esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Test Result of Superpave Method for SBS Modified Asphal  
#### 3.2.1 Dynamic Shear Rheological (DSR) Test  
The anti-rutting factor G*/sin\(\delta\) of SBS modified asphalt was tested to study the anti-rutting performance of SBS modified asphalt at the temperature of 45 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, 65 \({}^{\circ}\)C and 70 \({}^{\circ}\)C respectively. The larger the G*/sin\(\delta\), the better the high-temperature performance of asphalt, which means the high-temperature rutting resistance is better [16]. The modifier dosages were 4.5%, 5%, 5.5% and 6%, and the matrix asphalt was SK90# asphalt binder. The test results are shown in Figure 4.  
From Figure 4, for the matrix asphalt and SBS modified asphalt, the G*/sin\(\delta\) decreased with the increase of temperature, and the addition of SBS modifier can significantly improve the G*/sin\(\delta\) of asphalt binder, and enhance the high-temperature rutting resistance of asphalt [17].  
Figure 4: Rutting resistance factor of matrix asphalt and SBS modified asphalt at different temperatures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Source** & **df** & **F** & **Sig.** \\ \hline \multirow{2}{*}{Ductility} & 70\# matrix asphalt & 3 & 72.58 & 0.001 \\  & 90\# matrix asphalt & 3 & 36.47 & 0.002 \\ \hline \multirow{2}{*}{Elastic recovery} & 70\# matrix asphalt & 3 & 874.62 & 0.001 \\  & 90\# matrix asphalt & 3 & 153.87 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Variance analysis result of ductility and elastic recovery for SBS modified asphalt.  
The difference analysis of G*/sin\(\delta\) was conducted in different SBS modifier dosage, and the analysis results are shown in Table 6. The results showed that when the SBS modifier dosages increase from 0% to 5.5%, the high-temperature rutting resistance of SBS-modified asphalt increases continuously, and the difference between each pair of them was significant. When the SBS modifier dosages increased from 5.5% to 6%, there were no obvious difference between each pair of them, which meant the increase of high-temperature rutting resistance of asphalt was not significant when the modifier dosages increased from 5.5% to 6%.
## 2 Materials & Methods
### Control Model  
General position-based control is a method that forces the system to follow a specified trajectory. However, a high output torque is required for the system to track motion without dynamic interaction with the user [17]. In contrast, impedance control is a suitable control method for systems that require interaction with the wearer and is widely applied to the development of various rehabilitation assistance systems such as powered knee prostheses and exoskeleton systems [10; 13; 17]. The more residual muscles the wearer has, the less the load that the exoskeleton system needs to assist, allowing the selection of a low-power actuator, thereby reducing the volume and weight of the system.  
Based on previous studies, the output torque required for the knee joint can be expressed as a simple piecewise passive impedance function of linear spring (\(k\)) and damping (\(d\)) terms (Figure 8) [17; 18]. In this study, the output torque \(\tau_{out}\) was defined as a simple passive impedance model composed of user-customized torque \(\tau_{cu}\) and passive impedance terms, as shown in Equation (2) below, to generate a negative torque in the stance mode and active torque in the swing mode. The user customized load torque changes according to the patient's body condition and the degree of gait training; mathematical modeling was practically impossible. Therefore, the knee torque profiles for flexion and extension of each gait phase were experimentally set. The user customized torque profile is shown in Figure 8b. Table 2 shows the maximum user-customized load torque and impedance coefficients for each gait mode applied to the 6 min walk test. The mechanical friction of the knee joint was simplified by the Coulomb friction coefficient (\(c_{k}\)), and the variation in the friction torque according to the angular velocity was ignored.  
\[\tau_{out}=\tau_{cu}+k(\theta-\theta_{e})+d\dot{\theta}+\ c_{k}sgn(\dot{ \theta}), \tag{2}\]  
where \(\theta\) is the knee angle and \(\theta_{e}\) is the equilibrium knee angle at each gait mode.  
Figure 8: Control model for the knee joint. (**a**) A simple passive impedance model and the joint angle direction, and (**b**) The user-customized torque profile.
# A Conversation History-Based Q&A Cache Mechanism for Multi-Layered Chatbot Services
## 4 Cache Mechanism
### Cache Mechanism Algorithm  
The proposed algorithm saves some of the Q&A sets from the chatbot data to generate responses directly from the broker without connecting to the chatbots. The usage of frequency and relevance enables the algorithm to cache effectively. Algorithm 1 shows the cache mechanism algorithm.  
```
1 input:\(Q_{i}\) #user's question
2\(RelQ_{S_{i}}\) = \(RelQ\) [Q,Ck] #related questions from
3#correspendent chatbot
4 for\(RQ\)in\(RelQ_{S_{i}}\):
5\(FreqQ_{S_{i}}\) [\(RQ\)] = \(FreqQ\) [\(Q_{i}C_{k}\)] [\(RQ\)] #frequency of each \(RQ\) from
6#correspendent chatbot
7 endforfor\(RQ\)in\(RelQ_{S_{i}}\):
8 if\(RQ\)notin cache:
9\(cache.append(RQ)\)
10 endif
11 endfor
12 endfor
13 iflen(\(cache\))> size: #size of cache
14 for\(CQ\)in\(cache\): #cached question
15\(FreqCQ_{S_{i}}\) [\(CQ\)] = \(FreqQ\) [\(Q_{i}C_{k}\)] [\(CQ\)]: #frequency of each CQ
16 sorted(\(FreqCQ_{S_{i}}\)[\(CQ\)].keys(), \(FreqCQ_{S_{i}}\)[\(CQ\)].values())
17 endfor
18 endif \(cache=FreqCQ_{S_{i}}\):keys()[\(\cdot\)size]
```  
**Algorithm 1** Cache Mechanism.  
The input of this algorithm is a question from the chatbot service (\(Q_{i}\)). The algorithm uses the keyword-based relevance graph (\(RelQ\)) to obtain the next Q&A pairs and save them in \(RelQ_{S_{i}}\) (\(C_{k}\) is the id of the chatbot to get the required related questions). As Q&A sets are cached based on relevance and frequency, it is necessary to obtain the frequency of the relevant Q&A pairs (\(FreqQ_{i}\)) based on the conversation history. In other words, the frequency graph (\(FreqQ\)) saves information on how many times each relevant question was asked after \(Q_{i}\). The frequency helps the algorithm to sort relevant questions in reverse order to obtain the top Q&A sets that must be cached. The same is implemented with previously cached Q&A sets (\(CQ\)). The algorithm obtains the frequency of the previously cached Q&A sets (\(FreqCQ_{i}\)) after \(Q_{i}\) from \(FreqQ\) and unites it with \(FreqQ_{i}\). Then, only the top number (size) of Q&A sets are cached.
## 2 Background and Related Works  
Quantum key distribution (QKD) protocols leverage the principles of quantum mechanics to enable secure communication. Notable protocols include the Bennett-Brassard 1984 (BB84) protocol, the Ekert-91 (E91) protocol [9], and the measurement device-independent QKD (MDI-QKD) protocol [20].  
As an illustrative example, we consider the BB84 protocol--a prepare-and-measure scheme--to outline the specific implementation procedure of QKD. This example elucidates how the key distribution operates over long distances and across multiple paths.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 2 System Identification and TOT Decomposition
### System Identification Framework  
The system identification framework addressed in this paper considers a linear single-input single-output (SISO) scenario with zero-mean real-valued signals, where the main goal is to identify an unknown system characterized by the impulse response \(\mathbf{h}(n)=\left[\begin{array}{cccc}h_{0}(n)&h_{1}(n)&\cdots&h_{L-1}(n)\end{array} \right]^{T}\) of length \(L\), where \(n\) is the discrete-time index and superscript \({}^{T}\) denotes transposition. Also, let us consider that this time-varying impulse response evolves based on a simplified first-order Markov model, i.e.,  
\[\mathbf{h}(n)=\mathbf{h}(n-1)+\mathbf{w}(n), \tag{1}\]  
where \(\mathbf{w}(n)\) is a vector of length \(L\), which contains the samples of a zero-mean white Gaussian noise. In this context, \(\mathbf{h}(n-1)\) and \(\mathbf{w}(n)\) are uncorrelated. This standard model has been extensively involved in many analyses of time-varying systems in the framework of adaptive filtering algorithms [45; 46; 47]. In addition, it fits very well in echo cancellation scenarios [48]. Since \(\mathbf{w}(n)\) is white and Gaussian, its covariance matrix is a diagonal one, which can be expressed as \(\mathbf{R}_{\mathbf{w}}(n)=\sigma_{w}^{2}(n)\mathbf{I}_{L}\), where \(\sigma_{w}^{2}(n)\) is the noise variance and \(\mathbf{I}_{L}\) denotes the identity matrix of size \(L\times L\). It can be noticed that the uncertainties in \(\mathbf{h}(n)\) are captured by \(\sigma_{w}^{2}(n)\).
## 3 Results and Discussion
W, \(L\), \(\mu_{\text{sat}}\), C\({}_{\text{i}}\), \(V_{\text{th}}\) and \(I_{\text{ds}}\) are the channel width, channel length, saturation mobility, capacitance, threshold voltage and drain current, respectively, and \(V_{\text{ds}}\) and \(V_{\text{gs}}\) separately represent the source-drain voltage and gate-source voltage. The regions from which SS has been extracted are noted on the figures. The capacitance of the ZrO\({}_{2}\) film is around 130 nF/cm\({}^{2}\). The channel width (\(W\)) is 530 \(\upmu\)m and the length (\(L\)) is 270 \(\upmu\)m (\(W/L\) ratio is 1.96). The TFT with the ZrO\({}_{2}\) film achieved by spin-coating with 0.3 M precursor for six times (device A) had a saturation mobility of 12.7 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and an on/off ratio of 7.6 \(\times\) 10\({}^{5}\), while the TFT with the ZrO\({}_{2}\) film achieved by spin-coating with 0.6 M precursor for three times (device B) exhibited a saturation mobility of 10 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and an on/off ratio of 6.4 \(\times\) 10\({}^{5}\). Also, the device A had excellent bias stability, with an offset of the threshold voltage less than 0.6 V under positive bias, and negative bias stress over 3600 s. For comparison, [PERSON] et al. [26] fabricated the IGZO-TFT with bottom gate and staggered electrodes using atomic-layer-deposited Al\({}_{2}\)O\({}_{3}\) as gate insulator. The TFT device showed a threshold voltage shift of 1.5 V under 10 V gate voltage for 1 h. [PERSON] et al. [27] reported on the SiNx/IGZO-TFT with reactive sputtered SiOx as passivation layer, and the device exhibited a V\({}_{\text{th}}\) shift of 1.3 V after applying positive bias stress of 20 V for 10,000 s. However, in our study, the device B not only had a lower saturation mobility, but exhibited a larger threshold voltage shifting under the bias  
Figure 4: (**a**) Schematic showing the cross-section of IGZO-TFT with ZrO\({}_{2}\) dielectric layer. (**b**) Thickness of each layer. (**c**) Photograph of the fabricated TFT. Figure 5: Bias stability of the TFT with ZrO\({}_{2}\) film fabricated by spin-coating of 0.3 M precursor for 6 times. (**a**) Output curves and gate capacitance as a function of voltage. (**b**) Transfer curves with gate-leakage current curves of positive bias stress. (**c**) Plots of sqrt I\({}_{\text{ds}}\) (drain/source current) versus gate overdrive of positive bias stress. (**d**) Output curves and gate capacitance as a function of voltage. (**e**) Transfer curves with gate-leakage current curves of negative bias stress. (**f**) Plots of sqrt I\({}_{\text{ds}}\) (drain/source current) versus gate overdrive of negative bias stress. stress test, with a voltage shifting of 1.6 V under positive bias stress over 3600 s. Additional details are listed in Table 2. The trend of parameters in Table 2 is shown in Figures 7 and 8.
## 3 Results and Discussion
### Influence of Shear Rate
#### 3.1.2 MSCR Test  
For the purpose of studying the influence of shear rate on the high-temperature performance of SBS-modified asphalt, the SBS-modified asphalts were examined with the MSCR test. The calculated R and Jnr are shown in Figure 4.  
According to Figure 4, the SBS-modified asphalt prepared by low-speed shear (600 r/min) did not show a strong elastic network as it shows the lowest R with the highest Jnr. Additionally, it shows the highest temperature sensitivity as its R/Jnr varies noticeably with increased testing temperatures. These results are most likely attributed to the inhomogeneous distribution of SBS.  
On the other hand, SBS-modified asphalt prepared by high-speed shearing (4000, 5000, 6000 r/min) demonstrates a strong and even network structure, and the SBS is uniformly distributed. Hence the R and Jnr test results are significantly better than those prepared by low-speed shearing (600 r/min). The SBS-modified asphalt prepared at 6000 r/min shows the most uniform microstructure as well as the best MSCR results (high R and low Jnr).  
Figure 4: MSCR test results of SBS-modified asphalt under different shear rates (**a**) R, (**b**) Jnr.  
Figure 3: Fluorescence microscopy test of SBS-modified asphalt of different shear rates (100 times).  
Although the higher the shear rate, the better the performance of SBS-modified asphalt [33], the performance of SBS-modified asphalt prepared at 6000 r/min was only slightly better than that of SBS-modified asphalt prepared at 4000 r/min. For the purpose of efficiency, the recommended shear rate in this paper is 4000 r/min.
# Rapid and Accurate Crayfish Sorting by Size and Maturity Based on Improved YOLOv5
## 4 Results and Analysis
### Experimental Analysis
#### 4.3.2 Comparative Analysis of Speed and Accuracy of Different Object Detection Algorithms
YOLOv5-7.0 and YOLOv8 are currently considered to be highly outstanding object detection algorithms; in comparison to the algorithm proposed in this paper, they exhibit an improvement in accuracy by 0.3% and 0.9%, respectively. However, in terms of recall, mAP, model size, and speed, their performance falls short of the algorithm proposed in this study. Compared with the eight different object detection algorithms (SSD, Faster R-CNN, Centernet, YOLOv4, YOLOv5-6.0, YOLOv5-7.0, YOLOv, and YOLOv8), the mAP of the presented algorithm was higher by 12.4%, 35.5%, 37%, 61.2%, 1.7%, 0.6%, 1.5%, and 0.5%. This demonstrates the outstanding capabilities of the algorithm proposed for crayfish detection in terms of both speed and accuracy. In contrast to other object detection algorithms, the algorithm proposed in this study exhibited outstanding performance in both detection accuracy and robustness. This means that the proposed algorithm accomplishes the rapid and accurate detection of crayfish for different sizes and maturities, further improving the accuracy and efficiency of crayfish detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Model Size (MB)** &
\begin{tabular}{c} **The Processing Time** \\ **per Image/ms** \\ \end{tabular} \\ \hline SSD & 84.5 & 89.2 & 88.0 & 94.1 & **13.2** \\ Faster R-CNN & 57.0 & 82.2 & 72.9 & 108 & 81.0 \\ Centernet & 82.5 & 58.0 & 72.1 & 124 & 14.5 \\ \hline YOLOv4 & 66.8 & 62.5 & 61.3 & 244 & 32.1 \\ YOLOv5-6.0 & 94.6 & 94.2 & 97.1 & 14.0 & 15.9 \\ YOLOv5-7.0 & 95.9 & 96.1 & 98.2 & 14.4 & 19.8 \\ YOLOv & 94.8 & 96.6 & 97.3 & 35.1 & 18.7 \\ YOLOv8 & **96.5** & 96.1 & 98.3 & 22.0 & 17.5 \\
**Proposed** & 95.6 & **98.1** & **98.8** & **13.9** & 14.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed algorithm and other object detection algorithms. Figure 11: _Cont._
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 3 Results
### Choosing the Best Model and Best Input  
The unfolding of the significant interaction between model \(x\) input for correct classification (%) and Kappa coefficient for discrimination of five soybean cultivars are shown in Tables 2 and 3, respectively. By analyzing the unfolding of models within input, ANNs presented the highest mean correct classification and Kappa coefficient regardless of the input used. For the input within model splitting, the spectral bands (SBs) and spectral bands + vegetation indices (SBs + VIs) inputs had the highest mean correct classifications and Kappa coefficients and did not differ for ANNs, DT, REPTree, and RF models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **SBs * & **VIs** & **SBs + VIs** \\ \hline ANN & 92.18 Aa & 88.30 Ba & 91.12 Aa \\ DT & 85.88 Ac & 72.24 Be & 85.72 Ac \\ RBF & 80.94 Ab & 49.50 Be & 74.88 Af \\ REPTree & 82.92 Ad & 68.32 Bd & 82.46 Ad \\ RF & 89.62 Ae & 80.22 Bb & 87.94 Ab \\ SVM & 73.82 Bf & 78.86 Ab & 78.24 Ae \\ \hline \hline \end{tabular}  
* Means followed by equal lowercase letters in the same column and equal uppercase letters in the same row do not differ by the Scott–Knott test at 5% probability.  
\end{table}
Table 2: Unfolding of the significant model \(x\) input interaction for the correct classification (%) of five soybean cultivars using machine learning (ML) models and different inputs (vegetation indices—VIs, spectral bands—SBs, and SBs + VIs).  
Figure 5: Boxplot for the variables correct classification (%) and Kappa coefficient for discrimination of five soybean cultivars using machine learning (ML) models and different inputs (vegetation indices—VIs, Spectral bands—SBs and SBs + VIs).
## 3 Batch Authentication in the Vehicle-to-Grid (V2G) Network
### Existing Authentication Protocols in the Vehicle-to-Grid (V2G)
#### 3.2.4 Aggregated Identifier-based Protocols  
In 2012, [PERSON] _et al._[22] proposed an aggregated-proof-based privacy-preserving authentication scheme (AP3A), which works under home and visitor modes. Since, the vehicles are interacting with the different groups, different security and privacy requirements need to be considered. Using this approach, multiple battery vehicles (BVs) can be simultaneously authenticated by an AG to conserve communication resources without revealing the individual's privacy. Later, [PERSON] _et al._[87] suggested a battery status-aware authentication scheme (BASA) to address the battery-related issues of vehicles in the V2G networks. This scheme introduced an aggregated-identifier to hide each vehicle's identity from revealing the location related information. The scheme improves the anonymous power data transmission using an aggregated-status for data protection. However, both protocols [22,87], are inefficient since they generate large overhead during the V2G authentication.  
In summary, although some privacy-preserved authentication protocols exist for the V2G network, these protocols either generate huge overhead or do not present security analysis to justify that they defeat various security attacks. Therefore, there still remain many research challenges to develop a privacy-preserved, secure, and efficient authentication protocol in the V2G network.
# A New Model of Quasigeoid for the Baltic Sea Area
## 4 Quasigeoid Computation
### Calculation of Residual Geoid  
Having properly prepared gravity data, the calculation of \(N_{\Delta g_{zz}}\) Equation (6) was conducted with the spfour program. The method used in the program is based on an expansion of the spherical FFT method [48]. The program uses analytical transforms of the FFT kernel; this allows additional control over the effective integration radius. No spherical harmonic Wong-Gore modification of Stokes' integral was used in our computations (Figure 7).  
The residual ellipsoid geoid separations were calculated for three different geopotential models and then the terms N\({}_{\rm GM}\) calculated in Section 4.1 were added, thus obtaining three cogeoids.  
The next step was to calculate the term N\({}_{\rm H}\) from the Formula (4) and the quasigeoid corrections from the Formula (5). The following values were adopted in these formulae: G = 6.673 10\({}^{-11}\) m\({}^{3}\)kg\({}^{-1}\)s\({}^{-2}\), \(\rho\) = 2700 kg m\({}^{-3}\), and \(\gamma_{\rm m}\) = 9.798 m s\({}^{-2}\). To calculate the corrections, a terrain model is required. In our case, we used the SRTM 30 model from Section 3.2. The calculated indirect effect and corrections to the geoid are shown in Figure 8. The indirect effect is from 0.00 to \(-\)0.17 m while the geoid corrections are \(-\)0.5 to 0.32 m. Adding to the cogeoid indirect effect and then the quasigeoid correction, we received three versions of quasigeoid models. The final quasigeoid map of the Baltic sea for the GOCO model is shown in Figure 9.  
Figure 7: Residual cogeoid, in meters, in the case of the GOCO06s geopotential model.  
Figure 8: (**a**) Indirect effect, in meters; (**b**) quasigeoid correction, in meters.
###### Abstract  
In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital \"make-to-order\" (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system.  
Keywords:industry 4.0; make-to-order strategy; product configuration system; customer requirement modelling; Kano model +
Footnote †: journal: Applied Sciences