esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### ML for Interference Mitigation  
D2D transmission may be the source of severe interference to the other D2D links and the CUs. A survey of different popular and AI-based interference mitigation and RA approaches developed in D2D communications is provided in [24]. Additionally, the multi-agent actor critic (MAAC) is a newly proposed algorithm in [25] to mitigate interference by efficiently distributing the spectrum allocation. Moreover, the same paper proposes the neighbor-agent actor critic (NAAC) that uses neighbor users' historical information for centralized training leading to outage probability reduction and sum rate improvement for D2D links. Another RL based method for the RA problem was introduced in [26] based on the K-nearest neighbor algorithm that is utilized to choose the task offloading platform. Moreover, in [15], the concept of a limited D2D communication area is proposed based on ML.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 5 Conclusions  
The PM2.5 time series has three components (i.e., trend, period, and residual components). The trend component changes slowly over time, which can be understood as slow changes in climate conditions. The period component shows the change in each day, with a slightly higher value during the day, and is lower at night. This is in line with the laws of actual climate change. Moreover, the decomposition could reduce the modeling difficulty of PM2.5 data and obtain more accurate prediction results, especially for the long-term prediction.  
It is critical to choose the corresponding prediction models for different components. In this study, we used the ARIMA model to model and predict the trend component, and GRU networks were selected to model the period and residual components to capture the nonlinearity of the PM2.5 data.  
The experimental results show that, first, decomposition is necessary to effectively reduce the difficulty of modeling the PM2.5 data. Second, using different models, especially using the ARIMA model, for the trend component is more accurate than the GRU; and finally the long-term prediction performance was improved simultaneously with fewer parameters and lower calculating cost.  
Conceptualization, X.J. and N.Y.; Data curation, Y.B. and T.S.; Formal analysis, J.K.; Methodology, X.J. and N.Y.; Software, N.Y.; Supervision, X.W., Y.B., T.S., and J.K.; Validation, X.W., Y.B., and T.S.; Visualization, N.Y.; Writing--original draft, X.J. and N.Y.; Writing--review & editing, X.J. and N.Y.  
This research was funded by the National Natural Science Foundation of China (61673002, 61903009) and the Beijing Municipal Education Commission (KM201810011005, KM201910011010).  
The authors declare no conflicts of interest.
# _Sci_
## 1 Introduction  
The demand for data--raw facts--and its successful evolution to information--arranged in context--is growing, arguably at an exponential rate. This is true for the civil, commercial, and security sectors (i.e., defense; specifically, the U.S. Department of Defense). Civil space activities include those to explore space and advance human understanding: commercial activities are those where private companies and industries provide services with the intent of making a profit [1]. During 1991's Operation DESERT STORM in Iraq, the U.S. Department of Defense had access to ninety-nine mega-bits per second of bandwidth. In 2003, access for Operations ENDURING FREEDOM in Afghanistan and IRAQI FREEDOM had grown to thirty-two-hundred mega-bits per second. In 2007, U.S. Department of Defense planners projected the need for sixteen giga-bits per second by 2010 [2]. A rudimentary analysis concludes that the U.S. Department of Defense requires an order of magnitude increase in bps every nine years. The U.S. Department of Defense'sdemand for data, information, and intelligence is exceeding its satellite communications capacity, whether owner-operator or contracted. Transitioning to laser-based satellite communications, in part due to its orders of magnitude greater throughput, is becoming necessary for the U.S. Department of Defense. Laser communications could provide from ten to one-hundred times better data rates than radio, due to the higher bandwidth [3].  
This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to inform the U.S. Department of Defense initial capabilities documents (ICDs) or key performance parameters (KPPs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers, rather than traditional radio frequency sources for satellite uplink and downlink signal carriers, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth and large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploit (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.11. Navigation III  
In [79], the authors developed an autonomous navigation system for mobile robots that can operate in unknown environment settings without relying on pre-existing map. Sensor fusion and a DRL were used to guide the robot in an unknown environment. The proposed system contains three blocks, with the robotic vehicle setup on the left, the generation of global navigation points in the center, and the computation of local navigation actions on the right, as shown in Figure 12.  
The authors in [79] utilized the SAC algorithm with sensor data to determine the robot's location and orientation for the navigation points to develop motion control algorithms involving the robot's linear and angular velocities. A reward system helps the robot achieve local objectives quickly and effectively while preventing collisions. An ROS plays a vital role in the implementation and validation of the proposed navigation system. The authors conducted three experiments that offer strong evidence of the effectiveness and adaptability of the proposed autonomous navigation system. Combining DRL with a global planner based on heuristics offered significant advantages over traditional planning algorithms and learning-based navigation methods.  
The above-mentioned approach enhances the navigation effectiveness by improving the robot's ability to perceive its surroundings from sensor data. Their approach is able to adopt to dynamic environments and make real-time decisions. Future efforts may focus on incorporating RL algorithms, advanced network models, and integrating diverse sensor data to improve adaptability in challenging environments.
## 3 Evaluation and Application of PWV Derived from CPRAP
### Application of CPRAP-Derived PWV  
The comparison results above have verified the good performance of the proposed CPRAP for obtaining PWV using different water vapor detection techniques. The corresponding applications, such as drought and rainfall monitoring, are further performed using the CPRAP-derived PWV in this section.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 4 Strategies to Overcome These Barriers
### Lutein Esterification Gene and Mechanisms  
The understanding of the carotenoid esterification mechanism has made significant progress in recent years. The Pale Yellow Patel 1 (_PYPI_) gene has been identified and isolated in a tomato mutant [89]. Evidence indicates that mutation of the _PYPI_ gene resultsin the loss of xanthophyll esters. This is accompanied by decreasing total carotenoid yield and a reduction in fully developed plastoglobules in petals. Studies suggested that _PYP1_ is highly relevant to the xanthophyll esterification reaction in tomato flowers [89]. This is further supported by a study characterizing the Pale Yellow Patel (_PYP_)/Xanthophyll esterase (_XES_) gene family in citrus [94]. Results show that the transcript level of _PYP1_, 2 and 6 are correlated with carotenoid ester content in citrus, and the expression of _PYP_ accords with the low level of xanthophyll esters in yellow citrus. Similar results were found in petunias [95]. The _XES_ from morning glory, tomato and marigold have been expressed in petunias. This results in a strong positive correlation between the expression of _XES_ genes and xanthophyll esterification content. Besides, the expression of _XES_ leads to an increase in the percentage of xanthophyll esters in transgenic mutants.  
Unfortunately, the function of the _PYP_/_XES_ gene has not yet been characterized in vitro. The enzymatic condition and the substrate preference are still unclear. It was found that xanthophylls were not esterified after the _XES_ genes were expressed in petunia leaves. This implied that the _XES_ might prefer a lipophilic environment, such as plastglobules [95]. Therefore, comprehensive characterization of the _PYP_/_XES_ function in vitro is needed before the heterologous expression of microalgae and other species.  
Xanthophyll acyltransferase (XAT) was first isolated in bread wheat [82]. It was found that the function of this enzyme is necessary for carotenoid esterification, both in vitro and in vivo. The enzymatic condition of XAT was also characterized. Results show that XAT can catalyze the esterification reaction of lutein, zeaxanthin and \(\beta\)-cryptoxanthin with a wide range of acyl-donor substrates, such as triglycerides, 1,2-difycerides, lysophosphotidylcholine, free fatty acids, 1,3-diglycerides and monoglycerides.  
XAT can esterify lutein with a wide range of acyl-donor substrates. Triglycerides and free fatty acids can accumulate to high levels in the cytoplasm and the chloroplast of algal cells. Therefore, XAT is an excellent candidate to explore lutein esterification's effect on the accumulation of lutein in microalgal cells.
## 5 Vibration Velocity Results and Analysis
### Velocity Spectrum  
The interference signals, whose frequencies were relatively high, had less of an effect on the velocity spectrum because of the low sampling frequency. Figure 16 shows the pipe velocity spectrum of the test data (\(R=2.7\) m).  
The ground velocity spectrum was similar to the pipe spectrum, and the frequency components showed little difference. A comparison with the dynamic strain spectrum revealed that the frequency band of the strain spectrum was narrower than the velocity spectrum. The peak frequency of the strain was generally lower than the peak frequency of the velocity, and the main frequency band also moved toward the low-frequency region. In the velocity spectrum, there were several large peak frequencies, and the second peak bandwidth in the Z-axis direction accounted for a larger proportion. In the strain spectrum, the second peak frequency amplitude and bandwidth were weakened, and the first peak frequency band was dominant. It was difficult to find the variation law of peak frequency because of the many influencing factors and the large variation of the wave form near the explosive source. There was no change rule that was easily discernible by analyzing the main frequency given by the instrument or the center frequency of the main frequency band. Moreover, there was a sudden change in value. Therefore, the average frequency was used. When calculating the average frequency, the cut-off frequency was 200 Hz. Referring to the results in the literature [34], the main frequency appears to be mainly affected by the explosion source (the radius of the explosion cavity), charge mass, blast center distance, and the properties of the propagation medium. The formula for the average frequency is as follows:  
\[f_{c}=k(c/Q^{1/3})\left(R/Q^{1/3}\right)^{\alpha} \tag{11}\]  
After taking the logarithm of both sides, the following equation is obtained:  
\[\ln(f_{c}\cdot Q^{1/3})=\ln(k\cdot c)+\alpha\cdot\ln(\overline{R}) \tag{12}\]  
Using Equation (12), the fitted curves are shown in Figure 17, and the attenuation law is very obvious.  
Figure 16: The velocity spectrum of the pipe (\(R=2.7\) m and \(Q=100\) g).
## 4 Proposed Protocol
### Neighbor Determination  
After the random deployment of sensor nodes, they exchange hello packets. Initially just after deployment, nodes do not know their neighbors. In order to identify its neighbors, every sensor node broadcasts a hello packet that contains its measured pressure level and unique ID. It waits for a certain time to hear from its neighbors: nodes that are within its transmission range. This waiting time is modeled by  
\[\tau_{w}=\tau_{p}+\tau_{pr} \tag{11}\]  
where \(\tau_{w}\), \(\tau_{p}\) and \(\tau_{pr}\) represent the waiting time, propagation delay and processing delay, respectively. The propagation delay depends upon the distance between the transmitter and receiver and the speed of the acoustic wave. It is modeled by  
\[\tau_{p}=\tau_{sr,i}+\sum_{i,j\in n}\tau_{i,j}+\tau_{j,snk}\qquad n\geq 2,\ i,j \in n \tag{12}\]  
\[\tau_{p}=\tau_{sr,i}+\tau_{,snk}\qquad n=1,\ i\in n \tag{13}\]  
\[\tau_{p}=\tau_{sr,snk}\qquad n=0 \tag{14}\]  
where \(n\) is the number of hops for a packet and \(\tau_{sr,i}\), \(\tau_{i,j}\), \(\tau_{j,snk}\) and \(\tau_{sr,snk}\) are the propagation delays from a sender node to a forwarder \(i\), forwarder \(i\) to forwarder \(j\), forwarder \(j\) to the sink and from the sender to the sink, respectively. Upon receiving a response from a neighbor, the broadcaster node initially obtains the information about the neighbors' pressure levels and IDs. It then constructs a table that contains its number of neighbors with their corresponding IDs and pressure levels. The table is broadcasted. Every node undergoes this process. In this way, not only does a sensor node know about its own number of neighbors but its one-hop neighbors also know about it. When a broadcaster node does not hear back from any node within the waiting time, it sends the hello packet again and waits to receive a response from its neighbors. If it does not receive any response from its neighbors within the maximum waiting time \(max(t_{w})\), it adaptively increases its transmission range to the maximum threshold and broadcasts the hello packet to include one or more neighbors, as shown in Figure 2.  
Figure 1: Network model.  
In case (a), a sender node A has data to send but there is no node within its transmission range. Nodes B, C and D are in the proximity of its transmission range but not within it. Knowing this, node A increases its transmission range until node B lies within its range (becomes its neighbor), as shown in case (b).  
The transmission range is always increased to the maximum threshold independently of how many new nodes lie in it. Node A (and every other sensor node) declares no neighbor at all when the maximum number of rebroadcasts are reached with no neighbor at all even after increasing the transmission range. The neighbor-finding process is repeated after regular intervals of time as nodes die due to their limited battery life and change their positions with water currents. This ensures that data is forwarded to live neighbors, which reduces the probability of packet loss.
# Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm
## 3 Results and Discussion
### Training and Detection Time  
Table 5 presents the average network training time with training data, as well as the average time required to detect a single test image for the aligned parameters of the aforementioned trainings. Figure 10 shows a diagram with the comparative average detection time in each figure of the trainings 1-9.  
Detection time was also measured in full-resolution images (1024 \(\times\) 1024) obtained from an Optyx 3000 color sorter. The average time amounted to 0.37 s per image. The maximum conveyor belt speed in an Optyx 3000 color sorter is 3 m/s, i.e., \(\approx\)0.33 s duration for one image captured, considering that one pixel screened is 1 mm. If YOLOv3 detection time in a full-resolution image (1024 \(\times\) 1024) is \(\approx\)0.37 s, it can be concluded that this algorithm could be applied on these machines for its stated purpose. It should be noted that network training time depends on the processing unit used for training. Network training can be executed through GPU (graphics processing unit), where graphics card power is utilized, or CPU (central processing unit), where processor power is used. Training of convolutional neural networks is more convenient through GPU. As mentioned at the beginning of this paper, the power of the NVIDIA Tesla K80 graphics card was used, and this represents a middle class for training of deep learning algorithms. For example, when a deep learning virtual machine is created on Google Cloud, this card is set as default. The training set for this research did not require extensive computer resources; however, network training time would certainly be shorter if a more powerful graphics card was used.  
Figure 10: Comparative representation of detection time, in seconds, in 20 test images for all trainings.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Training** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Network training & 3.48 h & 9.15 h & 3.5 h & 7.788 h & 7.754 h & 4.89 h & 4.88 h & 9.141 h & 9.061 h \\ Test images & 0.045 s & 0.047 s & 0.056 s & 0.043 s & 0.043 s & 0.042 s & 0.044 s & 0.042 s & 0.044 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average network training time for each training and test image.  
Furthermore, the number and size of data in the training set, in addition to the above-mentioned hyperparameters, affect the length of training duration.
## 5 Major Test Results and Findings  
The full aeroelastic bridge model was tested in uniform flow for final verification of aeroelastic stability, as well as in turbulence for verification of the buffeting calculations. To investigate the aerodynamic stability under different wind attack angles, metal pads were placed at the bottom of the bridge piers and towers. The wind attack angles were adjusted to \(-3^{\circ}\), \(0^{\circ}\), and \(+3^{\circ}\) for the tests in this study.
## 6 Performance Evaluation
### Experimental Set Up
#### 6.1.3 Data Extraction  
In order to ensure the reliability of the extracted information, each configuration of the network has been executed for \(3,000\) s.  
To determine the average synchronization error, the sequence number of each packet has been used. Packets with the same sequence number are grouped to determine the average synchronization error in each group (Equations (11) and (12)). Once the average error for each sequence number has been obtained, the global average synchronization error is determined using Equation (13).  
\[AvgBeacon=\frac{\sum_{i=1}^{nodes}(GlobalTime[i])}{nodes} \tag{11}\]  
\[ErrorBeacon=\frac{\sum_{i=1}^{nodes}(AvgBeacon-GlobalTime[i])}{nodes} \tag{12}\]\[ErrorGlobal=\frac{\sum_{j=1}^{beacons}(ErrorBeacon[j])}{beacons} \tag{13}\]  
Other parameters with statistical relevance have been obtained: variance, typical deviation and maximum error.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
###### Abstract
In this paper, we outlined the on-site survey findings and present the methods for imaging seafloor surfaces using MGS equipment. Herein, we investigated the application of marine geophysical exploration equipment using sound waves through underwater excavation surveys at excavation sites in South Korea. To this end, the marine physics exploration equipment was operated and tested at the underwater excavation sites of Dangampo Sea in Taean, Chungcheongnam-do, Nakwoldo Sea in Yeonggwang, Joellanam-do, and Myeongnyangdae-ro, Jindo, Joellanam-do.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 2 Analysis of Traditional Interconnections in Space-Borne Microwave Systems
### Gold-Ribbon Wrap Bonding  
In the gold-ribbon wrap bonding, the ribbon is perpendicular to the direction of transmission line and forms an \"O \" shape [17]. Parallel gap-bonding equipment is used to complete the connection between the strip-line pad and the coaxial lead. The \"O\" shape is usually used for the interconnection of a non-coplanar coaxial seal to plane, such as the connection between the coaxial structure core and the substrate directly below, as shown in Figure 5.  
After analysis, the distance \(L_{2}\) from the seal to the substrate and the distance \(L_{1}\) from the extended end of the inner core to the gold ribbon have little effect on the stress. Therefore, the parameters \(L_{2}\), \(L_{1}\), and T are taken as constants, and the parameters \(g\), \(L_{4}\), \(P\), \(L_{3}\), and \(B\) are taken as variables for analysis. Because there are also ductile connections, the stress characteristics under the same conditions are similar to those of the above structures, as detailed in Table 2. On the other hand, due to the shorter distance between the abrupt changes of the electromagnetic field mode, the microwave matching characteristics are better than in the lap bonding. Its insert loss and reflection loss are shown in Figure 6, so this transmission structure can work in the Ka band. Based on the above, the stress characteristics and electromagnetic matching characteristics of the four structures are summarized and analyzed, as shown in Table 3 and Figure 6. Core wire winding soldering, gold-ribbon lap bonding, and gold-ribbon wrap bonding all have better temperature change and mechanical adaptability. The electrical performance of wrap bonding is similar to that of direct soldering. The microwave performance of the wrap bonding can be designed further. Since the gold ribbon in the wrap bonding increases the inductive parasitic parameters, the capacitive matching design is carried out on the  
Figure 5: Gold-ribbon wrap bonding structure. microstrip line, as shown in Figure 7. After matching, its reflection loss can be reduced by about 7 dB, as shown in Figure 8.  
Although the above microwave interconnection structures can solve the stress problem under high reliability requirements, they have poor microwave matching characteristics and it is difficult to apply them in higher frequency ranges. In addition, there is a problem that the inner core of the coaxial seal protruding inward shields the substrate below during assembly. The coaxial seal must be finished after the substrate is assembled, or when the process flow of soldering the shell and seals are first met, and the substrate must be tilted