esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 2 Methods
### Establishment of Vehicle Distance Measurement Model
#### 2.2.3 Traditional Geometric Ranging Method  
The traditional geometric ranging method calculates the distance between two vehicles by establishing the geometric relationship between the position of the feature point of the front vehicle on the image plane of the on-board camera and the position of the feature point of the front vehicle in the three-dimensional space. The pitch angle of the camera, that is, the angle between the optical axis of the camera and the horizontal road surface, was taken into account when establishing the model. The model structure is shown in Figure 5.  
In the above figure, P is the feature point of the front vehicle; O is the optical center of the camera; AB is the image plane of the camera; C is the center point of the image plane, and its pixel coordinate is (\(u_{0}\), \(v_{0}\)); P' is the imaging position of P in the camera image plane, and its pixel coordinate is (\(u_{p}\), \(v_{p}\)). CF is the optical axis of the camera; D is the projection point of the camera on the horizontal ground; G and E are the intersections of the upper edge of the camera image and the ground, and the intersection of the lower edge of the camera image and the ground, respectively; K is the projection point of the front end of the vehicle body on the horizontal ground; \(l\) is the longitudinal distance between the camera and the head of the vehicle; \(h\) is the height of the camera from the ground; \(f\) is the focal length in the pixel; \(\alpha\) is the angle between the camera optical axis and the horizontal road surface, that is, the pitch angle of the camera.  
According to the geometric relationship, the formula for calculating the longitudinal distance \(d\) between two vehicles can be obtained:  
\[d=h\times\tan\left(\left(90-\alpha\right)-\left(\arctan\left(\left(v_{p}-v_{0} \right)/f\right)\right)\right)-l. \tag{7}\]
_Algorithms_**2015**, 8, 1035-1051; doi:10.3390/a8041035  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment \({}^{\dagger}\)**  
**[PERSON] \({}^{1,2,3}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1,2,*}\)**  
\({}^{1}\) Life Sciences Group, Centrum Wiskunde & Informatica (CWI), Science Park 123,  
1098 XG Amsterdam, The Netherlands; E-Mail: [PERSON]\({}^{2}\) Centre for Integrative Bioinformatics VU (IBVU), VU University Amsterdam, De Boelelaan 1081A,  
1081 HV Amsterdam, The Netherlands; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Center for Computational Molecular Biology and Department of Computer Science,  
Brown University, Providence, RI 02912, USA  
\({}^{\dagger}\) This paper is an extended version of our paper published in Pattern Recognition in Bioinformatics. [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Lagrangian Relaxation Applied to Sparse Global Network Alignment. In Proceedings of the 6 th IAPR International Conference on Pattern Recognition in Bioinformatics, Delft, The Netherlands, 2-4 November 2011; pp. 225-236.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +31-20-592-4012.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 6 July 2015 / Accepted: 12 November 2015 / Published: 18 November 2015_  
**Abstract:** Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. We present a method for global network alignment that is fast and robust and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. We exploit that network alignment is a special case of the well-studied quadratic assignment problem (QAP). We focus on sparse network alignment, where each node can be mapped only to a typically small subset of nodes in the other network. This corresponds to a QAP instance with a symmetric and sparse weight matrix. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the open source software tool Natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative established and recent state-of-the-art methods.  
Keywords:global network alignment; bioinformatics; graph matching; network analysis; network comparison +
Footnote †: journal: Journal of the Royal Society Interface B
## 2 Proposed Architecture and Fundamentals of PPFD for Communication  
### Proposed Energy-Efficient Smart-Farming Architecture  
Figure 1 shows the proposed energy-efficient smart-farming architecture. The architecture consists of several relocatable smart-farming units, white LEDs for ceiling lights, uplink receivers, the edge computer, and the cloud.  
For making a complete RF-free environment, uplink and downlink communication utilize VLC technology. For the uplink, modulated green LED lights from the smart-farming unit are detected by an uplink receiver deployed above the vegetables near the ceiling lights. As the main computing unit, the edge computer monitors and controls the smart-farming units. According to the processed data, appropriate commands are made and sent back to each smart-farming unit through the downlink. For the downlink, the ceiling lights installed for a convenient working environment are reused as a transmitter. A downlink receiver placed near the grow LED lights receives the signal. To store the data for long-term use, the edge computer uploads it to the cloud.  
In this indoor smart-farming application, each smart-farming unit is composed of grow LED lights, a microcontroller, a receiver for downlink, and several sensors and actuators. A well-collimated RGB LED downlight is considered as the grow light for the vegetables. A key feature of the proposed smart-farming unit is that the green modulated light reflected from the leafy vegetables after photosynthesis is reused for uplink communication. Sensors monitor parameters such as PH, humidity, temperature, soil, and intensity. Actuators such as water pumps, fans, lamp drivers perform the action on the commands. All these sensors and actuators are connected to the microcontroller. In the case of uplink communication, the microcontroller sends the data recorded by each sensor to the edge computer through the uplink constructed by the modulated green LED light reflected from the leafy vegetables. This information is received by the uplink receiver placed near the ceiling lights. The edge computer processes the received data and generates the appropriate commands. In the case of downlink, such commands are sent back to the microcontroller using ceiling lights. The downlink receiver near the grow lights receives the signal. Based on these received commands, the microcontroller activates the required actuator devices.  
Since wired connections are limited in location and mobility, one of the main advantages of using wireless communication is achieving such locational flexibility for each smart-farming unit. Although the wired connections throughout the farming area can allow the RF-free environment, this can cause complex cable installation, difficulty in maintenances, and less flexible mobility of the individual farming unit.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 4 Shallow Underwater Acoustic Channel Model
### Random Channel  
Various fading models exist for RF channels and there is a general agreement between scientists and researchers over their usage. However, the domain of fading models for UWA channels is still open and lacks consensus. Experimental studies conducted suggest that a Rician fading model accurately represents the fading observed by acoustic signals in a UWA  
Figure 6: Shallow acoustic channel characteristics: (**a**) frequency dependent absorption; (**b**) channel impulse response.  
channel [52]. Researchers studied sea trial data, with Rician shadowed distribution having the closet fit [53,54]. The parameters selected in this work are \(m=0.4\) and \(k=2.0\)[54]. The value of spreading factor \(k=1.5\) can also be assumed [55]. The signal \(c_{2}(t)\) from Figure 5 can be represented as:  
\[c_{2}(t)=h_{r}(t)\ \bigotimes\ c_{1}(t)+n(t) \tag{16}\]  
where \(n(t)\) and \(h_{r}(t)\) are noise and Rician fading [56] impulse response modelled using Rician channel object in MATLAB R2019b by MathWorks, respectively. Figure 6b shows the sample of channel path gains and delay profile.
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT  
This section describes four major use cases of IoT and how data transmission works in these applications. As shown in Figure 1, the four important examples of IoT include the Internet of Vehicles (IoVs), Internet of Medical Things (IoMT), Industrial Internet of Things (IIoT), and Internet of Smart Grids (IoSG).
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Precipitation Estimation Model
of light rain, moderate rain, heavy rain and torrential rain, but the precipitation intensity is still underestimated for heavy rain and heavy rain. In the light-rain regions, some overestimations appear. The FY-4B/AGRI operational precipitation product cannot capture some light-rain and moderate-rain regions, and there are obvious overestimations in moderate-rain and heavy-rain regions. Figure 17: Precipitation estimation results from 0200 UTC to 0230 UTC on 18 August 2022: (**a**) GPM IMERG product, (**b**) FY-4B/AGRI operational precipitation product and (**c**) RF model. Figure 18: Precipitation estimation results of 1500 UTC\(\sim\)1530 UTC on 9 August 2022: (**a**) GPM IMERG product, (**b**) FY-4B/AGRI operational precipitation product and (**c**) RF model. Figure 19 shows the retrieval effect of the model on different underlying surfaces. The results show that the RMSE of the ocean is higher than that of the land. The day model is better for the underlying surface of the land, and there is no significant difference between the model under different underlying surfaces.
## 5 What Is C-RAN?  
Cloud-RAN is a RAN architecture that centralizes and virtualizes the baseband processing. The RAN architecture widely adopted today is referred to as distributed RAN, where the Radio Unit (RU) is located in the antenna tower, handling radio functions such as digital-to-analog conversion and up-conversion; the remaining processing is handled in the BBU. The BBU is located in a shelter close to the antenna tower and handles both time-critical and non-time-critical baseband processing. RANs represent a huge amount of under-utilized resources since the users tend to move around between different areas during the day. The users will usually be at home in the morning, then they go to work and come back home again in the evening, leading to much un-utilized capacity, since the mobile network is critical infrastructure that cannot be switched off when not in use. Hence, other ways must be found to increase resource utilization and add savings to the energy bill.  
A proposed solution is to move the baseband processing into a centralized data center, a BBU pool, where virtualized baseband processing can share the same hardware and assign additional capacity to areas where it is needed when it is needed. Hence, as described in Table 4, the C-RAN architecture incorporates both concepts of centralized and virtualized RAN. The concept was first proposed by IBM in 2010. Later, 3 GPP evolved for more agile deployment opportunities [110]. Here, the BBU was divided into a Distributed Unit (DU), handling the more time-critical functions, and a Centralized Unit (CU), handling more relaxed inter-site communication. Figure 13 illustrates the evolution of Cloud-RAN technology, highlighting the transition from distributed RAN to centralized RAN, and this progression has led to Cloud-RAN, where baseband processing is centralized and virtualized. Figure 14 illustrates the future mobile network architecture, where radio processing is divided into three units: RU, DU, and CU [110].  
Figure 13: The evolution of Cloud-RAN, going from distributed RAN with all functions located on the cell site, to centralized RAN, where baseband processing is centralized, leading to Cloud-RAN, where baseband processing is centralized and virtualized.
## 4 Key Design Requirements and System Components
### Layer 4: NW Resources  
Layer 4 contains the NW resources, which are powerfully deployed hardware managed and controlled by the NW providers. These resources, including cell towers, base stations, and various networking equipment, are pivotal in bridging the gap between NW subscribers and providers. By acting as intermediaries, NW resources facilitate the seamless flow of information, enabling voice, data, and multimedia communications to traverse vast distances with reliability and efficiency.
## 3 Results and Discussion
The latter might have been produced when lightning impulse electrical currents were conducted along the copper mesh and entered the composite panel to release energy at this location. Damage location data in Figure 10c show that the surface specimen was slightly tilted and the deterioration depth was about 1.8 mm apart from the top lamina. It can be seen that the lightning damage zone in Figure 10c presented in blue corresponds to that in Figure 10b presented in black. Figure 10: Echo amplitude, echo location and location data for composite-reinforced panel with full copper mesh filling: (**a**) echo amplitude; (**b**) echo location; (**c**) location data (Unit: mm). Figure 9: Echo amplitude, echo location, and location data for composite-reinforced panel with a local-sprayed aluminum coating: (**a**) echo amplitude; (**b**) echo location; (**c**) location data (Unit: mm). As a whole, delamination damage was developed with a ladder type along the thickness direction due to substantial lightning Joule heat, thermal shock, internal explosion, etc. The specimen with full-sprayed aluminum protection was more effective, although surface damage size was larger compared with the specimens with no protection and full copper mesh filling protection. The above analysis shows that the surface damage of the specimen with full-sprayed aluminum protection mainly comes into being by the melted aluminum layer when electrical currents are conducted along that layer. Based on ultrasonic C-scanning results analysis, there is a lower amount of lightning damage to composite-reinforced panels with full-sprayed aluminum coatings. Therefore, the full-sprayed aluminum protection can well prevent composite-reinforced panels from lightning strike damage. However, regarding aircraft weight loss, such composite-reinforced panels will be heavier than those with local-sprayed aluminum protection. Future work will place emphasis on the theoretical study of protection effects on damage quality, such as the damaged area and the maximum damage thickness due to lightning strikes in composite-reinforced panels, especially by the numerical simulation method, as was performed in previously published papers [6; 7].
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 7 Multichannel Data  
The Skoltech anomaly benchmark (SKAB) contains multichannel time series for evaluation of anomaly detection algorithms [16]. It consists of multiple instances of multivariate series associated with eight sensors monitoring conditions on an IIot testbed and two additional series indicating the presence of anomalies and change points, as illustrated in the example in Figure 10.  
To model each channel of SKAB, we use the time series of other channels as the exogenous series to predict the target time series (also with the time delay \(d=0\)). Again, the model is optimised with Levenberg-Marquardt backpropagation. The anomaly-free instances in SKAB are used as training data for the modelling. Meanwhile, the minimum and maximum segment lengths of collective anomalies are \(l=100\) and \(m=500\), respectively, and the penalties for collective and point anomalies to be \(\beta_{c}=75\) and \(\beta_{o}=2.5\), respectively. The parameter settings are tuned to ensure no labelling of non-anomalous data as anomalous. To cluster, we use fuzzy c-means clustering with ECDF and optimise for three clusters. There are a total number of 7826 data points within collective anomalies across a total of 22,274 data points in SKAB. NARX-SCAPA identifies 5698 of these, achieving an accuracy of 72.8%. Figure 11 shows the results of anomaly detection and clustering on an excerpt of the SKAB data. According to the anomaly indicators in the bottom-right plot, all anomaly regions are correctly detected and clustered according to the severity (an increase from green to yellow to red). It is worth noting that the original datasets do not provide any additional information about the anomaly types. Therefore, our clustering is mainly based on the structures of detected anomalies, and the severity of anomalies depends on the distance between the detected sequence and the baseline.  
Figure 9: Fuzzy c-means clustering with ECDF. (**a**) Online clustering with 10 days of data for clustering and the following 10 days of data for allocation. (**b**) Offline clustering with 20 days data.  
Figure 11: _Cont._  
Figure 10: Example of the multichannel time series from SKAB.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 3 Proposed Method
### Encoding of Smooth Blocks
To find the minimum distortion, the QA technique adjusts \(a_{s}\) and \(b_{s}\) to \(\hat{a}_{s}\) and \(\hat{b}_{s}\) by calculating:\[\hat{a}_{s}=\frac{a_{s}\rho_{00}+b_{s}\rho_{10}}{\rho_{00}+\rho_{10}} \tag{7}\]  
and:  
\[\hat{b}_{s}=\frac{a_{s}\rho_{01}+b_{s}\rho_{11}}{\rho_{01}+\rho_{11}} \tag{8}\]  
respectively, where \(\rho_{pq}\) is the number of bits with \(B_{s,j}=p\) and \(C_{a_{s}^{2},j}=q\), \((p,q)\in\{0,1\}\). For example, \(\rho_{01}\) indicates the number of bits with \(B_{s,j}=0\) and \(C_{a_{s}^{2},j}=1\). After adjustment of quantized values, the distortion due to the bitmap replacement will be smaller than that without adjustment.
## 6 Simulation Results
### Performance Evaluation
#### 6.2.3 Scenario 3  
For an intersection scenario, Figures 17-19 show that in terms of the group size and the frequency of group reformation, the performance of the HDTC scheme is superior to the other two schemes. For instance, with varying the cone degree, the HDTC method in group members is about \(60\%\) less for the number of group members than the other two methods (Figure 19 (bottom left)); the HDTC method in the number of in/out group members is 60%\(\sim\)70% less than the other two methods (Figure 19 (top right)). Moreover, in Scenario 3, the proposed HDTC scheme provides a longer connection time, which may stabilize the group formation (Figure 19 (top left)). However, due to a smaller group size, compared with the other two schemes, the HDTC scheme introduces additional 1\(\sim\)2 hops for sending the message to the sink (Figure 19 (bottom right)). Similar descriptions can be applied to the results depicted in Figures 17 and 18.  
Figure 15: Scenario 2: The connection time, the frequency of the link up/down, the group size and the number of hops with varying \(\theta\).  
Figure 16: Scenario 2: The connection time, the frequency of the link up/down, the group size and the number of hops with varying the lane headway.  
Figure 17: Scenario 3: The connection time, the frequency of the link up/down, the group size and the number of hops with varying the connection distance.  
Figure 19: Scenario 3: The connection time, the frequency of the link up/down, the group size and the number of hops with varying \(\theta\).  
Figure 18: Scenario 3: The connection time, the frequency of the link up/down, the group size and the number of hops with varying the vehicle speed.  
Although the system performances highly depend on the type of mobility model occurring in the network, in general, the longer connection time the main node (say, the vehicle _u_) has, the better it maintains the communication channel; the lower the frequency of the link up/down that the source node has, the more stable can the group be made [35]. Note that the above results suggest that different arguments may have different impacts on topology management. Thus, if we can acquire the traffic information in advance, some arguments may be adaptively adjusted to obtain a better network performance.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 3 Quality Estimation of Earth Surface Reconstruction
### Regression Model for Quality Estimation
#### 3.2.3 Evaluation Metrics  
In this paper, we utilize three evaluation metrics to assess the performance of the quality estimation model in our experiments: root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and coefficient of determination (\(R^{2}\)). The metrics are formalized as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\widehat{y_{i}}\right)^{2}} \tag{2}\]  
\[MAE=\frac{1}{n}\sum_{i}^{n}\left|\widehat{y_{i}}-y_{i}\right| \tag{3}\]  
where \(n\) stands for sample size, and \(\widehat{y_{i}}\) is the predicted quality value, while \(y_{i}\) is the true value.  
\[R^{2}=1-\frac{\sum\left(y_{i}-\widehat{y_{i}}\right)^{2}}{\sum\left(y_{i}- \widehat{y}\right)^{2}} \tag{4}\]  
where \(\widehat{y}=\frac{1}{n}\sum_{i=1}^{n}y_{i}\) is the mean value. The closer the value of \(R^{2}\) is to 1, the better the regression model performs.  
Figure 4: Feature coefficients after LASSO feature selection. (\(\lambda=1\times 10^{-6}\)).
## 4 Description of the Machine Learning Methods
### Correlation-Based Feature Selection  
The correlation-based feature selection (CFS) method [46] is based on the idea that good feature sets contain features that are highly correlated with the class, but not correlated with one another. It addresses the problem of feature selection through a correlation-based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula inspired from test theory is employed. CFS couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy, e.g., a greedy stepwise forward search.