esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study and Results
### Activity Identification  
IMU sensors were attached to the equipment using a plastic box with a magnetic bottom. For excavators, IMU sensors were attached to the bucket to maximize the activity identification accuracy. GPS sensors were also attached to the trucks to record their movements. The activities were recorded using a video camera. Unfortunately, one sensor fell off a truck, and one sensor malfunctioned. Thus, data from three trucks and two excavators were available for further analysis. Figure 6 shows the sensor attachment to the dump truck and excavator.  
The collected data were used to train an LSTM network to identify the activities following the steps described in [PERSON] & Louis [29]. For this study, three types of activities were identified from the dump truck, they are load, travel, and dump. In addition to these, all the wait times were also measured between those activities. Figure 7 shows the number of times each of those activities occurred during the data collection process. If travel occurred after _loading_, it was assumed to be _huling_ (i.e., the truck was moving from load area to dump area with soil) and if it happened after _dumping_, it was assumed to be returning (i.e., the truck was moving from dump area to load area empty).  
Figure 5: Schematic layout of the earthmoving site.  
Figure 6: IMU attachment to dump truck and excavator.
## 2 Principle of Heterodyne Phase Unwrapping  
### Solution of Phase Principal Value  
The four-step phase-shifting procedure [9; 10; 11] is the most commonly used method to obtain the phase principal value at present. The basic principle of the phase-shifting procedure is that the projection device is usually controlled chronologically. If more than three fringe patterns are projected, the grayscale distribution of the fringe patterns must conform to the sinusoidal transformation. In the law of transformation, the phase starting values of two adjacent fringe patterns differ by a fixed phase, that is, the constant phase. Displacement is also known as phase shift. The camera synchronously acquires these images, and after least squares or arctangent processing, phase information can be extracted. The distribution of the gray values of the four fringe images at coordinates (m,n) is shown in Formula (1):  
\[\left\{\begin{array}{c}I_{1}(m,n)=I^{\prime}(m,n)+I^{\prime\prime}(m,n)\cos[ \varphi(m,n)]\\ I_{2}(m,n)=I^{\prime}(m,n)+I^{\prime\prime}(m,n)\cos[\varphi(m,n)+\frac{\pi}{2 }]\\ I_{3}(m,n)=I^{\prime}(m,n)+I^{\prime\prime}(m,n)\cos[\varphi(m,n)+\pi]\\ I_{4}(m,n)=I^{\prime}(m,n)+I^{\prime\prime}(m,n)\cos[\varphi(m,n)+\frac{3\pi}{2 }]\end{array}\right. \tag{1}\]  
where \(I_{i}\) (\(m\), \(n\)) is the gray value of the \(i\)-th fringe pattern at the point (\(m\), \(n\)); \(I^{\prime}\) (\(m\), \(n\)) is the gray value of the image background; \(I^{\prime\prime}\) (\(m\), \(n\)) is the grayscale modulation of image; \(\varphi\) (\(m\), \(n\)) is the phase principal value needed to be solved. Then, the phase solution of the four-step phase-shifting method is shown in Formula (2):  
\[\varphi(m,n)\ =\ \arctan[\frac{I_{4}(m,n)-I_{2}(m,n)}{I_{1}(m,n)-I_{3}(m,n)}] \tag{2}\]  
According to Formula (2) and the trigonometric function property [12; 13; 14], the phase is wrapped in the interval \([-\pi,\pi]\) and exhibits a serrated distribution. The phase unwrapping is necessary to obtain the complete and continuous distribution of absolute phases.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Super-Resolution Generative Adversarial Network
\(D_{\theta_{D}}\) is the parameterized discriminator network. All the SRGAN models are trained on a server with NVIDIA TITAN RTX graphics processing unit (GPU) accelerators (24 GB memory) and were implemented in TensorFlow 2.8.0 and Python 3.8. The model parameters were set as follows: the number of residual blocks = 16, the model depth = 64, and the weights of loss were set according to experience as \(\alpha_{VGG}=3\times 10^{-5},\alpha_{X}=200,\alpha_{Gen}=1\). During the training phase, both the generator and the discriminator used the ADAM optimizer [36] with the parameters \(\beta_{1}=0.9,\ \beta_{2}=0.999\), the learning rate was set to 0.002 through repeated experiments, and the network was iteratively trained for 300 epochs with batch size = 4. In this paper, the gradient penalty strategy [37] was used for gradient updates, which calculates the gradient norm between the generator and discriminator by interpolating the sample space. The norm is then squared after subtracting 1, serving as the gradient penalty term. This approach ensures a more continuous and differentiable distance between the generator and discriminator, thereby enhancing the model's stability and convergence rate. Specifically, for each random interpolation point, the gradient penalty can be calculated using the following formula:  
\[ \begin{split}\hat{x}=ex_{real}+(1-\epsilon)x_{fake},\end{split}  \tag{6}\]  
Figure 7: The architecture of generator and discriminator network. \[L_{GP}=\lambda(\left\|\
abla_{\hat{x}}D(\hat{x})\right\|_{2}-1)^{2}, \tag{7}\]  
where \(x_{real}\) and \(x_{fake}\) denote the real and generated samples, respectively, \(\epsilon\) represents the interpolation factor, \(\
abla_{\hat{x}}D(\hat{x})\) is the gradient of the discriminator at the interpolation point \(\hat{x}\), and \(\lambda\) is the weight of the gradient penalty.
# Chromatographic Separation of Rare Earth Elements as MGDA Complexes on Anion Exchange Resins
## 3 Results and Discussion
### Sorption Behavior of REE
#### 3.2.1 The Effect of Temperature on REE Separation  
The temperature was found to have a small effect on the separation of the REE-MGDA complexes (Figure 5). Increasing the temperature enhanced the sorption of the MGDA-REE complexes. Higher temperatures caused the resin matrix to become more flexible and swollen, allowing for better mass transfer and enhancing the kinetics, which can be seen as a slightly faster process cycle. This caused a decrease in the size of the frontal peak group at the beginning of the chromatogram. The REE mass balance shifted to the right towards the second peak group, where we observe the retention of the MGDA-REE complexes. Temperature did not have noticeable effect on the desorption or retention. Higher temperatures than ambient (22 \({}^{\circ}\)C) could be used in the loading step of a two-step process to enhance the sorption of the MGDA-REE complex.  
Figure 4: Separation of REEs on IRA-410 using (**a**) HNO\({}_{3}\) and (**b**) HCl as the eluent (T = 298 \(\pm\) 2K; flowrate = 2.98 BV/h; feed volume = 0.025 BV; acid concentration = 10 mM).
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results  
In this section, the proposed frequency offset estimation scheme is simulated based on 5G NR PUSCH. The frame structure of 5G NR is simply introduced in the following Section 4.1, and the simulation results are presented in Section 4.2.
## 4 Data Analysis and Discussion
### Text Mining Analysis (Clustering)
#### 4.2.1 Clustering Results
The use of deep neural networks and Recurrent Neural Networks was still a popular area of research, but researchers were also exploring the use of artificial intelligence and genetic algorithms in new applications. Overall, these results highlight the rapid evolution of the field of machine learning and artificial neural networks. The focus has shifted from optimization and machine learning toward deep learning and artificial intelligence, with a strong emphasis on improving the stability and prediction capabilities of these models. The use of genetic algorithms, particle swarm optimization, and extreme learning machines in the early years have paved the way for research in the field, which has now expanded to incorporate exponential stability, adaptive control, and optimization techniques. A name is specified for each cluster by taking into account its relation with the set of words. This ensures accurate descriptions of the involved keywords and makes each cluster different from the others. The results offer an overview of the most adopted research topic per time period and help in assessing future research topics. Figure 6: The ten clusters for the period from 2007 to 2011. Figure 7. The ten clusters for the period from 2012 to 2015. Figure 8. The ten clusters for the period from 2016 to 2019.
## 3 Results
### Burned Area Map
#### 3.2.3 Results of the Burned Area Mapping Method  
To demonstrate the effectiveness and applicability of the new burned area mapping method, we applied it to five wildfire cases to derive burned area maps. The five cases are the Hot Pot Fire, Range 12 Fire, Powerline Fire, Tohakum 2 Fire, and Cinder Butte Fire which are shown in Figure 12a,c,e,g,i, respectively. The figures are the false-color composition RGB images of MODIS bands 7, 5 and 6 of the corresponding wildfires, which can provide supplementary information for burned area identification by visual interpretation. Also, Figure 12b,d,f,h,j present the detection results of the burned area in the five wildfire cases using the proposed method. In the burned area maps, the red pixels with the network output value greater than 0.8 are identified as the burned area while the white pixels with the value lower than 0.8 are the unburned area.  
Figure 12: _Cont._  
The detection results in the five wildfire cases, as shown in Figure 12, validate the effectiveness and applicability of the proposed method for burned area detection through visual interpretation with the false-color composition RGB images and the accuracy evaluation in Section 3.2.2. It can be seen that the main burned region can be effectively identified by the method; each burned region outline coincides with the dark purple region in the corresponding false-color composition images. Also, small burned patches can also be identified by our method, such as the dark purple patches in the upper-right corner of Figure 12c. Compared with the detection results by the traditional empirical method (Figure 9), the results of the same wildfire cases in Figure 12b,d by the new burned area mapping method are greatly improved. The commission error of unburned pixels that has similar spectral characteristics with burned pixels has been eliminated by our method. However, there are a  
Figure 12: (**a**), (**c**), (**e**), (**g**), and (**i**) are the false-color composition RGB images of MODIS bands 7, 5 and 6 in Hot Pot Fire, Range 12 Fire, Powerline Fire, Tohakum 2 Fire, and Cinder Butte Fire, respectively; (**b**), (**d**), (**f**), (**h**) and (**j**) are the proposed method’s detection results of the burned area for (**a**), (**c**), (**e**), (**g**) and (**i**), respectively.  
few pixels along the long river in Figure 12d that were identified as burned pixels by mistake. In view of the water mask work described in Section 2.3.2, the commission error appears due to the similar spectral characteristics of river bank pixels and burned pixels. Further research will be devoted to removing this type of commission error. The wildfire cases in Figure 12e,g,i involved the land cover types of water, vegetation and bare soil in addition to the burned area. From the detection results in Figure 12f-j, the new method can effectively separate the burned area from these unburned cover types. In general, the detection results indicate that the new method performs well in burned area detection.
## 4 Results and Discussion  
A simulation study was first performed using Matlab/SIMULINK software to demonstrate the functionality of the entire system. To verify the simulation study and its findings, a hardware prototype was also developed. Various experiments were carried out on the POVC and its integration with the MLI using a boost DC-DC converter. The output characteristics of the 17 L MLI operation are likewise examined in this section. The experiment employed a solar panel with the specifications listed in Table 3 under standard operating conditions (STC). In the experiment, power MOSFET model GW30 NC60 KD was used for all switches. The experimental setup is depicted in Figure 7.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
###### Abstract  
As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicles and reduce the probability of malicious nodes participating in the consensus, we propose to calculate the reputation of RSU nodes based on multi-weighted subjective logic (CRMWSL) model. Secondly, in order to ensure the efficiency of blockchain data consensus, we improve the consensus protocol of traditional double-layer PBFT, change the election method of the committee and the PBFT consensus process, and improve throughput by reducing the number of consensus nodes. For the committee, we combine the credibility value and hash method to ensure the credibility of nodes, but also to ensure a certain degree of election randomness. For the PBFT consensus process, the regional committee consensus is carried out first, and then the regional master node carries out the global consensus. Through experimental comparison, we show that the C-PBFT significantly reduces consensus time, network overhead, and is scalable for Internet of Vehicles.  
Internet of Vehicles; consortium blockchain; hierarchical consensus; PBFT; reputation value
## 3 Delay and Energy Consumption Analysis
### Delay Analysis  
Based on the AMC model presented in the previous section, we can now calculate the _average delay_, expressed in seconds, required to resolve the contention process.  
For FSA-ACK and FSA-FBP the _average delay_ can be formulated as  
\[\overline{T}_{C}^{\text{FSA-ACK}}=t_{0}\cdot T_{frame}^{\text{FSA-ACK}}, \tag{7}\]  
\[\overline{T}_{C}^{\text{FSA-FBP}}=t_{0}\cdot T_{frame}^{\text{FSA-FBP}}, \tag{8}\]  
where \(t_{0}\) is the average number of frames required to resolve the contention process, and \(T_{frame}^{\text{FSA-ACK}}\) and \(T_{frame}^{\text{FSA-FBP}}\) are the duration of a frame in FSA-ACK and FSA-FBP.  
Also, \(T_{frame}^{\text{FSA-ACK}}\) and \(T_{frame}^{\text{FSA-FBP}}\) can be expressed as  
\[T_{frame}^{\text{FSA-ACK}}=m\left(T_{data}+T_{ack}+2T_{IFS}\right)+T_{IFS}+T_{ FBP}^{\text{FSA-ACK}}, \tag{9}\]  
\[T_{frame}^{\text{FSA-FBP}}=mT_{data}+2T_{IFS}+T_{FBP}^{\text{FSA-FBP}}, \tag{10}\]  
where \(m\) is the number of contention slots per frame, \(T_{data}\), \(T_{ack}\), \(T_{IFS}\), \(T_{FBP}^{\text{FSA-ACK}}\) and \(T_{FBP}^{\text{FSA-FBP}}\) are the time of transmission of a data packet, an ACK, the duration of an IFS, and the time of transmission of a FBP.  
In contrast, for DFSA the _average delay_ can be formulated as  
\[\overline{T}_{C}^{\text{DFSA}}=\sum_{j=0}^{n-1}n_{0j}\cdot T_{frame}^{\text{ DFSA}}(j), \tag{11}\]  
where \(j\) is the number of end-devices that have succeeded in a given point in time, \(n_{0j}\) is the average number of frames for which the contention process remains with \(j\) end-devices with success, and \(T_{frame}^{\text{DFSA}}(j)\) is the duration of a frame.  
Also, \(T_{frame}^{\text{DFSA}}(j)\) can be expressed as  
\[T_{frame}^{\text{DFSA}}(j)=m_{j}\left(T_{data}+T_{ack}+2T_{IFS}\right)+T_{IFS }+T_{FBP}^{\text{DFSA}}, \tag{12}\]  
where \(m_{j}=\lceil\rho\left(n-j\right)\rceil\) is the number of contention slots in a frame where \(n-j\) end-devices have not succeeded yet, and \(T_{FBP}^{\text{DFSA}}\) is the time of transmission of a FBP.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 2 Current Status of Lunar Relay Navigation Satellite Networks
### Other Countries  
Japan was the first to successfully utilize lunar relay satellites in lunar exploration missions. In September 2007, Japan successfully launched the SELENE satellite, which consists of a main satellite and two sub-satellites. The primary mission of the main satellite and one sub-satellite is to measure the gravitational field of the Moon, while the other sub-satellite was responsible for relay communication with the Earth. The orbit of SELENE is a polar orbit with an orbital inclination of about 90\({}^{\circ}\). The main satellite was in a circular orbit with an orbital altitude of 100 km. The relay satellite is in an elliptical orbit with a perigee altitude of 100 km and an apogee altitude of 2400 km. The mission saw the breakthrough of many new technologies and achieved a series of results. At the end, it successfully impacted the Moon in June 2009.  
In 2014, OHB SE in Germany and the University of Stuttgart proposed a lunar relay communication satellite program operating at the Earth-Moon L4 and L5 libration points. The purpose is to provide support for unmanned and manned lunar probes, with an operating life of 10 years. In order to solve the problem of large-capacity data transmission, besides microwave relay links, laser communication links are also used.
# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 2 Related Work  
### Small Object Detection Method  
A few shot object detection algorithms have made significant progress in addressing challenges associated with detecting small objects, enhancing the performance of detection networks [21; 22]. However, current research predominantly concentrates on object recognition in natural settings, with limited exploration into aerial threat scenarios, characterized by their heightened complexity. Aerial threat environments are more intricate, increasing the likelihood of background-foreground confusion in detection models. Additionally, the non-cooperative nature of aerial threats complicates sample acquisition, making target feature extraction more challenging [23; 24; 25; 26].  
Deep learning-based methods for small object detection can be broadly classified into two categories: two-stage object detection methods that first propose candidate regions and then classify and regress them, represented by Faster R-CNN [27]; and one-stage object detection methods that directly predict object categories and coordinates from images without region proposals, exemplified by YOLO [28; 29; 30] and SSD [31]. However, both one-stage and two-stage methods encounter difficulties in effectively detecting small objects. Table 1 provides a comparison between classic small object detection algorithms, demonstrating that the current mainstream framework combines a backbone network with a feature pyramid structure.  
Figure 1: Lightweight Optimization Strategy for the YOLOv5s Algorithm.  
Challenges in small object detection include insufficient semantic information in low-level features leading to difficulties in detecting small objects; the limited availability of training data for small objects in popular datasets like Real World and COCO (Common Objects in Context), restricting the thorough learning of small object features during model training; and discrepancies between the scales of objects in classification datasets where backbone networks are trained and those in detection datasets, posing challenges in adapting to small object detection.
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 4 Combination of Pinned and Stiff Hangers  
In Section 3.1, it was shown that the live load acting on the whole deck, i.e., for symmetrical loads, the maximum positive bending moments near the springings appear for stiff hangers fixed in both ends. In certain cases, these bending moments could become the most adverse load combination in this zone and, therefore, determine the dimensions of this cross-section near the springings. To tackle this drawback of fixed stiff hangers, the bending moment distribution can be optimized at these zones by stiffening the central hangers only. Obviously, the exact number of hangers where the rotations are released and the location of the hinges must be specifically studied for a particular bridge. With this topological solution, the bending moment for \(q_{5}\) can be reduced at the deck ends (Figure 14).  
In Figure 14, it can be seen how the proposed configuration, where pinned and fixed hangers are combined in the same bridge, is almost as efficient as the configuration in which all the hangers are fixed at both ends. The bending moments are very similar. In addition, the bending moments decrease at the zone near the deck ends for symmetrical loads distributions, as it was intended. Besides, the effect of releasing rotation both at the top and the bottom of the hangers is shown.  
Figure 13: Effect of a Rectangular Hollow Section (RHS) 800 \(\times\) 800 \(\times\) 20 mm, for \(C_{A}\) = 0.20, 0.33 (reference bridge) and 0.50.  
Figure 15 shows how for the reference model, the bending moments near the deck end (encircled in a dotted line) are lower under \(q_{3}\) than under \(q_{1}\) when all the hangers are fixed. This fact does not change when the three last hangers at the deck end are pinned, and \(q_{1}\) continues to be the most adverse load case, as it is the usual in arch bridge design. On the contrary, when an alternative \(1000\times 1000\times 20\) cross-section for the hangers is used, \(q_{3}\) becomes the most adverse load combination for that zone. In this case, a combination of pinned and fully fixed hangers can be used to correct this fact.  
Figure 14: Bending moments for stiff and pinned hanger distribution.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Future ML Algorithms  
Some problems in D2D applications are inherently combinatorial. For example, in multi-hop communication tasks, the transmitter chooses a path (a sequence of intermediate devices) to the receiver to send the message. Therefore, the underlying problem can be formulated as an optimization problem over paths in the graph formed by intermediate devices. Appropriate problem formulations and combinatorial/adaptive ML algorithms could overcome problems such as clustering, NDS, and multi-hop relay probing. In addition, appropriate ML algorithms for the centralized and decentralized peer to peer network setting have to be addressed. Moreover the symbiosis relation between ML and communication communities have to help each other to solve future D2D problems via distributed learning.