esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Shortcomings and Prospects  
Currently, there are still directions for improvement in algorithms to further enhance their performance and application scope.  
Firstly, the interpretability of the algorithm can be improved. Although the Sac-BiLSTM algorithm performs well in terms of accuracy, its internal decision-making process is not easily understood. Further research can explore methods such as visualization to make the algorithm's decision-making process more transparent and interpretable to users, thereby enhancing trust in the algorithm.  
In addition, efficiency and real-time performance are also important aspects to consider. Despite achieving good accuracy, the Sac-BiLSTM algorithm has a high computational complexity and requires longer processing time. To meet the demands of real-time applications, further optimization of the algorithm's structure and parameters can improve its running efficiency.  
Lastly, the impact of data quality and scale on the algorithm should also be emphasized. The performance of the algorithm largely depends on the quality and scale of the training data. Future research can focus on constructing larger-scale, high-quality sentiment-annotated datasets to further enhance the algorithm's performance and generalization ability.  
In summary, looking ahead, the Sac-BiLSTM algorithm has broad application prospects in the field of text sentiment analysis. By improving the algorithm's interpretability, generalization ability, efficiency, and real-time performance, as well as focusing on data quality and scale, the algorithm's performance and applicability can be further enhanced, promoting its application in areas such as business analytics and user experience improvement.
## 5 Simulation Examples  
In order to validate the proposed method, a simulation was carried out on a certain 3 DJM. As shown in Figure 4, the dimensions of the 3 DJM are given by \(l_{A}=0.10\) [m], \(l_{B}=0.13\) [m], \(e_{A}=e_{B}=0.002\) [m] and \(h_{A}=h_{B}=0.08\) [m]. The VSDs were designed to fix onto the moving platform of the 3 DJM to extend the range of stiffness variation of each cable. The CAD model of the VSD is shown in Figure 5a. According to the diagram of the VSD, as shown in Figure 5b, the length of the cable in the VSD yields  
\[l=\sqrt{h^{2}+d^{2}-2 hd\cos\phi}, \tag{38}\]  
and the cable tension \(t_{c}\) applied on the VSD yields  
\[t_{c}=\frac{lk_{s}(\phi_{0}-\phi)}{hd\sin\phi}, \tag{39}\]  
where \(h\in\mathbb{R}\) is the height of the revolute joint, \(d\in\mathbb{R}\) is the length of the rigid-link, \(k_{s}\in\mathbb{R}\) is the stiffness of the spring, \(\phi\in\mathbb{R}\) is the angle of the rigid-link, and \(\phi_{0}\in\mathbb{R}\) is the initial value of \(\phi\). Then the stiffness of the VSD, denoted as \(k_{\text{VSD}}\in\mathbb{R}\), can be described by  
\[k_{\text{VSD}}=\frac{dt_{c}}{dl}=\frac{dt_{c}}{d\phi}\bigg{/}\frac{dl}{d\phi}. \tag{40}\]  
Given the parameters of the designed VSD, i.e., \(k_{s}=1.20\) Nm/rad, \(d=0.018\) m, \(h=0.03\) m, and \(\phi_{0}=0.53\) rad, the stiffness of the VSD can be approximated by a polynomial expression  
\[k_{\text{VSD}}\approx 8.005t_{c}^{2}-239.4t_{c}+5415. \tag{41}\]  
The corresponding stiffness-tension curve is shown in Figure 5c. For this 3 DJM, the lower limits of the cable tensions are given by  
\[T_{\text{min}}=\left(10,10,10,10,10,10\right)^{T}[N],\]  
Figure 4: CAD model of the 3 DJM. and the upper limits of the cable tensions are given by  
\[\mathbf{T}_{\text{max}}=(400,400,400,400,400,400)^{T}\;[N].\]  
The simulation examples are implemented for two different desired poses \(\mathbf{R}_{\text{des1}}\) and \(\mathbf{R}_{\text{des2}}\) with different loads, where  
\[\mathbf{R}_{\text{des1}}=\left( \begin{array}{ccc}0.992&-0.060&-0.111\\ 0.090&0.952&0.292\\ 0.088&-0.300&0.950\end{array} \right),\quad\mathbf{R}_{\text{des2}}=\left( \begin{array} []{ccc}0.950&0.088&-0.300\\ -0.111&0.992&-0.060\\ 0.292&0.090&0.952\end{array} \right). \tag{42}\]  
For each desired pose, three desired stiffness matrices are given. Thus, the simulation examples can be divided into six sub-cases, as listed in Table 1.
## 5 Simulations
### The Comparison with the ASMC in SSS  
We chose an ASMC that can deal with uncertain systems to compare with ADP in SSS. The ASMC can be designed as  
\[\begin{split} I_{s2}&=\ \widetilde{C}^{-1}\widetilde{A}(M_{1, s2}(\theta,0)\tilde{\theta}_{r}+\widetilde{A}^{-1}\widetilde{B}\dot{\theta}_{r}+G_{1, s2}(\theta,\dot{\theta},0,0)+\dot{\psi}+\varepsilon sa\mathrm{T}(s_{s}))\\ \dot{\theta}_{r}&=\dot{\theta}_{d}+\lambda(\theta_ {d}-\theta)\\ \dot{\psi}&=\mathrm{Y}^{\mathrm{T}}s_{s}\end{split} \tag{86}\]  
where \(\varepsilon=diag(40,35,20)\), \(\lambda=diag(25,25,45)\) and \(\mathrm{Y}=diag(240,185,200)\). The closed-loop stability proof with ASMC is similar to this paper [37].  
The payload mass is selected as 450 kg. The uncertainties and other controller parameters remain unchanged, consistent with previous simulations.  
The simulation results can be seen in Figure 13, indicating that the transient response of the ADP in SSS is significantly faster than that of the ASMC. The ASMC has few superiorities for trajectory tracking, and the longer settling time will inevitably lead to larger average errors. In particular, the settling time of joint angle 2 is more than 4 s (see Figure 13b). Moreover, the vibration suppression of the first-order mode becomes worse in the transition process when ASMC is used in SSS and other controllers remain the same as before.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Error** & **Without** & **Payload** & **Half** & **Full** & **Gradual** & **Sudden** & **Sudden** \\
**Accuracy** & **Joint** & **Payload** & **Payload** & **Payload** & **Change** & **Change (2 s)** & **Change (6 s)** \\ \hline RMSE & 1 & 1.29 & 1.16 & 0.97 & 1.03 & 1.12 & 1.12 \\ \((10^{-3})\) & 2 & 1.76 & 1.63 & 1.34 & 1.39 & 1.61 & 1.59 \\  & 3 & 2.24 & 2.07 & 1.79 & 1.92 & 2.03 & 2.01 \\ ISE & 1 & 5.43 & 5.18 & 4.91 & 4.99 & 5.05 &In summary, the composite controller proposed in this paper can adequately represent the manipulator trajectory tracking performances under six different cases because controller parameters always remain constant. Additionally, the control has greater robustness with system uncertainties, no matter how the payload mass is changed. Compared with the ASMC with the same robustness, the settling time of ADP is shorter.
## 2 Materials and Methods
### Unsupervised Machine Learning
#### 2.7.3 Agglomerative Hierarchical Clustering  
An agglomerative hierarchical procedure was coined by [PERSON] and [PERSON] in 1973. The approach of this algorithm is constructing a tree structure in bottom-up order. Initially, each object \(x\) in the data set is considered as a separate cluster and labelled as a singleton, denoted by \(\{x\}\). The clustering process then proceeds iteratively by merging the two closest clusters based on their similarity or distance. For instance, in the case of average linkage,the distance between two clusters, \(C_{p}\) and \(C_{q}\), can be computed as the mean of the distances between all pairs of points in \(C_{p}\) and \(C_{q}\). [53]  
\[D\big{(}C_{p},C_{q}\big{)}=(\frac{1}{n_{p}n_{q}})\sum\{d\big{(}x_{i},x_{j}\big{)} \mid x_{i}\in C_{p},x_{j}\in C_{q}\} \tag{11}\]  
Here, \(n_{p}\) and \(n_{q}\) denote the number of elements in clusters \(C_{p}\) and \(C_{q}\), respectively. The distance function \(d\big{(}x_{i},x_{j}\big{)}\) measures the distance between two data points \(x_{i}\) and \(x_{j}\).  
The merged cluster's node level for the joint branches of \(C_{p}\) and \(C_{q}\) is determined using the value of \(D\big{(}C_{p},C_{q}\big{)}\). The standard approach is monotonic, implying that if a cluster \(C\) is incorporated into another cluster \(C^{\prime}\), their respective node levels \(LC\) and \(LC^{\prime}\) follow an ascending order.  
\[C\subseteq C^{\prime}\Rightarrow L_{c}\leq L_{C^{\prime}} \tag{12}\]  
This property ensures the hierarchical tree could be built without branches crossing each other. A simplified description of the steps is as follows:  
* Assign each data point its own cluster.
* Compute the similarity information between every pair of clusters (dissimilarity or distance).
* Use a linkage function to group the data into new clusters recursively to build the hierarchical cluster tree, based on the similarity information achieved in the previous step.
* Determine where to cut the hierarchical tree into clusters.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 4 Analytical Model and Performance Analysis
### Performance Analysis
#### 4.4.1 Packet Transmission Delay
Solve the multivariate nonlinear equation to calculate \(P_{i,rec}\), \(\tau_{i}\), \(\eta_{i}\), \(P_{i,b}\), \(P_{i,f}\) and \(P_{i,arr}\) (\(i\in[e,s]\)) according to Equations (8), (9), (14), (17), (18), (28) and (30). 3. Calculate the average service time \(TS_{e}\) and \(TS_{s}\) according to Equations (40)-(47). 4. If \((2\lambda_{e}+2\lambda_{s})/(\mu_{e}+\mu_{s})\leq 1\), \(P_{e,emp}=1-2\lambda_{e}/(\mu_{e}+\mu_{s})\), \(P_{s,emp}=1-2\lambda_{s}/(\mu_{e}+\mu_{s})\), otherwise, \(P_{e,emp}=P_{s,emp}=0\). 5. If both \(P_{e,emp}\) and \(P_{s,emp}\) converge with previous values, then stop the algorithm; otherwise, go to Step 2 with new \(P_{e,emp}\) and \(P_{s,emp}\). **Definition 6** (_Queuing delay_). : _The queuing delay \(TQ_{i}\) is the duration from the time instant when an \(i\)\((i\in[e,s])\) packet arrives at the MAC layer queue to the time instant when it becomes the queue head. According to the Pollaczek-Khintchine mean value formula [46] for M/G/1, the expected queuing delay can be derived by_  
\[\left\{ \begin{split} TQ_{e}&=\frac{2\lambda_{e}[P_{TS _{e}}^{\prime\prime}(1)+P_{TS_{e}}^{\prime}(1)]}{2[1-2\lambda_{e}/(\mu_{e}+\mu _{s})]}\\ TQ_{s}&=\frac{2\lambda_{s}[P_{TS_{s}}^{\prime\prime}(1)+P_ {TS_{s}}^{\prime}(1)]}{2[1-2\lambda_{s}/(\mu_{e}+\mu_{s})]}\end{split} \right. \tag{48}\]
# Comparing the Diagnostic Accuracy of CBCT
## 2 Materials and Methods
### Test Methods  
After being included in this inquiry; each participant was thoroughly evaluated to gather updated information regarding their medical and dental histories, complaints, and demographic data,including, age, gender, education, marital status, occupation, and, address. Moreover, the participants were clinically examined by a well-trained oral surgeon and prosthodontist following previous investigations [30; 31; 32; 33; 34]. A dental mirror (15/16 inch; Hahnenkratt, Konigsbach-Stein, Germany) and a dental explorer probe (0700-9, anatomical handle single ended; ASA Dental, Bozzano, Italy) were used during the clinical examination of each participant on a dental unit. A CBCT (Index test) radiograph (SORDEX, Nahkelandtie 160 Tuusula, Finland) was taken for the upper and the lower jaws of each participant, in order to check for local problems, to help during implant treatment planning [35], and to measure Hounsfield values of jaw bone density (machine settings: Medium field of view, 0.2 mm voxel size,90 KV). Then, the GS values were calculated for each participant using the CBCT radiograph and associated computer software, using OnDemand 3D, Yuseong-gu, Daejeon, Korea (Figure 1). For each participant, the GS values were calculated at four regions; namely, anterior maxilla, posterior maxilla, anterior mandible, and posterior mandible. After that, each participant was requested to provide the investigator with documentation of the results of the reference test (DXA values) they had for screening, or the diagnosis of osteoporosis by medical professionals, as it allows high resolution images, precise short time scanning, and the stability of calibration (Figure 2) [3]. The T value information from the reference test was not available to the performers of the index test, but the same was provided to a statistician directly, as submitted by each participant who underwent the reference test. The assessors of the reference test were also not aware of the clinical presentation and GS values of the participants. For all participants, the assessment of GS was performed by one investigator (MGS). Intra-examiner reliability was confirmed by the recalculation of 10 duplicate GS evaluations by the same investigator (MGS) (Kappa was 0.89-0.95). Moreover, inter-examiner reliability was confirmed by recalculation of the same 10 GS evaluations by another investigator (MKA) (Kappa was 0.85-0.92), which is considered satisfactory [36; 37; 38].  
Figure 1: (**A**): Measurement of grayscale (GS) values using cone beam computed tomography (CBCT) technique in maxilla. (**B**): Graphical representation of corresponding GS value (HU).
# Pothole Classification Model Using Edge Detection in Road Image
## 4 Result and Performance Evaluation  
### Pothole Classification Using Edge Detection  
There are many places damaged by potholes and cracks on the road surface. If the external factors are continuously applied to the crack, a pothole is changed and generated. However, it is difficult to classify potholes from the image data of the road surface used in the field of image recognition. This is because there are various shapes on the road, such as lanes, shadows of various objects, and cracks. Also, potholes vary in size. Small potholes have little effect on the risk situation and are convenient for maintenance. On the other hand, the larger the pothole, the greater the impact on traffic accidents, vehicle damage, etc., and the higher the cost of maintenance. To classify potholes, therefore, edge detection is used to extract features and classification is carried out based on YOLO. Figure 6 shows the process of the pothole classification model using edge detection in road image.  
In Figure 6, there are three stages to classify potholes. In the first stage, image data is converted to a gray-scale to reduce operation costs. In addition, a preprocessing process is performed to remove the background excluding the potholes using object detection. The second stage is to extract the features of a pothole. A pothole is detected to extract the edge. In the third stage, images are classified according to the presence or absence of potholes, and the results are output. Figure 7 shows the road traffic pothole classification system. It implements the system using C++.  
The pothole classification system in Figure 7 is structured as follows. The system displays the current time and date. In addition, Seoul, Incheon, Gyeonggi-do, Joellanam-bukdo, Chungcheonnam-bukdo, Gyeongsangnam-bukdo, and Jeju-do are used to determine whether the road is damaged. Thus, the edge of a pothole is detected and classified to determine the presence or absence  
Figure 6: Process of the pothole classification model using edge detection in road image.  
Figure 7: The road traffic pothole classification system.  
of the pothole. Therefore, since it is known that a pothole exists in a specific area, the information on maintenance and road use etc. can be identified.
## 2 Algorithms
### Horspool  
First, the rightmost characters of window and pattern are compared; that means, \(a:=w[m-1]=s[t]\) is compared with \(p[m-1]\). If they match, the remaining \(m-1\) characters are compared until either the first mismatch is found or an entire match has been verified. This comparison can happen right-to-left, left-to-right, or in an arbitrary order that may depend on \(p\). In our analysis, we focus on the right-to-left case for concreteness, but the modifications for the other cases are straightforward. Therefore, the cost of window \(w\) is  
\[\xi^{p}(w)=\begin{cases}m&\text{ if }p=w,\\ \min\{i:1\leq i\leq m,\;p[m-i]\
eq w[m-i]\}&\text{ otherwise.}\end{cases}\]  
In any case, the rightmost window character \(a\) is used to determine how far the window can be shifted for the next iteration. The shift function ensures that no match can be missed by moving the window such that \(a\) becomes aligned to the rightmost \(a\) in \(p\) (not considering the last position). If \(a\) does not occur in \(p\) (or only at the last position), it is safe to shift by \(m\) positions. Formally, we define  
\[\mathit{right}^{\,p}(a) :=\max\left[\{i\in\{0,\ldots,m-2\}:p[i]=a\}\cup\{-1\}\right],\] \[\mathtt{shift}[a] :=(m-1)-\mathit{right}^{\,p}(a)\,,\text{ assuming }p\text{ fixed,}\] \[\mathit{shift}^{\,p}(w) :=\mathtt{shift}[w[m-1]]\,.\]  
For concreteness, we state [PERSON]'s algorithm and how we count text character accesses as pseudocode in Algorithm 1. Note that after a shift, even when we know that \(a\) now matches its corresponding pattern character, the corresponding position is compared again and counts as a text access. Otherwise the additional bookkeeping would make the algorithm more complicated; this is not worth the effort in practice. The lookup in the shift-table does not count as an additional access, since we can remember \(\mathtt{shift}[a]\) as soon as the last window character has been read.  
The main advantage of the Horspool algorithm is its simplicity. Especially, a window's shift value depends only on its last character, and its cost is easily computed from the number of consecutive matching characters at its right end. The Horspool algorithm does not require any advanced data structure and can be implemented in a few lines of code.  
```
1:text \(s\in\Sigma^{*}\), pattern \(p\in\Sigma^{m}\)
2:pair (number \(occ\) of occurrences of \(p\) in \(s\), number \(cost\) of accesses to \(s\))
3:pre-compute table shift\([a]\) for all \(a\in\Sigma\)
4:\((occ,cost)\leftarrow(0,0)\)
5:\(t\gets m-1\)
6:while\(t<|s|\)do
7:\(i\gets 0\)
8:while\(i<m\)do
9:\(cost\gets cost+1\)
10:if\(s[t-i]\
eq p[(m-1)-i]\)then break
11:\(i\gets i+1\)
12:if\(i=m\)then\(occ\gets occ+1\)
13:\(t\gets t+\texttt{shift}[s[t]]\)
14:return\((occ,cost)\)
```  
**Algorithm 1** Horspool-with-Cost
## 3 Results and Analysis
### Combination of GPS, GLONASS, and BDS for PPP Solutions
#### 3.2.2 Ambiguity-Fixed PPP Solutions  
In this section, the impacts of BDS and GLONASS observations to aid GPS ambiguity-fixed PPP are analyzed. The solutions are produced in the same way as the ambiguity-float ones. It should be mentioned that the fixing ambiguity strategy is only applied to GPS satellites. In order to do so, the additional FCB corrections are applied to GPS satellites. We also computed static and kinematic PPP solutions in this section.  
Statistical results of the convergence time are plotted in Figure 6 for static PPP solutions. It can be seen that the convergence time of each solution is reduced when the GPS float ambiguities are fixed to the integers by comparison with the float results in Figure 4. The GEO satellites slightly slow the convergence of combined solutions when IGSO observations are also used. The convergence is accelerated more by introduction of GLONASS satellites than BDS ones.  
The corresponding position errors are listed in Table 7. Different with the results of float-ambiguity solutions in Figure 4, the inclusion of any a single type of BDS observables does only improve the positioning accuracy in the east component. The other two components do not show significant differences. However, the positioning accuracy in all three directions is improved by adding GLONASS.  
For the kinematic solutions, the statistical results of convergence time and positioning accuracy are shown in Figure 7 and Table 8, respectively. Compared with that of ambiguity-fixed static PPP solutions in Figure 6, the convergence time is longer and the accuracy is relatively low. However,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**TYPE**} & \multicolumn{3}{c}{**STD (cm)**} & \multicolumn{3}{c}{**RMS (cm)**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline GPS & 0.82 & 0.54 & 1.52 & 1.16 & 0.75 & 1.80 \\ GPS + B & 0.72 & 0.53 & 1.53 & 1.06 & 0.74 & 1.88 \\ GPS + R & 0.63 & 0.51 & 1.28 & 1.02 & 0.73 & 1.59 \\ GPS + RIM & 0.56 & 0.49 & 1.25 & 0.94 & 0.72 & 1.52 \\ GPS + G & 0.79 & 0.56 & 1.57 & 1.15 & 0.77 & 1.85 \\ GPS + I & 0.60 & 0.52 & 1.47 & 0.98 & 0.74 & 1.83 \\ GPS + M & 0.74 & 0.54 & 1.50 & 1.10 & 0.75 & 1.80 \\ GPS + CI & 0.66 & 0.52 & 1.53 & 1.02 & 0.74 & 1.88 \\ GPS + GM & 0.80 & 0.55 & 1.55 & 1.14 & 0.76 & 1.84 \\ GPS + IM & 0.63 & 0.51 & 1.48 & 0.99 & 0.73 & 1.84 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Position error statistics of convergent static GPS ambiguity-fixed PPP solutions for all 20 sites.  
Figure 6: Distribution of convergence time in each 10 min and the averaged convergence time for the static GPS ambiguity-fixed PPP solutions for all sites.  
the same conclusion for different contribution of different types of BDS satellites as well as BDS as a whole and GLONASS satellites to the convergence time and positioning accuracy can also be drawn.
## 3 Results and Discussion
### Adsorption Isotherms  
Two isotherm models, Langmuir and Freundlich, are often used to determine the maximum pollutant capacity and to identify the adsorption process, the data were fitted to match the experimental data. The isotherm parameters and the values of the correlation coefficients (R\({}^{2}\)) are summarised in Table 3.  
The adsorption equilibrium data fit the Langmuir isotherm. The results show that the value of R\({}^{2}\) obtained from both isotherms was in the range of 0.817 and 0.977, assuming that the adsorption of MB is monolayered chemisorption. The maximum monolayer adsorption capacity was 1 mg\(\cdot\)g\({}^{-1}\) for sample C10 when H\({}_{2}\)O\({}_{2}\) was used. The separation factor S\({}_{\rm F}\) was in the range of 0.029 and 0.413 for all samples, showing that the adsorption of MB on fly ash-TiO\({}_{2}\) based materials is favourable. The adsorption mechanism of MB on ternary materials surface can be explained to the electrostatic interaction between negative charge of materials and positively charge of dye, showing that the process is chemisorption.  
The constants of the Freundlich model, K\({}_{\rm F}\) and 1/n ranged from 1.488 to 6.958 and from 0.516 to 3.3, respectively. If 1/n has a value between 0 and 1, then the adsorption is favourable. The MB adsorption on the A10, B10, and C10 samples were favourable in the absence of H\({}_{2}\)O\({}_{2}\). The high correlation coefficients values of Freundlich model from 0.81 to 0.977 indicate a high affinity between the materials surface and MB. These results indicated that some heterogeneity in the surface of the materials plays a role in the adsorption process, which is in agreement with the presence of both Cu\({}_{x}\)S and TiO\({}_{2}\) in the adsorbent structure.