esa-sceva/satcom-chunk-collection huggingface dataset:
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
###### Abstract  
A novel miniaturized single-fed circularly-polarized (CP) microstrip patch antenna operating in the Industrial, Scientific, Medical (ISM) band of 2.40-2.48 GHz, is comprehensively proposed for implantable wireless communications. By employing reactive loading in the arrow-shaped slotted patch to form slow wave effect and embedding V-shaped slots into patch to lengthen the current path, the proposed implantable antenna is minimized with the overall dimensions of 9.2 mm \(\times\) 9.2 mm \(\times\) 1.27 mm. The radiation patterns of the proposed antenna illustrate the performance of left-handed circular polarization. The simulated results show that an impedance bandwidth of 7.2% (2.39-2.57 GHz) and an axial ratio bandwidth of 3.7% (2.39-2.48 GHz) at the ISM band are achieved, respectively. Ex vivo measured results are in good agreement with the corresponding simulated ones.  
circular polarization; implantable antenna; reactive loading; slow wave effect +
Footnote †: journal:
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 4 Experimental Analysis
### Ablation Study
#### 4.4.2 RGB-D Acute Respiratory Distress Detection  
The experimental results in Table 1 show that the depth modality alone lacks sufficient information to capture chest retraction signs, which are crucial for ARD detection. Therefore, it is recommended to integrate the depth with RGB to enhance model robustness. To assess the effectiveness of multi-modality integration, we conduct experiments with two widely used fusion schemes: early fusion and late fusion. In the early fusion approach, the depth channel is integrated with the RGB channels, treating the depth data as a fourth channel, a method referred to as channel concatenation (CC). For the late fusion approach, we evaluate three variations: feature concatenation (FC), score averaging (SA), and feature concatenation with frozen base models for both modalities (FCF). The block diagram of the different types of multi-modality fusion schemes is presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**Precision**} & \multicolumn{4}{c}{**Recall**} & \multicolumn{4}{c}{**F1 Score**} \\ \cline{2-13} \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Min**} & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** \\ \hline \multirow{2}{*}{X3D} & **RGB** & 0.783 & 0.822 & 0.870 & 0.818 & 0.872 & 0.95 & 0.72 & 0.777 & 0.833 & 0.783 & 0.821 & 0.864 \\ \cline{2-13}  & **Depth** & 0.696 & 0.757 & 0.826 & 0.639 & 0.71
## 6 White Rot Fungi: An Overview
### Lignin-Degrading Enzyme System  
White Rot Fungi (WRF) are capable of producing extracellular lignin-degrading enzymes, like lignin peroxidase (LiP), manganese peroxidase (MnP), and increase. The excellent natural decomposition (of recalcitrant industrial chemicals) properties of WRF are mainly due to these extracellular lignin-degrading enzymes. These enzymes speed up the process of lignin oxidation and depolymerisation, which further assist WRF to approach the hemicellulose and cellulose components of lignocellulosic materials for the sake of energy and nutrient incorporation [48; 49].  
Lignin Peroxidase (LiP): LiP, a heme-containing enzyme, acts as a catalyst for the oxidation of various aromatic and non-aromatic compounds. LiP has the capability to directly attack the lignin by breaking the C-C and C-O bonds present within the complex structure of lignin. High redox potential is a usual characteristic of the LiP enzyme. This characteristic permits LiP to naturally decompose and oxidize several recalcitrant pollutants, like PAHs, textile dyes, and many phenolic compounds [50].  
Manganese Peroxidase (MnP): Another extracellular enzyme WRF produces is MnP. MnP is also a heme-containing enzyme, which assists in the oxidation of Mn(II) to Mn(III). In return, Mn(III) can degrade a variety of phenolic and non-phenolic compounds. Due to Mn(III) production, MnP can indirectly attack and degrade lignin through oxidation [51].  
Laccase: Another extracellular multicopper oxidase enzyme produced by WRF is lacase. Laccase helps in speeding up the oxidation of phenolic and non-phenolic compounds, with the associated reduction of molecular oxygen to water. Although the redox potential of laccase is lower than the other two enzymes, i.e., MnP and LiP, the enzyme has the capability to degrade and oxidize a range of pollutants, such as phenols, textile dyes, and endocrine-disrupting compounds [51].
# Spouted-Bed Gasification of Flame Retardant Textiles as a Potential Non-Conventional Biomass
## 3 Results
### Gas Evolution from Gasified Textile Pellets
#### 3.3.2 Cotton after Flame Retardant Removal  
Numerous components present in the developed gas from the gasification process are linked to different factors, which can influence the results of evolved gas. During the elevation of gasification temperatures for instance, components such as H\({}_{2}\)O, CO\({}_{2}\), CH\({}_{4}\) and other low-molecular-weight gasses continue to increase.  
The combustion of flame retardant cotton with phosphorus and nitrogen elements perhaps start with a catalyze reaction of dewatering, decarboxylation, and charring [38]. The flame retardant substrate on cotton textile resist complete combustion. Similarly, gasifying flame retardant on cotton hinders gas evolution and fragmented products. To obtain a quality gas composition with minimum impurities and maximum energetic conversion efficiency, the optimized parameters and conditions for gasification of textiles are vital.  
The optimal flow rate of particular shape and sized flame retardant cotton textile was found at 150 g/min for gasification. The flow rate was made starved occasionally to avoid overfeeding and plugging of pellets, which resulted in an irregular gas yield at variant places. Figure 6 shows non-constant gas yield due to rutted feeding of low-density fibrous cellulosic material into the designed small-scale gasification pilot unit. In given context, the gas yield with averaged composition values was calculated that will be discussed in the following section.  
Figure 7 shows the gas yield composition as a function of gasification temperature at the theoretical equivalence ratio of two samples. Carbon dioxide (CO\({}_{2}\)) yield higher in both samples, but gets slightly lower in the sample with removed flame retardant substrates as the temperature increased Figure 7B. Carbon monoxide (CO) and methane (CH\({}_{4}\)) yield was higher in flame retardant cotton at initial temperatures compared to ones with removed flame retardant, probably as of irregular feeding system. Whereas other components of evolved gas were in lower contents on both samples, the average gas yield components are discussed below. The concentration of acetylene (C\({}_{2}\)H\({}_{2}\)) and ethylene (C\({}_{2}\)H\({}_{4}\)) was in slightly low quantity in both samples from low to high temperatures. Oxygen (O\({}_{2}\)) was higher in the sample with flame retardant substrate removed.  
Figure 6: Evolved components in the gas composition from the gasification of flame retardant cotton pellets after flame retardant removal.
## 3 Path-Following Control Strategy with Deep Deterministic Policy Gradient
### Deep Deterministic Policy Gradient for Path-Following
#### 3.2.4 Implementation Details
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(\Delta t\) & Time period & 0.05 s \\ \(\alpha_{\mu}\) & Learning rate of actor network & 0.0001 \\ \(\alpha_{Q}\) & Learning rate of critic network & 0.001 \\ \(\tau\) & Target soft update rate & 0.001 \\ \(\gamma\) & Discount factor & 0.99 \\ \(T\) & Maximum time steps & 1,000,000 \\ - & Startup time steps & 25,000 \\ - & Evaluation per time steps & 5000 \\ - & Experience replay buffer size & 1,000,000 \\ - & Minibatch size & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameters of the DDPG agent. ```
Randomly Initialize critic network \(Q(s,a|\theta^{Q})\) and actor \(\mu(s|\theta^{\mu})\) with weights \(\theta^{Q}\) and \(\theta^{\mu}\)  Initialize target network \(Q^{\prime}\) and \(\mu^{\prime}\) with weights \(\theta^{Q^{\prime}}\leftarrow\theta^{Q}\) and \(\theta^{\mu^{\prime}}\leftarrow\theta^{\mu}\)  Initialize replay buffer \(R\) for\(t=1,T\)do  Initialize a stochastic path \(\mathcal{P}\) and random initial posture \([x_{1},y_{1},\theta_{1}]^{T}\) of vehicle  Initialize an OU process \(\mathcal{N}\) for action exploration  Observe initial state \(s_{1}\) while True do if\(t<T_{start}\)then  Select action randomly from action space else  Select action based on the policy \(a_{t}=\text{\bf clip}(\mu(s_{t}|\theta^{\mu})+\mathcal{N}_{t},\dot{\delta}_{ min},\dot{\delta}_{max})\) endif  Calculate steering command \(\delta_{t}^{*}=\text{\bf clip}(\delta t+a_{t}\Delta t,\delta_{min},\delta_{max})\)  Execute steering control, calculate reward \(r_{t}\) and transitions to new state \(s_{t+1}\)  Store transition \((s_{t},a_{t},r_{t},s_{t+1})\) in \(R\) if\(\text{\bf abs}(e_{p}(t))>e_{max}\) or \(\text{\bf abs}(\Psi(t))\geq\frac{\pi}{2}\)then  Logical value \(F=1\), Update reward \(r_{t}\) break elseif\(\|(x(t),y(t))-(x_{N_{w}},y_{N_{w}})\|<d\)then  Logical value \(H=1\), Update reward \(r_{t}\) break endif if Number of transitions \(\geq\) Minibatch Size then  Sample a random minibatch of \(N\) transitions \((s_{i},a_{i},r_{i},s_{i+1})\) from \(R\)  Set \(y_{i}=r_{i}+\gamma Q^{\prime}(s_{i+1},\mu^{\prime}(s_{i+1}|\theta^{\mu^{ \prime}})|\theta^{Q^{\prime}})\)  Update critic by minimizing the loss: \(L=\frac{1}{N}\sum_{i}(y_{i}-Q(s_{i},a_{i}|\theta^{Q}))^{2}\)  Update the actor policy using the sampled policy gradient: \[\
abla_{\theta^{\mu}}J\approx\frac{1}{N}\sum_{i}\
abla_{a}Q(s,a|\theta^{Q})|_{ s=s_{i}}\
abla_{\theta^{\mu}}\mu(s|\theta^{\mu})|_{s=s_{i}}\]  Update the target networks with soft update strategy: \[\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{ \prime}}\] \[\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^ {\prime}}\] endif endwhile endfor
```  
**Algorithm 2** Training Process of Path-Following Control Strategy for Car-Like Vehicles
# GNSS-Based Monitoring Methods for Mining Headframes
## 4 Correlation Analysis and Risk Assessment
### Risk Assessment  
The above correlation analyses demonstrate that in the headframe deformation, the sheave wheel distance is an important influencing factor for deformation quantities, while the height factor has a minor effect on deformation quantities. For scientifically assessing the risk thresholds for headframe deformation, the monitoring data are preliminarily divided into two categories.  
For monitoring stations with the same sheave wheel distance, STL-L, XTL-L, and STL-R (the data from monitoring station XTL-R are not used given its severe obstruction by the headframe), their height effect is ignored, and they are grouped into one category, designated as group 1. The four monitoring points farther from the sheave wheel, JC01, JC02, JC03, and JC04, are grouped into another category, designated as group 2. After the monitoring data are categorized into these two groups, data preprocessing is conducted using the data cleaning methods from the second part of this paper. With the aid of SPSS software, descriptive information of the data from the two groups of monitoring points is obtained, as shown in Table 3.  
On the basis of the statistical results of the two sets of data, the fluctuation principle method [34] is used to estimate the risk thresholds. The maximum and minimum values of \(\Delta\)X, \(\Delta\)Y, and \(\Delta\)H are set as the maximum warning values. In accordance with this method, data within the threshold K (taking 0.75 times the maximum or minimum value) are defined as the normal zone, data between the maximum and minimum values of \(\Delta\)X, \(\Delta\)Y, \(\Delta\)H, and K are defined as the moderate zone, and data exceeding the maximum and minimum values  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Direction** & **Quantity** & **Minimum** & **Maximum** & **Average** & **SD** \\ \hline \multirow{3}{*}{Group 1} & \(\Delta\)X/mm & 639 & \(-\)34.30 & 63.63 & 4.61 & 9.14 \\  & \(\Delta\)Y/mm & 639 & \(-\)22.50 & 18.82 & \(-\)1.94 & 6.56 \\  & \(\Delta\)H/mm & 639 & \(-\)45.88 & 53.04 & 8.57 & 14.35 \\ \hline \multirow{3}{*}{Group 2} & \(\Delta\)X/mm & 854 & \(-\)0.99 & 15.48 & 7.18 & 2.73 \\  & \(\Delta\)Y/mm & 854 & \(-\)3.72 & 8.83 & 0.57 & 1.91 \\ \cline{1-1}  & \(\Delta\)H/mm & 854 & \(-\)14.92 & 18.85 & 3.84 & 4.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data description for group 1 and group 2.
# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## 2 The Optimized SDM Parameter Identification Method Based on Genetic Algorithm
### Genetic Algorithm Basic Function Description  
Although many advanced genetic algorithm tools are available [18; 19; 20], a basic version of GA has been selected for implementing the proposed method. This choice has been made because the main objective of this paper is to implement the technique on a low-cost digital platform, thus suitably for the online operation. The GA code has been developed in C/C++ starting from the free-download version available in [21] and distributed under the GNU Lesser General Public License license.  
The GA starts by randomly generating the individuals of the initial population. The number of individuals (\(N\)) is the population size. Each individual represents a solution of the problem to be solved, and the elements composing the individuals are called genes. For the SDM parameter estimation problem, each individual is composed of five genes representing the values of (\(I_{ph}\), \(I_{s}\), \(\eta\), \(R_{s}\), \(R_{h}\)); hence, the individual is a vector of five elements. Differently from [21], in the proposed approach,the five genes of one individual of the initial population are initialized with the guess solution, which is computed as discussed in Section 2.4.  
The GA evolves by modifying the population emulating the biological evolution; in fact, the new individuals are obtained by means of the following processes:  
* Selector function: the individuals that survive and reproduce are selected by evaluating the cumulative fitness function.
* Crossover function: the individuals created by the Selector function can swap genes with another individual of the population (i.e., the other parent); therefore, these children inherit genes from both parents. The percentage of individuals created with this function is determined by parameter \(P_{C}\).
* Mutation function: the individuals created by the Selector function can be subject to a random mutation of their genes. The percentage of individuals created with this function is determined by parameter \(P_{M}\).
* Elite function: the individuals with the best fitness function in the current population are preserved in the next generation. The number of preserved individuals is specified using parameter \(N_{E}\).  
The Crossover, Mutation and Elitefunctions have been implemented as shown in [21]. On the other hand, the Selector function of [21] has been modified so as to speed up the execution on the embedded platform as much as possible, as discussed in Section 4.  
On the basis of the values of \(P_{C}\), \(P_{M}\) and \(N_{E}\), the individuals move differently in the research space from one generation to another. The GA makes the population evolve until the maximum number of generations (\(N_{g}\)) is reached. The individual with the best fitness in the last generation will be the optimal solution.
## 4 Discussion  
We introduced TFNetPropX, a comprehensive web-based RNA-seq data analysis tool. The system provides user-friendly access, allowing users to perform gene, gene-set, and network-level analyses with a single analysis request. This enables easy exploration of disease-specific genes, gene sets, and gene networks focused on targeted TFs. Two case studies demonstrated the utility of TFNetPropX in generating biologically meaningful results. Unlike the existing web tools, our system generates interactive plots of results, overcoming the limitations of static plots. Additional information about genes, GO terms, and biological pathways resulting from the analysis is easily obtained by providing a link to the term's web page for convenience. In addition, even if there are no KO data, if there are gene expression data for two classes, such as condition or phenotypic subtype, it is possible to select the specific TF that the user wants to focus on and perform a biological analysis of the differences in conditions, focusing on the specific TF. We believe that our web tool represents a tool that will help researchers who want to obtain and analyze gene expression data, by facilitating analysis and providing various insights, helping in biological understanding and developing various biological hypotheses.  
Nonetheless, this study has several limitations and areas necessitating enhancement. There is a trade-off between ease of use and specificity of analysis, which needs to be improved to allow more detailed analysis options to be easily performed by the user. Although the running parameters were reduced for ease of use, the user still needs to set the threshold for DEG identification, and the results will vary depending on this value; thus, the user needs to find appropriate parameters according to the data. There is also a need for enhanced network analysis capabilities, potentially including subnetwork features, network clustering, and comprehensive network information extraction. The integration of other types of networks is also being considered, such as disease-gene networks, drug-gene networks, and the integration of multi-omics data with networks. This would provide more comprehensive information to the user, but it is very challenging to analyze these heterogeneous networks. Future work should consider these aspects, to further improve the analytical capabilities and functionality of the system.  
[PERSON] designed the project. [PERSON] designed and implemented data analysis algorithm. [PERSON] and [PERSON] biologically interpreted the case study analysis results. [PERSON] and [PERSON] wrote and revised the paper. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2022 Research Fund of Myongji University.  
**Institutional Review Board Statement:** Not applicable.  
Figure 6: TF Network visualization using the Cytoscape.js. The network analysis result is visualized in a network. The connectivity and interactions of _Gt/2 ird1_ and its downstream genes can be easily observed.
## 6 Conclusions
Although it has been proven that satellite image resolution was relevant to the final results (_i.e._, the best OA on single images was achieved using the WV2 dataset), the use of multi-temporal images along growing season turned out to be crucial to improve the discrimination between different crops. In that sense, the use of pan-sharpened orthoimages obtained from free available Landsat 8 scenes resulted in being sufficiently successful. However, bearing in mind the small area of plastic greenhouses and the complexity of the task addressed in this work, it seems obvious that using higher resolution time series, both temporal and spatial, would result in better outcomes. In that way, the new Sentinel-2 satellites could offer a valuable source of data in the near future. The optimal temporal windows for collecting the satellite imagery time series in order to identify the autumn greenhouse crops should include more temporal data within the growing season from June to March. It is important to note that greenhouse coverings are usually whitewashed again in February. Regarding our imagery time series from May to November, the crucial collection periods proved to be July and August. Likely, adding more satellite images within this period of time could lead to better results. The methodology applied in this work could be adapted to other intensive agricultural areas using plastic covering, such as Morocco (e.g., Bir Jdid, Oulidia, Essaouira and Agadir) or China. However, further works are needed to better understand the influence of the type of plastic (material, color, thickness) on crop detection.
## 3 Model Validation for Sand-Fines Mixtures
### Evaluating the Critical Particle Size  
Figure 2 shows the relationship between porosity and particle size for various spherical glass beads packs from literature [41, 51].  
At particle size above 0.150 mm, porosity of beads remains approximately the same despite the change in particle size (Figure 2), since particles has the same shape characteristics and their packing is governed by gravity. However, as particle size decreases below 0.150 mm, porosity started to increase with decrease in particle size (Figure 2). This is because as particle size decreases, the effect  
Figure 2: Porosity as function of particle size for glass beads (in grey and black circles) and natural sands and fines (in blue). Data sources for the sand and fines: [52, 53, 54, 55, 11, 56]. The red dashed line represents the particle size value of 0.15 mm.  
of inter-particle forces becomes more important than gravity and thus, causing fines aggregation or conglomeration that leads to higher porosity [40; 57; 58]. In order to find \(d_{cri}\) for sand and silt particles, we would need to construct relationship for porosity as function of particle size. It is however important to note that porosity of natural sand is function of particle shape too [9; 52; 59]. To rule out the effect of particle size alone, we used published dataset for only sand and fines with similar shape characteristics (i.e., roundness of 0.6 and sphericity of 0.8), measured at same condition (i.e., loose packing from maximum void ratio measurements). The porosity-particle size relationship is compared with the trend for glass beads (Figure 2). It can be noticed that 0.150 mm is a good estimate for \(d_{cri}\) of the natural sands and salts.  
The natural sands and fines in Figure 2 has lower roundness and sphericity than glass beads which, as suggested by [PERSON] et al. [52], can explain the higher porosity of the sands. Despite of the different shape and porosity values observed for sand in comparison with glass beads at the same particle size, the transition from cohesive to non-cohesive behavior seems to occur at the same \(d_{cri}\).