esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with [PERSON] and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads.  
satellite self-organizing; routing algorithm; OMNeT++ +
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 3 Delayed Combination of Encoded Features
### Delayed Combination  
We selected or designed four kinds of feature encodings, which are promising for NER task: pre-trained word embedding, contextual embedding, character-level CNN/LSTM-based word encoding and CNN-based sentence encoding using a dictionary. These feature encodings are combined at suitable positions of the bidirectional LSTM CRF network according to the characteristics of each feature encoding.  
The pre-trained word embedding and contextual embedding were learned from the context of each word. That is, their major role is to maintain and propagate the contextual information of each word to the latter layers. So we decided to pass both of them through each separate bidirectional LSTM blocks to further learn or fine-tune the contextual information from the training data as shown in Figure 4.  
On the contrary, character-level CNN/LSTM-based word encoding is better to bypass the bidirectional LSTM blocks because this word encoding learns only the character compositions within a word, not contextual information of a word. Passing this encoding through the bidirectional LSTM blocks may cause loss of character composition information in that encoding. Early combination of this encoding with other pre-trained feature encodings at the embedding layer may disturb correct learning  
Figure 4: The pre-trained word embedding and contextual embedding passed through its own bidirectional LSTM blocks.  
Figure 3: CNN-based sentence encoding using dictionary  
of that encoding because in general dominant features (i.e., pre-trained ones) are first propagated to the latter layers.  
Our dictionary representation is also not passed through the bidirectional LSTM blocks. The representation is obtained by employing a partial match strategy and passing through word CNN. This partial match may cause many false matches and give a negative effect on the performance. To soften this problem, we employed CNN-based sentence encoding. As a result, we combine (i.e., concatenate) the above feature encodings at the fully-connected layer directly after the bidirectional LSTM blocks. The combined encoding further passes fully-connected layer and CRF layer to find the best chainable label sequences. A graphical illustration of our delayed combination model is given in Figure 5.  
The early combination may cause some useful information to mix or collide with other encodings and, as a result, disappear before the output layer. The graphical difference between the early combination and the delayed combination is depicted in Figure 6.  
Both the delayed combination and early combination models are used for comparison in experiments, as explained in the next section.
## 3 Conventional Positioning Solutions in 5G Networks
### Pure 5G Network-Based Positioning
For position estimation, they used statistics-based expectation maximization and the subspace-spaced algorithm. The results based on the sounding reference signals in a line-of-sight scenario prove that VA is capable of providing 3D UE positioning with sub-meter accuracy in 5G networks without requiring multiple cells or multiple antennas. Partly similar to the previous study, [PERSON] et al. [40] conducted their experiments in a virtualized indoor office scenario with only one mmWave Base Station (BS). In their solution, an improved Least Mean Square (LMS) algorithm is used to refine the multipath AoA estimation based on the motion characteristics of the user devices. Then, an Unscented Kalman Filter (UKF) is used to estimate the position based on the AoA and ToA distributions of the different mmWave paths. According to the results, their proposed method is able to achieve a positioning accuracy of less than one meter. [PERSON] et al. [41] investigated the performance limits of Vehicle-to-Vehicle (V2V) relative positioning for vehicles with multiple antenna arrays. AoA and TDoA measurements are used to estimate the position and direction of the vehicle in platooning and overtaking scenarios. These are used to obtain the Cramer-Rao bound for lateral and longitudinal positioning error. The results are compared to the 5G new radio vehicle-to-everything [42] Positioning Error Bound (PEB) requirements: longitudinal PEB less than 0.5m and lateral PEB less than 1m. In the overtaking scenario, the requirements are met if the longitudinal offset between vehicles is less than 22.46 m. In the platooning scenario, the distance between the vehicles cannot exceed 17.07 m in order to keep the positioning error under the limit value.
# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
###### Abstract  
Several studies have explored the linkages between phenology and ecosystem productivity across the Amazon basin. However, few studies have focused on flooded forests, which correspond to c.a. 14% of the basin. In this study, we assessed the seasonality of ecosystem productivity (gross primary productivity, GPP) from eddy covariance measurements, environmental drivers and phenological patterns obtained from the field (leaf litter mass) and satellite measurements (enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer/multi-angle implementation correction (MODIS/MAIAC) in an Amazonian floodplain forest. We found that ecosystem productivity is limited by soil moisture in two different ways. During the flooded period, the excess of water limits GPP ([PERSON]'s correlation; rho = \(-0.22\)), while during non-flooded months, GPP is positively associated with soil moisture (rho = 0.34). However, GPP is maximized when cumulative water deficit (CWD) increases (rho = 0.81), indicating that GPP is dependent on the amount of water available. EVI was positively associated with leaf litter mass ([PERSON]'s correlation; r = 0.55) and with GPP (r = 0.50), suggesting a coupling between new leaf production and the phenology of photosynthetic capacity, decreasing both at the peak of the flooded period and at the end of the dry season. EVI was able to describe the inter-annual variations on forest responses to environmental drivers, which have changed during an observed El Nino-Southern Oscillation (ENSO) year (2015/2016).  
tropical wetlands; floodplain phenology; eddy covariance; GPP; MODIS; MAIAC; seasonality +
Footnote †: journal: Remote Sensing, 2019, 11, 1530; doi:10.3390/rs11131530
## 1 Introduction  
The basalt fiber reinforced plastics bar (BFRP bar) is a new type of material with the advantages of a light weight, corrosion resistance, high tensile strength, and good fatigue resistance. Replacing ordinary steel bars with BFRP bars can effectively solve the shortcomings of steel bars, which easily corrode in concrete structures and add significant structural weight [1]. They are widely used in concrete structures such as bridges, roads, and wharfs [2]. Many experts and scholars have conducted in-depth research and analysis on the failure modes, mechanical properties, and bonding properties of BFRP bar concrete beams [3; 4; 5; 6; 7; 8]. The low elastic modulus of BFRP bars causes large deformation of the concrete components and an increase of crack widths. The stress-strain relationship of the material is linear, and there are no problems related to brittle failure caused by the yield stage. To a certain extent, the application of its materials in engineering has been limited [9]. The chopped fiber and steel wire mesh composite component could have a significant complementary and reinforcing effect, effectively improving the flexural behavior and crack resistance of the concrete component and improving the brittleness of the matrix material [10; 11].  
In recent years, global forest coverage and resource storage has been declining, but bamboo has shown a trend of steady growth, indicating that bamboo is a plant material with great potential; and its green, environmentally friendly, and economical features can be recommended. In addition, adding an appropriate amount of bamboo fiber to concrete, like other chopped fibers, could improve the tensile properties of concrete, control the development of early cracks, and significantly improve ductility and toughness [12; 13; 14; 15]. Therefore, this paper took BFRP bar concrete beams reinforced with bamboo fiber and steel wire mesh as the research object, and conducted flexural tests and numerical simulation of the experimental beams by changing the length of the bamboo fiber and the layout of the steel wire mesh. We explored the effects of bamboo fibers and steel wire mesh on the crack development, deformation, and flexural capacity of BFRP bar concrete beams under separate and composite actions, as well as the feasibility of numerical simulation analysis. Analyzing the data of crack resistance, deformation, and flexural behavior, we summarized the best selection method for bamboo fiber length and steel wire mesh layout in concrete, and we established a calculation method for the flexural bearing capacity. This addressed a research vacancy regarding BFRP bar concrete beams reinforced with bamboo fiber and steel wire mesh, and provides a theoretical basis for the engineering applications of these materials.
## 3 Proposed MPUSMs-IMFEAO for Personalized Recommendation
### Interactive Multifactorial Evolutionary Optimization Algorithm with MPUSMs
#### 3.2.2 MPUSM-Based Recommendation List  
Two selection processes for Top-N items are designed here: coarse and fine selection to obtain a pre-recommended list and a Top-N list, respectively. During the coarse selection stage, although population diversity has already been enhanced by changing the coding form and adjusting the crossover and mutation operators, sometimes, an evolved population may not be sufficient to provide enough unduplicated individuals. For this reason, a new initial population generated by probability models is again evolved to provide more non-repeating individuals. In addition, MPUSM separates user's preferences, so that user's attention to different dimensions of preferences can be determined based on recently assessed items. Therefore, the roulette wheel selection is applied, which not only takes into account each preference's importance but also provides more randomness. According to (11), the probability of roulette is calculated. Moreover, the preference dimension to which the item belongs is determined by calculating the magnitude of the Euclidean distance of the item from the \(K\) clustering centers:  
\[F=[f_{1},f_{2},\cdots,f_{K}] \tag{11}\]  
where \(f_{k}\) denotes the probability of attention to the \(k\)-th dimensional preference and \(f_{k}=w_{k}/W\). \(w_{k}\) represents the number of items belonging to the \(k\)-th cluster in the latest \(W\) historical evaluations.  
The population is divided into \(K\) parts by skill factors, so it is easy to extract diverse and non-repeating individuals for the pre-recommended list using the roulette. At this time, individuals with higher rating are given priority for extraction. Once the population has no more non-replicated individuals, a newly created population is re-evolved for filling the pre-recommended list. After the pre-recommended list is filled, the individuals are transformed into nearest real items in an item pool using the minimum Euclidean distance. Then, they need to be evaluated with MPUSMs, at which point the list is still divided into \(K\) groups.  
During the fine selection stage, a Top-N list containing the \(N\) most desired real items is generated. Currently, a pre-recommended list contains items on multiple dimensional preferences, and each item is evaluated with utmost precision MPUSMs. Thus, the roulette is used again to draw \(N\) items from the pre-recommended list to form the Top-N list.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.2.4 Data Editing  
Workshop participants described a range of base functionality that EASY should include such as polygon, line and point sketch tools printing and basic map viewing (e.g., zoom, pan, print) so that landholders can define their property boundaries, measure paddock areas and print a map of the property for use in planning and discussions with extension officers and other stakeholders. Definition of property boundaries would also enable access to models and their outputs with the geographic extent of information provided controlled through access protocols.
###### Abstract  
To investigate the microscopic pore evolution characteristics of Zhanjiang Formation structural clay during the disturbance process, unconfined compressive strength tests, scanning electron microscopy (SEM), and X-ray diffraction (XRD) were conducted on disturbed samples subjected to various disturbance conditions after vibrational disturbance. Based on the evolution characteristics of the microstructure, the microscopic pore characteristics of the disturbance damage of Zhanjiang Formation structural clay were examined. The results indicate the following. (1) The porosity in three-dimensional visualization images of the microstructure reconstructed by ArcGIS 10.1 increases with the disturbance degree, showing a linear growth trend. (2) The correlation analysis between macroscopic mechanics and microscopic pores shows that the unconfined compressive strength of Zhanjiang Formation structural clay is mainly affected by its porosity, with a significant linear negative correlation. Based on this, a reasonable regression model between the microscopic porosity and the unconfined compressive strength has been established. The model can rapidly estimate the unconfined compressive strength from porosity data, facilitating the assessment engineering properties of the soil. (3) The microscopic pore structure of Zhanjiang Formation structural clay exhibits prominent Menger fractal characteristics. The three-dimensional pore fractal dimension has a certain positive correlation with the disturbance degree, and can be utilized to characterize the pore structure and complexity, serving as a significant parameter for the quantitative evaluation of the pore structure characteristics of Zhanjiang Formation structural clay. Consequently, the complexity of the pore structure of the engineering soil can be evaluated by the pore fractal dimension. (4) The impact of disturbance on soil is primarily manifested in the structural changes in secondary clay minerals, transiting from a relatively intact to a fully adjusted state. During this process, interparticle pores continuously increase, pore structure complexity increases, and interparticle cementation weakens, resulting in the continuous degradation of unconfined compressive strength. This study contributes to a deeper understanding of the disturbance damage characteristics of Zhanjiang Formation structured clays from a microscopic pore perspective, providing a theoretical basis for the engineering construction and operational maintenance in regions with Zhanjiang Formation structural clay.  
Zhanjiang Formation structural clay; disturbance damage; microscopic pore; fractal characteristics; damage mechanism +
Footnote †: journal: Applied sciences
## 2 Technical Background
### Review of the ADMM Method  
The ADMM method is an algorithm for constraint minimization. ADMM is a powerful technique in linearly constrained optimization problems for splitting a large problem into smaller ones. It has been widely used in many areas including machine learning, signal processing, and other fields [34; 35; 36; 37]. It can be decomposed into several sub-vectors, that is, its objective function can also be decomposed into:  
\[f(x)=\sum_{i=1}^{r}f_{i}(x),\ x_{i}\in\mathbb{R}^{n_{i}},\ \sum_{i=1}^{r}n_{i}=n \tag{2}\]  
with variable \(x\in\mathbb{R}\), and where \(f\) are convex. Then the large-scale optimization problem can be transformed into the distributed optimization problem. Accordingly, the equality constraint matrix \(Ax=b\) is also divided into:  
\[A=[A_{1},A_{2},\ldots,A_{r}],\ Ax=\sum_{i=1}^{r}A_{i}x_{i}=b \tag{3}\]  
where \(A\in\mathbb{R}^{r\times n}\) is the measurement matrix, and \(b\in\mathbb{R}^{r}\) is the projection vector. Therefore, the augmented Lagrange objective function \(L_{p}(x,\lambda)\) can be written as follows:  
\[L_{p}(x,\lambda)=\sum_{i=1}^{r}\left(f_{i}(x_{i})+\lambda^{T}A_{i}x_{i}- \lambda^{T}b+\frac{\rho}{2}\left\|\sum_{i=1}^{r}(A_{i}x_{i})-b\right\|\right) \tag{4}\]where \(\rho>0\) is the penalty parameter, \(\lambda>0\) is a regular term parameter, and \(L_{\rho}\) is the standard Lagrangian for the problem. The penalty function obtained is still the same as that obtained in the augmented Lagrange multiplier method. Then, the dual ascending method is adopted to obtain a decentralized algorithm that can carry out parallel operations:  
\[x_{i}^{k+1}=\text{argmin}L_{i}(x_{i},\lambda_{i}),\;i=1,2,\ldots,r \tag{5}\]  
\[\lambda_{k+1}=\lambda_{k}+\rho_{k}\left(\sum_{i=1}^{r}A_{i}x_{i}^{k+1}-b\right) \tag{6}\]  
The \(x_{i}\) can be updated independently, because \(x_{i}\) updates are done in an alternating or sequential manner, and they are alternating direction multiplicators.
## 2 Materials and Methods
### Spatial Panel Lag Model (SLM)  
After conducting the LM test for spatial dependence, we apply the Spatial Panel Lag Model (SLM) to verify the presence of spatial dependence or the similarity of observations in neighboring areas. This model captures the spatial effects of surrounding regions by including a spatially lagged dependent variable. Moreover, the SLM serves as the baseline model for comparison with the Spatial Dynamic Panel Lag Model (SDPD IV) to determine which provides the most accurate estimation results. The conclusion will be drawn based on the model that yields the most reliable outcomes. The mathematical expression of the model is as follows:  
\[y_{i}=\rho W_{i,j}y_{j}+x_{i}\beta+u_{i} \tag{4}\]  
where \(\rho W_{i,j}y_{j}\) is the vector of the dependent variable \(y_{j}\) multiplied by the spatial weight matrix \(W_{i,j}\), which represents another spatially lagged dependent variable; \(x_{i}\) is the vector of explanatory variables; and \(\beta\) is a vector of parameters. Using this model our EKC model is analyzed using Equation (5):  
\[LnEnergycons\_pc_{i,t}=a+\rho WLnEnergycons\_pc_{i,t}+\beta_{1}LnGPppc_{i,t}+ \beta_{2}LnGPppc_{i,t}+\beta_{3}LnPop\_dens_{i,t}+ \tag{5}\] \[\beta_{4}LnLandtemp\_night_{i,t}+\beta_{5}LnRainfall_{i,t}+\beta_{6 }LnWindspeed_{i,t}+\beta_{7}LnRoaddens_{i,t}+\beta_{8}COVID_{i,t}+\mu_{i}+ \lambda_{i}+\varepsilon_{i,t}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tests** & **Statistic** & **df** & \(\mathbf{p}\)**-Value** \\ \hline Moran’s I & 2.554 & 1 & 0.011 \\ \hline Spatial Error & & & \\ \hline Lagrange multiplier & 1.406 & 1 & 0.236 \\ \hline Robust Lagrange multiplier & 1.212 & 1 & 0.271 \\ \hline Spatial Lag & & & \\ \hline Lagrange multiplier & 2.154 & 1 & 0.142 \\ \hline Robust Lagrange multiplier & 1.961 & 1 & 0.161 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spatial correlation test results.  
In this equation, \(\rho\) is the spatial autoregressive coefficient indicating the spatial spillover effect of energy consumption per capita (\(Energy\)cons_pc), and \(WLnEnergy\)cons_pc\(i,i\) is the spatially lagged dependent variable of energy consumption per capita (\(Energy\)cons_pc).  
We aim to utilize the full SLM model as a benchmark for comparison with the SDPD IV model, determining which provides more accurate estimation results based on the lowest root mean square error (RMSE) and mean absolute error (MAE) values.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Data Fusion and Sharing
#### 4.3.2 Centralized  
The centralized data fusion and sharing mechanism needs high-power communication equipment to perform the operation smoothly. Furthermore, it shares all the information through the fusion center with other devices in the network and leads to provision of more accurate information about the operation [37]. At the early stage of deployment and operation for the shorter distance, centralized sharing performs more efficiently. However, when it comes to longer distances and later part of the operations, it will be much more complicated to reposition or control the device [38].
## 2 Materials and Methods
### Soil pH Mapping Framework
#### 2.3.2 Modeling for Each Scenario  
We leveraged the RF regression algorithm to predict soil pH employing the R packages of \"randomForest\", \"raster\", and \"caret\" by the open-source statistical language R v4.4.2 (R Studio, Boston, MA, USA). RF has been widely used in DSM and usually provides a high accuracy. It is easy to execute and is also insensitive to noise and overtraining. RF is an ensemble learning algorithm by combining multiple independent decision trees [54]. RF first randomly selects bootstrap samples of observations, and a decision tree is fitted to each subsample. Then, the decision tree randomly selects explanatory variables with _mtry_ features (_mtry_: number of variables randomly sampled as candidates at each split). All splits of the tree are examined with explanatory variables, and the best split at each step is determined to build the decision tree. The final modeled value is the average across all decision trees.  
For each scenario, variable selection was first completed by MGFS based on the whole soil sample sets to improve computing efficiency and ensure consistency across sample folds. Then, based on selected variables, 10-fold cross-validation was performed to evaluate soil pH prediction performance. At each fold, the number of variables randomly sampled as candidates at each split (_mtry_) was fine-tuned by grid-search and cross-validation. The number of trees (_ntree_) was held constant at the default value of 500. Our experiment indicated that, when the number of trees increased to 500, the accuracy tended to stabilize. The variables' relative importance was assessed by the increase in the mean square error (%_IncMSE_) as a result of randomly shuffling variable values. Finally, the soil pH map was produced by the RF model fitted by all the soil samples under each scenario. To feed categorical variables such as crop rotation to the models, the one for hot encoding was used by converting each categorical value into a new categorical column and assigning a binary value of 1 or 0 to those columns.
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 5 Estimation and Analysis of Inter-system Biases  
According to the experimental arrangement of the manuscript, three groups of experiments are designed to study some clear characteristics of ISBs, which need to be verified by the measured data. The results of the experiments over 28 days showed very clear agreements.