates all possible \(n\)-binary strings. Clearly, \(\{(\)- - - -\()\}\) is trivially complete.  
We note that \(S\) is a set and so it does not contain duplicate strings. We assume this to be true also when we represent a set of \(m\) strings of length \(n\) as an \(m\times n\) array of zeros and ones.
# Research on a Bone Stick Fragment Stitching Method Based on Improved Corner Detection
## 2 Improved Corner Detection Method
### Multi-Scale and Multi-Stage Corner Detection Method  
#### 2.2.1 FAST Corner Detection  
First, the FAST corner detection algorithm is used to perform a preliminary corner detection on the maximum outer contour of the acquired bone stick fragment. The FAST corner detection algorithm can quickly screen a large number of potential corners, reduce computational complexity, and maintain a high detection speed, which is very suitable for processing a large number of bone stick fragment contour point coordinates [18]. Due to the complex edge features of the bone stick fragment, the goal of the preliminary detection stage is to capture as many possible corners as possible, especially those that are located at the break edge and in its adjacent area.  
The FAST corner detection algorithm mainly determines whether a pixel is a corner by quickly determining the gray level in its neighborhood. The main algorithm steps are as follows:  
1. Neighboring pixel selection. For each contour point \(p\), a circular area with a radius of 3 centered at this point is selected, containing 16 pixels which are numbered \(p_{1}\), \(p_{2}\), \(\ldots\), \(p_{16}\). This selection method helps to quickly determine whether the gray level changes around this point are sufficient to make it a corner.
2. Gray-scale comparison. The gray value of the central pixel is set to be \(I_{P}\), and a threshold \(t\) is defined. Then, for each neighboring pixel \(p_{i}\), if \(I_{p_{i}}>I_{p}+t\) or \(I_{p_{i}}<I_{p}-t\) is satisfied, it is considered that \(p\) may be a corner.
3. Continuity determination. Whether or not there are more than \(n\) consecutive pixels in the neighborhood that meet the above gray difference conditions is checked. If so, point \(p\) is marked as a corner. This strategy can quickly screen out significant corners from a large number of points, reducing computational complexity.
4. Non-maximum suppression. Non-maximum suppression is performed on the detected corners to eliminate redundant corners, retain the corners with the strongest response in a local region, and obtain the initial corner set \(C_{\text{FAST}}\).  
Among these, the selection of parameters has a significant impact on the detection results. The threshold parameter \(t\) controls the sensitivity to gray-scale changes, and its setting is directly related to the effect of preliminary corner screening. It is usually adjusted based on experimental sample data to ensure that a sufficient number of potential cornerscan be detected in complex edge areas, especially broken edges. The minimum neighborhood connectivity parameter \(n\) affects the corner determination conditions. A higher value can effectively reduce the generation of pseudo-corners and thus improve the accuracy of corner detection. By selecting the parameter reasonably, it can be ensured to the greatest extent possible that as many corners as possible are retained on broken edges and in key feature areas, thereby providing higher quality data support for subsequent refined detection.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 4 Design Procedures of the One-Sided Exponential AEWMA Scheme
### Optimal Design Procedures  
[PERSON] and [PERSON] (see [8]) transformed the multi-objective optimization problem into a progressive optimization problem for two different shift sizes using a hierarchical and weighted approach, providing an optimization algorithm for the AEWMA scheme based on ARL. However, this algorithm is complex and computationally intensive. [PERSON] (see [9]) introduced a sequential search method to determine the approximate optimal parameters for the AEWMA scheme, which is both simpler and requires less computational effort. In this study, we also employ this sequential search method for the one-sided exponential AEWMA scheme. The optimization design procedures are outlined as follows:  
1. Determine the required \(MRL_{0}\), the small process mean shift \(c_{1}\), and the large process mean shift \(c_{2}\).
2. For the given small shift \(c_{1}\), under the constraint of \(MRL_{0}\), determine the corresponding smoothing parameter \(\lambda^{+}(\lambda^{-})\) for the optimal one-sided exponential AEWMA scheme.
3. Select a small constant \(\xi\) to constrain the performance loss that might result from introducing the threshold parameter \(k\) at the shift \(c_{1}\).  
Figure 3: Contour plot of \(MRL_{1}\) values for the upper-sided exponential AEWMA scheme when \(MRL_{0}=200,c\in\{1.04,1.1,1.2,1.4\}\).  
Set the smoothing parameter \(\lambda^{+}(\lambda^{-})\) obtained in step 2 for the one-sided exponential AEWMA scheme.
5. Given the constraint that the percentage increase in \(MRL_{1}\) for the one-sided exponential AEWMA scheme monitoring the small shift \(c_{1}\) is less than \(\xi\), find the threshold parameter \(k\) and \(UCL^{+}(LCL^{-})\) that minimize the \(MRL_{1}\) value for the large shift \(c_{2}\).  
The optimal design procedures, though computationally demanding, can be effectively employed in industrial production by pre-optimizing parameters for specified \(MRL_{0}\), \(c1\), and \(c2\). This facilitates the pre-determination of optimal parameters, which can then be directly integrated into production monitoring systems. With these parameters in place, computational efficiency is greatly enhanced, ensuring the approach is well-suited for real-time monitoring in industries with critical timing constraints.