esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Human Thorax Using Ultrasound Traveltime Tomography with Supervised Descent Method
## 4 Numerical Experiments
### Details of the Training Set and the Training Process  
When using the traditional gradient-based method to solve the traveltime tomography problem, the key step is computing the Jacobian matrix and the inverse of the Hessian matrix. However, only elements traversed by paths can be sensed in the traditional method. Usually, only few elements are traversed, which means the Hessian is generally sparse and hard to invert. In a traditional method, computing the inverse of Hessian needs the help of regularization and is time-consuming. To solve these problems, information of all the elements is added to the training set and the average descent direction is learned offline, which makes the online predicting fast and stable.  
The training set is composed of a homogeneous low-velocity background (1500 m/s) and a high-velocity rectangular block (2200 m/s). The block has two sizes (4 \(\times\) 8 and 8 \(\times\) 4). The block moves one element at a time and covers the whole DoI (38 \(\times\) 57) to make sure  
Figure 5: The transmitted waveform and the received waveform from transmitter No. 9 and receiver No. 15. The blue line is the received waveform corresponding to the left blue scale. The yellow line is the transmitted waveform corresponding to the right yellow scale. The position of the marker is calculated using AIC method, which marks the waveform is received/transmitted. The difference of the two positions is the traveltime of the signal, which is 155.75 \(\upmu\)s.  
Figure 6: The absolute value of the traveltime data difference between SPR method with \(n_{nodes}=3\) and k-Wave simulation. Colorbar: 0-4.5 \(\upmu\)s.  
that all the elements in the DoI are sensed. When moving to the edge of the DoI, the part of the block out of the region is cut. Totally \((38+7)\times(57+3)+(38+3)\times(57+7)=5324\) training models are generated. One of the training models is shown in Figure 7. Thus, the training process learns the sensitivity of traveltime with respect to velocity, which is inherently embedded with the prior knowledge on spatial correlations of velocity.  
During the training process, the initial model is set to be a homogeneous background with velocity of 1500 m/s. The maximum training step is set to be 5 for convenience. The predicting process will also last for 5 iterations and use the same initial value. The normalized model misfit is shown in Figure 8.
###### Abstract  
The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil-Sen trend analysis, the Mann-Kendall test, and the correlation coefficient) based on the 2001-2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global Land Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CI\({}_{\text{CS}}\)) and the growing season mean LAI (LAI\({}_{\text{GS}}\)) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.  
clumping index; spatio-temporal variation; driving factors; Sanjiang Plain; geographical detector model; trend analysis 202132797. [[https://doi.org/10.3390/ns13142797](https://doi.org/10.3390/ns13142797)]([https://doi.org/10.3390/ns13142797](https://doi.org/10.3390/ns13142797))
## Application of Thermoeconomics in HVAC Systems
###### Abstract  
In order to achieve a sustainable society, the energy consumption in buildings must be reduced. The first step toward achieving this goal is to detect their weak points and analyze the energy-saving potential. to detect the units with higher consumption and cost. Exergy is very useful for analyzing pieces of equipment, systems or entire buildings. It measures not only the quantity of energy but also its quality. If the exergy is combined with economic analysis, this gives rise to thermoeconomics, and the system can be checked systematically and optimized from the perspective of economics. In this work, exergy methods and thermoeconomic analysis were applied to a building thermal system. Due to its complexity, it is necessary to adapt some concepts to translate the exergy application from industry to buildings. The purpose of this work is to overcome these shortcomings and to deal with energy-saving actions for buildings. To this end, a thermoeconomic study of a facility that covers the heating and domestic hot water (DHW) demands of 176 dwellings in Vitoria-Gasteiz (Basque Country) using two boilers and two cogeneration engines was analyzed. The irreversibility associated with each piece of equipment was quantified, and the costs associated with resources, investment and maintenance were calculated for each flow and, consequently, for the final flows, that is, electricity (11.37 c/kWh), heating (7.42 c/kWh) and DHW (7.25 c/kWh). The results prove that the boilers are the lesser efficient components (with an exergy efficiency of 15%). Moreover, it is demonstrated that micro-cogeneration engines not only save energy because they have higher exergy efficiency (36%), but they are also economically attractive, even if they require a relatively high investment. Additionally, thermoeconomic costs provide very interesting information and underscore the necessity to adapt the energy quality in between the generation and demand.  
every construction; thermoeconomics; energy savings; heating and DHW facilities Article
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 2 Related Work
### Localization Using Antenna Arrays  
Antenna arrays are widely used in signals' direction of arrival (DOA) estimation today. ArrayTrack [11] presents the design and experimental evaluation of an indoor localization system that uses MIMO-based (Multiple Input and Multiple Output) techniques to track wireless clients at a very fine granularity in real-time. The ArrayTrack AP system is built on two specialized devices called Rice WARP (Windows Advanced Rasterization Platform) FPGA (Field-Programmable Gate Array) based wireless radios. However, these specialized devices are inconvenient for daily use. Besides, there are some other related works using antenna arrays for localization, e.g., [12; 13; 14; 15; 16; 17; 18].  
[PERSON] et al. [19] use MIMO interference nulling to eliminate reflections off of static objects and focus the receiver on a moving target. Then, it tracks human beings by treating the motion of a human body as an antenna array and tracking the resulting RF (radio frequency) beam. This research utilizes a technique called inverse synthetic aperture radar (ISAR), which has been used for mapping the surfaces of the Earth and other planets. ISAR uses the movement of the target to emulate an antenna array. There are some other related works based on RF beam, e.g., [20; 21; 22].
## 3 Numerical Results
### Effect of Drone Azimuth Rotation
To help readers grasp the overall trends and identify key findings of the azimuth rotation effect more effectively, a summary of the results is presented in Table 6 as follows:  
It must be noted that this work focuses on the low-altitude scenarios for LED-based drone visible light communications, where the involved drone is near the ground. Typically, these low-altitude scenarios include, but are not limited to, emergency communication, safe-protection, intelligent transportation, indoors, tunnels, and others. Therefore, the link distance between the drone and the user is quite short and less than several meters. Unlike the laser diode-based drone free space optic links for long-range transmission, the studied LED-based drone visible light communications for very short-range connection is less susceptible to the influence of external disturbance, especially atmospheric turbulence. As future work, we plan to extend the link range to tens of meters, even several hundred meters, to investigate the link performance and robustness of LED-based drone visible light communications under distinct atmospheric turbulence conditions.
# Impact of Temperature on the Moisture Buffering Performance of Palm and Sunflower Concretes
## 4 Numerical Study and Discussions
As it is shown in Figures 7 and 8, these vegetal particles have a tubular structure with interconnected pores very permeable to water vapor. The MBV values vary from 1.44 to 3.33 and from 2.01 to 4.33 g/(m\({}^{2}\)%RH) for SC and PC when the temperature varies from 10 to 35 \({}^{\circ}\)C, respectively. According to the classification of  
Figure 5: Influence of temperature on the MBV of the studied bio-based materials (PC and SC) and their classification. Figure 6: Vapor pressure and density of water vapor as function of temperature. moisture buffer values of building materials suggested in the NordTest project [7], it can be seen that sunflower concrete is classified as good for the studied temperatures, ranging from 10 to 15 \({}^{\circ}\)C, and is classified as excellent beyond this temperature range, while for the palm concrete case, it is classified as excellent for all temperatures, as shown in Figure 5. The difference between the moisture buffering capacity of the two bio-based materials can be explained by sorption curves, as shown in Figure 1. It can be seen that they are similar when the RH is lower than 50% RH, but after that, the palm concrete has a higher adsorption capacity compared to SC. This reflects a very high hygroscopic property of palm concrete which is very interesting for regulating the surrounding relative humidity thanks to its sorption capacity of vapor water. This present study shows that the temperature influences the MBV of bio-based concretes and the classification of the moisture buffer capacity. Note that in the studied range of temperatures, the MBV increases quite linearly as a function of the temperature due to the increasing water vapor pressure as well as the density of the water vapor (\(\rho_{\text{v,e}}\)) with increasing temperature. The numerical results permit establishing a relationship between the MBV and temperature. Two linear models are proposed and presented in Figure 9, \(\text{MBV}_{\text{PC}}(\text{T})\) and \(\text{MBV}_{\text{SC}}(\text{T})\) for PC and SC concretes, respectively:  
\[\text{MBV}_{\text{PC}}(\text{T})=0.0928\,\text{T}+0.9848 \tag{10}\]  
\[\text{MBV}_{\text{SC}}(\text{T})=0.0759\,\text{T}+0.5959 \tag{11}\]  
Figure 8: SEM longitudinal cross-section of sunflower pit [49]. Figure 7: SEM microstructure view of date palm fiber [48]. Based on the obtained results, a regression coefficient (slope) of 0.0844 for other hygroscopic bio-based materials can be defined, but further experimental studies need to be conducted to verify this assumption.
## 4 Conclusions  
Due to the effect of reinforcing bars on the transmission of the electromagnetic wave of GPR, the on-site inspection of the contact condition between the concrete lining and surrounding rock for hydraulic tunnels often cannot provide accurate results. In this paper, the SASW method is used as an alternative to perform this evaluation task, and the following conclusions are obtained.  
1. The finite element analysis results of different uniform or layered models show that the SASW method can accurately identify the interface contact condition between the concrete lining and bedrock, especially the existence of voids.
2. When the receiver spacing is 1.0-1.5 times that of the thickness of the target object to be inspected, the quality of the collected signal data is the best, which is conducive to improving the accuracy of defect identification.
3. Under a certain reasonable range, the impact duration has an insignificant effect on the phase spectra and dispersion curves of a concrete-weak layer model.
4. The results of the in-situ SASW inspection are consistent with those of the finite element model analysis, which can accurately tell whether the voids exist at the concrete-bedrock interface. It shows the SASW method may be applied as an effective supplement to the ground penetrating radar in inspecting the densely reinforced concrete tunnels.
5. The data processing program of the SASW method based on the MATLAB platform is accurate, convenient, and worth promoting.  
Conceptualization, X.L. ([PERSON]) and X.L. ([PERSON]); Methodology, X.L. ([PERSON]) and M.L.; Software, X.L. ([PERSON] and X.L. ([PERSON]); Writing-Original Draft Preparation, X.L. ([PERSON]); Writing-Review & Editing, X.L. ([PERSON]), J.H., and M.L.; Funding Acquisition, X.L. ([PERSON]) and Y.X.  
This research was funded by the National Key R&D Program of China (No.2018 YFC0407102), the IWHR Basic Research Fund (No. SM0145B632017, SM0145B952017, SM0145C102018), and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, IWHR (Grant No. SM0145B252018).  
The authors would like to appreciate the anonymous reviewers for their constructive suggestions to improve the quality of the paper.  
The authors declare no conflict of interest.  
Figure 20: Broken weak interlayer between concrete and bedrock.
## 6 Determinant Factors of Obesity
### Neurohormonal Regulation of Appetite
#### 6.1.2 Adipokines  
The most significant adipokine implicated in hunger modulation is leptin. The discovery of this peptide has had a substantial impact on the understanding of appetite regulation. Leptin is synthesized within white fat cells. Leptin receptors are exhibited by neurons within the hypothalamic centers responsible for appetite control. By activating POMC neurons and inhibiting NPY/AgRP neurons, leptin inhibits the sensation of hunger and leads to the feeling of satiety, thus reducing food intake and lipid accumulation. Furthermore, leptin also plays a role in modulating energy use and carbohydrate metabolism, reducing weight gain. Several genetic mutations altering the effect of leptin, the function of its receptor, as well as the POMC-aMSH pathway have been described [154; 155]. They are further detailed in Section 6.2.2.  
Despite the beneficial effects of leptin, it seems that circulating leptin levels correlate with BMI. This phenomenon can be explained by the decrease in leptin receptor sensitivity, which leads to an increase in peripheral leptin secretion [156; 157]. This mechanism is similar in principle with the hyperinsulin found in individuals with decreased peripheral insulin sensitivity, a condition which most commonly precedes the development of type 2 diabetes [158].
## 7 Performance Analysis: Reliability and Scalability
### Experiment Results  
This subsection details the performance analysis conducted given different parameters. The key concerns were related to the reliability and scalability of the system based on the changing requests.
## 4 Implementation of the Proposed UniChain System
### A University Node Reads a Record from Another University Node  
The process of reading a record that is stored in a university node from another university node utilizes the timed-based deposit-box mechanism to increase both the accessibility and the security of EARs systems.  
Suppose that there are two university nodes, a and B, where University B would like to read a specific student's record from University A. University B generates a request to read the record first, signs that generated request by its private key for authorization, and then encrypts the signed request with the public key of University A. Over HTPPS, the encrypted signed request will be sent to University A. Upon receiving the request, University A decrypts the request with its private key and then decrypts it with the public key of University B to ensure that the university is the one that it claims to be.  
First, Node A will send the ID of the student to its SRHC to return the associated RC address. University Node A then sends the filename of the record to the RC to retrieve the associated ACC address. University Node A then sends the Ethereum address and the access level request to the ACC. The ACC then forwards the Ethereum address of University B and its request for the NC to verify whether University B is an authorized university registered on the system. Upon receiving the verification from the NC, the ACC generates a new entry with the Ethereum address of Node B, the Access Level, and the \"request new level\" status, and the timestamp of the last status update. The ACC requests a change in the access level from the file owner (i.e., student), and updates both its status field to \"waiting approval\" and last-update. If the student accepts the request, the ACC updates the access level with \"temp-read\" for the applicable file with the \"approved\" status and the last-update. Once the request has been approved, the ACC sends a notification to University Node B indicating that a new access level is assigned to it. The ACC also notifies the RC to create a new entry in the DPC with the Ethereum address of University B.  
The RC then sends the Ethereum address of University B, and the file name to University Node A. The DB Manager retrieves the record and forwards the record with the received public key to the Cipher/Decipher Manager for encryption. The DBC will be updated by storing the encrypted file for a certain time specified by the Time field. Over HTTPS, University A sends the DPC address to University B to access the requested record by decrypting it using its private key. The DB Manager of Node B will update the LC entry with an encrypted log indicating the process of reading the record. The LC updates its status with \"new log\" to indicate that the new log has not been added to the blockchain yet, and the timestamp of the last status update.
## 5 Analysis on Influence Factors of Seismic Performance
### Initial Prestress  
Prestressed tendons in PSCFST piers can protect the self-centering capacity of piers, which are usually considered to be energy dissipation components. Therefore, the prestress size is also taken into account to explore the seismic performance of PSCFST piers when the initial prestress size is 52 kN, 103 kN and 155 kN, respectively. Figure 14 shows the hysteretic curve and skeleton curve of the PSCFST pier under different prestress.  
Figure 14: Comparison diagram of hysteretic characteristics. (**a**) Hysteretic curve under different prestress. (**b**) Skeleton curve under different prestress sizes.  
Figure 13: PEEQ (equivalent plastic strain nephogram). (**a**) PSCFST pier with external energy dissipation ring. (**b**) Traditional PSCFSt pier.  
Comparing the hysteretic curve and skeleton curve of the PSCFST pier under different prestress, it can be seen that when the initial prestress is 52 kN and 103 kN, the curve trend tends to be the same. When the maximum displacement is 38.9 mm, the horizontal force is 76.6 kN and 78.5 kN, respectively. When the initial prestress increases to 155 kN, it can be seen that the curve changes. When the maximum displacement is 39.7 mm, the horizontal force is 101.6 kN. It may be that the initial prestress is increased to 155 kN, which makes the connection between segments closer. Due to the extrusion of prestress, the steel pipe and concrete can more effectively participate in the reciprocating motion, which improves the lateral bearing capacity of the PSCFST pier.  
Compared with the cumulative energy dissipation curve of the pier (as shows in Figure 15), it can be seen that although the lateral bearing capacity of the PSCFST pier has been improved, the energy dissipation capacity in the three groups of models is the same, and the area of the energy dissipation ring is basically the same. It can be inferred that the size of initial prestress will not affect the energy dissipation capacity of the PSCFST pier.  
Compared with the friction energy dissipation curves of PSCFST pier under different prestress sizes (as shows in Figure 16), it can be obtained that with the increase in prestress, the friction energy dissipation of the pier has increased significantly, indicating that the increase in prestress will improve the integrity of pier and increase the friction between segmental joints, so that piers can dissipate more energy under the same displacement conditions.  
Figure 16: Friction energy dissipation curve under different prestress.  
Figure 15: Cumulative energy dissipation curve under different prestress sizes.