esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Communication in IoT
### Commonly Used Sensors for Smart Farming and Heavy Metal Identification  
Sensors for Soil Moisture (SM) have been used in crop fields for decades to measure water content. The use of handheld/manual soil moisture technology is increasingly being replaced by automated technologies, since there were difficulties in manual soil moisture readings in remote production areas. In the past decade, technology has been developed for wireless data collection, providing managers and users with real-time access to soil moisture data, resulting in more successful water management decisions. Some of the prominent sensing devices to measure soil moisture comprise gravimetric sampling, resistive sensors, capacitive sensors, and Ground Penetrating Radar (GPR) [93]. Gravimetric sampling is a direct and normal SM measurement tool [94]. SM is determined by a proportion of dry soil mass to wet soil mass including pores. It needs the manual drying of soil samples taken from the field and oven sampling [94]. The electrical conductivity of water and the measuring of resistance changes based on soil water content are primarily resistive sensors, such as granular matrix sensors. This method includes sensor calibration for precise SM reading. Intelligent irrigation-based measurement to maintain soil moisture levels is significant to improve plant productivity and quality. On the other hand, soil moisture sensors these days are expensive, i.e., the ECHO-EC5 soil moisture sensor costs around USD 169 [95]. In order to overcome the cost constraint factor, [PERSON] et al. [95] proposed an RFID-based GreenTag sensor to maintain and improvise plant productivity and quality. In addition, RFID sensors can be combined with biosensors comprising aptamer and DNA-based properties which can be used to detect heavy metals at nanoscale and large scale levels pertaining to food safety monitoring. A heavy metal detection-based biosensor is composed of genetically modified bacterial cells and a green fluorescent signal amplifier which detects the presence of arsenite in foods [96; 97]. Its arsenic detection lasts for an hour with a detection range of 5-140 \(\upmu\)g/L. Other methodologies pertaining to biosensors, i.e., aptamers and graphene electrodes, have also been used to detect arsenic with the possibility of being developed as simple and easy-to-use low-cost devices [97; 98]. The EC-5 series sensors were also used by [PERSON] et al. [99] for field-specific calibration and evaluation in sandy soils. Nonetheless, EC-5 sensors have turned out to be helpful to reveal soil water content dynamics in different soil depths post rainfall conditions. The ECHO series has other variants of sensors; i.e., ECHO-EA10 can be used for medium
## 4 Proposed Scheme
### Exploiting Overhearing
Note for dense graphs the network throughput may be severely limited by contention and interference. However overhearing compensates such limitation particularly for the applications retrieving the extreme data value, since unnecessary transmissions can be further suppressed by overhearing in denser networks, which in turn reduces subsequent contention for channel access. Figure 6: An example of overhearing. ```
\(z_{i}\gets F_{X_{i}}(x_{i})\), FrameCount \(\leftarrow\) 1, \(c\gets 0\)  NeedToTX\(\leftarrow\)TRUE  TXSlot\(\leftarrow\) SelectSlot() // Minislot selection loop // Begin infinite loop  SlotCount\(\gets 1\) whileSlotCount\(\leq m\)do // Begin SlotCount loop if(detect that medium is busy) then if(this node is the receiver) then [Receive the packet] \(z\leftarrow\) received data if\(z\geq z_{i}\)then // Selective forwarding  NeedToTX\(\leftarrow\) TRUE \(z_{i}\gets z\)  TXSlot\(\leftarrow\) SelectSlot() // Minislot selection endif else if(overhearing is possible)then // Overhearing [Overhear the transmitted packet] \(z\leftarrow\) overheard data if\(z\geq z_{i}\)then  NeedToTX\(\leftarrow\) FALSE \(z_{i}\gets z\) endif endif endif  SlotCount\(\gets m\) // Exit from SlotCount loop else if(NeedToTX=TRUE) &(SlotCount=TXSlot)then [Transmit packet with data \(z_{i}\)] if(Collision occurred) then \(c\gets c+1\) else  NeedToTX\(\leftarrow\) FALSE // Successful transmission \(c\gets 0\) endif  SlotCount\(\gets m\) // Exit from SlotCount loop endif endif  SlotCount\(\leftarrow\)SlotCount+1 [Wait until the next minislot starts] endwhile // End SlotCount loop FrameCount\(\leftarrow\)FrameCount+1 [Wait until the next frame starts] endloop // End infinite loop
```  
**Algorithm 1** The proposed scheme. The whole procedure which combines priority-based channel access, selective forwarding and overhearing is given in Algorithm 1. The SelectSlot() subroutine used in Algorithm 1 is provided in Algorithm 2.
## 5 Conclusions  
Under quasi-static conditions, cyclic shear tests were carried out to investigate the surface damage and roughness degradation of artificial rock joints with natural morphologies under different normal stresses. The 3D roughness parameters were measured and compared in 36 directions. The main conclusions are as follows:  
1. The polar plot of the 3D roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\) of the joint surfaces analyzed in 36 directions demonstrates that, as the number of shear cycles increases, the polar curve of the joint surface roughness shrinks to the original direction. That is, it gradually develops from a circular to an elliptical shape, with the shear direction as the minor axis.
2. The roughness degradation rate and the shear cycles are mutually interrelated with the normal stress level. A larger normal stress results in faster degradation rates and smaller residual roughness parameter values. The roughness degradation rate is approximately uniform with the shear cycles under low normal stress (0.4 MPa). When the normal stress increases, the roughness first degenerates quickly and then slowly.
3. Influenced by the cyclic shear direction, the 3D roughness of the joint surfaces degenerates anisotropically, and the most serious damage is sustained along the shear direction. An RDF (\(r=a/(n+b)+c\)) under cyclic shearing is proposed, and the residual roughness \(c\) is linearly related to normal stress. The joint roughness after cyclic shears with different normal stresses can be predicted.
4. The wear area ratio \(r_{w}\) of the specimen's lower block surfaces becomes severer as the number of shear cycles increases, and the increment rate of \(r_{w}\) is first fast and then slow. A fitting formula of the joint surface wear under cyclic shearing is proposed, and it could aid in the assessment of the cumulative degree of wear in jointed rock masses.  
Conceptualization, B.L.; Formal analysis, H.S.; Investigation, Y.Z.; Supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (Grant No. 41672311).  
The data used to support the findings of this study are available within the article.  
The authors declare that they have no conflict of interest.
# Multi-Objective BiLevel Optimization by Bayesian Optimization
###### Abstract  
In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who reacts, each aiming to optimize their own objective. Many real-world decision-making processes have various objectives to optimize at the same time while considering how the decision-makers affect each other. When both features are combined, we have a multi-objective bilevel optimization problem, which arises in manufacturing, logistics, environmental economics, defence applications and many other areas. Many exact and approximation-based techniques have been proposed, but because of the intrinsic nonconvexity and conflicting multiple objectives, their computational cost is high. We propose a hybrid algorithm based on batch Bayesian optimization to approximate the upper-level Pareto-optimal solution set. We also extend our approach to handle uncertainty in the leader's objectives via a hypervolume improvement-based acquisition function. Experiments show that our algorithm is more efficient than other current methods while successfully approximating Pareto-fronts.  
multi-objective bilevel optimization; Bayesian optimization; hypervolume improvement; Gaussian process; decision-making under uncertainty +
Footnote †: journal:
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 4 Simulated Leak Piping System
### Tests of Detecting Leakage Signals  
Ten experimental cases were established including no leakage, leakage of the main pipe, leakage of the branch pipe, flow in the pipe, and several cases of different amounts of leakage. The water pressure was 0.18 MPa when there was no flow in the pipe and was 0.17 MPa with the flow in the pipe. Here, as a variable, the leakage signal was measured when there was leakage without a change in the water pressure, and also when there was a change in the water pressure due to a large amount of leakage. Table 4 summarizes the experimental conditions described above.
## 3 Results and Discussion
#### 3.1.3 Web-Based and Mobile Applications for Healthcare Monitoring
A real-time web application, developed with CSS and HTML, displays IoT device data via MQTT messages and alerts medical personnel to critical changes, publishing data to a private cloud server [110]. Finally, a Web3-type monitoring system was proposed that reduces the burden on healthcare providers and maintains a sustainable health status by establishing mutual oversight among the elderly [93]. Table 4 summarizes the studies related to web-based and mobile applications for healthcare monitoring. A detailed description of each listed work can be found in Table A1, which is organized by year of publication.
## 2 TBM Selection Decision-Making Method
### Text Project Overview and Required Data Acquisition  
In order to verify the model in this paper, three projects with different types of TBM are selected. The TBM used in these three projects has a very good tunnelling effect in actual tunnelling. The specific project overview is as follows:  
Project 1. A hydraulic in Xinjiang. The project has a total length of 540 km, including Xe, KS and SS tunnels, which are mainly constructed by open TBM. According to the geological survey report, TBM passes through eight regional fault fracture zones. The basic seismic intensity of the region is level 7. The lithology of the tunnel is Variscan granite, Cretaceous mudstone and sandstone. The open TBM has a good tunnelling effect in this project. This paper selects the K2 + 310-K4 + 310 section of the KS tunnel, which is representative of this project, for verification.  
Project 2. Rail transit project in the southwest. The strata exposed along the TM mountain tunnel section of a rail transit project in southwest China are mainly quaternary artificial fill, residual slope and landslide deposit, in addition to the Jurassic ZLJ, ZZC and Triassic XJH, LKP and JLJ formations. The TM mountain tunnel, a rail transit project in southwest China, is a two-tunnel, two-line, light-rail-dedicated tunnel. Tunnel mileage: YCK5 + 830-YCK11 + 460.908, tunnel length: 5630.908 m; among them, the TBM construction method is adopted for YCK 6 + 000-YCK 9 + 500 (about 3500 m). The single-shield TBM used in the project has good adaptability in this section.  
Project 3. A tunnel project in the west. The tunnel is located in South Asia and belongs to the X-range. Most of the terrain is incomplete, gullies are well-developed and gully beams are alternate. The main exposed strata in the project area are Neogene Cenozoic (N1)--Quaternary Pleistocene (Qp) X Group sandy mudstone and Quaternary Holocene (Qh) loose deposits. Middle Siwalik (MSA): The lithology is mainly sandstone-mudstone interbedding with pseudoconglomerate strips, in which sandstone accounts for 60-70% and mudstone accounts for 30-40%. Thick layers are dominant. The rock mass in the sandstone-concentrated section is shallow weathering with high strength. The rock mass in the mudstone-concentrated section is weak and low strength. Local calcareous cemented sandstone is softened and loose. Due to the difference in weathering degree between sandstone and mudstone, the topography of the exposed section is incomplete and the gullies and beams are inter-related. Double-shield TBM used in the project has good adaptability in this section.  
In this chapter, the above three projects are selected to verify the TBM selection decision-making method proposed by Section 2. See Table 4 for the data required by the model.
## 5 Summary and Conclusions  
This study focused on the diversity between green up and senescent segments of vegetation greenness seasonal cycles, which correspond to periods when terrestrial ecosystems generally release and uptake carbon dioxide, respectively. Therefore, a set of vegetation asymmetries in growth length (AsyL), vegetation greenness (AsyV) and growth rate (AsyR) were defined to characterize detailed differences in vegetation growth shapes during the two halves of the growing season. Long-term trends and the spatial distribution of these asymmetric indices were analyzed. Conclusion are drawn as follows.  
1. For the long term, the asymmetric indices of vegetation growth in Northeast China have significant interannual variability with an approximate periodicity of ten years, especially for AsyR and AsyV. The total time series of AsyV and AsyL exhibit a synchronous profile that is opposite to that of AsyR. Spatially, the average annual values and long-term trends of AsyR and AsyV are arranged with clear cluster patches that are approximately consistent with the geographic distribution of primary vegetation cover types. The AsyL, nevertheless, shows a relatively complex spatiotemporal pattern. AsyR reveals that the senescence rate in cropland and grassland was faster than the green up rate, while the opposite was true for the forest types. Trends in AsyL indicate that all plant types show a longer growing season in the senescent phase than the green up phase. However, spring mean greenness is much greater than autumn greenness in all vegetation types, except conifer forest.  
The combined effects of climatic constraints on vegetation asymmetries are pronounced when viewed both spatially and temporally. Asymmetries in temperature (AsyTemp) and rainfall (AsyPrcp) show similar temporal profiles as the vegetation asymmetries, and they jointly control changes in the three asymmetrical indices, with a roughly equivalent contribution identified by the relative importance (RI) analysis. AsyTemp and AsyPrcp contribute more to changes in AsyR and AsyV than in AsyL, while climatic asymmetries are negatively linked to AsyR, but positively correlated to AsyV and AsyL. Overall, these asymmetric indices provide for the potential to effectively depict detailed seasonal states of vegetation growth. Shifts of magnitude and sign in vegetation asymmetries over Northeast China follow a certain pattern in location and time, which helps us to understand the seasonal cycle of these terrestrial ecosystems. In future research, it will be necessary to derive more shape parameters of plant growth cycles to combine with other observed vegetation bio-physiological (such as GPP, LAI, PPAR and flux) and model data to reveal the influence of asymmetry on vegetation seasonal processes.  
This research was funded by National Natural Science Foundation of China grant number 41601478 and National Key Research and Development Program grant numbers 2018 YFB0505301, 2016 YFC0500103. The APC was funded by 2018 YFB0505301, 2016 YFC0500103.  
The author declares no conflict of interest.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### HSDFG Scheduling
#### 4.3.2 Instance Selection  
\(level(\alpha_{i})\) is the level value of instance \(\alpha_{i}\), which represents the sum of the execution times of all instances contained in the longest path from \(\alpha_{i}\) to the endpoint instance, as defined below.  
\[level(\alpha_{i})=\sum_{\alpha\in S}et(\alpha) \tag{17}\]  
\(S\) in Equation (16) denotes the set of all instances contained in the longest path from \(\alpha_{i}\) to the endpoint instance. In the LSEFT algorithm, the level value of an instance is used as the priority when scheduling, and the instance with the higher level value among all instances is scheduled for execution first. In the level value calculation of the PAPS algorithm, if there are many endpoint instances, then additional vertices need to be created to connect these endpoint instances. We propose an improved method for level value calculation, the reverse traversal to solve for level. The main idea is to use the reverse traversal of multi-tree nodes and the Algorithm 2 is described.  
```
1:HSDFG
2:each instance level value \(level(\alpha_{i})\)
3:Put all instances of HSDFG are in reverse order into queue \(F\), each instance has a set of precursor instance \(pre(ins)\)
4:\(level(ins)=et(ins)\), \(ins\in F\)
5:forall\(ins\in F\)do
6:forall\(pins\in pre(ins)\)do
7:\(new\_level=et(pins)+level(ins)\)
8:if\(level(pins)<new\_level\)then
9:\(level(pins)\gets new\_level\)
10:endif
11:endfor
```  
**Algorithm 2**Calculate Level Values of Instances  
The HSDFG is reconstructed from the SDFG, and the reconstruction process is also a simulated execution of the SDFG. Instances of actors in the SDFG need to be created, the order in which each actor is created is recorded, and all instances are added to the queue \(F\) in reverse order. Using this special structure, the exact level value can be obtained by traversing all instances in the HSDFG once.  
If there are many instances with the same level value, we consider them to be at the same level and add them to the queue \(D\). \(num_{t}(D)\) represents the number of instances in the queue \(D\) at time \(t\). For the instances in the to-be-scheduled queue \(D\), we will use the short-job-first policy, defined as follows:  
\[min(et(\alpha)),\ \alpha\in D \tag{18}\]
# An Improved Lightweight Dense Pedestrian Detection Algorithm
## 1 Introduction
This research investigates a lightweight pedestrian detection model framework to fulfill real-time pedestrian identification in congested scenarios. In summary, some structural optimizations are made on the basis of the original YOLOv5 to make the model more suitable for the specificity of real-time detection. The contributions of this paper can be summarized as follows. 1. Based on YOLOv5 to further reduce its number of parameters and computation, for the backbone feature extraction network part, GhostNet, a lightweight network, is used to replace the original CSPDarknet53, and, for the neck part, GSConv and VoV-GSCSP are used to replace it, reducing the space required for model storage, while significantly improving the detection speed in under-computing scenarios. 2. For the problem of overlapping prediction frames in dense scenes, the original IoU loss function cannot solve the prediction frame screening task well when the targets are close together. We employ SIoU as the loss function in this paper, introduce the vector angle between the real frame and the prediction frame, and redefine the related loss function to increase the model's accuracy in crowded scenarios.
## 4 Experiments
### Results
#### 4.3.2 Results of Driver Tracking  
The tracking efficacy is affected by the confidence threshold and the IoU threshold. Therefore, experiments are conducted to evaluate the tracking performance under various configurations of these parameters. The assessment is based on three metrics: MOTA, precision, and recall. As shown in Figure 5, the optimal performance is achieved when both the confidence threshold and the IoU threshold are set to 0.4, resulting in an MOTA score of 0.957, a precision of 0.97, and a recall of 0.991.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Image Size** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Latency (ms)** \\ \hline YOLOv7 & 640 px \(\times\) 640 px & 0.7454 & 0.6806 & 0.7029 & 0.4086 & 12.5 \\ YOLOv7-w6 & 1280 px \(\times\) 1280 px & 0.812 & 0.726 & 0.78 & 0.503 & 30.9 \\ YOLOv7-e6 & 1280 px \(\times\) 1280 px & 0.822 & 0.745 & 0.781 & 0.514 & 49.3 \\ YOLOv7-d6 & 1280 px \(\times\) 1280 px & 0.827 & 0.7434 & 0.7964 & 0.5101 & 60.6 \\ YOLOv7-e6e & 1280 px \(\times\) 1280 px & 0.838 & 0.703 & 0.776 & 0.513 & 71.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results of personnel and feature object detection using YOLOv7 series models.
## 5 Conclusions and Future Works  
The hybrid intelligent model described in this research predicts the output temperature of a solar panel, taking into account the input temperature, the flow through the panel, and the solar radiation. This type of model could be used to measure, for example, the thermal energy absorbed by a solar collector without using thermal energy measurement equipment.  
The model has been created with a real dataset recorder over two years to ensure that all climatology conditions are included in the dataset. Moreover, different subsets were separated from the beginning of the modeling process to validate and test the final model. The testing was performed with the 5% of the samples that had been isolated from each cluster; this test has made it possible to select the best hybrid configuration that has nine local models with artificial neural networks and support vector machines for regression.  
The validation dataset has been isolated from the rest of the dataset, at the very begging of data processing, and it therefore does not consider the clusters. The validation test has been performed with new data and the model has been used in real time; each sample is used as input and internally assigned to a local model to calculate the output. The performance values obtained in this test represent a prediction with less than 4 \({}^{\circ}\)C in MAE or less than 14.5% in MAPE (0.0255 and 11.3191 with normalized values). The obtained MSE was 30.5010, and the NMSE was 0.1144. These results demonstrate that the approach predicts more accurate values in comparison to global models.  
Regarding future lines of research, it would be interesting to consider increasing the predicted horizon in order to predict the signal values in a future time. Moreover, it may be possible to create new models for the rest of the systems; this research only focused on solar thermal energy, but the bioclimatic house has many systems that could be studied.  
Data curation, E.J. and R.A.V.V.; methodology, J.A.L.V. and T.P.; project administration, M.-I.F.-I. and J.L.C.-R. ([PERSON]); software, J.-L.C.-R. ([PERSON]) and A.G.-B.; validation, H.Q. and S.T.-A.; writing--original draft, P.C. and A.-J.P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.