esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Correspondence Analysis  
#### 4.3.1 Time-Series Analysis  
Figure 7 demonstrates the correspondence analysis results for the entire period, starting from the second quadrant in 2016, moving counterclockwise, and reaching the first quadrant in 2022. The period 2016-2017 was surrounded by important future technologies related to \"big data\", \"cloud\", and \"robots\"; thus, this period is categorized as the recognition stage. Meanwhile, 2018 is more closely associated with the terms \"new\", \"growth\", \"positive\", and \"investment\". It is now considered that AI has been recognized independently among related technologies and has become a unique area to invest in; thus, this period is called the investment stage. The period 2019-2020 is the stage of incorporation into strategy; accordingly, the terms \"environment\", \"expansion\", and \"increase\" are mentioned in 2019, whereas \"management\", \"group\", \"value\", and \"deployment\" dominate in 2020. This indicates that the perception of AI evolved to be integrated into business strategies in response to the environmental change underlying AI expansion. A group response policy is evident. Simultaneously, the terms \"operations\", \"RPA\", \"production\", and \"ef  
Figure 6: Co-occurrence network.  
ficiency\" are close to one another. Accordingly, it can be stated that AI technology will be used to improve production efficiency to achieve AI-related strategies. This period is called the strategization stage.  
The 2021-2022 period can be considered the commercialization and monetization stage. In 2021, the terms \"business\", \"development\", and \"implementation\" were prominent, indicating that AI business was being implemented. In 2022, the words \"sales\", \"segment\", \"revenue\", and \"accounting\" were dominant, reflecting the commitment to using AI in accounting results.  
In summary, integrating AI technology into business structures involves the process of the recognition, investment, strategization, commercialization, and monetization stages, as depicted in Figure 8.  
Figure 8: Stages of AI perception.  
Figure 7: Time\(-\)series-based correspondence analysis.
## 3 Results and Discussion
### Effects of Resin Content
#### 3.4.2 Internal Bond Strength  
Figure 14a shows a definitive relationship between the resin content of the produced particleboard and the internal bond property. While increasing of UF resin ratio in the produced particleboard led to a positive influence by improving the internal bond property of particleboard, increasing the resin content of the produced particleboard from 10% and 12% to 14% improved the internal bond significantly. The internal bond value of particleboards with lower resin content was 0.002 MPa, whereas that of boards produced with a higher resin content was 0.016 MPa. This results trend agreed with [9], who found out that the internal bond of the particleboards made from eggplant stalks increased with an increase in ratio of urea formaldehyde and melamine urea formaldehyde adhesives.
## 4 Results
### Local Parameter of CLUE-S Model for LULC Prediction  
The conversion matrix of LULC change for LULC prediction of three scenarios, which were assigned by considering the transition probability matrix of LULC data between 2009 and 2019 (Table 9), is presented in Tables 10-12. Meanwhile, elasticity values, as probability values for the urban and built-up area, paddy field, field crop, para rubber, perennial trees and orchards, forest land, waterbody, rangeland, wetland, and miscellaneous land were 1.00, 0.84, 0.22, 0.37, 0.67, 0.92, 0.93, 0.38, 0.45, and 0.29, respectively. The results of LULC prediction data for Scenario I, which were simultaneously allocated based on elasticity values and conversion matrix of LULC change (Tables 9 and 10), the land requirements (Table 13), and driving factors on LULC change for specific LULC type location preference (Table 8), are presented in Table 14 and Figure 4. Meanwhile, the deviation between the estimated land requirements and predicted LULC data in 2029 under Scenario I is reported in Table A4 in Appendix B. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**LULC Types**} & \multicolumn{1}{c}{\multicolumn{1}{c}{\multirow{2}{*}{**LULC Types**}}} & \multicolumn{1}{c}{\multirow{2}{*}{**LULC Types**}} & \multicolumn{1}{c}{\multirow{2}{*}{**FC**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RB**}} & \multicolumn{1}{c}{\multirow{2}{*}{**PO**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RB**}} & \multicolumn{1}{c}{\multirow{2}{*}{**PO**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RB**}} & \multicolumn{1}{c}{\multirow{2}{*}{**PO**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RB**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RO**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RB**}} & \multicolumn{1}{c}{\multirow{2}{*}{**PO**}} \\ \cline{3-3} \cline{5-12} \multicolumn{1}{c}{} & **UR** & **PD** & **FC** & **RB** & **PO** & **FO** & **WB** & **RL** & **WL** & **ML** \\ \hline \multirow{9}{*}{**LULC**} & Urban and built-up area (UR) & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Paddy field (PD) & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 \\  & Field crop (FC) & 1 & 0 & 1 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 1 \\  & Para rubber (RB) & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\  & Perennial trees and orchards (PO) & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 \\  & Forest land (FO) & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 1 \\  & Water body (WB) & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 \\  & Rangeland (RL) & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 1 & 0 \\  & Welland (WL) & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 \\  & Miscellaneous land (ML) & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular} Note: 0 is not allowed, and 1 is allowed.  
\end{table} 
Table 10: Conversion matrix of possible LULC change between 2019 and 2029 for Scenario I (Trend of LULC evolution).
## 2 Experimental Analysis
### Test Set-Up and Procedure  
The plan dimension of the test box used for the test set-up was 1.5 m \(\times\) 0.9 m \(\times\) 0.6 m. The tank includes a strong loading frame and one loading system that allows soft soil and the stone column materials to be loaded (Figure 3). The present model tests were carried out in accordance with other researchers who carried out comparable testing. The footing loading was imposed on a stone column installed in the middle of a clay bed prepared in a big test tank [19; 35]. The tank's plan dimension was chosen such that the test findings would not be influenced by the tank's boundaries. The loading system for this test was stress controlled, where the loading rate was established by a hydraulic jack with a capability of 20 kN. The test steps entail implementing a vertical monotonic load to the clay treated with a stone column and calculating the load-displacement behaviour of the softer clay. The vertical application of load was made after the column of stones was installed, utilising a plate placed at the centre of the column and a bed of clay.  
In the current study, single stone columns were subjected to 48 tests. A robust plate of steel with a 200 mm diameter along with a thickness of 25 mm was utilised for the plate used for loading in all solitary stone column testing. Table 4 summarises the outcome of this testing. For all single stone column testing, an L/D ratio (stone column's length to its diameter) of the above five was employed; as a minimum, L/D = 4 is necessary to prevent the bulging collapse mode [43]. In the case of VESCs and HRSCs, various encasement lengths were used, as shown in Table 4 and Figure 2. Single stone column tests were conducted on the columns with 50, 75, and 100 mm diameters using a 200 mm loading plate. The area replacement ratio is determined as the proportion of the cross-sectional area of stone columns to the entire area of the foundations and is indicated in the literature. In these experiments, the percentages were 6.25, 14.06, and 25% for the columns with the diameters of 50, 75, and 100 mm, respectively.
## 4 Results
### Determination Attributes Improvement Cost  
To minimize the risk of biased judgments resulting from reliance on a sole expert's opinion, we disbursed the extracted attributes and corresponding attribute words to six experts for independent review. This approach guaranteed that each expert, leveraging their unique professional expertise, would base their judgments solely on their thorough comprehension of the attributes. Consequently, a more balanced and comprehensive evaluation of the cost of attribute improvement was achieved.  
Since both courses belong to the programming category and share identical attributes, it is reasonable to presume that the cost of enhancing these attributes would be comparable. Therefore, in order to streamline the ranking process, we did not ask experts to individually rank each attribute for each course. Instead, we requested a unified ranking for the attributes across both courses. The resulting rankings are presented in Table 3.  
Table 3 reveals that while there exist minor disparities in experts' assessments regarding the cost of attribute improvement, the overall trend remains consistent. All experts agree that enhancing attributes such as \"Teaching level\", \"Course organization\", and \"Professional knowledge\" involves a relatively high cost. Conversely, attributes like \"Teacher's expressive ability\", \"Exercise\", and \"Supporting materials\" are considered easier to improve.  
This finding resonates strongly with our intuitive understanding. The enhancement of attributes such as teachers' professional knowledge, teaching level, and course organization is deeply ingrained in their extensive learning and experience. Consequently, these aspects are inherently challenging to improve in the short term. In contrast, other attributes, which are not intrinsically tied to teachers, can be relatively easier to improve upon.  
The calculation of Formula (5) requires a set of rankings, but our six experts have provided us with a total of six sets of rankings, which makes it impossible for us to directly bring the results of Table 3 into Formula (5) for calculation. Nonetheless, considering that there is little difference in the judgments of the experts, we directly calculate the averages of the rankings across six experts for each attribute and then bring it into Equation (5) to calculate the improvement cost.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attributes** & **Expert 1** & **Expert 2** & **Expert 3** & **Expert 4** & **Expert 5** & **Expert 6** \\ \hline Teacher’s expressive ability & 7 & 9 & 10 & 10 & 10 & 10 \\ Teaching level & 2 & 1 & 2 & 1 & 1 & 1 \\ Teaching method & 8 & 5 & 6 & 5 & 5 & 6 \\ Exercise & 9 & 6 & 7 & 9 & 7 & 7 \\ Learning threshold & 3 & 3 & 5 & 6 & 4 & 4 \\ Course organization & 5 & 3 & 1 & 2 & 3 & 3 \\ Supporting materials & 10 & 8 & 8 & 8 & 8 & 8 \\ Platform & 6 & 8 & 8 & 7 & 9 & 9 \\ Experience of learning & 4 & 6 & 4 & 4 & 5 & 5 \\ Professional knowledge & 1 & 2 & 3 & 3 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ranking results of improvement cost of all attributes.
## 3 Background  
### Discrete Event System  
A discrete event system (DES) is a time event system, in which each discrete state is updated based on asynchronous events that occur over time. In a discrete event system, the state of the system is changed dynamically while the system is being simulated. Various modeling methods exist for a discrete event system. Among them, DEVS is used for modular, layered formalism, including a discrete event system based on state transition, a continuous state system represented by differential equations, and a hybrid system in which both exist. DEVS is also used for object-oriented modeling and analysis. A real system modeled using DEVS can be depicted as a set of atomic and coupled models.  
Figure 1 displays a DEVS atomic model of DES, and the following specifications represent its formal representation. An atomic model of DEVS is basically represented by three variables and four functions. The three variables include \(X\), \(Y\), and \(S\), and the four functions represent an external state transition function, an internal state transition function, an output function, and a time advance function. X is the set of input events, and Y is the set of output events. The S is the set of sequential states, which cover all states of the modeling system. \(\sigma_{Ext}\) is the external state transition function with system state S, input X, and e is the elapsed time since the last state transition. \(\sigma_{Int}\) is the internal state transition function. \(\lambda\) is the output function of the system. \(ta\) is the time advance function, which can take any real value between 0 and inf. The time advance function is used as an operation trigger for the internal state transition function.  
The state variables with discrete values in DEVS are updated when the model receives input from outside of the model. In addition, if the input is not received for a certain period of time, the internal state transition function is operated after waiting for a predetermined time, and the state variable is updated. The amount of time necessary to wait for an input is determined by the time advance function. With this operation, unlike other system modeling methods, the DEVS modeling system has a different structure in which the state of the system is changed not only by external input but also by an internal transition.
## Methods and Materials
### Methods
#### 2.1.2 Random Forest Model  
Random Forest (RF) is a classifier created by [PERSON] that combines multiple decision tree classifiers using the bagging method [36; 37]. Random Forest combines multiple weaker decision trees, also known as weak classifiers, to improve the overall prediction accuracy. The approach takes inspiration from the Bagging method, and the so-called Bagging is:  
1. Randomly sample N training samples from the training set to create a new training set;
2. Train M submodels on the new training set;
3. For classification tasks, use a voting method to determine the final class prediction, which is based on the most frequent predictions amongst all submodels. For regression tasks, the predicted value is obtained through simple averaging of the submodels' predictions.  
In general, integration algorithms can be broadly classified into three categories: Bagging, Boosting, and Stacking. Random Forest uses the bagging method, and the fundamental concept behind the Bagging method is to create several individual predictors, each of which makes independent predictions. The final prediction of the integration model is obtained by averaging or majority voting of the predictions made by these individual evaluators [38; 39]. In this paper, the construction and evaluation of the model are realized by the use of the Random Forest algorithm. The specific process is shown in figure (Figure 2). For the Random Forest modeling process, this article used the Python package scikit-learn (version 1.0.2).  
Figure 1: Schematic diagram of missing value filling process. Complete the filling process from left to right as shown in the picture. Use blue squares for missing values and orange squares for filling values.
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 5 The Proposed Cluster-Based VANET Routing Protocol  
Due to the random node mobility, a major challenge is how to route data packets in a desert and similar scenarios of communication, particularly when the source and the destination are out of the DSRC transmission range. Maintaining a routing table, as in proactive methods, is not an optimal solution, and repetitive path finding before each packet delivery, as in reactive routing, can also be exhaustive. Therefore, specific routing solutions are needed. A routing strategy only based on the location information of the nodes can satisfy the requirements of VANETs in a desert. The problem is that the majority of location-based protocols mainly rely on road side units (RSU), road intersections and other assumptions, which may not be available in a desert. This paper proposes a cluster-based VANET routing protocol (CBVRP). The proposed algorithm covers three communication scenarios, as follows.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 4 Experimental  
### Implementation Details  
The dataset to evaluate the LLCT was the Online Tracking Benchmark (OTB) [1], an authoritative dataset that has been used to compare the performance of other trackers (Section 4.3.1). It contains 50 sequences and has many challenging attributes. Moreover, we also evaluated the LLCT performance on the classic evaluation datasets and benchmarks UAV123 [4] (Section 4.3.2). The proposed tracker was implemented in MATLAB 2016b on an industrial computer with Intel i7-8700 3.70 GHz CPU and Nvidia GeForce GTX 1080 GPU. The CNN feature was not used to improve the real-time performance of the  
Figure 4: Hardware and software framework of vision tracking system for coal mine drilling robot.  
Figure 3: Selection of the low-illumination detection area. In the full view (1280 \(\times\) 720), the darkest pixel (green circle) is close to the target, but the brightest (red circle) is not. The maximum RGB values close to the target are [33 33 33]. The global brightness of the image is 128, but the brightness close to the target is only 16.5.  
operation, and only manually selected features (histogram of oriented gradients (HOG) and color names) were used. Therefore, the separate graphics card was disabled to reduce power consumption.  
The regularization parameter \(\lambda\) was 0.01, and the learning rate \(\eta\) was 0.025. The standard deviation of the Gaussian function output \(y\) was 1/16 of the translation target size. The padding of the filter was twice the size of the initial target. The scale filters were interpolated from \(N=17\) scales to \(N*=33\) scales by interpolation, and the scale factor \(a=1.02\) was used. The re-detection threshold \(T_{r}\) was 0.2 for the activation detection module and \(T_{a}=0.4\) for the detection result. Note that the threshold setting here is only a fraction of the long-term filter \(\mathbf{f}_{\text{Long}}\). The confidence parameters of the LCT response were 0.9 times the maximum response and 0.75 times the APCE (or CSRM) response. The long-term memory template was updated when both its parameters exceeded the set threshold. The purpose of this was to test the effect of multi-confidence settings on the trackers' performance. The exposure control parameter \(k\) of the LLCT was 1. The lighting intensity threshold \(T_{L}=48\). The number of iterations for the image enhancement was 1 (Section 4.4).  
The OTB-2013 dataset [1] results were evaluated by the overlap precision (OP), distance precision (DP), and tracking speed (FPS). The success plots show the DP rate [0, 1] in 20 pixels and the area-under-the-curve (AUC) of the OP rate.
## 3 Performance of the Optical Wireless Link
### Outage Probability Estimation  
Consider a receiver that constantly measures the instantaneous SNR of the receiving pulses, provided that this value is larger than the sensitivity threshold, \(\gamma_{j,th}\); other  
Figure 4: Expected irradiance of a pulse under the joint effect of GVD and TJ as a function of propagation distance ratio, \(z/z_{c2}\).  
wise, the link is thought to be interrupted. The probability of the link not being available is estimated through the OP [20; 41; 42; 43; 44; 45], \(P_{out}\), provided by the cumulative density function (CDF) of irradiance from the lowest possible SNR value, \(\gamma_{j,\min}\equiv\gamma_{j}(I_{j,\min})=B^{2}\overline{\gamma}_{j}\exp \left(-T_{sl}^{2}/2T_{pw}^{2}\right)/\left(E\big{[}I_{j}\big{]}\right)^{2}\), up to a given \(\gamma_{j,th}\)[20].  
\[P_{out}=F_{\gamma_{j}}\Big{(}\gamma_{j,th}\Big{)}=\int\limits_{\gamma_{j,\min }}^{\gamma_{j,th}}f_{\gamma_{j}}\big{(}\gamma_{j}\big{)}d\gamma_{j}. \tag{12}\]  
By substituting \(q=T_{pw}\sqrt{\ln\left(B\sqrt{\overline{\gamma}_{j}/\gamma_{j}}/E\big{[}I_{ j}\big{]}\right)}\), the above equation is transformed into  
\[P_{out}(q_{th})=\frac{2}{\sqrt{2\pi}\sigma_{T}}\int\limits_{q_{th}}^{q_{0}} \exp\left(-\frac{q^{2}}{2\sigma_{T}^{2}}\right)dq, \tag{13}\]  
where \(q_{0}\equiv q\big{(}\gamma_{j,\min}\big{)}=T_{sl}/2\) and \(q_{th}\equiv q\Big{(}\gamma_{j,th}\Big{)}=T_{pw}\sqrt{\ln\left(B\sqrt{ \overline{\gamma}_{j}/\gamma_{j,th}}/E\big{[}I_{j}\big{]}\right)}\). Then, the OP in terms of average SNR and for a certain SNR threshold is simply generated as follows:  
\[P_{out}\Big{(}\overline{\gamma}_{j};\gamma_{j,th}\Big{)}=\frac{1}{Z}\left\{ erf\left[\frac{T_{sl}}{2\sqrt{2}\sigma_{T}}\right]-erf\left[\frac{T_{pw}\sqrt{ \ln\left(B\sqrt{\overline{\gamma}_{j}/\gamma_{j,th}}/E\big{[}I_{j}\big{]} \right)}}{\sqrt{2}\sigma_{T}}\right]\right\}. \tag{14}\]
# A Simple Method to Estimate the In Situ Performance of Noise Barriers
## 2 Proposed Model  
In this section a simple method will be described to obtain the insertion loss provided by a noise barrier without requiring specific measurements or a complete vibroacoustic simulation of the behaviour of the acoustic screen.  
Consider the simple scenario of a noise barrier illustrated in Figure 1. The effect of the acoustic screen can be understood through the Huygens-Fresnel-Kirchhoff principle. Energy from the plane wave impinging the noise barrier can travel through the screen or can pass over it, and both contributions will arrive to each measurement position. If \(\varepsilon\) is the portion of the energy that arrives to a particular measurement position propagating above the acoustic screen, the portion that travels through the screen will be \((1-\varepsilon)\tau\), \(\tau\) being the corresponding sound transmission coefficient. Then, if no reflections are considered, the frequency-dependent Insertion Loss can be written as:  
\[IL_{i}=-10\log[(1-\varepsilon_{i})\tau_{i}+\varepsilon_{i}] \tag{5}\]  
The frequency dependent transmission factor, \(\tau_{i}\), is related to the sound reduction index by the expression:  
\[R_{i}=10\log(\frac{1}{\tau_{i}}) \tag{6}\]  
Figure 1: Schematic representation of a noise barrier, showing the application of the Huygens–Fresnel–Kirchhoff principle.  
Notice that incoherence is assumed between the sound not interfered by the acoustic screen and the one that goes through it. Nevertheless, the third octave averaging performed in all of the aforementioned standards reduces the effect of coherent terms in the sum of the two contributions.  
The portion of energy not interfered by the noise barrier, \(\varepsilon\), can be obtained from the numerical simulation of the particular geometry under study, forcing the screen to be completely rigid and reflective (\(\tau=0\)), which is the easiest case to be simulated, without taking into account the vibrational problem inside the noise barrier. In that case, Equation (5) is written as  
\[IL_{i\;rigid}=-10\log[\varepsilon_{i}] \tag{7}\]  
In other words,  
\[\varepsilon_{i}=10^{-IL_{i\;rigid}/10} \tag{8}\]  
As a result, the final performance of an acoustic screen can be obtained by knowing the reduction index, \(R\), commonly provided by the manufacturer, and the particular value of \(\varepsilon\) that, as commented before, can be obtained from the simulation of the limiting case in which the noise barrier is completely stiff and reflective. In other words, there is no need to perform a vibroacoustic simulation of the acoustic screen that takes into account the transmission of sound through the material or materials it is made of. Notice that, for that particular case, \(\varepsilon\) can be easily obtained with common numerical methods used in acoustics (such as FEM, FDTD, BEM or MFS) through simple 2D numerical simulations in vertical plane, given that the partial sound insulation of the acoustic screen is not needed.