esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Research Study of Vehicle Tracking  
Due to recent advancements in object detection, object tracking is one of the significant components in multiple object tracking. There are some traditional methods like Multiple Hypothesis Tracking (MHT) [37] and Joint Probabilistic Data Association Filter (JPDAF) [38] that impact the results of object tracking. However, data association can be performed by dividing the received observations into tracks. For better surveillance, Multi-Target Tracking (MTT) is an essential component. MHT is broadly viewed as the most crucial technique for performing the data association problem in MTT frameworks [39]. In the JPDAF, data association can be achieved by generating a single-state hypothesis with weighting. The rapid advancement in deep learning and computer vision technologies for self-driving cars can improve the object-tracking performance. Recently, several tracking algorithms have been introduced to improve the accuracy and robustness of vehicle tracking.  
Nevertheless, for tracking the object, [PERSON] et al. [40] introduced an algorithm called Simple Online and Real-Time Tracking (SORT) in 2016, which is one of the most robust approaches in terms of speed for tracking multiple objects. This approach was introduced to achieve detection-based online tracking. It combines the Kalman filter [41], which predicts the current frame speed based on the target frame while moving the target object, and the Hungarian [42] algorithm. In this, the data association on each frame enhances multiple objects' tracking performances. The SORT algorithm accomplishes an execution 20 times faster than other state-of-the-art trackers [43]. However, it fails to maintain accuracy due to occlusions and viewpoint changes. [PERSON] et al. [40] introduced the enhanced version of SORT named Deep SORT. Deep SORT resulted in a better performance for occlusions and a change in viewpoint by embedding a convolutional neural network technique called deep appearance-based metric and the data association by motion-based metrics. Deep SORT first predicts the trajectory using the Kalman filter, then detections in the current frame are matched with the predicted trajectory. Finally, the Kalman filter is updated and applied in Multiple Object Tracking (MOT) for assigning different IDs to every vehicle [43].
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem
### Diffusion Process and Its Inverse
#### 2.3.1 Permutation Layer Based on the Modified 2-D Cat Map and Its Reverse  
To permute a substituted block \(s_{l}(k)\), utilizing the improved 2-D cat map, we transform it to a matrix form \(s_{l}(i,\ j)\) such that [24]:  
\[s_{l}(i,\ j)=s_{l}(k),\ \ k=(i-1)\times M+j;\ \ i=1,\cdots M;\ \ j=1,\cdots M \tag{19}\]  
The equation of the modified 2-D cat map [47] and its optimized implementation [24] are given below:  
\[\left[\begin{array}{l}i_{n}\\ j_{n}\end{array}\right]=Mod\left(\left[\begin{array}{cc}1&u_{d}\\ \upsilon_{d}&(1+u_{d}\upsilon_{d})\end{array}\right]\left[\begin{array}{c}i \\ j\end{array}\right]+\left[\begin{array}{c}r_{id}+r_{jd}\\ r_{jd}\end{array}\right],\left[\begin{array}{c}M\\ M\end{array}\right]\right) \tag{20}\]  
\[\left\{\begin{array}{l}i_{n}=Mod((i+u_{d}\times j+Z_{1}),M)\\ \\ j_{n}=Mod((Z_{3}+Z_{2}\times j),M)\end{array}\right. \tag{21}\]  
with \(Z_{1}=r_{id}+r_{jd}\), \(Z_{2}=u_{d}\times v_{d}+1\), and \(Z_{3}=\ \upsilon_{d}\times i+r_{jd}\).  
The values of \(Z_{1}\) and \(Z_{2}\) are calculated once per round and the value of \(Z_{3}\) is calculated \(M\) times per round \((with\ M=\sqrt{|p_{l}|}=32=2^{q}-1)\).  
The 2-D cat map is bijective since each permuted element's \((i_{n},j_{n})\) position is unique. Despite it being reversible, the modulo operation on the cat map prevents it from being an invertible function. Thus, the same following relation can be used to perform the permutation and the reverse permutation:  
\[sp_{l}(i_{n},\ j_{n})=s_{l}(i,\ j),\ \ i=1,\cdots M,\ \ j=1,\cdots M \tag{22}\]  
The dynamic key \(Kd\) is formed by four elements \(Kd=\left\{u_{d},v_{d},\ r_{id},r_{jd}\right\}\), \(each\in[1,\ M-1]\) and it is fed by the PRNG-CS. The size \(Kd\) is \(|Kd|=4\times q=20\text{-bit}\).
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 3 Network Model
### Communication Model  
We adopt the protocol model in [1] as our communication model in this paper. Concretely, let \(r\) denote the transmission range of _ad hoc_ nodes, node \(X_{j}\) can receive the packets from node \(X_{i}\) if the following conditions hold:  
(1) Node \(X_{j}\) is under the coverage of node \(X_{i}\), _i.e._,  
\[\left|X_{i}-X_{j}\right|\leq r\]  
(2) For any other node \(X_{k}\) which is delivering packets over the same time slot,  
\[\left|X_{k}-X_{j}\right|\geq\left(1+\Delta\right)\left|X_{i}-X_{j}\right|\]  
where \(\Delta\) defines the size of guard zone.
## 3 Results and Discussion
### Influence of Jet Velocity on the Erosion Performance of HBS
The stress distributions of HBS at different jet velocities, with a standoff distance of 1.5 cm and a nozzle diameter of 1.5 mm, are illustrated in Figure 9. It can be seen that the erosion width of the crater orifice and the erosion depth increase with the increase in jet velocity, and the stress concentration zone at the bottom of the pit at a jet velocity of 140 m\(\cdot\)s\({}^{-1}\) is significantly larger than that of the former two. This result indicates that the increase in jet velocity truly enhances the vertical erosion and the erosion of the side of the crater by the rebounding water. In conclusion, an increase in jet velocity corresponds to an increase in erosion depth and erosion volume, which is consistent with the conclusions of Sections 3.1.1 and 3.1.2, respectively. Furthermore, the increase in jet velocity enhances the efficiency of the jet erosion of HBS. Therefore, it is suggested that, in the process of using a high-pressure water jet to exploit marine NGH, the jet velocity should be increased as far as possible given equipment capacity to improve the mining efficiency.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 3 Numerical Experiments
### Dataset Description  
To evaluate the effectiveness of the hybrid prediction framework, we use the ship deficiency prediction problem and the PSC inspection dataset as a case study, representing an essential issue for maritime transportation.  
To identify ships that are potentially deficient or pose a higher detention risk for port authorities, the Tokyo Memorandum of Understanding (MoU) introduced a ship selection scheme in 2014, namely, the new inspection regime (NIR) [20], to evaluate the risk level of ships in Asia-Pacific regions. The NIR considers seven features related to the characteristics and historical inspection records of a ship, including ship type, ship age, ship flag performance, ship recognized organization (RO) performance, ship company performance, the number of deficiencies within the previous 36 months, and the number of detentions within the previous 36 months. Each candidate value of a certain feature is assigned a fixed weighting point, and a ship's risk level is determined by the sum of the seven features' weighting points.  
The weighted-sum method introduced by the NIR assumes a straightforward additive relationship between the weighting points and ship deficiencies. However, as highlighted by [PERSON] and [PERSON] [21], the reality is not that simple. The inherent interdependence between features, or the so-called coupling effect, makes a linear additive model like NIR potentially ineffective. This is where nonlinear models, especially machine learning models, come to the fore. As validated by [PERSON] et al. [22], Bayesian network (BN) models have showcased superior performance in predicting ship deficiencies compared with the NIR's linear model. This, coupled with other noteworthy contributions by [PERSON] et al. [23], [PERSON] and [PERSON] [24], and [PERSON] et al. [25], further solidifies the argument for a departure from simple linear models to more intricate, nonlinear models.  
For our study, the goal is to predict the number of deficiencies for a ship. We rely on the seven features, as highlighted by the NIR, that are suspected to be in direct correlation with a ship's deficiency count. The dataset consists of PSC inspection records from January 2015 to December 2019, specifically from the port of Hong Kong. These records were sourced from the Tokyo MoU database. For the sake of data quality, incomplete records were excluded, resulting in a final dataset of 3026 inspection records.
## 2 Materials and Methods
### FBGs Response to Irradiation  
There are numerous reports on testing the durability of fiber optic sensors, in particular FBG-based sensors, to high dosage and high dose rate environments. Studies conducted by [PERSON] et al. on the effects of high dosage \(\gamma\)-radiation on FBG sensors provided insight into the performance expectation of the test specimens incorporated in our experiments [18]. [PERSON] et al. specifically detailed the FBG wavelength shift induced by exposure to 1 MGy and 4 MGy of gamma radiation on Ge-doped and pure silica fibers, respectively, with a dose rate of 50 Gy/s. These results determined the selection of fibers for use in our experiments. Ge-doped fiber was employed in the UV and FS specimens, while pure silica fiber was used in the RAD specimens. In our experiments, the expected radiation induced wavelength shifts derived from the data by [PERSON] et al. was expected to be \(\pm\)0.040 nm without any consistent direction (higher/lower wavelength). Coupled with the uncertainty of \(\pm\)0.005 nm in the wavelength measurement by the device used for these tests, the results suggested an expected performance of the RAD test specimen within the measurement uncertainty of the FBG even when the specimens were not exposed to high radiation doses. This further emphasizes the radiation resistance of FBGs and their efficacy for use in the proposed experiments.
## 2 Related Work
### Relation Classification Model Based on Recurrent Neural Network  
The relationship classification model based on the RNN [20] can learn long-distance word sequences and overcomes the shortcomings of CNN-based models that only focus on local information. Bidirectional RNN is an early neural network model applied to relational classification tasks. [PERSON] et al. [21] used bidirectional RNN to extract the features in sentences and used the extracted semantic information to perform relationship classification tasks. There are many problems faced when using bidirectional RNNs to perform relation classification tasks. Issues include vanishing and exploding gradients, poor-quality semantic features, etc. [PERSON] et al. [22] used BiLSTM in relational classification tasks in order to solve the problem of the exponential rise or decay of gradients. At the same time, elements such as POS tags and named entity information were added to the relationship classification model. Several combined relational classification models followed, combining RNNs with attention mechanisms. For example, Att-BiLSTM [23] used the combination of BiLSTM and attention to improve the performance of the relationship classification model. Models such as Bi-RNN + Bi-LSTM [24], BiGRU MCNN Att [25], and Bi-LSTM + LET [26] combine neural networks such as the RNN, GRU, BiLSTM, and CNN for relational classification tasks and have achieved good performance.
## 3 Modified Hybrid Integration Processing  
This section will introduce the modified hybrid integration algorithm (MHI). Initially, the communication information was compensated for, and the frequency modulation symbol frame was established. Next, the MHI algorithm is elaborated, including the coherent intra-frame integration and hybrid inter-frame integration of subfragments. Finally, the calculation results for the detection probability (\(P_{d}\)) and false alarm probability (\(P_{fa}\)) are provided. The detailed flowchart of the MHI is shown in Figure 1.  
Figure 1: Detailed processing flowchart of the MHI.
## 4 Results
### TOPSIS Results  
In this section, the goal is to achieve the best plan. As mentioned earlier, due to the optimization of the two objective functions and the fact that each objective function without the base state contains 9 different states, there are 18 different states total. Of these, 9 are with the first objective function and 9 are with the second objective function. For each of these 18 cases, the results for each of the objective functions were obtained. For example, with the use of the first objective function, we determined the result of optimization with a genetic algorithm for each of the functions. Figure 17 shows the TOPSIS technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Mode**} & **Total Cost** & **Minimum** & **Site \& Size** & \\  & & **(*1065)** & **Voltage** & **of DG** & & **Site of SM** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Base} & 0.992 & 0.9038 & - & - \\  & & & (B18) & 0.9389 & & \\  & & 1 & 1.628 & B(33) & 1.75.0(B12) & - \\  & & & 0.9537 & & \\
2 & 2 & 1.702 & B(33) & 1.856(B8) & - \\  & & & 0.9548 & & \\  & & 3 & 0.9205(B33) & B(33) & 1.890(B8) & B6, B25, B30, \\  & & & & 0.93(B3) & & B31, B32 \\  & & 1 & 0.9115(B31) & 0.9618 & 1.352(B14) & - \\  & & & & B(31) & 0.623(B33) & - \\
3 & 2 & 0.9826(B25) & 0.9689 & 0.988(B17) & - \\  & & & & B(25) & 1.011(B33) & - \\  & & & 0.9745 & 0.953(B14) & B7, B14, B18, B20, B32 \\  & & & & 0.9678 & 0.959(B13) & \\  & & 1 & 0.9578 (B31) & B(31) & 0.507(B16) & - \\  & & & & 0.534(B33) & & \\
4 & 2 & 0.9512(B25) & B(25) & 0.417(B18) & - \\  & & & & 0.862(B33) & & \\  & & & 0.9785 & 0.700(B15) & & \\  & & & & 0.430(B18) & B8, B29, B30, B31, B32 \\  & & & & 0.870(B28) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results in improved voltage profile function.  
Figure 17: TOPSIS employed in the planning of SPL-CHHP.  
Thus, since there are 18 states and 2 functions, the number of alternatives will be equal to 2, and the number of indicators will be equal to 18.  
As shown in Table 5, the most suitable plan under which both functions can be simultaneously optimized is Index Number 9. The ranking of all indexes is shown in this table. Index 9 is related to State 3 of Scenario 4, with the objective function of cost reduction. This index demonstrates a state in which both functions can be at their best at the same time.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Mechanical Properties and Deformation Stability of Soils
#### 3.2.2 Dynamic Modulus Test  
Figure 11a shows the dynamic stress-strain curves of intact soil (S), SSL and SCLC. It can be seen that the stress increased with increasing strain, and the slope of these curves gradually decreased, showing that the dynamic modulus decreased. Among the test soil specimens, the dynamic modulus of SSLC was the largest, followed by SSL, and finally S. The dynamic modulus test results were consistent with the above static triaxial strength and bearing capacity results. The point line diagram of the dynamic modulus curve was obtained using the dynamic stress-strain curves, as shown in Figure 11b. Unlike the trend seen for the stress-strain curve, the dynamic modulus decreased with increased axial strain, mainly because increased deformation leads to plastic reinforcement and elastic attenuation, so dynamic modulus decreases.  
Dynamic modulus is an important parameter to evaluate the subgrade deformation in construction. In order to better analyze the dynamic modulus test results, Equation (2) converted from Equation (1) (Hardin-Drnevich model) was used to fit the curve of dynamic modulus versus strain:  
\[\sigma_{d}=\frac{\varepsilon_{d}}{A+B\varepsilon_{d}}, \tag{1}\]  
\[E_{d}=\frac{1}{A\varepsilon_{d}+B}, \tag{2}\]  
in which \(A\) and \(B\) are fitting parameters. It is worth noting that when the longitudinal strain (\(\varepsilon_{d}\)) is close to 0, the dynamic modulus \(E_{d}\) is equal to 1/B, which is usually defined as the maximum dynamic modulus \(E_{d\max}\). According to Equation (1), the values of \(A\), \(B\) and \(E_{d\max}\) of the test soil are calculated and listed in Table 4. From Table 4, the parameters \(A\) and \(B\) of intact soil were the highest, and \(A\) and \(B\) of SSLC were the lowest. The maximum dynamic moduli of the test soil (S, SSL and SCLC) were 118.20 MPa, 140.85 MPa, and 255.75 MPa, respectively, where the intact soil was the lowest, SSL was intermediate, and SSLC was the highest. These results showed the good dynamic modulus characteristics of the improved soils with SL and cement, including SSL and SSLC.  
Figure 11: The dynamic modulus test results: (**a**) dynamic stress-strain curve; (**b**) dynamic re-bound modulus curve.
## 4 Results and Discussion
### Discussion  
The UBGH2 field case study validated that the LSTM model could generate synthetic log data in distant wellbores. Input variables were selected primarily based on the correlation with the output variable Vp. For the UBGH2 wellbores, RHOB had the highest correlation coefficient while GR had the lowest, as summarized in Table 1. Nonetheless, the Vp-learning model obtained the highest performance among the Vp-estimation results with a single input variable by capturing the nonlinear relationship between GR and Vp. Furthermore, the highest performance was derived using GR, RHOB, TNPH, and PEF as input variables.  
The use of all five logging types by adding RT in the input set yielded a similar performance. The \(R^{2}\) of using all five logging types was 0.3 less than the best performance on average. Thus, Figures 6-8 illustrate that using all given input variables does not guarantee improved performance for the learning-based predictive model. Furthermore, our results imply that the LSTM is suitable for capturing nonlinearity among the inputs and output. Except for wellbores with similar names (e.g., UBGH2-1_1 and UBGH2-1_2), Fold 1 and the other folds had \(R^{2}\) values higher than 0.80. The influences of similar data distribution of training, validation, and test datasets were analyzed through Figures 7 and 8. The more similar the data distribution, the more accurate performances were obtained from the model. Therefore, the data distribution affects the overall performance of the designed model.  
Due to the unavailability of core Vp data, this study focused on the synthetic generation of Vp logs. If available, the proposed LSTM approach could be used to integrate core and  
Figure 12: Comparison of synthetic and reference Vp profiles in the UBGH2 field: (**a**) training wellbore data (red curves), (**b**) validation wellbore data (green curves), and (**c**) test wellbore data (blue curves).  
well-logging data. Our future research will use a learning-based model to generate high-resolution logging data compatible with core data.
## 2 Materials and Methods
### Grey Fuzzy Approach
In this case, the GRC coefficient values from 0 to 1 are divided into fuzzy sets or linguistic values, for example, LOW, MEDIUM, and HIGH. The fuzzifier uses the Gbell membership function to fuzzify the GRC of each feature. The mathematical form of this function is shown in Equation (7). \[triangle(x;a,b,c)=\max\biggl{(}\min\biggl{(}\frac{x-a}{b-a},\frac{c-x}{c-b} \biggr{)},0\biggr{)} \tag{7}\]  
This function has the task of changing the linguistic values between zero and one. In Equation (7), x indicates the range of input variables, and a, b, and c are the linguistic value parameters. The generation of fuzzy rules (if-then rules) follows. The Mamdani fuzzy inference system (FIS) was developed in an effort to improve the systematic approach to generating fuzzy rules from a given input-output dataset. One of the main applications of the Mamdani FIS is when the consequences of the rules are fuzzy. This provides more flexibility to the fuzzy model for the grey fuzzy approach. The general structure of a fuzzy rule in a Mamdani fuzzy inference model [39] has the format, Equations (8) and (9), or in mathematical form:  
Figure 4:0  
Figure 4:1  
Here, \(A\) and \(B\) are linguistic values defined by fuzzy sets on domains \(X\) and \(Y\), respectively. The if-part of the rule \"\(X\) is \(A\)\" is called the antecedent or premise, while the then-part of the rule \"\(Y\) is \(B\)\" is called the consequent or conclusion. The input of an if-then rule is the current value of the input variable and the output is generally defuzzified. The resulting fuzzy sets are combined from the inference value of each input rule using the aggregation operator. Depending on the system, it may not be necessary to evaluate every possible input combination, since some rarely or never occur. This type of evaluation, usually performed by an experienced operator, allows fewer rules to be evaluated, simplifying the processing logic and perhaps even improving the performance of the fuzzy logic system. Finally, a defuzzification method is used to change the fuzzy inference output to a non-fuzzy value. The defuzzification is achieved using the Centroid defuzzification method (COA) under the membership function, Equation (10):  
Figure 4:2  
where \(y^{\prime}\) are the defuzzified outputs (the output for a given input vector predicted by the grey fuzzy relational grade GFR values in this study); \(\mu\)_Ei_ and \(\mu\)_Fi_ = the aggregated memberships functions; \(y\) = the output variable (the center value of the regions). The fundamental role of fuzzy logic in this approach is that, based on the GRC coefficient of each input variable, the degree of fuzzy reasoning is determined. The maximum value of this coefficient is the best solution.
## 3 System Design and Implementation
### Software Design  
The software design is divided into two modules: gateway and host computer. The gateway module is divided into the establishment of UDP communication, the design of protocol conversion, and the realization of AUDP mechanism. The host computer module is divided into data transmission and reception (via socket programming), and the realization of the AUDP mechanism. Figure 9 shows the software design framework.  
Figure 8: USART to RS485 circuit.  
Figure 7: Power supply module circuit.