esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Annual Ecosystem Water Use Efficiency, Trends and Drivers  
#### 3.3.1 Spatial Distributions of Mean Annual GPP, ET, and eWUE across the Horn of Africa  
The spatial distribution maps for mean annual GPP, ET and eWUE across the Horn of Africa during 2000-2014 can be observed in Figure 7. The highest values of GPP and ET were concentrated in southwestern highlands of Ethiopia and Kenya with shallow extents in coastal area of Kenya and Southern Somalia. The accuracy of the mean annual GPP was evaluated using GPPEC from Global FLUXCOM observations and obtained R2 and RMSE values of 0.78 and 5.4 g C m\({}^{-2}\), respectively. The mean annual MODIS ET values was also compared to the annual averaged ET from the monthly evapotranspiration product of USGS early warning system for the study area and scored R\({}^{2}\) value of 0.76. Some areas in the eastern lowlands had low values of GPP and ET as these areas received low annual precipitation and represented areas of sparse vegetation.  
Figure 6: (**a**) Pixel-based correlations of annual mean NDVI versus land surface temperature (**a**) and versus precipitation (**b**) over the Horn of Africa during 2000–2019.  
The mean annual eWUE in the Horn of Africa was 1.58 g C kg\({}^{-1}\) H\({}_{2}\)O having large spatial variability with a standard deviation of 0.51. The highest mean annual eWUE were observed in large parts of Ethiopia followed by Eritrea, while Somalia and Djibouti had the lowest mean annual eWUE. We also extracted zonal statistics based on the ESA land cover types (Figure 2). The highest mean annual eWUE were found in the cropland and forest land cover types, and the least mean annual eWUE values were observed in the sparse vegetation and wetland areas. The shrubland which is the most dominant land cover (38.9%) in the Horn of Africa had relatively lower mean annual eWUE, whereas the grassland and sparsely vegetated lands had higher standard deviations and eWUE values.
## 7 Evaluation and Results
### Localization Ratio  
Localization ratio represents the quantity of localized nodes divided by the total number of UNs. It also indicates the coverage of localization that MA can make. The higher the localization ratio is, the more successful the model. The localization ratio is given as  
\[L_{avg}=\frac{RN}{N} \tag{10}\]  
where \(RN\) is the total number of successfully localized nodes (reference nodes) and \(N\) is the total number of the original distributed unknown nodes.  
A set of 250 unknown nodes is used with different movement distances, \(d_{max}\) with the default value of standard deviation of noise, \(\sigma=3\). Since the localization ratio is almost the same in both WCL and WCWCL, we only show one of them here. Figure 17 presents the localization ratio for the implemented path models.  
When \(d_{max}\) = 35, all paths, including FLPPL and NLA_MB, provided insufficient ratio of localization. The very short movement allowance had its negative impact on the number of localized nodes. With increasing the maximum distance, the localization ratio improves. FLPPL is the only model that takes the number of neighbours of each node in its movement decision. Hence, it showed a superior performance starting with \(d_{max}\) = 70 till \(d_{max}\) = 210. Another reason is that nodes in FLPPL are able to share their own location, which helps to spread the localization information. This feature is applied in NLA_MB as well. However, while NLA_MB provides a competitive localization error, the other static models offered higher localization ratio in most cases. The distance  
Figure 17: Localization ratio all mobility models in both WCL and WCWCL.  
between each two points in the static models is farther than those in NLA_MB which means more nodes will receive the localization information than those in NLA_MB.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 5 System Description
### Basic Procedures
The substation processes it at its reception. 6. Pairing and payment. The substation monitors all the offer and request messages. When a request, with index \(j\), is received, and an offer, with index \(i\), is available:1. The substation broadcasts a message indicating the offer-request pairing: \[\text{Pairing}_{i,j}=\{\text{``Pairing''},i,j,Pub_{i}\}\] with \(Pub_{i}\) being the public key included in the paired offer message. 2. Upon receiving it, the requesting smart meter takes an unspent e-coin, denoted by \(Coin\), from its wallet and generates a random bitstring denoted by \(Receipt\). Then, the requesting meter generates and broadcasts the following message: \[\text{Payment}_{i,j}=\{\text{``Payment''},i,j,Enc_{Pub_{i}}(Coin,Receipt), \mathcal{H}(Receipt)\}\] 3. The meter who post the \(i\)th offer decrypts the ciphertext included in the previous message, extracts \(Coin\) from the resulting plaintext and checks the digital signature embedded in it. It also asks the substation to check whether \(Coin\) has been spent before by sending a message encrypted under the substation's public key, which includes a one-time ephemeral public key generated by the meter. 4. If the previous checks are satisfied, the meter is allowed to withdraw a new e-coin (the e-coin sent as a payment has been recorded as spent by the substation). The message requesting the withdrawal of this new e-coin has to include a digital signature verifiable under the ephemeral public key sent in the previous step. 5. Next, the meter broadcasts the following message: \[\text{Receipt}_{i,j}=\{\text{``Receipt''},i,j,Receipt\}\] 6. The substation checks that the hash digest of \(Receipt\) matches the value \(\mathcal{H}(Receipt)\) included in the Payment\({}_{i,j}\) message. In such a case, the transaction is completed. 7. Finally, the substation removes the \(\text{Offer}_{i}\) message from the pool of available offers. Figure 2 depicts the messages transmitted during the trading process. The process begins with a meter sending an \"offer\" message and another one sending a \"request\". When the substation has received them, it starts an execution of the \"pairing and payment\" process. As a result of this process, the offering meter will pour energy into the microgrid which is consumed by the requesting meter. Figure 2: Messages exchanged during the trading process. The initial “offer” and “request” messages are paired by the substation. Next, an e-coin payment is made, and the validity of the coin is validated. Finally, the “receipt” message concludes a proper transaction.
## 5 Decentralized Algorithm  
The centralized strategy of resource allocation proposed above is processed on the CU with the global CSI at the beginning of each TTI. It is possible that the time delay caused by processing is too long to guarantee the effectiveness of a large-scale system involving numerous TPs and UEs. An alternative method for shortening the time delay is a decentralized strategy that distributes calculations to each TP instead of the CU. Under a decentralized strategy, global CSIs are shared between TPs at the first place. Then, the resource allocation is processed at each TP independently and simultaneously, according to the known CSIs and a given strategy. In this way, the time delay of processing can be significantly decreased, even in a large-scale network. However, due to the lack of knowledge about the scheduling results of other TPs, the accuracy of the decentralized one is unavoidably worse than the centralized one.  
In this section, we propose a decentralized strategy with the similar CE-based scheduling and KKT-based power allocation to the centralized proposed above. Simulation results presented in Section 6 will prove that the decline of system performance under the proposed decentralized strategy is acceptable.
## 3 Data Assimilation Strategy  
A data assimilation system is generally composed of a dynamic model, observation operator, assimilation algorithms, and experiment datasets, including forcing data, model parameters, and observations [7]. In this study, we developed a LPJ-DA modeling framework by using LPJ-DGVM as the model operator to simulate energy and mass exchanges between the surface vegetation and land-atmosphere. To make observations consistent with the state physical and space variables, we linked assimilation algorithms and LPJ-DGVM. We used Equation (1) as an observation operator to convert the simulated foliage projective cover (FPC) state variable into the observed LAI variable when new observations became available. We applied two different assimilation algorithms, EnKF and PODEn4 DVar, as discussed in Sections 3.2.1 and 3.2.2, respectively, to utilize the observation information of GLASS LAI [46], as discussed in Section 2.4. This measure was performed to update the FPC state variable so that the related variables (GPP in this study) can be obtained. A schematic diagram of the LPJ-DA modeling framework is shown in Figure 2.
# Aortic Pressure Control Based on Deep Reinforcement Learning for _Ex Vivo_ Heart Perfusion
###### Abstract  
In _ex vivo_ heart perfusion (EVHP), the control of aortic pressure (AoP) is critical for maintaining the heart's physiologic aerobic metabolism. However, the complexity of and variability in cardiac parameters present a challenge in achieving the rapid and accurate regulation of AoP. In this paper, we propose a method of AoP control based on deep reinforcement learning for EVHP in Langendorff mode, which can adapt to the variations in cardiac parameters. Firstly, a mathematical model is developed by coupling the coronary artery and the pulsatile blood pump models. Subsequently, an aortic pressure control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. This method enables the regulation of the blood pump and the realization of closed-loop control. The control performance of the proposed DDPG method, the traditional proportional-integral-derivative (PID) method, and the fuzzy PID method are compared by simulating single and mixed changes in mean aortic pressure target values and coronary resistance. The proposed method exhibits superior performance compared to the PID and fuzzy PID methods under mixed factors, with 68.6% and 66.4% lower settling times and 70.3% and 54.1% lower overshoot values, respectively. This study demonstrates that the proposed DDPG-based method can respond more rapidly and accurately to different cardiac conditions than the conventional PID controllers.  
ex vivo heart perfusion; aortic pressure; cardiac parameters; deep reinforcement learning
## 1 Introduction  
Premolar extraction is a method that has been continuously implemented since [PERSON] proposed to overcome the lack of dentoalveolar discrepancies. However, in patients with moderate crowding, the selection of premolar extraction and non-extraction is made in consideration of the skeletal pattern of the patient and the effect on the facialesthetics. Molar distalization is a nonextraction treatment modality used to correct Class II or Class III molar relationships [1, 2] and to relieve crowding without adverse arch expansion and interdental reduction, which can jeopardize both esthetics and stability [3, 4]. There have been many attempts to distalize molars with extraoral and intraoral distalizing appliances. The main disadvantages of extraoral anchorage devices such as headgear are the need for patient compliance and the fact that they are esthetically unacceptable [5, 6, 7]. To overcome these limitations, many intraoral methods were used to distalize molars. Pendulums [8], distal jets [9], magnets [10], Franzulum appliances [11], and several other methods can be used as intraoral appliances; however, the common and unwanted side effects of intraoral appliances are anchorage loss at the reactive part, flaring of the incisors, distal tipping, and rotation of the distalized molars [12, 13, 14, 15].  
To reduce the impact of these consequences, the use of temporary anchorage devices (TADs), such as miniplates and miniscrews, has become a new orthodontic treatment strategy over the past decades [16, 17, 18, 19, 20, 21]. TADs provide stationary anchorage for various tooth movements without the need for active patient compliance and with no undesirable side effects. The nature of absolute anchorage allows for the retraction of the anterior teeth with simultaneous distal movement of the posterior teeth [22].  
Several clinical case reports showed the efficacy of TADs and the efficiency of the treatment mechanics in distalization of the whole dentition. However, there are few studieswith adequate numbers of subjects evaluating the treatment effects of these mechanics with cephalometric analysis, and no study evaluated post-treatment changes of the distalized dentition. Post-treatment stability is not a separate problem in orthodontics but one to be considered in diagnosis and treatment planning [23]. Thus, it is as important to investigate the post-treatment stability of total arch distalization as it is to demonstrate the overall effectiveness of this procedure. Hence, the purpose of this study was to quantify the treatment effects and post-treatment stability of total arch distalization in adults. We also determined whether initial skeletal pattern and treatment changes were correlated with post-treatment changes during retention.
## 2 Simulations  
Simulations in \"COMSOL Multiphysics\" were performed in order to understand the nature of the NPW in more detail. As we are interested in the development and propagation of the NPW, a time-dependent analysis was chosen. Because of a small leak, laminar flow was assumed. The tube with the crack, as described in Section 1.3, is represented by a large cylinder (the tube) connected to another smaller cylinder (the crack). Both cylinders contain air that is assumed to be initially at rest. The air is at an absolute pressure of 20 kPa (vacuum). When the crack is opened, air will enter through the small cylinder (the crack). Therefore, a pressure condition (equal to the ambient pressure) is set to this inlet surface. The no slip condition is applied to all other walls. In order to interpret the development and propagation of the NPW, the plots only contain the pressure information in the range of 20,000-20,200 Pa (as is expected from Equation (6)).  
Figure 3 shows the pressure results of the computational fluid dynamics analysis (CFD) obtained in a section in the longitudinal direction of the tube. The abrupt opening of the crack results in a discontinuity of the pressure at the crack location. Assuming that the capillary pressure is below the ambient pressure, the low pressure on the inside of the capillary meets the relative higher pressure of the atmosphere. That discontinuity results in a local increase of density and pressure at the crack location. The pressure wave further propagates through the crack and expands as a spherical wave front in the tube. Once in the tube, the spherical wave front forms two plane waves that travel in opposite directions at the speed of sound (c) through the capillary.  
The last time step in Figure 3 clearly indicates that when the NPW arrive at the end of the tube (acoustically seen as a hard wall), the amplitude of the NPW doubles because the incident wave is in phase with the reflected wave. The reflected wave then travels back to the other side of the tube where it will be detected by the other sensor. The amplitude of the arriving NPW is 100 Pa (doubled at the wall).
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 6 Conclusions  
ACM technology is one of the effective measures to resist rain attenuation in Ka-band satellite communication. The accuracy of SNR estimation is one of the main factors that affect the performance of the ACM system. This paper establishes the channel model of Ka-band satellite communication, and derives and analyzes the DA ML and M\({}_{2}\)M\({}_{4}\) estimators for the SNR of CPM in Ka-band fading channel. The SNR fitting curve, NMSE, and CRLB were used as indices for analysis of the performance of the two estimators. The two estimators cannot achieve the CRLB, and the estimation performance in the medium and high SNR worsens with the increase of SNR. When the SNR is low, the performance of the DA ML estimator is better than the M\({}_{2}\)M\({}_{4}\) estimator. The higher the number of observation points, the better the estimation performance of the two. When the SNR is high, the two estimators have similar estimation performance, and the observation points have no influence on the estimation performance. The two estimators in the commonly used SNR range have a better estimation performance and can be approximated as unbiased. Therefore, CPM can be a competitive modulation mode of the ACM technology in Ka-band satellite communication.  
This research work was supported by the Open Research Fund of State Key Laboratory of Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing (grant no. 2016-ZW-KFJ-01), the National Natural Science Foundation of China (grant no. 61403093), the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology (grant no. 2015_SGIIT_KFJ)-DH_03), and the Fundamental Research Funds for the Central universities (grant no. HEUCFP201769).  
[PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
# Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
## 5 Discussion
### Point Cloud Density and Deformation  
Some of the materials seemed to have a problematic surface. However, point clouds with a relatively high density were obtained without surface treatments. Granite contains pieces of shiny minerals. Nevertheless, we achieved a value of 101,012 points in the scanned area. Furthermore, woven carpet is a material with very fine elements, for which we assume problematic scanning and, therefore, also a very low density of the cloud of points, but this was not confirmed. For this material, we achieved relatively high average values of 169,861 points in the scanned area.  
However, we cannot take into account only the numerical data, but must also consider the less significant variability of the points that we can see on the specific results and the results of individual point clouds, which is described in the results section. For example, granite achieved a relatively high point cloud density, but the resulting deformations and the absence of points in some parts did not allow the creation of a model corresponding to reality.
## 3 Results and Discussion
### Results of Calorimetric and Thermogravimetric Tests
\begin{table}
\begin{tabular}{c c c c c} \hline
**The Type** & **CO** & **CO2** & **SO2** & **NO\({}_{\mathrm{x}}\)** \\
**of Pellets** & **[mg\(\cdot\)m\({}^{-3}\)]** & **[\%]** & **[mg\(\cdot\)m\({}^{-3}\)]** & **[mg\(\cdot\)m\({}^{-3}\)]** \\ \hline P1 & 114.23 \({}^{\mathrm{b}}\)\(\pm\) 2.7 & 1.96 \({}^{\mathrm{a}}\)\(\pm\) 0.07 & 5.42 \({}^{\mathrm{b}}\)\(\pm\) 0.14 & 62.14 \({}^{\mathrm{a}}\)\(\pm\) 1.9 \\ P2 & 90.12 \({}^{\mathrm{a}}\)\(\pm\) 3.3 & 1.72 \({}^{\mathrm{a}}\)\(\pm\) 0.09 & 2.02 \({}^{\mathrm{a}}\)\(\pm\) 0.15 & 129.45 \({}^{\mathrm{b}}\)\(\pm\) 2.9 \\ P3 & 75.61 \({}^{\mathrm{b}}\)\(\pm\) 3.5 & 1.42 \({}^{\mathrm{a}}\)\(\pm\) 0.08 & 6.01 \({}^{\mathrm{b}}\)\(\pm\) 0.17 & 45.56 \({}^{\mathrm{a}}\)\(\pm\) 2.5 \\ P4 & 59.81 \({}^{\mathrm{a}}\)\(\pm\) 2.2 & 1.22 \({}^{\mathrm{a}}\)\(\pm\) 0.10 & 1.88 \({}^{\mathrm{a}}\)\(\pm\) 0.11 & 78.94 \({}^{\mathrm{b}}\)\(\pm\) 1.6 \\ P5 & 81.92 \({}^{\mathrm{b}}\)\(\pm\) 2.4 & 1.57 \({}^{\mathrm{a}}\)\(\pm\) 0.09 & 5.88 \({}^{\mathrm{b}}\)\(\pm\) 0.19 & 82.24 \({}^{\mathrm{a}}\)\(\pm\) 1.7 \\ P6 & 66.90 \({}^{\mathrm{a}}\)\(\pm\) 1.9 & 1.39 \({}^{\mathrm{a}}\)\(\pm\) 0.09 & 1.94 \({}^{\mathrm{a}}\)\(\pm\) 0.16 & 121.21 \({}^{\mathrm{b}}\)\(\pm\) 2.1 \\ \hline \end{tabular} Statistically significant differences marked by different letters (\(p\leq 0.05\)). The data were analyzed separately for each type of materials.  
\end{table} 
Table 5: Average values of gases emitted during the combustion of pellets.
## 3 Proposed Work
### Assessment Smart Contract (SC)
(a) Automated marking  
The ADDSUBMISSION transaction might quickly deliver automatic evaluation findings. Test files for automated marking are maintained on the blockchain, ready to be delivered along with students' submission files to the appropriate extrinsic automated marking service. Just one simple automatic marking service was implemented in our demonstration system. It was a standalone web application, which performed a string equivalency check. Although this was merely a simple equivalency check, it demonstrated the blockchain's capacity to perform automated tests. (b) Assessor marking  
The course's educator uses the SUBMITRESULT transaction to upgrade a submission's evaluation outcome on the blockchain for assessor marking. Grades could be calculated in various methods, including by the educator, the applications, or the blockchain SC. Because the grades estimation formula would already be kept on every blockchain peer in the pre-defined evaluation rule, we chose to develop the last of these in our system. Teachers can use a grade description grid to submit grades. Smart contracts produce final rates based on pre-determined percentages and regulations. Teachers could then use comments to make changes to the final mark. This gives a way to make mark moderating more transparent. Figure 3: Diagram representing transactions (blue arrows) for an assessment effort. Credential generation  
The availability of evaluation transactions on the blockchain provides a reliable and unchangeable record of a student's progress. Our platform's credentials are invariably traceable to past transactions, which anyone can verify and make public if the learner so desires. Teachers can demonstrate and authorize a certificate on the blockchain using the SIGNCERTIFICATE transaction, which is optional. It is used to mimic the explicit \"degree conferral\" procedures in real-world institutions. It also acts as the last stage for any automated due diligence before a learner receives a certificate for a course. Multiple signatures may be required based on the syllabus or course structure. ```
transaction ADDSUBMISSION (input data) CREATE Submission objects if assessment is automated then GET test files from the blockchain POST files to the marking service GET results from marking service UPDATE Submission asset with returned results if results are a pass then UPDATE Certificate end if else ASSIGN a teacher to the submission end if Transaction rejected if conflicts occur return Transaction Accepted end transaction SUBMITRESULT (input data) COMPUTE result UPDATE Submission object if Submission graded as pass then UPDATE Certificate end if end transaction
```  
**Algorithm 2**ADDSUBMISSION and SUBMITRESULT transactions in the assessment SC
## 2 Materials and Methods
### Satellite Products
#### 2.3.2 MODIS FAPAR Product  
Different series of MODIS FAPAR products have been widely used in modeling GPP. The latest version is Collection 6 (C6) MOD15A2H, which is composited at an eight-day interval with a spatial resolution of 500 m. The main retrieval method is based on the 3-D radiative transfer (RT) model setting canopy spectral properties for a given biome type [49]. A Look-up-Table (LUT), which is generated by 3-D RT, is used to retrieve FAPAR [57]. A back-up method based on the statistical relationships between NDVI and FAPAR is chosen as an alternative when the main RT retrieval method fails [58].