esa-sceva/satcom-chunk-collection huggingface dataset:
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment  
In the V2X environment, the driver-vehicle unit can obtain massive amounts of traffic information of various units, including multiple vehicles, in the system. Based on this, the connection and interaction relationship between the object vehicle and other units will be established. With this relationship, characteristics of the object vehicle's car-following behavior will differ from that in the previous traffic environment without V2X technology. These changes should be considered in modeling car-following behavior to make the results suitable for the V2X environment. As the core of traffic flow theory, the car-following model is the basis of theory applications such as analyzing the characteristics of traffic flow, evaluating the energy consumption and emission. In other words, the car-following model is the basis for comprehensively understanding the traffic flow in the V2X environment. Considering this, the relevant studies are reviewed from three aspects, that are the impacts of V2X technology, modeling the car-following behavior in the V2X environment, and the applications of the car-following models in this section.
## 5 Simulation Analysis
### Simulation Results and Analysis of Fusion Algorithm
#### 5.2.3 Simulation under Dynamic and Temporary Static Obstacle Environments  
To further verify the reliability of the hybrid algorithm in this study, the following experiment adds temporary static obstacles and dynamic obstacles to the 20 \(\times\) 20 raster maps and makes a simulation comparison experiment with the hybrid algorithm [34]. The experimental results are shown in Figures 10 and 11, and the performance pairs are shown in Table 4.  
As shown in Figure 10, the depicted elements are as follows: the blue triangle represents the starting point, the blue circle represents the destination point, the red solid line represents the actual travel path, the black dashed line represents the global path, the asterisk represents local target points, the blue solid line represents the trajectory of the moving obstacle, the black solid triangle signifies the starting point of the moving obstacle, the black five-pointed star symbolizes the endpoint of the moving obstacle, the gray square denotes the added temporary static obstacle, and the asterisk represents local target points. Figure 10a,c are the results from Ref. [34], while (b) and (d) are the results from this study. Based on the findings from Figure 10a,b, it is evident that both the hybrid algorithm mentioned in the literature and the hybrid algorithm introduced in this study can effectively detect dynamic obstacles, making adjustments to their motion directions and paths as needed. However, this study stands out due to the incorporation of an enhanced A* algorithm that takes into consideration the vehicle's dimensions, resulting in smoother planned paths. This, in turn, offers improved local target points for subsequent AGV local path planning. Furthermore, adaptive adjustments and optimizations have been implemented for the initial orientation angle of the AGV, leading to even smoother paths. Examining Figure 10c, it is apparent that the literature's approach neglects the AGV's dimensions, leading to a collision between the planned path and an introduced static obstacle. Conversely, the path devised in this study adeptly circumvents obstacles, ensuring a safe and obstacle-free passage to the target point.  
Figure 11a is the results from reference [34], while (b) is the results from this study. Further insights from Figure 11 reveal that, in comparison to the literature, linear velocity demonstrates greater stability and angular velocity exhibits reduced fluctuation. Additionally, Table 4 showcases a notable 12.6% reduction in running time for the hybrid algorithm implemented in this study. In summation, the AGV path planning algorithm, which leverages the enhanced A* and DWA fusion as proposed in this study, not only enhances safety but also ensures stability. These advancements collectively make it a safer, more stable, and operationally efficient solution for AGV navigation.
## 3 Results and Discussion  
### Analysis of Variance (ANOVA)  
This section presents the statistical assessment of the parameters related to UHI modeling. After evaluating the descriptive statistics shown in Table 2, three key statistical tests were applied to verify the assumptions required for parametric analysis: normality, independence, and homoscedasticity. Table 3 summarizes the outcomes of these tests for each parameter, including the Kolmogorov-Smirnov D statistic (normality) assessed per climate zone, and [PERSON]'s F statistic (homogeneity of variances) and Kruskal-Wallis chi-squared statistic (intergroup differences) applied across the entire dataset. * The Kolmogorov-Smirnov-Lilliefors test returned \(p\)-values below 0.003 for all parameters and climate zones, indicating that none of the distributions were normal. * [PERSON]'s test, calculated using the median for increased robustness, also yielded \(p\)-values < 0.003, confirming the lack of homogeneity of variances. * As a result of these violations of parametric assumptions, the Kruskal-Wallis H test was applied, revealing statistically significant differences between SCS climate zones for all parameters (\(p\) < 0.003; FPR < 5%). However, the altitude, NDVI, and NDWI parameters have a greater influence compared to the rest of the parameters. Since intergroup differences were confirmed, a Holm post hoc test was conducted to determine which climate zones differed significantly for each parameter. The test identified statistically significant differences across all zone pairs for all parameters, with the only exception being the Buildings on Both Sides parameter between SCS 2 and SCS 3, where no significant difference was observed. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **WC * & Alt. * & NDVI & NDWI & BBS * & SVF & **DtS * & Coastal & LST \\ \hline Minimum & 0.00 & \(-\)2.34 & \(-\)0.58 & \(-\)0.90 & 0 & 0.02 & 0.00 & 0 & 12.10 \\ \hline Maximum & 2736.11 & 1195.79 & 0.98 & 0.77 & 2 & 1.00 & 316,592.90 & 1 & 31.50 \\ \hline Mean & 0.63 & 255.61 & 0.19 & 0.02 & 1.23 & 0.76 & 76,441.88 & 0.53 & 24.40 \\ \hline Median & 0.38 & 94.87 & 0.15 & 0.10 & 1 & 0.80 & 9312.14 & 1 & 23.20 \\ \hline SD * & 7.21 & 329.17 & 0.16 & 0.30 & 0.81 & 0.19 & 98,632.11 & 0.25 & 3.66 \\ \hline \hline \end{tabular}  
* WC = Wind Corridor, Alt. = Altitude, BBS = Buildings on Both Sides, DtS = Distance to the Sea, SD = Standard Deviation. BBS is a multinomial parameter (taking values of 0, 1 or 2), while Coastal is a binary parameter (taking values of 0 or 1).  
\end{table} 
Table 2: Descriptive statistics for the parameters considered in the multifactorial UHI characterization. Due to the consistency and clarity of the results, and to avoid redundancy, the full matrix of pairwise \(p\)-values has been omitted and is instead discussed narratively.
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 1 Introduction
But the distance between the two device users greatly affects the performance of D2D communication [35]. Among the emerging technologies, D2D communication and massive MIMO are key enablers in achieving 5G targets [36]. The possibilities and challenges are discussed here [37]. Advanced wireless techniques, energy efficiency optimization, and non-convex problems are also discussed [38; 39] as well as the SWIPT-based EE optimization challenge for D2D communications supporting IoT networks with UAV assistance [40; 41]. In addition, a joint resource management approach that ensures the quality of service (QoS) of both cellular and D2D communications while improving the overall energy efficiency of D2D communications is discussed in this paper [42]. The objective of this paper is to develop an algorithm for high-energy efficient device-to-device communication. The remainder of this paper is set out as follows. The system model is explained briefly in Section 2. The proposed modified derivative algorithm is explained in Section 3. The simulation findings are summarized in Section 4. The comparison of simulation results is summarized in Section 5. The conclusion of the paper is given in Section 6.
## 4 Experiment Evaluations
### Rmo_SODSDA Efficiency
#### 4.2.3 Experiment Results  
We randomly selected 10 pairs of values of \(P_{lb}\) and \(P_{ub}\) for the experiments and took the median value. The results of our experiments are shown in Figure 1 with error bars, where the lateral axis represents varying values of the number of roles, and the vertical axis represents changes of query time including truth assignments for \(HC\), encoding for \(SC\), and execution time by using the QMaxSAT solver.  
Figure 1 demonstrates that query time increased slightly as the number of roles increased in the _exact_ and _max_ cases. Particularly, the time was less than 10 milliseconds with up to 300 roles. In the _min_ case, query time irregularly floated up and down with the increasing number of roles. While time taken for different match requests remained the same in the preprocessing and clause-updating stages, the time taken using the QMaxSAT solver was longer in the _min_ case than in the other two cases. However, total query time was still below one second in any case.  
In order to further validate the efficiency of the RMO_SODSDA, we considered a similar set of experiments as the ones performed in the work of [37]. More precisely, our experiments were set as follows: \(30\leq|R|<300\), \(|U|=20\), \(|P|=70\), \(|C|=5\). Similar to the experiments in the previous UAQ setting section, we took the median value over 10 randomly selected pairs of values of \(P_{lb}\) and \(P_{ub}\) for the experiments. We compared the performance of our method with the results based on the MAX-SAT solver in Figures 2-4 with error bars, where the lateral axis represents varying values of the number of roles, and the vertical axis represents changes of query time.  
It can be observed from Figures 2 and 3 that the query time of our method did not obviously vary. It was less than 20 milliseconds with up to 290 roles. In contrast, the query time in the work of [37] tended to grow linearly up to around 160 milliseconds. The total query time of our method was lower because all hard and soft clauses were completely encoded in the preprocessing stage, while in the work of [37], all clauses were encoded in the querying stage. Therefore, our method outperformed the MAX-SAT solver in the _exact_ and _max_ cases.  
It can be observed from Figure 4 that the query time of our method did not obviously vary when \(|R|\) changed from 30 to 190. However, the query time irregularly floated up and down when \(|R|\) changed from 210 to 290, up to around 240 milliseconds in this paper and around 400 milliseconds in the work of [38]. Therefore, our method did not outperform the MAX-SAT solver in the _min_ case.  
Figure 1: Comparison of the different scenarios.
## 6 Results
### Start-Up Speed
#### 6.2.2 Throughput of Small File Transmission  
We further evaluated the impact of the start-up speed on the small file transfer performance by testing the throughput of four schemes for transferring small files. We set the bottleneck link bandwidth to 80 Mbps and tested four rate control schemes under link latency conditions of 30 ms, 35 ms, 40 ms, and 45 ms, transmitting files of different sizes from 3 KB to 90 MB (3 KB, 9 KB, 30 KB, 90 KB, 300 KB, 900 KB, 3 MB, 9 MB, 30 MB, and 90 MB). The transmission throughputs of the four transmission rate control schemes obtained in the experiment are shown in Figure 6.  
Figure 6 shows the variation in the link throughput with the amount of transmitted data, and the subtitles show the total delay of the links in the experiment. The graph shows that the smaller the file, the more significant the impact of the start-up speed was on the throughput. The larger the file, the closer the throughput was to the bottleneck bandwidth. Due to its fast start-up speed, the Nodectl scheme had the best throughput performance under the same file size. When the file size was 3 MB, Nodectl had the greatest improvement compared with other schemes. We present the bandwidth utilization of a 3 MB file transfer in Table 4. Table 4 shows that although the bandwidth utilization of the Nodectl scheme decreased with an increasing link delay, under the same link delay conditions, Nodectl approximately doubled the performance compared with the other three schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bandwidth \(\times\) Rtt** & **NewReno** & **Cubic** & **Bbr** & **Nodectl** \\ \hline
10 Mbps \(\times\) 20 ms = 25 KB & 121 ms & 121 ms & 139 ms & 41 ms \\
10 Mbps \(\times\) 80 ms = 100 KB & 596 ms & 596 ms & 702 ms & 162 ms \\
80 Mbps \(\times\) 20 ms = 200 KB & 168 ms & 168 ms & 194 ms & 40 ms \\
80 Mbps \(\times\) 80 ms = 800 KB & 820 ms & 820 ms & 920 ms & 160 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time when infighting bytes reach BDP.  
Figure 5: Inflighting bytes.
# Smart Sensing of PSC Girders Using a PC Strand with a Built-in Optical Fiber Sensor
###### Abstract  
This paper presents a multi-functional strand capable of introducing prestressing force in prestressed concrete (PSC) girders and sensing their static and dynamic behavior as well. This innovative strand is developed by replacing the core steel wire of the strand used in PSC structures with a carbon fiber-reinforced polymer (CFRP) wire with a built-in optical Fiber Bragg Grating (FBG) sensor. A full-scale girder specimen was fabricated by applying this multi-function strand to check the possibility of tracking the change of prestressing force at each construction stage. Moreover, dynamic data could be secured during dynamic loading tests without installing accelerometers and made it possible to obtain the natural frequencies of the structure. The results verified the capability to effectively manage the prestressing force in the PSC bridge structure by applying the PC strand with a built-in optical sensor known for its outstanding practicability and durability.  
b ridge; girder; prestressed concrete; PC strand; optical sensor; prestressing force management +
Footnote †: journal: Applied Sciences
## 3 Model Architecture
### Attention Enhanced Spatial-Temporal Graph Convolutional LSTM  
The ASTGC-LSTM also uses three gates: the input gate \(i_{t}\), forgetting gate \(f_{t}\), and output gate \(o_{t}\). The input \(X_{t}\), hidden state \(H_{t}\), and cell memory \(C_{t}\) are graph structure data, and the graph structure is generated by the Delaunay algorithm. Because of the graph convolution operator in the ASTGC-LSTM, cell memory \(C_{t}\) and hidden state \(H_{t}\) can obtain temporal dynamics, and contain spatial structure information. Figure 1 describes the structure of one ASTGC-LSTM layer. Figure 2 describes the structure of the ASTGC-LSTM unit. Equation (1) describes the function of ASTGC-LSTM unit.  
\[\begin{array}{l}i_{t}=\sigma(W_{xi}*gX_{t}+W_{hi}*gH_{t-1}+b_{i})\\ f_{t}=\sigma\Big{(}W_{xf}*gX_{t}+W_{hf}*gH_{t-1}+b_{f}\Big{)}\\ o_{t}=\sigma(W_{xu}*gX_{t}+W_{ho}*gH_{t-1}+b_{o})\\ u_{t}=\tanh(W_{xx}*gX_{t}+W_{hc}*gH_{t-1}+b_{c})\\ C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot u_{t}\\ \widehat{H_{t}}=o_{t}\odot\tanh(C_{t})\\ H_{t}=f_{att}\Big{(}\widehat{H_{t}}\Big{)}+\widehat{H_{t}}\end{array} \tag{1}\]  
where \({}^{*}g\) denotes the graph convolution operator and \(\odot\) denotes the Hadamard product. \(\sigma(\cdot)\) is the sigmoid activation function. \(u_{t}\) is the modulated input. \(\widehat{H_{t}}\) is an intermediate hidden state. \(W_{xi}*gX_{t}\) denotes a graph convolution of \(X_{t}\) with \(W_{xi}\). \(f_{att}(\cdot)\) is an attention network that can select the discriminative information of key nodes. The \(H_{t}\) as the output aims to strengthen the information of key nodes, not weaken the information of non-focus nodes, so as to maintain the integrity of spatial information.  
The attention network adaptively focuses on key nodes through a software attention mechanism that automatically measures the importance of the key nodes. The principle of the spatial attention network is shown in Figure 2. The intermediate hidden state of the ASTGC-LSTM contains rich spatial structure information and temporal dynamics, which is beneficial to guiding the selection of key nodes, so as to obtain the non-uniform degree weight sequence and strengthen the importance of different nodes in different types of actions.  
The attentional network is used to meet critical points of attentional adaptation through a software attentional mechanism that automatically measures the importance of joints. The principle of the spatial attention network is shown in Figure 3. The intermediate hidden state of the ASTGC-LSTM contains rich spatial structure information and temporal dynamics, which is helpful to guiding the selection of key nodes, so as to obtain the weight sequence of the non-uniformity degree.  
Figure 1: The structure of one ASTGC-LSTM layer.  
Figure 3: Illustration of the spatial attention mechanism principle.  
Figure 2: The structure of an ASTGC-LSTM unit. Compared with LSTM, the inner operator of the ASTGC-LSTM is graph convolution. In order to highlight more discriminative information, the attention mechanism is used to enhance the characteristics of key nodes.
###### Abstract  
Recent advancements in information and communication technology have driven various organizations, including businesses, government agencies, and institutions, to digitize and manage critical documents. Document digitization mitigates spatial constraints on storage and offers significant advantages in transmission and management. However, while digitization offers many benefits, the development of image processing software has also increased the risk of forgery and manipulation of digital documents. Digital documents, ranging from everyday documents to those handled by major institutions, can become targets of forgery, and the unrestricted distribution of such documents may cause social disruption. As a result, research on digital document forgery detection has been actively conducted in various countries, with recent studies focusing on improving detection accuracy using deep learning techniques. However, most of the document image datasets generated for the development of deep learning models are English-based documents. Consequently, forgery detection models trained on these English-based datasets may perform well on English documents but may not achieve the same level of accuracy when applied to documents in other languages. This study systematically examines the necessity of language-specific datasets by analyzing the impact of visual complexity on forgery detection accuracy. Specifically, this study analyzes differences in forgery characteristics between English and Korean documents as representative cases and evaluates the classification performance of a forgery detection model trained on an English dataset when applied to both English and Korean documents. The experimental results indicate that forged document images exhibit distinct visual alterations depending on the language. Furthermore, the detection performance of models trained on English-based datasets varies according to the language of the training and test data. These findings underscore the necessity of developing datasets and model architectures tailored to the linguistic and structural characteristics of each language to enhance forgery detection efficacy. Additionally, the results highlight the importance of multilingual datasets in deep learning-based forgery detection, providing a foundation for the advancement of language-specific detection models.  
digital document; forgery detection; Korean; dataset +
Footnote †: journal: Applied sciences
###### Abstract  
Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Sentinel-1 SAR data for the years 2014-2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.  
Jenks Natural Break classification; logistic regression; tidal flat; topography; Sentinel-1; synthetic aperture radar (SAR) +
Footnote †: journal: Remote Sensing  
17 December 2021  
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