esa-sceva/satcom-chunk-collection huggingface dataset:
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 5 Luojia-1A Satellite POD Results and Discussion
### Tuning the Pseudo-Stochastics Parameters
The statistic results of Luojia-1A satellite orbit in the radial, along-track, and cross-track directions are listed in Table 6, and the detail of the orbit error is shown in Figure 13. Panel (a) shows the error of the Luojia-1A satellite orbit without adding any pseudo-stochastic parameters. The fluctuation of the along-track error is pronounced, in which the error of the start epoch is larger than 30 cm. In the cross-track direction, there is nearly 10 cm constant error. As shown in the other three panels, the errors of three directions are reduced by adding the pseudo-stochastic parameters. The shorter spacing that the piece constant parameters are set, the more precise the result of Luojia-1A satellite orbit achieved, especially in the beginning of overlap comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accelerations Interval** & **Radial** & **Along-Track** & **Cross-Track** & **3D** \\ \hline w/o & 2.43 & 11.17 & 12.83 & 17.184 \\
30 min & 1.30 & 3.66 & 0.62 & 3.933 \\
15 min & 0.84 & 2.37 & 0.77 & 2.630 \\
10 min & 0.74 & 1.99 & 0.73 & 2.245 \\
6 min & 0.99 & 1.66 & 0.52 & 2.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The RMS precision of Luojia-1A with different piecewise constant accelerations intervals (Unit: cm). Figure 12: The 3D precision of Luojia-1A with different settings for the pseudo-stochastic parameters.
## 5 Conclusions  
We first obtain the schedulability test of SSF-EDF. Then, we study three different processor allocation strategies of the EDF scheduling algorithm on the performance asymmetric multiprocessor platform. The experiments show that BSF-EDF can schedule more task sets than the other two processor allocation strategies. SSF-EDF has the lowest number of task migrations, indicating that it has less preemption latency. SSF-EDF has the highest effective processor utilization, indicating that it utilizes the processor more efficiently.  
The design of the best scheduling strategy for performance asymmetric multiprocessors still remains unsolved and is an important research problem. In this paper, inspired by the global EDF scheduling strategy, we select the best or slowest processor for EDF scheduling [16]. The purpose of this consideration is to reserve fast processors for later tasks. This provides an opportunity to improve schedulability. The simulation results illustrate that the idea of reserving fast processors with a certain strategy for subsequent tasks is useful, which is what BSF-EDF achieves. Specifically, it rationally allocates the current task according to its need for resources. In this way, the processor resources can be fully utilized while the on-time completion of the current task is guaranteed. Thus, BSF-EDF can schedule the most task sets. The number of schedulable task sets of SSF-EDF is less than that of BSF-EDF. However, SSF-EDF has the smallest number of task migrations and the highest effective processor utilization.  
Conceptualization, P.W. and Z.L.; methodology, P.W., Z.L. and T.Y.; software, P.W. and Z.L.; validation, P.W., Z.L. and T.Y.; formal analysis, P.W. and Z.L.; investigation, P.W., Z.L., T.Y. and Y.L.; resources, P.W., T.Y. and Y.L.; data curation, P.W. and Z.L.; writing--original draft preparation, P.W. and Z.L.; writing--review and editing, P.W., Z.L., T.Y. and Y.L.; visualization, P.W., Z.L. and Y.L.; supervision, P.W. and Z.L.; project administration, P.W., Z.L., T.Y. and Y.L; funding acquisition, P.W., T.Y. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 62006146, 62001143, and 62002210) and the National Natural Science Foundation of Shandong Province, China (No. ZR2020 QF006).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Comparison of EPU values of BSF, FSF and SSF under different system utilization values (\(m=10\)).
# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 2 Problem Statement  
We consider a straight section of pipeline (AC/water/gas/oil) which is enclosed with two stations, one at each end of the section of the pipeline. The pipeline (channel) can run for a few meters to a few kilometers. This channel should be effectively checked; at the end of the day, the entire length of the channel should be secured, and the detected data should be transmitted to a specific node named sink or base station. The sink node should be at one of the stations. In this manner, it is accepted that there is a pipeline with length L, with sensor nodes SN (N1, N2... Nn) and two stations are disseminated at the closures of the channel. A sink node S should be placed at one of the channel's closures near one of the stations. Sensors and also the sink node are expected to have restricted abilities including the communication range and the size of the buffer. Here, two scenarios exist, one is to establish a new pipeline to connect two stations efficiently, and the other is to efficiently manage the connection between existing stations. The problem can be generalized in that there are multiple channels, which are interconnected with each other in some pre-specified manner. This paper discusses the establishment of a network of pipelines or channels with minimum number of nodes such that the entire area is covered in a secure, robust manner and data is transferred to the BS with a minimum number of nodes. The energy efficiency and lifetime of the network are also considered while designing the solution of the proposed problem. For setting up a pipeline, there are multiple straight pipelines connected in a manner to common BS as shown in Figure 2.
# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 4 Original DAWN and YOLOv5 Results  
After running our experiments with four batches for 32 epochs, SiLU generally outperformed all the other activation functions among the YOLO5s and scored the highest mAP at 80%, approximately 5 percentage points higher than the closest activation function in YOLO5s. Figure 6 shows the detected objects with their scores using the SiLU function. Although SiLU scored the highest mAP among the YOLOv5s, if we observe Figure 6, the hard sandstorm represented in the bottom left scene and the distant location of the objects prevented the model from detecting all the people in the scene. The model failed to detect the second person. Failure to detect all the elements in a scene is considered a high risk in real-life situations. The detailed SiLU metrics are shown in Figure 7, after running the model for 32 epochs. The top left chart shows the precision result, with a score of 79%, while the top right chart shows the recall result reaching 76%. The bottom chart shows the resulting mAP which reached 80%.  
Figure 6: YOLO5s model predicted objects using SiLU.  
Figure 7: SiLU result after 32 epochs with YOLO5s model.  
In the case of YOLO5m, the results were different. LeakyReLU achieved the highest mAP and highest precision. ReLU had a slightly better performance in terms of detecting the relevant objects in the scene (recall), since it achieved 70%, whereas SiLU and LeakyReLU scored 69% and 12%, respectively. Table 3 shows a summary of the YOLOv5 experimental results.  
Figure 8 shows part of the results when we detected objects using YOLOv5s version 5 on the testing video frames with SiLU as an activation function. From the figure, we can see that the detection score for the black vehicle on the right reached 92%. However, if we observe the other side of the road, the YOLOv5s with SiLU could not detect objects in the far distance. The model did not detect the white truck on the other side.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup
### Numerical Approach Validation  
E779A is one of the most popular propellers used by many scholars because of its substantial experiment results [46; 47; 48]. Because cavitation experiments have been conducted in Italian Navy cavitation tunnel facility in 2006, in this paper, E779A propeller was selected to test the numerical simulated feasibility. The geometry and boundary conditions are shown as Figure 1, the outer cylindrical domain is external flow field and the inner one is rotational domain. The advance ratio is defined as \(J=U_{\infty}/(nD)\), where \(U_{\infty}\) represents the oncoming flow velocity, while \(n\) and \(D\) are rotational speed and diameter of propeller, respectively. In order to compare with cavitation experiments data, outlet boundary was set into static pressure that varied according to cavitation number \(\sigma=(p_{out}-p_{v})/(0.5^{\circ}\rho{U_{\infty}}^{2})\), where \(p_{out}\) is outlet pressure, \(p_{v}\) is saturation pressure under local temperature.  
The SST \(k\)-\(\omega\) turbulence model and Z-G-B cavitation model were used in the cavitation simulation of E779A propeller. Figure 2 shows the comparison between calculation and experiment results. In Figure 2, \(K_{t}\) is the thrust coefficient and \(K_{q}\) is the torque coefficient which are defined as \(K_{t}=T/(\rho n^{2}D^{4})\) and \(K_{q}=q/(\rho n^{2}D^{5})\), respectively, where \(T\) is the thrust and \(q\) is the torque of propeller. The propulsion efficiency \(\eta\) is defined as \(\eta=K_{t}/K_{q}{}^{\circ}\eta/(2\pi)\). It can be seen from Figure 2 that numerical results agree well with the experimental data, which indicates that the numerical approach employed by this paper is reliable.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 5 Simulation Results
### Applicability to DBC  
Figure 9 shows the effect of the DBC for harvesting probability and conditional BER. The harvesting probability increases as the number of multipaths in forward channel \(L^{+}\) increases. The multipath diversity reinforces the incident power at tag antenna in (5), improving efficiency of ambient energy harvesting in both the TD-AmBC and the SC-AmBC. However, multipath is harmful to BER performance in conventional TD-AmBC. To resolve ISI caused by DBC, simply filtering ISI-affected samples in detector [2; 23] incurs significant loss in SNR as the number of multipath \(L^{+}\) increases. Moreover, this limits the operational range of backscatter applications such as inventory control in warehouse and grocery stores [4]. Thus, we can observe the trade-off between the harvesting probability and the conditional BER in the TD-AmBC where ISI is not exploited.  
On the other hand, the proposed SC-AmBC can fully exploit the ISI based on D-MPA and D-CEA. Without these algorithms, we can partially exploit the ISI for performance improvement. As shown in Figure 9, traditional MPA for one-way channel [35; 36; 44] shows worse BER than those of TD-AmBC, especially in DBC. However, if we add the weighted-sum of ISI for information calculation in Equation (38) using D-MPA, the conditional BER decreases as \(L^{+}\) increases, which is in sharp contrast to the conventional TD-AmBC detectors and the baseline SC-AmBC with MPA. Therefore, the proposed D-MPA with D-CEA provides multipath diversity for both the energy harvesting and the information decoding by fully exploiting ISI in DBC.
The thirteen spectral bands (443-2190 nm) and HR imaging capabilities in visible and near-infrared bands at 10 m spatial resolution have been already tested for archaeological prospection (for example, References [15; 16]). On the contrary, it is still to be assessed how Sentinel-2 can be used systematically to detect prominent features and changes in heritage sites during both ordinary times and crisis. To the best of our knowledge, no research has been published yet to test Sentinel-2 for this specific capability. This is, therefore, the aim and overarching research question of this paper. In particular, we explore the value of Sentinel-2 time series for multi-temporal HR assessment for event verification and hotspot mapping. Our appraisal is structured around a selection of case studies in Syria and Libya that we investigated using Sentinel-2 and Google Earth time series (Section 2) to examine two scenarios (Section 3):  
1. The local heritage has been impacted by event(s) known based on precise background information (that is, including the location, time of occurrence, and typology of the event). Therefore the features to search for in the satellite images can be anticipated, are very distinctive, and easy to interpret in the cloud- and fog-free visibility conditions (Section 3.1);
2. The heritage site is known to be a hotspot of specific threats to conservation (for example, intense and repeated looting) and the history of incidents may suggest that further events can happen, and their manifestation can be captured even at HR (Section 3.2). The results of the Sentinel-2 time series analysis were validated based on integration with VHR imagery--that is, DigitalGlobe and CNES/Airbus images available in Google Earth--and background knowledge from published literature and incident reports. When no Sentinel-2 data were available before a certain date of interest, we used the pre-existing Google Earth time series to identify the trends and dynamics of the investigated studies for further verification in the more updated Sentinel-2 time series. This was the case in the demonstration site of Cyrene where there was an opportunity to investigate an anthropogenic process of a potential threat to heritage conservation that manifests at the surface through distinctive features changing in time. We, therefore, discuss how systematic Sentinel-2 acquisitions taken with short revisiting time can be used for the dynamic detection of such features (Section 3.3). We then test the regional scalability of the method by extending the analysis across the entire Libyan region of Cyrenaica with a focus on coastal heritage sites affected by urban sprawling (Section 4). We finally outline future research directions in this field accounting for the emerging opportunities offered by machine learning (Section 5).
# Learning Translation-Based Knowledge Graph Embeddings by \(N\)-Pair Translation Loss
## 2 Related Work  
Knowledge graph embedding has been studied steadily for the past couple of years since well-developed knowledge graphs have become available [21; 22; 23; 24]. Among various embedding models, translation-based embedding has become one of the widely-used approaches to knowledge graph embedding due to its simplicity and efficiency. Since TransE [9] embeds entities and relations in the same vector space, several variants such as TransH [13], TransR [12], TransD [10], StransE [25], TransG [14], TransE-RS [26], TransAt [27], KGLG [28], and NTransGH [29] have been developed by extending or modifying TransE. Detailed explanations for translation-based variants can be found in the studies of [PERSON] et al. [2] and [PERSON] et al. [3].  
Even if translation-based knowledge graph embeddings yield promising results, they suffer from slow convergence and poor performance compared to other knowledge graph embeddings [16]. These problems of translation-based embeddings arise partially from the fact that the margin-based ranking loss used for training translation-based embeddings adopts only one negative triple per each update of learning parameters and some of the negative triples in which quality is low contribute little to training the embeddings, and this is called the _zero loss_ problem. As a result, some previous studies focused on alleviating the zero loss problem by improving the quality of negative triples at the moment when the negative triples are generated [15; 16; 17; 30]. One direction to improve the quality is to adopt the adversarial training framework [19] for training the knowledge graph embeddings [15; 16]. In this direction, a generator calculates a probability distribution over a set of candidate negative triples and provides a high-quality negative triple to a discriminator. Then, the discriminator, which is usually trained with the margin-based ranking loss, receives the negative triple from the generator as well as the positive triple and calculates a score of each triple. While the discriminator is trained with a marginal loss between positive and negative triples, the generator is trained using rewards from the discriminator. Another direction is to track the losses of negative triples and generate a difficult negative triple according to a loss distribution [17,30]. Although the studies above can generate high-quality negative triples, they require extra modules such as a generator or cache, and thus the number of parameters to be trained increases.
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 2 Current State of Space-Constrained Scheduling
As mentioned in the introduction, a lack of studies considers the effect of the stacking of trades, in which productivity declines due to an increase in resources within a physically limited work area. In space-constrained scheduling, it is crucial to resolve workspace interference. There are various strategies for resolving workspace interference, including changing workspace locations, workspace sizes, activity sequences, construction methods, activity execution patterns, and deferring the start times of noncritical activities [3; 6; 14; 19]. Most of the existing studies have applied manual adjustment or rule-based heuristic methods for resolving workspace interference. Heuristic methods are easy to understand and intuitive. However, they are difficult to apply to large-scale network problems, and they have the limitation of being unable to guarantee global solutions. On the other hand, meta-heuristics, such as GA, simulation annealing (SA), and tabu search (TS), are widely used for schedule optimization because they can find quick and trustworthy near-global solutions within a large-scale solution space. However, few studies have used meta-heuristics for SCS optimization. A study by [PERSON] et al. [5] is one of the few studies that used meta-heuristics for space-constrained scheduling. [PERSON] et al. [5] noted that there are limitations to using manual methods to establish a reasonable workspace schedule because large-scale construction projects consist of many activities that have logical (preceding-succeeding) relationships, and they proposed a GA-based methodology for resolving workspace interference. [PERSON]'s method searches for a solution that minimizes space interference by adjusting the start times of noncritical activities within the TF (total float). That is, the solution found by GAs is an adjusted set of activity start times. Obviously, the strategy of adjusting the start times of noncritical activities may be the most preferred method of resolving workspace interference because it incurs no penalty, such as an increase in construction completion time or cost. However, changing activity execution patterns is another strategy that has no penalty [3; 14; 20], but it was not considered in the search for an optimal solution. [PERSON]'s method has the limitation of being unable to present a different resolution strategy when workspace interference problems cannot be resolved by adjusting activity start times.
## 4 Conclusions  
Meteorology forecasts from the raw WRF model have relatively high error levels. Previous studies have demonstrated that forecast performance could be improved by post-correction processes. In this aspect, this study built and tested a straightforward and auto-optimizing AutoML model to bias-correct the raw forecasts. The AutoML is not only strong in terms of modeling capabilities, but it is also easy to construct and fit due to its automatic workflow. This method was implemented and evaluated to post-process forecasts from 13 stations in northern Xinjiang. In general, forecast performances were significantly improved over raw forecasts. For temperature, the average RMSE values decreased from 3.24 \({}^{\circ}\)C to 2.34 \({}^{\circ}\)C, which was a large percentage decrease of 28%. As for relative humidity, mean RMSE values at the 13 stations decreased from 19.54% to 11.54%, or a percentage change of 41%. In addition, biases were also significantly decreased. The post-processed forecasts also have a relatively enhanced performance stability, which will help in forecast data applications.  
Compared to three other MOS methods, the ensembled AutoML model also had a better performance with lower error levels. RF, MLR, and moving error post-correction models, respectively, have larger RMSE values than the AutoML model, with mean differences of 0.11, 0.07, and 0.09 \({}^{\circ}\)C for temperature and 0.55%, 0.61%, and 1.12% for relative humidity. The post-correction performance was significantly improved from the original forecasts in almost all forecast processes. A complete circle of forecasts was corrected all at once rather than by each lead hour.  
The model could be readily used in post-processing forecast field data by training it with multiple stations within a region. It should also be applicable to other forecast variables. In terms of limitations, the model is generally time consuming compared to a  
Figure 15: Comparison of the raw forecast with post-corrections to observations for: (**a**) temperature and (**b**) RH of a forecast process at station 51359 starting at 0:00 on 1 August 2019.  
single model, since it needs to test many parameters. The post-correction model could be updated on a weekly or monthly time scale.  
Conceptualization A.M.; methodology, J.L.; software, J.L. and H.L.; validation, H.Z. and H.L.; formal analysis, H.L.; investigation, A.M.; writing, original draft preparation, J.L.; writing, review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China, grant number 2018 YFC1507105; the National Natural Science Foundation of China, grant number 41875023, 41801019; Central Scientific Research Institute of the Public Basic Scientific Research Business Professional, grant number IDM2018010.  
The data used in this paper can be provided upon request by reaching [PERSON] ([EMAIL_ADDRESS]).  
Thanks to [PERSON], a respectable, responsible and resourceful scholar, who has provided me with valuable guidance in every stage of the writing of this paper.  
The authors declare no conflict of interest.