esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Biome-BGC model is a classic forest ecosystem carbon cycle model driven by remote sensing and plant trait data, and it has been widely applied in various regions of China over the years. However, does the Biome-BGC model have good applicability in all regions of China? This question implies that the rationality of some applications of the Biome-BGC model in China might be questionable. To quantitatively assess the overall spatial applicability of the Biome-BGC model in China's vegetation ecosystems, this study selected ten representative forest and grassland ecosystem sites, all of which have publicly available carbon flux data. In this study, we first used the EFAST method to identify the sensitive ecophysiological parameters of the Biome-BGC model at these sites. Subsequently, we calibrated the optimal values of these sensitive parameters through a literature review and the PEST method and then used these to drive the Biome-BGC model to simulate the productivity (including GPP and NEP) of these ten forest and grassland ecosystems in China. Finally, we compared the simulation accuracy of the Biome-BGC model at these ten sites in detail and established the spatial pattern of the model's applicability across China. The results show that the sensitive ecophysiological parameters of the Biome-BGC model vary with spatial distribution, plant functional types, and model output variables. After conducting parameter sensitivity analysis and optimization, the simulation accuracy of the Biome-BGC model can be significantly improved. Additionally, for forest ecosystems in China, the model's simulation accuracy decreases from north to south, while for grassland ecosystems, the accuracy increases from north to south. This study provides a set of localized ecophysiological parameters and advocates that the use of the Biome-BGC model should be based on parameter sensitivity analysis and optimization.  
forest ecosystem carbon cycle; carbon flux; Biome-BGC model; spatial applicability; data-model fusion +
Footnote †: journal: _forests_
## 3 Test Results  
### General  
For illustration, all of the specimens were labeled according to the following rule: the first group of characters represent the sheathing material (GPB: gypsum plasterboard); the second group of characters represent the loaded edge distance (10, 15 or 20 mm); the third group describes the number of layers for sheathing (S for single-layer or D for double-layer); and the last group indicates the temperature for the experiment (20 \({}^{\circ}\)C (ambient temperature) or 100 \({}^{\circ}\)C-500 \({}^{\circ}\)C). For instance, GPB20D-20 refers to a specimen with double layers of gypsum plasterboard and a loaded edge distance of 20 mm at ambient temperature. Figure 5 gives the typical load-displacement curve for the specimen GPB20D-20. Tables 1 and 2 summarize the average results and coefficient of variation (COV) for each series. \(F_{\text{mT}}\) represents the shear strength of the specimen at \(T\)\({}^{\circ}\)C; \(\Delta_{\text{mT}}\) is the recorded displacement corresponding to \(F_{\text{mT}}\) at \(T\)\({}^{\circ}\)C; \(\Delta_{\text{uT}}\) is the recorded displacement corresponding to 0.8\(F_{\text{mT}}\) on the post-peak branch of response at \(T\)\({}^{\circ}\)C; \(\Delta_{\text{eT}}\) is the recorded displacement corresponding to 0.4\(F_{\text{mT}}\) at \(T\)\({}^{\circ}\)C; and \(K_{\text{eT}}\) is the initial shear stiffness of the screw connection at \(T\)\({}^{\circ}\)C and \(K_{\text{eT}}=0.4F_{\text{mT}}/\Delta_{\text{eT}}\). \(E_{\text{T}}\) represents the absorbed energy of the connection at \(T\)\({}^{\circ}\)C, which is the area under the load-displacement curve up to \(\Delta_{\text{uT}}\). In addition, the characters for the failure mode in Tables 1 and 2 were grouped as follows: \(B\) represents the breaking of the loaded sheathing edge (Figure 6a); \(T\) represents the screw tilting (Figure 7c); and \(F\) represents the flexing fracture of the sheathing (Figure 7c). For instance, B/B + T indicates the breaking of the loaded sheathing edge alone or combined with screw tilting. In Tables 1 and 2, the scatter of the test results is significant, except for \(F_{\text{mT}}\). Both \(F_{\text{mT}}\) and \(K_{\text{eT}}\) of the screw connection decreased with increasing temperatures. However, \(K_{\text{eT}}\) of some series at 100 \({}^{\circ}\)C (for instance, GPB20S-100 and GPB15D-100) became much higher than that of the series at ambient temperature, and \(\Delta_{\text{mT}}\) of the series at 100 \({}^{\circ}\)C became much lower than those of the series at ambient temperature and 150 \({}^{\circ}\)C. No reasonable explanation is currently offered for such a phenomenon. Moreover, Tables 1 and 2 show that the shear strength of the double-layer sheathing connection was higher than but not twice that of the shear strength of the single-layer sheathing connection. Therefore, the linear superposition method is not applicable, which should be taken into consideration in the design of CFS walls with double-layer sheathing. The effect of double-layer sheathing is discussed separately in this paper.  
Figure 5: Typical load–displacement curve of (gypsum plasterboard) GPB20D-20.
## 4 Conclusions  
This study investigated a correlation of execution in the RIS-aided system cell framework and DAS-aided system cell framework, wherein a simple and precise structure for  
Figure 6: Energy efficiency vs. SNR with varied N.  
Figure 7: Area spectral efficiency vs. SNR with variable \(q\).  
the performance measurement under realistic channel presumptions was introduced. The analysis of the downlink ergodic capacity with regards to the RIS framework and DAS framework under the Nakagami-m fading channel and path loss attenuation, with the presence of interference from co-channel BSs, was simplified by utilizing an MGF-based approach. It is observed that, under ideal RIS channel assumptions and conditions, utilizing RIS at the cell edge provides better ASE than DAS, especially when the traffic activity factor (co-channel interferences) decreases. The EE for RIS improves as the number of antennas, N, increases, in comparison to DAS. However, at a high SNR of 20 dB, the ASE gap for both systems decrease to 0.005 bits/s/Hz/km\({}^{2}\), while at a lower SNR, the difference in ASE between the two systems is greater. Both systems have highest ASE when the channels in the system are all in used.  
N.A.K., A.M. and M.Y.B.A. were involved in the analysis and system model design; [PERSON] and [PERSON] produced and verified the analysis and simulation; [PERSON], [PERSON] and [PERSON] carried out the formal analysis; [PERSON] and [PERSON], writing--original draft preparation; [PERSON], [PERSON] and [PERSON], writing--review and editing; M.Y.B.A., help in securing the funding. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Telekom Malaysia Research and Development (TMRND), grant number MMUE/220001; and Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS) number MMUE/190229 (FRGS/1/2020/TK02/MMU/03/1).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiments
### Results  
Figure 11 illustrates the accuracy of the classification of 3 DCNN model on the train and validation sets. The plots suggest that the model has a good fit on the problem.  
We compare our results on the hand gesture dataset against another state-of-the-art method in Table 1. Overall, DL algorithms showed higher accuracy than the traditional ML model. The 3 DCNN model achieves 92.6% accuracy, while SVM and CNN are 60.50% and 64.28%, respectively, which are 32.1% and 28.3% lower than the 3 DCNN result. The results showed that the 3 DCNN network outperforms SVM and 2 DCNN in video classification on our hand gesture dataset. One reason for this result is that learning multiple frames in 3 DCNN provide the flexibility of a decision boundary.  
In terms of the training time and testing time of convolutional neural networks, the training time of the 3 DCNN showed higher run-time than SVM and 2 DCNN. However, all architectures could recognize the hand gesture in less than 5.29 s, which shows the feasibility of implementing neural networks to real-time systems.  
Table 2 shows the results of hand gesture recognition by our proposed method when using ensemble learning. It can easily be seen that the ensemble learning method outperforms the single 3 DCNN in terms of video classification with an ensemble of 15 3 DCNN models, which achieves 97.12% accuracy. The experimental results indicate that the 3 DCNN ensemble with five models is substantially more accurate than the individual 3 DCNN on the dataset. However, when the number of models increases to 10 and 15, there is no significant change. On the other hand, ens  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Training Time** & **Testing Time** & **Accuracy (\%)** \\ \hline SVM & 21.94 m & 2.57 s & 60.50 \\
2 DCNN & 50.00 m & 3.46 s & 64.28 \\
3 DCNN & 1h35 & 5.29 s & 92.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results with support vector machine (SVM) and two-dimensional convolution neural network (2 DCNN).  
Figure 11: The accuracy of 3 DCNN model during training.  
models also required a long training time, which is related to the expensive computational cost of a computer.  
The confusion matrix of the 3 DCNN with 15 models ensemble is shown in Table 3. As expected, the highest accuracy was obtained in the easier gestures of'swipe left (SL)','swipe right (SR)', and'meaningless (M)', while the lowest accuracy was obtained in the harder gestures 'zoom in (ZI)' and 'zoom out (ZO)'. The accuracy was reduced in the 'drag' gesture because, with the starting point and ending point, the gesture was sometimes confused with others owing to fast hand movement.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 2 SDT Concept Development and Methods Used
### Validation of the Model  
Once the model is formed, it should be tested in order to determine its accuracy and reliability. The validation of the model is then the fourth step of the process, and different measures of evaluation have been used for that purpose.  
Validation measures, i.e., numerical evaluation measures, include the acceptable range of values in a specific operating condition based on standard deviation (SD) \(\sigma\), as used in [9; 10; 18], relative error (RE), as used in [15; 18; 27], root-mean-square error (RMSE), as in [41], persistency index, as used in [41], mean absolute error (MAE), as used in [41], mean error (ME), as used in [10], and the squared sum of residuals and correlation coefficient R, as used in [11].  
Graphical evaluations of model responses have also been used, such as scatter plots, as used in [9], and histograms and linear robust regression, as used in [8], and direct comparisons of the model response to measured data have often been used, e.g., as in [15; 17; 18; 20; 21], and the validation method of the model was sometimes not specified, as in [14].  
Once it becomes available, the new data can also be used for recalibrating the model, which makes the whole process iterative. Once it is recalibrated, the model must go again through the validation process before it can be used.
## 5 Understanding Decentralised Identity Systems
### Hyperledger Indy
#### 5.4.1 Technical Architecture  
Indy's key components include validators, stewards, the DLT, DIDs, ZKPs, and the Plenum consensus algorithm. Validators are the essential nodes in charge of maintaining the distributed ledger. These validators keep the system running and consistent by validating and adding transactions to the blockchain, safeguarding the system's integrity. Indy uses multiple blockchains each for specific purposes across its network: the Domain Transactions (Domain TXs) blockchain manages domain-specific data and transaction metadata; the Pool Transactions (Pool TXs) blockchain manages validator setups and operations; and the Config Transactions (Config TXs) blockchain stores the updates to network parameters and policies [63].  
As a permissioned network, Indy handles transaction validation and ledger maintenance via steward-managed validators. Indy is designed to be distributed across several virtual machines and is generally containerized with Docker to facilitate management. The fault-tolerant Plenum consensus algorithm supports dynamic configuration modifications, but its scalability is limited by the computational requirements of its trust-based consensus mechanism. Transaction latency varies with the number of validators and transaction rates, and write latencies are consistently lower than read latencies [63].
# Ecology of Gamebirds in Namha National Protected Area, Lao People's Democratic Republic
## 4 Discussion  
### Species Detection and Occupancy  
Several extremely shy species (Table 1) that might have otherwise gone undetected were documented with camera-traps (cf. [34]). Gamebirds, especially pheasants, are often shy due to hunting, and therefore, they are under-accounted for in avifaunal inventories [5]. The camera-traps were set to detect species using lower strata of the forest, with eight primarily terrestrial species of birds recorded (the six gamebirds of this study as well as Rusty-naped Pitta (_Hydrornis oatesi_) and White's Thrush (_Zootheria aurea_); Table S1).  
Of the six species of gamebirds detected, all were assessed by IUCN as Least Concern with Decreasing Populations [7], but none are endemic and all have a broad geographic distribution [35]. Silver Pheasant and Gray Peacock-Pheasant are on the list of Category 1 Protected Species in Lao PDR. Despite their reclusive nature, we are fairly confident that we detected all species of extant pheasants in the region, as they accounted for 53% (\(n=159\)) of the 297 total avian detections made (Table S1). Moreover, these three species of pheasants were the only three accounted for by [PERSON] et al. [9] and [PERSON] et al. [10] in northern Lao PDR, as well as in extensive market surveys [16], appearing in the same order of relative detection as in our camera trapping study.  
The smaller partridges were more rarely detected; whether this is a consequence of their smaller size not triggering the camera-traps or their relative rarity is uncertain. Of the 31 detected species of birds represented, the majority were only detected on one or two occasions (Table S1). In light of the few partridge detections (\(n=10\) events total, range = 1-5 events/sp.), it is likely that they were either too small to trigger the camera traps, or rare in the region relative to pheasants. That virtually no partridge were found during eight years of market surveys [16], despite other species of small birds being common, supports the idea that partridge are indeed rare and seldom encountered.  
We did not obtain any records of Japanese Quail (_Coturnix japonica_), Blue-breasted Quail (_Synoicus chinensis_), Chinese Francolin (_Francolinus pintudeanus_), Mountain Bamboo Partridge or Green Peafowl (_Pavo muticus_), all of which geographically overlapped the region of our study site on range maps (e.g., [7]). These five species were not expected, as they all occupy more open habitats, including grassland, scrub, swamp and open woodland, and in some cases at higher altitudes [35] than our camera traps were located.
## 3 Analysis of Experimental Results
### Numerical Model Development and Validation  
The RFPA2D cyclic loading module was used for the numerical simulation. The RFPA software used in this paper, Rock Failure Process Analysis System (RFPA), is a tool based on an elastic damage model that takes into account the non-uniformity of rock materials and the distribution of defects using the intrinsic relationship of statistical damage randomness of defect distribution. We first established a two-dimensional plane strain model with a length \(\times\) height of 50 mm \(\times\) 100 mm with 50 elements in the X direction and 100 elements in the Y direction. The homogeneity of the model is 3, the density is 2477.95 kg/m\({}^{3}\), the damage criterion is the Mohr-ulomb criterion, the number of cyclic loading is 1000 times, the Poisson's ratio is 0.25, and the uniaxial compressive strength is 18.264 MPa. The model is numerically modeled according to the corresponding dimensions, and Figure 7 shows a cyclic disturbance model based on the Mohr-Coulomb failure criterion. In the sample model, a pre-pressure is applied to the upper boundary, followed by a sinusoidal cyclic loading with a setting of 1000 times, and no force is applied to the lower boundary. Taking the initial average stress of 10.96 MPa as an example, the elastic modulus of each loading step is shown in Figure 8, which can reproduce the macroscopic crack development of the specimen. The evolution of acoustic emission signals is shown in Figure 9.  
As can be seen in Figure 8, at the beginning of the cyclic disturbance loading, the specimen is relatively intact, and there are no noticeable cracks on the surface. As the axial stress increases, shear cracks are created with an inclination of 60 degrees, which gradually penetrate through the specimen. The acoustic emission events of the siltstone specimen during the damage evolution are shown in Figure 9. The center of each circle in the figure represents the location of the acoustic emission event, and the size of the circle represents the amount of energy released by the event. Each circle represents an acoustic emission event, where a white circle represents an acoustic emission event resulting from compression-shear failure, and a red circle represents an acoustic emission event from tensile failure. At  
Figure 6: Typical macroscopic damage characteristics of siltstone under different initial average stresses.
# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 2 Network Model and Design of Queues
### Design of Energy Queue  
Each node maintains an energy queue that relates to the residual energy amount. The energy queue size of node \(n\) in time slot \(t\) is \(E_{n}(t)\), which is updated as:\[E_{n}(t+1)=E_{n}(t)-\sum_{i\in O(n)}e_{ni}^{T}(t)-e_{n}^{i\equiv n}(t)\, \tag{11}\]  
where \(e_{n}^{T}(t)\) represents the energy consumption for data transmission from node \(n\) to node \(i\) in time slot \(t\). Here, the superscript \(T\) denotes _Transmission_. \(t_{n}^{T}(t)\) denotes the duration of the data transmission between node \(n\) and node \(i\) in time slot \(t\). Given that the duration of the data transmission between any two nodes in a time slot cannot be greater than the duration of the time slot, the following inequality can be derived:  
\[0\leq t_{ni}^{T}(t)\leq t_{slot}. \tag{12}\]  
Under the assumption that the time for scheduling and routing is much shorter than the data transmission duration, we can derive:  
\[e_{ni}^{T}(t)=p_{ni}^{T}(t)\cdot t_{ni}^{T}(t)\approx p_{ni}^{T}(t)\cdot t_{ slot}. \tag{13}\]  
\(e_{n}^{i\equiv n}(t)\) is the energy consumption in control packets sensing/processing by node \(n\) in time slot \(t\). In this paper, we establish that the packets sensing/processing power of each node is a constant \(p^{i\equiv n}\). The following equality can be derived:  
\[e_{n}^{i\equiv n}(t)=p^{i\equiv n}\cdot t_{slot}. \tag{14}\]  
The initial energy in \(E_{n}\) is set to be \(\theta_{n}^{E}\), and \(\theta_{n}^{E}-E_{n}(t)\) is the energy consumption of node \(n\) in time slot \(t\).
## 2 System Model
### Power Consumption Model  
Typical BS is composed of many power consumption components such as the power supply, cooling, baseband (BB), RF components, PA, and loss factors for DC-DC and feeding cable, etc. It is difficult to estimate how technology will evolve, and designing an exact model is out of the scope of this paper. In this paper, we define a sum power, \(P_{sum}\), as a tractable power consumption model, which includes some of the important LSAS power consumption components that can be expressed as:  
\[P_{sum}=P_{PA}+P_{Cir}, \tag{8}\]  
where \(P_{PA}\) is the power consumption of the PA and \(P_{Cir}\) is the power consumption of circuits, which includes baseband power consumption, \(P_{BB}\), and RF front-end power consumption, \(P_{RF}=N_{t}P_{rf}\) where \(P_{rf}\) is the RF front-end power consumption of each antenna. It is true that the PA is also a kind of circuit; however, since its operation is significantly important, and different from other circuits, we differentiate it from other circuits. Due to the beamforming effect, \(P_{PA}\) can decrease as \(N_{t}\) increases, while \(P_{Cir}\) increases as \(N_{t}\) increases.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Same Expert, Same Session (ESSESS)**} & \multicolumn{4}{c}{**Same Expert, Different Session (SEDS)**} & \multicolumn{4}{c}{**Different Experts (DE)**} \\ \cline{3-14}  & & & **Mean** & **Median** & **SD** & **Worst** & **Mean** & **Median** & **SD** & **Worst** & **Mean** & **Median** & **SD** & **Worst** \\ \hline \multirow{4}{*}{Liver} & \multirow{4}{*}{Automatic vs. Manual} & DC & 0.90 & 0.89 & 0.05 & 0.87 & 0.79 & 0.79 & 0.04 & 0.73 & 0.70 & 0.68 & 0.04 & 0.60 \\  & & CD (mm) & 1.4 & 1.4 & 0.8 & 1.8 & 2.3 & 2.4 & 0.9 & 2.7 & 3.3 & 3.2 & 1.0 & 4.0 \\  & & HD (mm) & 3.0 & 2.8 & 0.9 & 3.7 & 5.3 & 5.6 & 1.1 & 5.7 & 7.7 & 7.9 & 1.3 & 9.3 \\ \cline{2-14}  & & \multirow{2}{*}{Manual vs. Manual} & DC & - & - & - & - & 0.78 & 0.79 & 0.05 & 0.72 & 0.69 & 0.67 & 0.05 & 0.60 \\  & & CD (mm) & - & - & - & - & 2.7 & 2.6 & 1.1 & 2.9 & 3.5 & 3.5 & 1.2 & 4.1 \\  & & HD (mm) & - & - & - & - & 5.7 & 5.8 & 1.4 & 6.1 & 7.8 & 7.9 & 1.6 & 9.3 \\ \hline \multirow{4}{*}{Prostate} & \multirow{4}{*}{Automatic vs. Manual} & DC & 0.95 & 0.95 & 0.01 & 0.94 & 0.93 & 0.93 & 0.01 & 0.92 & 0.87 & 0.88 & 0.02 & 0.82 \\  & & CD (mm) & 1.2 & 1.1 & 0.6 & 2.0 & 1.7 & 1.7 & 0.7 & 2.6 & 2.8 & 2.8 & 0.7 & 3.4 \\  & & HD (mm) & 3.6 & 3.3 & 0.9 & 4.9 & 5.1 & 5.3 & 0.9 & 6.3 & 7.9 & 7.6 & 1.0 & 10.5 \\ \cline{2-14}  & & \multirow{2}{*}{Manual vs. Manual} & DC & - & - & - & 0.92 & 0.92 & 0.02 & 0.91 & 0.87 & 0.87 & 0.02 & 0.83 \\  & & CD (mm) & - & - & - & - & 1.7 & 1.4 & 0.8 & 2.5 & 2.8 & 2.9 & 0.8 & 3.4 \\  & & HD (mm) & - & - & - & - & 5.3 & 5.1 & 1.1 & 6.9 & 7.9 & 7.5 & 1.2 & 10.6 \\ \hline \multirow{4}{*}{Lung} & \multirow{4}{*}{Automatic vs. Manual} & DC & 0.89 & 0.89 & 0.05 & 0.87 & 0.85 & 0.84 & 0.05 & 0.81 & 0.76 & 0.76 & 0.04 & 0.68 \\  & & CD (mm) & 1.3 & 1.3 & 0.7 & 1.6 & 1.5 & 1.6 & 0.8 & 2.0 & 2.0 & 2.0 & 0.9 & 2.4 \\  & & HD (mm) & 2.6 & 2.5 & 0.9 & 3.1 & 3.2 & 3.4 & 1.0 & 4.1 & 5.4 & 5.5 & 1.1 & 6.4 \\ \cline{2-14}  & & \multirow{2}{*}{Manual vs. Manual} & DC & - & - & - & 0.84 & 0.81 & 0.06 & 0.80 & 0.75 & 0.75 & 0.05 & 0.66 \\  & & CD (mm) & - & - & - & - & 1.8 & 1.9 & 1.0 & 2.2 & 2.2 & 2.3 & 1.1 & 2.6 \\  & & HD (mm) & - & - & - & - & 3.6 & 3.7 & 1.1 & 4.3 & 5.8 & 5.8 & 1.4 & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Automatic vs. Manual and Manual vs. Manual evaluation metrics (Dice coefficient (DC), centroid displacement (CD), and Hausdorff distance (HD), averaged over 10 patients per tumor site, and averaged over each of the ESS, SEDS, or DE matches). SD: standard deviation. Figure 2: Example MR images of patients with the best and worst Automatic vs. Manual and Manual vs. Manual intra- or inter-observer variability Dice coefficient (DC). For each patient (P#), red box shows the tumor location with an enlarged image patch centered at tumor, along with manual and autoconut (green and magenta lines) or two manual contours (yellow and red lines). Images shown for each pair of Automatic vs. Manual and Manual vs. Manual comparisons are different dynamic images of the same patient. Detailed comparison methods are written in the figure.