esa-sceva/satcom-chunk-collection huggingface dataset:
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 3 Experiments
### Implementation Details  
The implementation and execution of all experiments were performed on the Keras platform (version 2.2.4-tf) on the Tensorflow backend (version 2.1.0) using Python programming language (version 3.7.4). The open-source Python package Pydicom (version 1.4.2) and NiBabel (version 3.0.2) were used for loading and processing medical images. To use graphics processing unit acceleration, a single TITAN RTX was exploited by the CUDA toolkit (version 10.1) and cuDNN library (version 7.6).  
For the common baseline \(B\), The model depth was set to 5, and the filter number of convolutional layers per level was 64, 128, 196, 256, and 512. All convolutional operations used a filter size of 3, and max-pooling operations halve each axis. This setting was applied equally to Spider U-Net and the other comparative models for a fair comparison. For Spider U-Net, \(T\), i.e., the number of \(B\) replicas forming the warp path, was set to 5. The weft path was implemented with a bidirectional convolutional LSTM with 512 convolution filters and replaced the deepest convolution layers of the warp path. \(S\), i.e., the stride parameter of SS, was set to 3 and 5, respectively, for the training and inference phase. The SS strategy was also applied to 3D U-Net and FCN-RNN.  
The implementation details of comparative models are as follows. 2D U-Net was implemented with a single \(B\) using 2D operations for all layers (e.g., convolution, deconvolution, and max-pooling), and the input was 2D images. 3D U-Net was also based on a single \(B\), and the differences with 2D U-Net was that 3D operations were applied and the input was volumes with a stack of 16 images. As \(B\) has four downsampling paths, at least 16 images were required simultaneously for 3D U-Net. For the cardiac MRI dataset in particular, the image stack size was set to 8 because the length of the available image sequence was usually less than 16. In this case, the last downsampling step of 3D U-Net was changed into the 2D operation which halved the x- and y-axis only. For FCN-RNN, the warp and weft paths of the Spider U-Net implementation were applied to FCN and RNN component individually. In other words, the only difference between our customized FCN-RNN and Spider U-Net was the placement of the weft path.  
The focal dice loss [26] with Laplace smooth factor 1 was used as a loss function to deal with the harsh class imbalance of the 3D BVS dataset. Adam optimizer was adopted with an initial learning rate of 0.001. The training was performed in an end-to-end manner without pre-training. The early stopping strategy was adapted when the validation loss was not improved within 10 epochs. The best model with the lowest validation loss was selected as the final model for evaluation.
## 2 Materials and Methods
### Rao-Blackwellized Particle Filter-Based Human Tracking  
#### 2.3.1 Rao-Blackwellized Particle Filter (RBPF)  
Kalman filter and Extended Kalman filter are two representative estimation methods used for object tracking using the Bayesian filtering method. They work on linear state space models and non-linear state space models, respectively, and many estimation problems are solved using these two filtering methods. However, these methods assume the Gaussian approximation, which may not work in certain real-world scenarios. A sequential importance resampling algorithm is proposed to solve the non-linearity and non-Gaussian problems. This algorithm first represents the posterior distribution of the state vector using particles with weight factors. The resampling step then removes the low-weight particles and duplicates the high-weight particles to reduce the variance [37]. The set of particles is  
Figure 4: Human body detection process using a generated depth image.  
then reweighted and updated recursively. The Sequential Importance Resampling (SIR) algorithm [38] produces the following filtering distribution. Equation (5) represents the filtering for a weighted set of particles \(\left\{\left(w_{k}^{(i)},x_{k}^{(i)}\right):i=1\cdots N\right\}\).  
\[p(x_{k}|y_{1:k})\approx\sum_{i=1}^{N}\omega_{k}^{(i)}\delta(x_{k}-x_{k}^{(i)}) \tag{5}\]  
Rao-Blackwellization refers to a kind of marginalization in which the parts that can be evaluated analytically in the filtering equation are separately calculated and the remaining parts are calculated using the Monte Carlo sampling method [39]. Thus, Equation (5) is divided into two parts, as shown in Equation (6).  
\[p(u_{0:k},x_{0:k}|y_{1:k})=p(x_{0:k}|u_{0:k},y_{1:k})p(u_{0:k}|y_{1:k}) \tag{6}\]  
where \(x_{k}\) is the state, \(y_{k}\) is the measurement, and \(u_{k}\) is an arbitrary latent variable. Here, the first term is analytically solved using a Kalman filter or an extended Kalman filter and the second term is solved using the Monte Carlo sampling method.  
\[\begin{array}{l}p(u_{0:k}|y_{1:k})\propto p(y_{k}|u_{0:k},y_{1:k-1})p(u_{0: k}|y_{1:k-1})\\ =p(y_{k}|u_{0:k},y_{1:k-1})p(u_{k}|u_{k-1})p(u_{0:k-1}|y_{1:k-1})\end{array} \tag{7}\]  
where we have used the Markovian property \(u_{k}\). Importance distribution can be used to obtain the following weight recursion:  
\[\omega_{k}^{(i)}\propto\frac{p(y_{k}|u_{0:k-1}^{(i)},y_{1:k-1})p(u_{k}^{(i)}|u _{k-1}^{(i)})}{\pi(u_{k}^{(i)}|u_{0:k-1}^{(i)},y_{1:k})}\omega_{k-1}^{(i)} \tag{8}\]  
This is a mathematical formulation of RBPF [40].
# Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data
## 7 User Study
### Objective Performance  
Figure 12 shows participants' performance in the experiments. Most participants found over nine patterns (Figure 12a) with a few queries (Figure 12b). Moreover, most participants (except for two) could complete the experiment within 30 min (Figure 12c).  
**Insight 1**: The strong performance shows the effectiveness and usability of RBVE in automatically identifying patterns of a batch of visualizations generated randomly.  
Two participants took more than 40 minutes (Figure 12c) to complete the experiments. By observing their screen recording videos, we found that they conducted more queries and identified more pattern classes from the filtered visualizations with low indicator scores. More frequent retraining of the PC led to longer experiment time. We carefully checked their identified patterns and found that the differences between these classes are not very significant.  
**Insight 2**: New patterns determined by PCs are sometimes different from human perceptions. Participants with strict (or relaxed) criteria for pattern classes may merge (or separate) patterns that contain subtle differences, thus creating fewer (or more) pattern classes. Although the indicator can improve the automation of identifying new patterns by filtering a few candidates, it still cannot replace human decision. We will study how to further reduce human intervention in determining new pattern classes in the future.  
The number of queries (Figure 12b) is higher than that of patterns (Figure 12a), indicating it is unnecessary to update the PC in each query. Moreover, we found that PC retraining mainly occurred in the early stage of each experiment and gradually decreased in frequency as the number of patterns included in it increased, as shown in Figure 13.  
**Insight 3**: The above phenomena further illustrates the effectiveness of the PC in improving IDE efficiency. The RBVE framework can avoid frequently updating PCs. As the number of included pattern classes increases, the PC will pick fewer samples with low indicators, gradually decreasing its update frequency. In the later stage of the experiment, the PC has recognized most data patterns, thus making the IDE automated without any updates.  
Figure 12: Objective performance of participants in experiments. The red line represents the average value of each variable. (**a**) Number of new patterns. (**b**) Number of queries. (**c**) Completion time.
## 5 Conclusions  
In conclusion, we have investigated in vivo incorporation of iridium, ruthenium, aluminum, and zinc luminescent organometallic complexes into _Phaeodactylum tricornutum_ diatom microalgae. Except for ZnQ\({}_{2}\), the complexes penetrate cells and stain frustules without altering diatom viability even 7 days after their addition. The microalgal species still retains its capability of adhesion by cell-cell and cell-substrate interactions after the uptake of the organometallic emitters, thus forming luminescent biofilms of living microorganisms. Moreover, a hard acid-oxidative treatment--based on sulfuric acid, chloridic acid, and hydrogen peroxide--of doped diatoms allows isolation of biosilica valves while retaining both their pristine nanostructured morphology and the emission properties of the incorporated transition metal complex. The evidence of incorporation of complexes into frustules was provided by confocal and bidimensional fluorescence microscopies as well as by FTIR-ATR and Raman spectroscopies. This biotechnological protocol represents a straightforward method of preparation of biosilica doped with luminescent organometallic complexes, with the benefit of easily achieving silica nanostructures, never reported so far, that combine photoluminescence of organometallic complexes with structural properties of diatom valves optimized by nature over a billion years. Moreover, the film-forming properties of the semi-benthonic diatom can be exploited to self-assemble in vivo thin luminescent biosilica-based biofilms that may have potential application in photonics. Although the reasons why ZnQ\({}_{2}\) is not suitable as dopant for _Phaeodactylum tricornutum_ are not clarified, our results show the suitability of the method for preparation of light-emitting iridium, ruthenium, and aluminum-doped biosilica valves with integer nanostructure, and can pave the way to a variety of biohybrid materials for photonics and optoelectronics.  
[PERSON] contributed to diatom culture and feeding experiments; [PERSON] contributed to complex synthesis and characterization, experimental design, and paper writing; [PERSON] and [PERSON] performed confocal microscopy analysis; [PERSON] contributed to Raman analysis; [PERSON] contributed to the spectroscopic characterization of biosilica; [PERSON] contributed to planning all the experiments and paper writing. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Commission through EU project 800926-HyPhOE (Hybrid Electronics based on Photosynthetic Organisms), H2020-MSCA-ITN-2019 project 860125--BEEP (Bio-inspired and bionic materials for enhanced photosynthesis).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 3 Design
### CoAP Request  
In order for a client to send a CoAP request to a CoAP group, it needs to know the IP multicast address of that group as well as the \(Group_{DID}\) and the corresponding DID document; \(Group_{DID}\) is later used for verifying CoAP responses (see Section 3.5). A CoAP client learns the required information using DNS. The IP multicast address of the CoAP group is stored in a type \(A\) DNS record that maps the CoAP group FQDN to an IP multicast address. The DID document that corresponds to the \(Group_{DID}\) is stored in a _TXT_ DNS record of the group FQDN, which is a similar approach to that used by \"DNS-Based Authentication of Named Entities\" [18].  
CoAP clients construct their request and send it to the appropriate IP multicast address. Requests include a _token_, which is then used to match the received responses (the token format and usage are defined in Section 5.3.1 of [4]).
## 2 Concept of Cooperative Optimization  
The theoretical structure of the cooperative optimization method proposed in this paper can be illustrated as in Figure 1.  
The model can be separated into two sections: one is the forecasting of load demands and RES generations--the accuracy of the forecast can significantly influence the availability of the optimization results--and the other is the main part of the cooperative optimization. In the optimization process, forecasting data are derived according to the mathematical character of EVs and RESs, sent to the dispatching and allocation sections. The dispatching model will dispatch the operation of microgrid system including EVs and RESs, and the allocation section can optimize the installation capacities of EVBCDIs (Electric Vehicle bidirectional charging/discharging infrastructure) and RESs. An allocation module generates the initial system allocation. A dispatching model then optimizes the operation of the system according to the system allocation and feeds the results back to the allocation module. The system allocation is then updated by an allocation module according to the data from the dispatching module. Through several repetitions of this interaction, both allocations and dispatching of the system are finally optimized. Details of the cooperative optimization process will be discussed in following sections.  
Figure 1: Brief process of cooperative optimization of EVs (Electric Vehicles) and RESs (Renewable Energy Sources).  
In this paper, three respects of cooperative optimization are considered.  
Firstly, RESs and EVs are cooperatively optimized. By optimally dispatching EVs, energy generated by RESs can be redistributed across different times and spaces, and the utilization efficiency of RESs can be improved. Furthermore, EVs can also profit from interacting with RESs and participating into the operation of the system.  
Secondly, microgrids in the regional multi-microgrid system are cooperatively optimized. With the ATSET of EVs, energy can be transmitted through different microgrids, and microgrids with lower electricity prices indirectly sell their energy to microgrids with higher electricity prices. Through such a cooperation, microgrids with lower prices can sell energy to make profits, and microgrids with higher prices can purchase electricity form EVs with lower prices.  
Thirdly, economic dispatching and system allocation are cooperatively optimized. An economic dispatching of the system can reduce the cost of a redundant installation of RESs and EVCDIs, and an appropriate system allocation can in turn better exploit the potential of generation and energy storage units to serve the load with less costs.
## 12 Problem Formulation  
We assume there are \(m=8\) candidate responses, each with a corresponding base cost, base effectiveness, base risk, and duration. Let \(x_{i}\) be a binary decision variable for response \(i\), where \(x_{i}=1\) if response \(i\) is chosen and \(x_{i}=0\) otherwise. The total cost, total risk, and the maximum duration among the chosen responses must each not exceed specified limits. A random threat factor \(\alpha\) scales the base effectiveness of each response, so the realized effectiveness of response \(i\) is \(\alpha\cdot\text{baseEffect}_{i}\).  
The goal is to _maximize_ total effectiveness while remaining within a cost limit, a risk limit, and a time limit. In practice, we _minimize_ the negative effectiveness plus large penalty terms for any constraint violations. Specifically, we define the following:  
\[\text{TotalEffect} = \sum_{i=1}^{m}\left(\alpha\cdot\text{baseEffect}_{i}\right)x_{i},\] \[\text{TotalCost} = \sum_{i=1}^{m}\text{baseCost}_{i}\,x_{i},\] \[\text{TotalRisk} = \sum_{i=1}^{m}\text{baseRisk}_{i}\,x_{i},\] \[\text{MaxDuration} = \max_{1\leq i\leq m}\{\text{duration}_{i}\,x_{i}\}.\]  
If any limit is exceeded, a penalty term is added. For example, exceeding the cost limit (CostLimit) by \(\Delta\) results in a penalty proportional to \(\Delta\) with a large penalty coefficient, \(\beta_{\text{cost}}\). Similar penalties are applied to the risk and time constraints. The composite objective function then becomes as follows:  
\[\text{Objective}(\mathbf{x}) = -\text{TotalEffect}\] \[+ \beta_{\text{cost}}\,\max\{0,\,\text{TotalCost}-\text{CostLimit}\}\] \[+ \beta_{\text{risk}}\,\max\{0,\,\text{TotalRisk}-\text{RiskLimit}\}\] \[+ \beta_{\text{time}}\,\max\{0,\,\text{MaxDuration}-\text{TimeLimit}\}.\]  
We then minimize this objective with respect to the binary vector \(\mathbf{x}\). Because of its combinatorial structure and the uncertain threat factor, \(\alpha\), we employ metaheuristic solvers to find good solutions.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 5 Simulation Results
### Effect of the Number of UAVs on TDOA Localization Performance  
The purpose of this simulation is to compare the localization performance with different numbers of UAVs (i.e., \(M=3,4,5\)).  
Figure 6 shows the evolution of RMSE corresponding to varying values of \(M\). As can be expected, as \(M\) becomes larger, a lower and more stable RMSE is obtained.  
We also notice that the objective function has a growing number of local parameters causing sensitivities to initialization with \(M\) increases. This may lead to suboptimal solutions if not initialized properly. Hence, the initialization of the target plays an important role in SDP methods. The results in Section 3 can be helpful for a proper choice of the initial measurements.
## 6 Comparison with Theoretical Approaches
### Theoretical Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Models** & **RMSE** & **MSE** & **MAE** & **R\({}^{2}\)** & **Mean** & **Std** & **CoV** \\ \hline JGJ 3-2010 & 391.8 & 153,537.5 & 220.4 & 0.72 & 0.93 & 0.44 & 0.471 \\ ACI 318-19 & 448.8 & 201,421.6 & 258.2 & 0.67 & 0.91 & 0.47 & 0.512 \\ [PERSON] & 584.8 & 3,011,535 & 285.9 & 0.58 & 0.76 & 0.28 & 0.384 \\ [PERSON] and [PERSON] & 291.1 & 68,166.2 & 172.1 & 0.87 & 1.31 & 0.39 & 0.292 \\ \hline \end{tabular}
\end{table} 
Table 8: Predictive performance metrics of the theoretical model. Figure 9: _Cont_. Based on the comparative analysis of the four RCSW shear capacity calculation models, the [PERSON] and [PERSON] model performed the best, with an RMSE of 261.1 and an MAE of 172.1. The R\({}^{2}\) reaches 0.87, indicating its high prediction accuracy and reliability. This is followed by the JGJ 3-2010 model, which, despite its relatively low RMSE (391.8) and MAE (220.4), is slightly inferior to [PERSON] and [PERSON] in explaining the variability of the actual data, with an R\({}^{2}\) of 0.72. The ACI 318-19 model, which has a high RMSE (448.8), an MAE of 258.2, and an R\({}^{2}\) of 0.67, also showed lower prediction accuracy, especially with a CoV of 0.512, indicating greater volatility in its predictions. The Sanchez-Alejandre model showed the worst prediction performance, with the highest RMSE (584.8) and MAE (285.9) and an R\({}^{2}\) of only 0.58, suggesting that it was weak in its ability to account for the variability in the data. In summary, the Hwang and Lee model performs better than the other variants, especially in the high-precision prediction of shear-bearing capacity, while the performance of Sanchez-Alejandre is unsatisfactory.
## 3 Results
### HPLC/DAD Analysis After Fermentation  
Even though the Folin-Ciocalteu (F-C) assay [34] is a standardized method to determine the phenolic content of food products and dietary supplements [63], an accurate HPLC/DAD analysis of the polyphenol types and contents was performed to confirm these results. Thus, the results of this analysis showed that, from the 13 polyphenol patterns, 6 peaks exhibited significant concentrations (see Figure 2).  
These results confirm that both beers exhibit a similar polyphenol profile with a few polyphenols (phloroglucinol, catequin and ferulic acid) showing slightly higher concentrations in the beer produced with \(Sc\) (see Figure 2). Therefore, the higher antioxidant activity found in the beer produced with \(Sc_{b}\) could be attributed to antioxidant compounds other than polyphenols, secreted by the probiotic yeast. Thus, for example, the \(Sc_{b}\) yeast was found to produce (1\(\rightarrow\)3)-\(\beta\)-d-glucan, which exhibits antioxidant and immunomododulation properties very suitable for anti-infective and antitumor therapy [64]. In addition, literature reports also suggest that \(Sc_{b}\) produces certain antioxidant metabolites responsible for its anti-inflammatory activity [58; 60].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Yeast Strain** & \begin{tabular}{c} **TPC** \\ **(mg GA/L)** \\ \end{tabular} & \begin{tabular}{c} **RSA** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} \(\rho_{wort}\) \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} \(\rho_{beer}\) \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} \(ABW_{t}\) \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} \(ABV\) \\ **(\%)** \\ \end{tabular} \\ \hline \(SF\)-04 & \(0.1597\pm 0.0373\) & \(11.51\pm 0.36\) & \(1028\) & \(1008\) & \(1.87\) & \(2.39\) \\ \(Sc_{b}\) & \(0.1545\pm 0.0192\) & \(16.80\pm 0.31\)* & \(1028\) & \(1014\) & \(1.29\) & \(1.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total phenolic content (TPC) determined by the Folin-Ciocalteu method and expressed in mg of gallic acid (GA) per L, antioxidant activity (RSA) obtained by the stable free radical 2,2-Diphenyl-1-picrylhydrazyl (DPPH) technique and expressed in % by Equation (1), wort density (\(\rho_{\text{wort}}\)), beer density (\(\rho_{\text{beer}}\)), alcohol percentage by weight (ABW\({}_{t}\)) and by volume (ABV) of the craft beer fermented with _Saccharomyces cerevisiae_ (\(Sc\)) and _Saccharomyces cerevisiae_ _var. boulardii_ (SF-04) yeast strains. Results of TPC and RSA shown as mean \(\pm\) standard deviation. * Statistically significant differences (\(p<0.05\)).
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 4 Test and Verification of Seed Metering Device
### Single Factor Test Results
#### 4.4.3 Effect of Hole Depth on Each Index  
Figure 12 shows the single-factor test results of the effect of hole depth on the seeding performance. The results showed that when the rotation speed of the metering wheel was 90 r/min and the diameter of the hole was 11 mm, the variation coefficient of seeding uniformity decreased first and then increased with the increase in depth. The seed rate increased with the increase in rotation speed. The seed rate of 5.5-7.5 mm hole depth was 143.62-178.09 g/min, which met the seed rate index of 150.10-178.29 g/min.  
When the hole depth is too tiny, large particles of seeds are not filled by the hole, causing seeds to be brushed back to the filling area, resulting in filling instability. When the depth of the hole is too large, the time for seeds to fall out of the hole is prolonged, which may lead to seeds in the hole failing to unload. The seeds in the hole are returned to the filling area with the rotation of the metering wheel, which affects the subsequent seeding operation and the seeding performance. Therefore, the hole depth has a significant influence on the seeding effect.  
Figure 11: Relationship curve between hole diameter and indexes.  
Figure 12: Relationship curve between hole depth and each index.  
Combined with the single factor test results, when the operating speed of the machine is 4.7 km/h, the rotational speed of the metering wheel is 80-100 r/min, the diameter of the hole is 10-12 mm, and the depth of the hole is 5.5-7.5 mm, the variation coefficient of the uniformity of the metering device is the minimum. The seed rate is 150.1-178.3 g/min. Therefore, the rotational speed of the metering wheel is 80-100 r/min, the hole diameter is 10-12 mm, and the hole depth is 5.5-7.5 mm.