esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
By linking ground measurements of NEE with MODIS data, it was demonstrated that VI time series have significant potential for predicting the onset of the growing season in evergreen conifers. The work presented in this paper is based on potentially the longest PRI time series in satellite-based vegetation studies to date, made possible by the MAIAC-processed MODIS data. While no long-term greening trends were identified at the study site over 13 years, the MAIAC-derived NDVI and PRI allowed detection of inter-annual variations in SGS dates, which could not be achieved using the standard MODIS NDVI product. The range of results generated from each dataset highlights some inherent issues with the lack of reliable, standardized methods for deriving phenology from satellite-derived VIs and flux data. The MODIS data could not describe the timings of senescence accurately since the magnitude of variation in the estimated EGS dates was of the same order as the magnitude of inter-annual variation, even in the ground data.  
The results of the study are encouraging and provide support for additional work to confirm the relationships observed and to further explore the usefulness of MODIS PRI for estimating phenological events. It would be interesting to see how well PRI performs in a wider range of vegetation types and ecosystems, even if the value of the index particularly lies in the potential for improving estimates in evergreen forests. Future work could involve processing of spatially continuous MAIAC time series data to study regional patterns of phenology. Pixel-based analysis of time series of images is for instance facilitated by TIMESAT software developed by [PERSON] and [PERSON] [36]. The program is currently limited to data with even time intervals, but efforts are being made to develop new ways to incorporate irregular time series as well as extending the smoothing techniques into the spatial domain [51].  
The study demonstrates that there are significant advantages in utilizing MAIAC-processed data for detecting phenology, in terms of both the improved noise reduction and the possibility of studying long-term PRI time-series. The use of satellite-derived PRI for detecting phenology could potentially enable systematic monitoring of changing patterns in the seasonality of evergreen vegetation and thus reduce significant uncertainties in global carbon budgets associated with boreal regions.  
We thank the National Centre of Excellence (272041) and ICOS-FINLAND (281255) funded by Academy of Finland, and the Academy of Finland (Project # 1288039). We further offer thanks to [PERSON] for his support in preparing the flux dataset used in this analysis.  
[PERSON] was the Principal Investigator and supervisor of [PERSON]. [PERSON] carried out all analysis and writing, with written contributions from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. [PERSON], [PERSON] and [PERSON] provided the processed MAIAC MODIS data. [PERSON] oversaw the running of the eddy covariance system, which provided the flux data to [PERSON], and this project.  
The authors declare no conflict of interest.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 3 Performance Evaluation
### Performance Evaluation According to the Number of UTs  
In Scenario 3, we show the QoS performance according to the traffic load and the number of UTs in the satellite network. The VBDC was applied in the resource allocation. The burstiness was one. The delay constraint was from 3-10 s with consideration of the resource allocation delay. In this scenario, the total traffic load was constant and the data transmission rate of each UT was the total traffic load divided by the number of UTs. Thus, the data size to be transmitted in each ST decreased with increasing the number of UTs. Figures 12 and 13 show the throughput and the 95 th percentile end-to-end delay according to the traffic load for the spectral efficiencies of 1, 2, and 3 bps/Hz. In Figure 12, it is shown that the maximum throughput was slightly reduced with increasing the number of UTs. For the spectral efficiency of 3 bps/Hz, the maximum throughputs for 50 UTs, 75 UTs, 100 UTs, 125 UTs, and 150 UTs were about 18 Mbps, 17.87 Mbps, 17.68 Mbps, 17.41 Mbps, and 17.13 Mbps, respectively. In the transmission of the data of a small size, the probability that the time slot of MF-TDMA was not fully filled increased, resulting in reducing the maximum throughput slightly. For this reason, the performance of the 95 th percentile end-to-end delay in the heavy traffic was degraded by the number of UTs, as shown in Figure 13. Figures 14 and 15 show the satisfaction ratio and the goodput according to the traffic load for the spectral efficiency of 3 bps/Hz. As mentioned above, it was indicated that the performance of the satisfaction ratio and the goodput in the heavy traffic was degraded by increasing the number of UTs.  
Figure 12: Cont.  
Figure 12: The network throughput at the application layer according to the traffic load (Mbps) when the number of UTs varies from 50–150 (VBDC).  
Figure 13: Cont.  
Figure 14: Cont.  
Figure 13: The 95 th percentile delay according to the traffic load (Mbps) when the number of UTs varies from 50–150 (VBDC).  
Figure 14: The satisfaction ratio when the spectral efficiency is 3 bps/Hz when the traffic load varies from 5-20 Mbps and the number of UTs varies from 50–150 (VBDC).  
Figure 15: The goodput when the spectral efficiency is 3 bps/Hz when the traffic load varies from 5–20 Mbps and the number of UTs varies from 50–150 (VBDC).  
Consequently, for IoT services such as remote control, telemetry, and surveillance of sensing data of a small size, the small data size had the effect of a slight reduction of the QoS performance. Its impact should be considered in the QoS policy in the satellite network. For example, by consideration of the data size and the maximum throughput in the network planing of the QoS policy, the number of UTs should be limited in the satellite network to prevent network congestion in dedicated networks for the IoT service.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 2 RF Energy Harvesting Principles
### Rectifier Circuit Design and Topologies  
The rectifier functions by converting the harvested RF signal by the receiving antenna to DC output voltage. The main design bottleneck in the rectifier's design is to generate DC voltage from low power input. There are two types of rectifiers: diode-based rectifiers and metal-oxide-semiconductor field-effect transistor MOSFET rectifier. When compared to MOSFET circuits, the diode-based rectifier has a lower forward voltage drop; hence, it is preferred in applications. The ability of low forward voltage diodes to achieve higher PCE makes it an optimal choice. For rectenna applications, Schottky barrier diodes are popularly adopted [104]. The half-wave rectifier is the basic rectifier topology; it is composed of a single diode D1, as depicted in Figure 5a. However, for most applications, a half-wave rectifier is insufficient despite its simplicity of design. Therefore, the full-wave rectifier is preferred. Figure 5b depicts the circuit design of a full-wave rectifier. Alternatively, the bridge rectifier (depicted in Figure 5c) exhibits better reverse breakdown voltages, higher power efficiencies, and fewer output ripples compared to its counterparts [105].  
Applications driven by high power need are enabled through a rectifier, known as the voltage multiplier, in boosting of AC to DC conversion. The voltage multiplier encompasses numerous single rectifiers connected in series [106]. The Cockcroft-Walton voltage multiplier is the commonly used topology (depicted in Figure 5d). This rectifier is similar to a full-wave rectifier, which integrates numerous stages to achieve higher voltage gains. A modified version of the Cockcroft-Walton's configuration is the Dickson multiplier, shown in Figure 5e, in which a shunt capacitor is integrated to minimize parasitic effects. The Dickson multiplier is popularly considered for low-power devices despite the tradeoffs in high power conversion efficiency due to the leakage current by the high threshold voltage within the diodes. Furthermore, the output voltage shows a significant reduction under high resistive load, thereby reducing the current supply. Nevertheless, the MOSFET-based diode has a fast switching speed but suffers from electromagnetic interference and thermal runaway; a high threshold voltage is also needed, restricting RFEH circuits' performance [107]. The Dickson multiplier is suitable for low-power applications enabled by MOSFET technology; it can be realized in system on chip (SoC) solutions by substituting the diode with a negative channel metal oxide semiconductor (NMOS), as depicted in Figure 5f.  
Figure 4: Different forms of common impedance matching networks.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 4 Proposed Method  
### Mobility-Based Federated Learning  
With respect to federated learning, this work considers two models: a conventional model and a mobility-based model. In particular, the vehicular knowledge networking introduced in the previous section efficiently identifies and resolves federating issues in the processes of client selection and communication, such as bias in training data, fairness, and non-IID arising from two features of IoV (vehicle mobility and the time-space validity of data) [3]. In this sense, this work takes it as our mobility-based federated learning model. Figure 3 illustrates the model's architecture and operations. Each vehicle periodically collects its own semantic information (i.e., knowledge) that includes its location, training environment, and kinematic information (step 1 in the figure). In the process of participant selection, vehicles share their own knowledge with a coordinator, who then identifies which vehicles have the necessary data for model training and invites appropriate ones to join.
## 3 Experiment and Analysis
### Field Test Design  
In order to evaluate the accuracy and reliability of the proposed AMG combined cycle slips detection and repair method, two experiments are designed. In Experiment 1, the BDS observation data under a severe multipath environment were collected from Yingxue Lake on the Shandong Jianzhu University (SDJZU) campus to evaluate the influence of the multipath on the cycle slips' detection. In Experiment 2, the BDS observation data in a weak multipath environment was collected from a roof of SDJZU to verify the feasibility of the proposed algorithm. The experimental scene is shown in Figure 2, and in both experiments, the GNSS base station was located on the top of another building. The data acquisition time was DOY96 and DOY100 of 2022, and the sampling interval was 1 s. The GNSS equipment was the chncav i70 II receiver, which could receive BDS3 dual-frequency signals. The sampling time of the two experiments was 2000 and 2500 epochs, respectively. In order to test the performance of the cycle slips' detection and repair in a multipath environment, pseudorange and carrier phase observations of the B1c and B2a frequency of the C38 satellite (MEO satellite) and C40 satellite (IGSO satellite) in Experiment 2 were selected, and the multipath effect was simulated in 500-2500 epoch of the observed data, and the cycle slips of 1-8 cycles were artificially added. The experimental data before and after adding simulated cycle slips were processed and analyzed with the method proposed in this paper.  
Figure 2: Experimental area and scene: (**a**) severe multipath environment; (**b**) weak multipath environment.
## 4 Experimental Results
### Experimental Results on Synthetic Images
However, our presented approach requires longer CPU time than the variant approach of [PERSON] and [PERSON] [8], because the former adopts a complex high-order approximation scheme. Figure 2: The original height maps: (**a**) the sphere; (**b**) the vase. Figure 3: Experimental results of the synthetic sphere: (**a**) intensity image generated by Figure 2a with the parameter set: \(\sigma=0.2\). (**b**) reconstructed height map using the variant approach of [8]; (**c**) error map of (**b**) with Figure 2a; (**d**) reconstructed height map using the approach of [20]; (**e**) error map of (**d**) with Figure 2a; (**f**) reconstructed height map using our presented approach; (**g**) error map of (**f**) with Figure 2a. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{3}{c}{**Sphere**} & \multicolumn{3}{c}{**Vase**} \\ \cline{2-7}  & **MAE** & **RMSE** & **CPU time** & **MAE** & **RMSE** & **CPU time** \\ \hline Variant approach of [8] & 0.6903 & 0.8380 & 0.006 s & 0.5464 & 0.6515 & 0.005 s \\ Approach of [20] & 1.0637 & 1.4887 & 0.276 s & 0.6852 & 1.0867 & 0.217 s \\ Presented approach & 0.0134 & 0.0415 & 0.093 s & 0.0793 & 0.1537 & 0.067 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of approaches for the synthetic images. Figure 4: Experimental results of the synthetic case: (**a**) intensity images generated by Figure 2b with the parameter set: \(\sigma=0.2\). (**b**) reconstructed height map using the variant approach of [8]; (**c**) error map of (**b**) with Figure 2b; (**d**) reconstructed height map using the approach of [20]; (**e**) error map of (**d**) with Figure 2b; (**f**) reconstructed height map using our presented approach; (**g**) error map of (**f**) with Figure 2b.
# Modularity-Based Incremental Label Propagation Algorithm for Community Detection
## 2 Methods and Materials
### Evaluation Metrics
#### Modularity-based label updating  
Incremental label propagation can greatly reduce the randomness of LPA to obtain more stable community partition. However, when there are two or more nodes having the highest intensity in step (3), one has to randomly select one from them so that the solution derived from incremental label propagation may not be unique. Moreover, the solution may not be the best partition. To improve the performance of our method, MILPA uses the modularity gain as an optimization function to fine-tune the labels of nodes based on the initial community partition. In particular, one calculates the modularity values before and after a node is moved to the community of its neighbors from its original community. This can be seen in Figure 1, before and after node \(i\) in community \(C_{1}\) is moved to community \(C_{2}\), the modularity respectively is  
\[Q_{before}=\frac{l_{c_{1}}}{m}-\left(\frac{d_{c_{1}}}{2m}\right)^{2}+\frac{l_{ c_{2}}}{m}-\left(\frac{d_{c_{2}}}{2m}\right)^{2}, \tag{6}\]  
\[Q_{after}=\frac{l_{c_{1}}+k_{i_{c_{1}}}}{m}-\left(\frac{d_{c_{1}}+k_{i}}{2m} \right)^{2}+\frac{l_{c_{2}}-k_{i_{c_{2}}}}{m}-\left(\frac{d_{c_{2}}+k_{i}}{2m} \right)^{2}. \tag{7}\]  
where \(k_{i,c_{1}}\) denotes the number of edges connected to node \(i\) in community \(C_{1}\) and \(k_{i,c_{2}}\) denotes the number of edges connecting node \(i\) with nodes in community \(C_{2}\).  
Therefore, one has the modularity gain  
\[\Delta Q=Q_{after}-Q_{before}=\frac{1}{m}\bigg{[}k_{i,c_{1}}-k_{i,c_{2}}-\frac{ k_{i}(d_{c_{1}}-d_{c_{2}}+k_{i})}{2m}\bigg{]}. \tag{8}\]  
If the number of the label of a node's neighbors is more than two, there will be multiple communities for the node to move in. One can compute the \(\Delta Q\) generated by each move and select the community with the largest \(\Delta Q\) (if \(\Delta Q>0\)). If there are two or more communities with the optimal \(\Delta Q\), then one moves this node to one of them randomly. If the maximum of \(\Delta Q\) is not positive, then this node remains in the original community. Besides, when a node is moved to a new community, its label is also updated. Until the labels of all nodes no longer change or a specified number of iterations is reached, our MILPA method yields the final community partition.
## 2 Governing Equations for Fractured Unsaturated Porous Media under the Non-Isothermal Condition
### Balance of Moisture  
Moisture movement in multiphase porous media can be divided into two flows in liquid and vapor phases. The movement of moisture is under the influence of temperature and moisture content gradient and can be given as follows:  
\[\frac{q}{\rho_{w}}=\frac{q_{\text{v}up}}{\rho_{w}}+\frac{q_{\text{l}ig}}{\rho _{w}}=-D_{T}\
abla T-D_{\theta}\
abla\theta-D_{w}\
abla Z \tag{1}\]  
In Equation (1), the first, second, and third terms of the right-hand side indicate the moisture movement due to temperature gradient, moisture content gradient, and elevation difference. \(D_{T}\) and \(D_{\theta}\) denotes the thermal moisture diffusivity and isothermal moisture diffusivity. \(\
abla Z\) is the elevation difference and \(D_{w}\) is the gravitational coefficient or the water permeability that takes effect from non-isothermal conditions of unsaturated porous media (Figure 1b) with the following form:  
\[D_{w}=K_{w}=\left(\frac{\
u_{r}}{\
u_{T}}\right)K_{w}(S_{w})K_{int}(n) \tag{2}\]  
where \(\
u_{r}\) and \(\
u_{T}\) are the dynamic viscosity of water at the current temperature and reference temperature, respectively. \(K_{w}(S_{w})\) is the relative permeability that reflects the fluid properties [23], and \(K_{int}\) can be obtained as follows:  
\[K_{int}(n)=k_{w0}\ 10^{q_{w}e}I \tag{3}\]  
where \(k_{w0}\) is a reference permeability, \(\alpha_{w}\) is the material properties, and \(e\) is the void ratio of porous matrix.  
Assuming phase change between liquid and water vapor, the continuity equation for the moisture phase of deformable porous media in non-isothermal condition could be given as follows:  
\[\frac{\partial\theta_{m}}{\partial t}=\frac{\partial(nS_{w}\rho_{w}+n(1-S_{w })\rho_{v})}{\partial t}=-div(\rho_{w}(U+V)) \tag{4}\]where \(U\) and \(V\) are water and water vapor velocities, respectively; \(\theta_{m}\) is the moisture content; \(n\) is the solid phase porosity; \(\rho_{w}\) and \(\rho_{v}\) are water and vapor densities, respectively; and \(S_{w}\) is the degree of saturation \((S_{w,r}\leq S_{w}\leq 1)\), where \(S_{w,r}\) is the residual degree of saturation. One novelty of works of [PERSON] et al. ([6; 24]) is the incorporation of the changes of saturation degree due to the thermal effects into the state surface of saturation degree based on experimental evidence. This state surface is shown in Figure 1a, and can be given as follows:  
\[S_{w} = \left[(1-S_{w,r})(1+(\alpha_{VG}|s|)^{n_{VG}})^{-(1-\frac{1}{n_{VG }})}+S_{w,r}\right]\exp(d_{s}(T-T_{0})) \tag{5}\]
## 2 Materials and Methods
### Case-Study
#### 2.4.3 Ground-Truth Data  
The ground-truth data were obtained from 107 vineyard blocks in the Languedoc-Roussillon region (Figure 3). These were all commercial productive blocks with characteristics representative of the Languedoc-Roussillon wine region in terms of plantation density, management practices and diversity of varieties. All blocks were non-irrigated. These blocks were selected because they all showed some effect related to the heat wave, such as stalled development, leaf burn and leaf drop.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sentinel-2 Band** & **Central Wavelength** & **Bandwidth** & **Spatial Resolution** \\  & **(nm)** & **(nm)** & **(m)** \\ \hline Band 1–Aerosol & 442.7 & 21 & 60 \\ Band 2–Blue & 492.4 & 66 & 10 \\ Band 3–Green & 559.8 & 36 & 10 \\ Band 4–Red & 664.6 & 31 & 10 \\ Band 5–Vegetation Red Edge & 704.1 & 15 & 20 \\ Band 6–Vegetation Red Edge & 740.5 & 15 & 20 \\ Band 7–Vegetation Red Edge & 782.8 & 20 & 20 \\ Band 8–NIR & 832.8 & 106 & 10 \\ Band 8A–Vegetation Red Edge & 864.1 & 21 & 20 \\ Band 9–VNIR & 945.1 & 20 & 60 \\ Band 11–SWIR & 1613.1 & 91 & 20 \\ Band 12–SWIR & 2202.4 & 175 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral bands for the Sentinel-2 satellite considered by the analysis.  
The severity of this effect on each of the 107 vineyard blocks was assessed locally by the winegrowers and advisors. Severity was evaluated several weeks after by an estimation of percentage yield loss. There was an acknowledgement that it was sometimes difficult to attribute the losses to the heat alone. Figure 6 summarizes the distribution of the 107 blocks in relation to estimated yield loss.
## 3 Analysis of Numerical Simulation Result
### Analysis of TBM Cutterhead Model
#### 3.3.3 Correlation between Compressive Force and Cutterhead Torque  
The calculated compressive force on the cutterhead could be evaluated as part of the TBM's total thrust. Figure 16a shows that the compressive force and torque on the cutterhead were related by a regression equation with an exponential function. The cutterhead torque and TBM thrust would thus be closely related as the TBM advanced. Figure 16b shows the ratio of compressive force and torque as the TBM advanced. The ratio gradually decreased as the cutterhead's rotation increased from 1.0 to 3.0 \(\text{rev}/\text{min}\).  
Table 7 shows the calculated average and the SD of the ratio for different operating conditions. Case 2 shows the highest average ratio of about 3.72, and Case 11 shows the lowest SD of the ratio. Overall, the average and SD of the ratio decreased as the cutterhead RPM increased. The ratio of thrust and torque was a critical indicator for evaluating a TBM's excavation performance. If the TBM met adverse geological conditions, the ratio increased abruptly. As successful excavation generally showed low fluctuation in the ratio as the TBM advanced, Case 11 had the most appropriate conditions in terms of operational management.  
Figure 16: (**a**) Correlation of compressive force and torque. (**a**) Torque with respect to compressive force and (**b**) compressive force/torque with respect to simulation time.  
Figure 15: (**a**) Driving power of the TBM cutterhead with respect to TBM advance. (**b**) Average driving power with respect to cutterhead RPM.