esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation  
In this section, a two-tier downlink HWSN is considered with MIMO-SWIPT, where a femtocell sink node is deployed with a macrocell sink node, as shown in Figure 1. Following [40], the smart antenna technology is applied for the sink nodes. Moreover, the femtocell sink node serves \(K\) HP-SNs and \(L\) LP-SNs, whereas the macrocell sink node serves \(M\) M-SNs. For the sake of notational simplicity, it is assumed that the \(m\)-th M-SN, the \(k\)-th HP-SN and the \(l\)-th LP-SN are denoted by M-SN\({}_{m}\), HP-SN\({}_{k}\), and LP-SN\({}_{l}\), respectively. The system parameters that we have considered are defined in Table 1. In the smallcell of this HWSN system, two different types of receivers are considered: (i) one cluster of HP-SNs (such as low-power receivers), denoted by \(\mathbb{K}=\{\text{HP-SN}_{1},\ldots,\text{HP-SN}_{K}\}\), which is located far from the femtocell sink node, (ii) one cluster of LP-SNs (such as sensors), denoted by \(\mathbb{L}=\{\text{LP-SN}_{1},\ldots,\text{LP-SN}_{L}\}\), which are located close to the femtocell sink node. It is assumed that each M-SN and HP-SN have single receive antenna, LP-SN is equipped with \(N_{E}\) receive antenna, whereas the macrocell sink node and femtocell sink node are equipped with \(N_{M}\geq M\) antennas and \(N_{F}\geq K+LN_{E}\) transmit antennas, respectively. This system operates as follows: the macrocells share their frequency spectrum with the femtocell to improve the spectral efficiency, whereas the confidential messages from the macrocell sink node and the femtocell sink node may be intercepted by LP-SNs. Additionally, all HP-SNs employ the PS scheme to perform information decoding (ID) and energy harvesting (EH) simultaneously, as shown in Figure 2.  
Figure 1: The system model of MIMO Secured HWSN with SWIPT. As an active user in the HWSN, we assume that LP-SN can harvest energy to extend its battery life from the ambient radio frequency (RF) signals. However, LP-SNs may be potential eavesdroppers due to intercepting the information from the femtocell sink node to HP-SNs. Thus, to provide secure transmission in this HWSN, the femtocell sink node applies the AN to transmit beamforming, which not only acts as interference to the LP-SNs, but also supplies energy to the HP-SNs. The secure signal vector \(\mathbf{s}\) from the macrocell sink node and \(\mathbf{x}_{s}\) from the femtocell sink node can be written as  
\[\mathbf{s} = \sum_{m=1}^{M}\mathbf{v}_{m}s_{m},\ m=1,...,M,\] \[\mathbf{x}_{s} = \sum_{k=1}^{K}\mathbf{w}_{k}s_{s,k}+\mathbf{z},\ k=1,...,K,\]  
where \(\mathbf{v}_{m}\in\mathbb{C}^{N_{p}}\) and \(\mathbf{w}_{k}\in\mathbb{C}^{N_{s}}\) define the transmit beamforming vector from the macrocell sink node aiming at M-SN\({}_{m}\) and from the femtocell sink node aiming at HP-SN\({}_{k}\), \(s_{m}\) and \(s_{s,k}\) are the information-bearing signal intended for M-SN\({}_{m}\) and HP-SN\({}_{k}\) with satisfying \(\mathbb{E}\{s_{m}^{2}\}=1\) and \(\mathbb{E}\{s_{s,k}^{2}\}\ =\ 1\), respectively, and \(\mathbf{z}\) represents the energy-carrying AN.
## 4 Simulation Verification  
The simulation parameters and environment for this run are shown in Table 2 below.  
Following the approach outlined in reference [12], the factor graph selection in this simulation experiment fixes the first six layers of the factor graph and reorders the last four layers. Simulations are conducted for a total of 4! factor graph configurations. The decoder corresponding to the original factor graph is denoted as \(L_{24}=(l_{g},l_{8},\cdots,l_{0})\) (with the sequence number determined by the magnitude of the four-digit number arranged in four layers), as shown in Figure 5. After simulation verification, it is found that \(L_{23}=(l_{g},l_{8},l_{6},l_{7},l_{5},\cdots,l_{0})\), \(L_{18}=(l_{8},l_{9},l_{7},l_{6},l_{5},\cdots,l_{0})\), \(L_{17}=(l_{8},l_{9},l_{6},l_{7},l_{5},\cdots,l_{0})\), \(L_{22}=(l_{9},l_{7},l_{8},l_{6},l_{5},\cdots,l_{0})\) are the factor graphs with the best decoding performance, which aligns with the results in reference [12]. Therefore, in this experiment, when \(L=5\), the \((L_{24},L_{23},L_{22},L_{18},L_{17})\) factor graph is selected as the BP decoder; when \(L=10\), the \((L_{24},L_{23},L_{22},L_{21},L_{19},L_{18},L_{17},L_{14},L_{12},L_{10})\) factor graph is chosen as the BP decoder. (Note: The specific factor graphs selected for \(L=5\) and \(L=10\) are omitted here and should be filled in based on the actual simulation results).
## 4 Practical Explanations and Implications
### Channel Decoder Advancement  
Every time we make adjustments to \(\widehat{\alpha}(u,\zeta)\), \(\widehat{\Omega}(u,\zeta)\), and \(\widehat{\widehat{\mathbf{h}}}^{(u)}(\zeta)\) for a particular user \(u\), we have to recalculate the a posteriori probability \(\Pr\left(\widehat{d}_{z}^{(u)}(m)\middle|\mathbf{y}_{u},\widehat{\alpha}(u, \zeta),\widehat{\Omega}(u,\zeta),\widehat{\widehat{\mathbf{h}}}^{(u)}(\zeta) \right)\). This calls for a reset of the channel decoder, which in turn leads to several rounds. In order to lessen this burden, we use an embedded assessment method [53], in which the channel decoder is not refreshed when the parameters \(\widehat{\alpha}(u,\zeta)\), \(\widehat{\Omega}(u,\zeta)\), and \(\widehat{\widehat{\mathbf{h}}}^{(u)}(\zeta)\) are upgraded, but the extrinsic and a priori probabilities from the previous round of the channel decoder are preserved. In this case, the extra work required by the recommended iterative process is manageable.
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 2 Materials and Methods  
### Site-Level Observations  
We selected 197 sites from the FLUXNET2015 dataset for model development and validation, each with at least 1-year consistent observations of carbon and water fluxes, as well as meteorological measurements after 2001 (Figure S1 and Table S1). These sites were categorized into ten PFTs according to the International Geosphere-Biosphere Program (IGBP) land cover classification scheme. At each site, meteorological variables including total shortwave radiation (SW, W m\({}^{-2}\)), surface air pressure (Ps, hPa), air temperature (Tair, K), total precipitation (Pre, mm), wind speed (Wind, m s\({}^{-1}\)), and vapor pressure difference (VPD, kPa) were observed at half-hour intervals. In addition, carbon and water fluxes, such as net ecosystem exchange (NEE) and latent heat (LH), were measured simultaneously. In this study, we used the carbon fluxes of GPP (umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) derived from the mean values of daytime (GPP_DT_VUT_MEAN) and nighttime (GPP_NT_VUT_MEAN) fluxes based on the NEE-partitioning methods. For water fluxes, ET (mm hr\({}^{-1}\)) was calculated from the observed LH using the following method [40]:  
\[ET=\frac{LH}{a\cdot\rho_{water}} \tag{1}\]  
Here, \(a\) is the latent heat of the vaporization of water (2.45 KJ g\({}^{-1}\)), and \(\rho_{water}\) is the density of water (1 g cm\({}^{-3}\)). All datasets were aggregated into 8-day timescales to build ML models.
## 4 Discussion
### Comparison of the ANN-Based ET Model with Existing ML-Based ET Models  
ML algorithms have been more and more widely used to estimate ET on a regional or global scale. In this study, the most widely used ANN algorithm is used to improve the accuracy of the PM equation to estimate cropland ET on a regional scale. There are also many studies that use other ML algorithms to estimate ET, e.g., [PERSON] et al. [63], [PERSON] and [PERSON] [64], [PERSON] et al. [29], [PERSON] and [PERSON] [59], [PERSON] et al. [31], and [PERSON] [65]. These studies literally showed different performances of different ML-based ET models. However, it should also be noted that the performance metrics of ET models could vary between different regions, validation data sources, temporal scale of validation, and so on. For example, the ML models estimating the reference ET usually show higher performance metrics than the actual ET models [64; 66; 67], as reference ET was calculated from only a few meteorological factors. If different data sources are used in modeling ET using the ML algorithm, the efficiency of the ET model can also be different. For example, [PERSON] et al. [67] showed that the performance of the ML algorithm (R\({}^{2}\) = 0.701-0.995, RMSE = 0.106-0.637 mm d\({}^{-1}\)) in estimating reference ET were significantly different between eight meteorological stations that represented the eight main climate types of China. [PERSON] et al. [31] showed similar results in modeling reference ET using the ML over nine meteorological stations in the arid region of Northwest China (R\({}^{2}\) = 0.844-0.969, MAE = 0.268-0.635 mm d\({}^{-1}\)). The ET model focusing on the daily scale also produces different performance metrics from the hourly scale ET model. [PERSON] and [PERSON] [65] revealed better performances of the deep learning-based models in estimating daily reference ET on a daily scale as compared to the models on an hourly scale, with R\({}^{2}\) increased from 0.78-0.88 to 0.87-0.91, and RMSE decreased from 0.56-0.73 to 0.47-0.60 mm d\({}^{-1}\). The above studies show that the performance of the ET models can differ under different temporal scales. The performance metrics of the hybrid model in our study are in line with the range of those ML-based ET models.
## 5 Summary and Conclusions  
A study was performed in which a frequently used BLE for dual-wall configurations that are commonly used on spacecraft to protect them against the MMOD environment was modified to more properly include the effects of any stuffing between the walls as well as the actual material properties of the walls and the impacting projectile. By comparing the predictions for ballistic limit diameter of the modified and original versions of this BLE, we found that there was, overall, a marked improvement in the response prediction ability once the modifications were introduced into the NNO BLE. This improvement was also evident when comparing original and modified NNO BLE predictions against empirical response data that were not used in developing these modifications. Using this modified version of the NNO BLE for these kinds of dual-wall systems will result in assessed mission risk values that would be more reflective of the actual spacecraft wall designs being used.  
This research was supported by funding from the NASA Engineering Safety Center.  
Not applicable.  
The data used in this study are wholly available in this article.  
The author wishes to acknowledge the support provided by the NASA Engineering Safety Center that made this study possible. The author is also grateful to [PERSON], [PERSON], and [PERSON] for performing the hydrocode simulations that were used in this study, and to [PERSON] and the NASA team that performed the recent tests that were part of this study.  
The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Original NNO BLE** & \multicolumn{2}{c}{**Modified NNO BLE**} \\ \hline P predicted as P & 40 & 45\% & 42 & 47\% \\ \hline P predicted as NP & 4 & 4\% & 1 & 1\% \\ \hline NP predicted as P & 29 & 33\% & 14 & 16\% \\ \hline NP predicted as NP & 16 & 18\% & 32 & 36\% \\ \hline \end{tabular}
\end{table}
Table 5: Overview of Original and Modified NNO BLE Performance.
## 3 Results and Discussion
### Catalyst Reusability Tests  
Besides the activity, the reuse of a heterogeneous catalyst is indeed very important for all the reactions, including the transesterification in our case. The transcarbonation reaction of glycerol with ethylene carbonate was carried out for 4 reaction cycles under optimal reaction conditions (that is to say, amount of catalyst loaded into the reactor: 2% \(w/w\) glycerol, EC:Gly (molar ratio: 2:1), temperature 80 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, 100 \({}^{\circ}\)C and 110 \({}^{\circ}\)C, the reaction time fixed at 4 h). Having a cycle count of 4 is a well-accepted practice in catalyst stability reports in the open literature [46; 47]. Additionally, at the end of each cycle, the catalyst was removed by centrifugation and oven-dried at 35 \({}^{\circ}\)C overnight, then reused for the next reaction cycle using fresh glycerol and glycerol carbonate. According to Figure 14, the recycled crude smectic catalyst could be successfully recycled with a slight loss of catalytic activity observed after each reaction cycle. In more detail, the conversion of glycerol decreased from 65.5% to 35.2 at 80 \({}^{\circ}\)C, at 90 \({}^{\circ}\)C the conversion is observed from 82.1% to 62%, from 85.2% to 65.8% at 100 \({}^{\circ}\)C after 4 reaction cycles, and at 110 \({}^{\circ}\)C a conversion equal to 98.3% in the first cycle was decreased to 81% in the fourth cycle.  
These data reveal that the catalyst can be used for at least four operating cycles with a drop in yield of 10.1% in glycerol carbonate compared to that using the fresh catalyst. However, the trend observed in the yield at 4 h changes notably from the lowest temperature, with a clear grace-period behavior, to the highest temperature, where a stable 80% of the yield with fresh catalyst at 4 h is appreciated in the fourth cycle. This smectite is rich in metallic carbonates, which probably act as a mild basic catalyst. Carbonates are known to reduce their activity in the presence of water and carbon dioxide, due to a progressive neutralization to bicarbonate, with low alkalinity, while water build-up in the reaction zone due to the clay hygroscopicity reduces catalyst activity in the transcarbonation  
Figure 14: Reusability cycles for crude smectic catalyst at different temperatures, (**A**) at 80 \({}^{\circ}\)C, (**B**) at 90 \({}^{\circ}\)C, (**C**) at 100 \({}^{\circ}\)C and (**D**) at 110 \({}^{\circ}\)C. The standard % error range is \(\pm\)0.5 to 5%.  
reaction by promoting carbonate hydrolysis to CO\({}_{2}\) and ethylene glycol [43]. Therefore, operating at temperatures higher than 100 \({}^{\circ}\)C ensures a low water environment.
## 3 Tunnel Stability Based of In-Site Monitoring
### Analysis of Surface Settlement  
The time graph for vertical displacement of each point is shown in Figure 4, and the surface settlement curve is shown in Figure 5.  
It can be seen from Figure 4 that with the advance of the working face, the settlement of each measuring point shows the following main processes [22; 23; 24]: (1) Sharp deformation. It lasted about 18 days, and the settlement rapidly occurs during this initial phase. On average, each monitoring point experiences approximately 76% of its final settlement. (2) Slow deformation. It lasted about 12 days. A slower settlement process ensues, and the average settlement at each measuring point contributes to about 20% of the final settlement. (3) Deformation stabilization. After monitoring for 31 days, the subsidence amount of each  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Primary Supports**} & \\ \hline
**Anchor** & **Reinforcing Mesh** & **Shotcrete** & **Steel Arch** & **Secondary Lining** \\ \hline \(\varphi\)22, L = 6.0 m, spacing is \(75\times 150\) & \(\varphi\)8(double layers), \(25\times 25\) cm & C25, thickness is 28 cm & I22a, spacing is 75 cm & C35 reinforced concrete, thickness = 50 cm \\ (longitudinal \(\times\) ring) & & & & thickness = 50 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of tunnel supports.  
Figure 3: Monitoring point layout of vault subsidence and horizontal convergence.  
Figure 2: Monitoring point layout of surface settlement.  
measuring point is very small, and the subsidence rate approaches zero, indicating that surface subsidence has reached a stable state.  
Figure 5 shows the following: (1) The change trend of the curve for settlement of the surface is consistent with that calculated by Peck theory [1; 19], and the final surface settlement is symmetrically distributed with the tunnel center line as the symmetry axis. (2) The smaller the distance between the monitoring point and the central line of the tunnel, the greater the settlement. On the contrary, the greater the distance between the monitoring point and central line of the tunnel, the smaller the settlement. The settlement of measuring point No.5 above the middle line of tunnel is the largest (15.36 mm), and the values for measuring points No.1 and No.9 are 6.20 mm and 5.42 mm, respectively. (3) The calculated width coefficient of the ground subsidence tank is 1.91, and the maximum ground subsidence value is 15.25 mm, which is only 0.72% less than the field monitoring value. The determination coefficient is 0.981 and residual sum of squares is 1.229, thus indicating that Peck theory can well predict the surface settlement law of shallow soft-rock tunnels.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 5 Hierarchical Manipulation Planning
### Motion Level Planning  
After sampling the object poses on the table and generating the corresponding grasps, the motions to move the object and change the grasp poses are generated in motion level planning. The primitive motions include the following:(i) pivoting motion, which is a transfer motion to change the object configuration without changing the grasps, (ii) tumbling motion, which includes both transfer and transit motions to rotate the object around one edge and change its base surface, and (iii) regrasping of an object in SP involving transit motion, which changes the grasp configurations without affecting the object configuration, and regrasping of an object in USP, which involves both transit and transfer motions.
## 6 TTL Estimation
### Results under \(L\)-Type Offset  
#### 6.2.1 Impact of Pointing Angle Noise  
The same conditions as in Section 6.1.1 were applied, except that the offsets were set to \(\Delta x=1500\,\mathrm{mm}\) and \(\Delta y=\Delta z=0.5\,\mathrm{mm}\) for each satellite. Four linear and two quadratic coupling factors \(\{p_{x,1},p_{y,1},p_{z,1},p_{x,2},p_{y,2},p_{z,2}\}\) were estimated. Figure 11 shows the TTL estimation error and Table 6 shows the TTL coupling factors under different cases. Taking the angle biases into account, the large longitudinal offsets yield additional linear coupling terms according to Equation (29). These terms were absorbed by the linear coupling factors in the TTL estimation. The pitch and yaw angle biases \(\{\Delta\theta_{y,1},\Delta\theta_{z,1},\Delta\theta_{y,2},\Delta\theta_{z,2}\}\) we used here are set to \(\{500,-300,-400,700\}\,\mathrm{\SIUnitSymbolMicro rad}\). As a result, the quadratic coupling factors deviate from the input values (see Table 6) and the desired values of the linear coupling factors \(p_{y,i}\) and \(p_{z,i}\) (\(i=1,2\)) can be computed by the following equation:  
\[p_{y,i} =-\Delta\theta_{z,i}\Delta x_{i}-\Delta y_{i}, \tag{53}\] \[p_{z,i} =-\Delta\theta_{y,i}\Delta x_{i}+\Delta z_{i}.\]  
Figure 10: Input and estimated TTL error as well as their difference under different laser frequency noise (the ASD model in Equation (1) was multiplied by a factor to generate the laser frequency noise model in each case.) (**a**) \(1\times\mathrm{ASD}[\delta f_{\mathrm{cav}}](f)\); (**b**) \(10\times\mathrm{ASD}[\delta f_{\mathrm{cav}}](f)\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Laser Frequency Noise** **(ASD Model)**} & \multicolumn{4}{c}{**Coupling Factor**} \\ \cline{2-5}  & \(\mathbf{p_{y,1}}\) (\(\
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 2 Materials and Methods  
### Test Site  
The study area is located in north-eastern Poland (Figure 2). Biebrza Wetlands is a unique ecosystem being under RAMSAR Convention for peatlands, as well as its highly diversified flora and fauna, especially needs and sedges with grasses. The Biebrza National Park was established (in 1993) within the study area as a wetland site of global networking program NATURA 2000. The study area covers a total area of 59,223 ha of which 45% are wetlands. The area is mostly flat, with an elevation of approximately 105 m above sea level. The Biebrza River, which runs through the area, is a naturally occurring lowland river that provides a distinctive reference point for lowland valley mires and river floodplains [29]. There are approximately 70 various types of natural and semi-natural plants documented in vegetation reports [30; 31]. To better understand the ecological value of wetlands, we focused on several dominant habitats, such as rush, sedge, sedge moss, and need communities. According to the European Environment Agency's (EEA) latest report on wetlands, certain areas of the wetlands still require restoration, particularly those that have been degraded. Our study investigated three main types of plant communities found within the wetland ecosystem: peatlands dominated by sedges, sedge mosses, and reeds (Figure 1). The research site is situated in one of the coolest regions in Poland, where the average daily air temperature throughout the year is only 6.6 \({}^{\circ}\)C due to the prevailing temperate continental climate. The coldest month is January, when the average air temperature is around \(-\)4.2 \({}^{\circ}\)C, and the warmest month is July, with an average temperature of 17.5 \({}^{\circ}\)C. Snow coverage usually lasts for a maximum of 140 days, and the average annual precipitation ranges from 550 to 650 mm. Additionally, the growing season in the wetlands is less than 200 days, making it one of the shortest in the country [32].
# Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases
## 3 Test Case Generation Using GAs
### Problem Definition  
Test case generation is related to the SUT. Since a tester cannot test the SUT with all the possible values of input parameters, a test criterion is used to consider which inputs to apply. The tester defines the test criterion and methodology for generating test cases. Generating an optimal test case using a GA can be formally outlined as \(ST=\{SUT,\,C,\,P_{GA},\,G,\,Sc\}\), where \(SUT\) denotes the software under test, \(C\) denotes the test criterion, \(P_{GA}\) is the adopted control parameters in the GA, \(G\) indicates the number of generations, and \(Sc\) is the stopping criterion. The fitness function is associated with the test criterion and is defined as \(P_{GA}=\{Ps\), \(F\), \(Sel\), \(Cro\), \(Mut\}\), where \(Ps\) is the population size, \(F\) indicates the fitness function, \(Sel\) denotes the selection operator, \(Cro\) denotes the crossover operator, and \(Mut\) is the mutation operator.  
The test criterion \(C\) affects the test requirement \(TR\). This research uses a branch coverage criterion. Test case generation for the branch coverage criterion can be from the source code, a requirement specification, or a development model. When using the branch coverage criterion, each decision statement is executed to obtain all possible outcomes at least once. The decision statements of source code are obtained from if, switch, or loop statements. Let \(D\) represent a set of decision statements in \(SUT\) and \(P\) denote a set of parameters in \(D\). Each decision statement \(d\in D\), and the possible outcomes of \(d\) are true or false. Therefore, generating test cases according to the branch coverage criterion can be defined as \(TR=\{d=true,d=false\}\).  
The value of parameter \(p\in P\) in the decision statement determines the value of \(d\). The challenge lies in providing the value of the parameters to reveal the software's behavior. A suitable value of a parameter will execute as many decision statements as possible. The percentage of executed decision statements can be computed as \(Coverage=(D_{exec}/D_{Sut})\times 100\), where \(D_{exec}\) represents the number of executed decision statements in \(SUT\), and \(D_{Sut}\) is the total number of decision statements in \(SUT\).