esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
###### Abstract  
This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes was analyzed using several machine learning tools including the synthetic minority oversampling technique (SMOTE), information gain (IG), Relieff, and least absolute shrinkage and selection operator (LASSO), along with various classifiers (BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest). We identified 439 differentially expressed genes (DEGs), of which only three were down-regulated (AT3G20810, ATIG31680, and ATIG30250). The performance of the top 20 genes selected by IG and Relieff was evaluated using the classifiers mentioned above to classify stressed versus non-stressed samples. The random forest algorithm outperformed other algorithms with an accuracy of 97.91% and 98.51% for IG and Relieff, respectively. Additionally, 42 genes were identified from all 30,380 genes using LASSO regression. The top 20 genes for each feature selection were analyzed to determine three common genes (AT5G44050, AT2G47180, and AT1G70700), which formed a three-gene signature. The efficiency of these three genes was evaluated using random forest and XGBoost algorithms. Further validation was performed using an independent RNA_seq dataset and random forest. These gene signatures can be exploited in plant breeding to improve stress tolerance in a variety of crops.  
LASSO; information gain; Relieff; classifiers; random forest +
Footnote †: journal: _Algorithms_  
0000-0001-9993-2278-22010-0
## 2 Selectivity
Experimental results elsewhere indicated that the uptake capacity and biosorption yield of one metal ion were reduced by the presence of the other metal ion; the selectivity order of Pb\({}^{2+}>\) Cu\({}^{2+}>\) Zn\({}^{2+}\) was found [20]. Although biosorption for reclaiming single precious metal was frequently reported, the actual subsistent adsorptive competition among different metal ions sometimes shows diverging reinforcement or prohibition for different species. Another study tried to screen bacteria that are able to absorb certain precious metals with high selectivity under competitive conditions. For binary metal system, the adsorption parameters of extended-Langmuir model were modified by introducing a selectivity factor of the solute [21]. A mass action law was able to predict the ion exchange equilibrium with Na-loaded algae, considering a ternary system Na\({}^{+}\)/H\({}^{+}\)/Cd\({}^{2+}\) or Na\({}^{+}\)/H\({}^{+}\)/Pb\({}^{2+}\)[22]. The selectivity coefficients of the Na-loaded algae increases in the following order: Na\({}^{+}<\) H\({}^{+}<\) Cd\({}^{2+}<\) Pb\({}^{2+}\), which indicates that carboxylic and sulfonic groups have a higher preference (affinity) to Pb\({}^{2+}\), followed by Cd\({}^{2+}\), H\({}^{+}\) and Na\({}^{+}\). The higher affinity of lead ion to sulfonic groups was attributed to the hard and soft acid and base theory. A mass transfer model, considering that the ion exchange limiting step is the intraparticle ion diffusion, was then able to fit the concentration profile of all ionic species at the liquid and solid phases. Adsorption behaviors of non-conventional and cost-effective adsorbent prepared from orange waste has been investigated for various metal ions [23]. Separation factors for Fe(III) and Zr(IV) over Zn(II) at around pH 2, derived from individual adsorption isotherm by fitting a linear curve, suggested that there was an excellent selectivity for transition metals over alkaline/alkaline earths. The selectivity of a bio-based dithiocarbamate modified chitosan, using Pb(II) as imprinted ions and the non-imprinted beads for lead ions over other metal ions were evaluated from the selectivity coefficient, defined as the ratio of the distribution ratios of the Pb(II) ions and other coexistent metal ions [24]. Cross-linked imprinted chitosan polymer has been recently prepared from chitosan, using citric acid-cadmium complex as template and glutaraldehyde as the cross-linker [25]. Therefore, this may explain the following attempt and proposal.
###### Abstract  
This article presents the results of an assessment of the combustion process of blends of n-hexane and canola oil. Tests were conducted for pure canola oil and its blends with n-hexane, with a max. n-hexane content of 20% by volume. The tests were carried out using the constant volume combustion chamber (CVCC) method as well as a diesel engine. For comparison purposes, the results for typical diesel fuel are also presented. Tests on the self-ignition properties of the n-hexane-canola oil blend, conducted in a CVCC according to the normative method for diesel fuel, showed little effect on the combustion process. However, previous tests conducted on a diesel engine of a passenger car showed a favorable effect of the n-hexane addition to canola oil on the combustion process in the engine, the performance and environmental parameters obtained. This shows that for some fuels, the evaluation of self-ignition and combustion properties in a constant volume combustion chamber, under conditions corresponding to diesel fuel tests, is not sufficient. The findings of this research may be beneficial in optimizing the diesel engine combustion systems fueled by renewable fuels. As the results of the tests have shown, the standardized method for determining the combustion process of the CVCC method does not always fully reflect the results obtained in engine tests. The results obtained by the CVCC method can be successfully used when comparing fuels, but when considering the selection of fuel for the engine, the results obtained in engine tests should be relied on first and foremost.  
canola oil; n-hexane; diesel engine; combustion process; derived cetane number Article
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 3 Multi-Pedestrian Tracking Method Based on IVDM
### Multi-Pedestrian Tracking Methods Based on IVDM  
Pedestrian tracking not only provides trajectory information but also provides valuable information for behavioral analysis. However, in crowded scenes, a large number of targets may be occluded, resulting in missing and blurred features, which seriously affects the function of detection-based tracking methods [16]. When the video surveillance fields of view do not overlap and the pedestrians are heavily occluded, the \"1-n\" pedestrian identity phenomenon results. Existing tracking algorithms still lack a flexible approach to dealing with heavily occluded targets and thus perform poorly in complex scenarios where heavy occlusion occurs frequently [33]. To address the above situation, based on the improved detection method in the previous section, we introduce pedestrian identity validity judgment into the pedestrian tracking process, which performs \"occlusion perception-occlusion ratio \(k\) calculation-pedestrian identity validity discrimination\" on unmatched targets between different frames (Figure 4).  
The module performs a series of calculation and discrimination operations on unmatched targets between different frames, such as \"occlusion perception-calculation of occlusion ratio \(k\)-pedestrian identity validity discrimination\", which determines the degree of occlusion of pedestrians detected by the surveillance video based on the magnitude of the coefficient k of the proportion of occlusion of pedestrians in the frame and categorizes the occluded pedestrians into valid ID\({}_{\text{Y}}\) and invalid ID\({}_{\text{N}}\) through \(k\). In essence, the above process is used to discern whether or not the identity of the detected pedestrian has validity. The most successful associations in pedestrian tracking often occur in the cascade matching section. Therefore, we have incorporated the IVDM into the cascade matching process, as depicted in Figure 5.  
Figure 4: Multi-pedestrian tracking method based on IVDM.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.1 Switch Design  
Ternary Content Addressable Memory (TCAM) is the common choice for storing a flow table in a switch. Along with TCAM, OpenFlow switches are usually designed with Static Random Access Memory (SRAM) or Field-Programmable Gate Array (FPGA) and a Graphics Processing Unit (GPU) or Central Processing Unit (CPU) [555]. OpenState has an abstraction as a superset of OpenFlow primitives to enable stateful handling of OpenFlow rules using extended finite state machines inside forwarding devices, which augment the capability and flexibility of the switches [296]. The Programming Protocol Independent Packet Processor (P4) allows the controller to specify the high-level functionality of the switches, which will be compiled into a control flow graph that can be mapped to different switches [556]. As the memory of switches is limited and may not be sufficient to store all rules when the network becomes large, several strategies, such as timeout and eviction mechanisms, flow rule aggregation, flow rule split and distribute, flow rule caching, etc., can be employed to store the rules in the limited memory [557]. In order to cope with increasing demands, such as in the case of a KDN, switches' memory, processing, and communication capacities should be upgraded. There have been attempts to improve security in switches by providing switches with an additional virtualization layer that provides virtualized security functions in the form of security applications or security service chains where the controller is responsible for activating virtualized security functions [558]. Therefore, it is better to have switches in KDN to have such virtualized security measures in the form of access control, encryption, and intrusion detection.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Planning Phase
#### 4.2.3 System Performance  
Different metrics can be defined to express the ILS performance, depending on the output type provided by the system [16]. Point-based ILSs give the coordinates (\(X\),\(Y\)) where the tag may be located. In these cases, the error can be calculated from the Euclidean distance between the estimated tag location (\(X\)',\(Y\)) and the exact one (\(X\),\(Y\)). Furthermore, system precision is defined as the radius of the disk where the tag is positioned under a certain probability (usually established as 90%). However, in a room-level ILS case, the output of the localization engine is the room ID (\(R_{i}\)) in which the tag is positioned. Thus, error cannot be calculated from the Euclidean distance, but as the percentage of times the true room (_i.e._, the room where the object is located) is estimated. This is usually known as the classification success probability (\(P_{S}\)), which can be mathematically expressed as follows:  
\[P_{S}=\frac{\text{Number of successful estimations}}{\text{Total estimations}} \tag{3}\]  
In addition to \(P_{S}\), which represents a global system performance parameter, we will also consider the success probability associated with a pair of rooms \(i\), \(j\) (\(P_{Sij}\)), defined as the probability of selecting the room \(i\), room \(j\) being the true room where the tag is located. Therefore, an \(RxR\) probability matrix (\(PM\)) is obtained when all combinations are calculated. This matrix is interesting because it offers detailed information about the correlation between rooms and how likely two rooms may be confused. Note that the main diagonal of the \(PM\) matrix (\(P_{Sij}\) with \(I=j\)) corresponds to the success probability per room, _i.e._, the success probability to locate the tag in room \(R_{i}\), when the tag is located in that room.  
\[PM=\left(\begin{array}{ccc}\text{P}_{\text{S11}}&\ldots&\text{P}_{\text{S1R }}\\ \vdots&\ddots&\vdots\\ \text{P}_{\text{SR1}}&\ldots&\text{P}_{\text{SRR}}\end{array}\right) \tag{4}\]  
These metrics offer valuable information that can be employed in the experimental test phase to analyze the accuracy of the system, allowing the analysis of the system performance in a general way or, specifically, in a particular room.
# Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices
## 4 Discussion
Figure 11: Comparison between the deduced K\({}_{\rm KC}\)\(-\)rock brittleness index B4 relationships, i.e., Equation (11) in Table 3, and relationships from [23], i.e., Equations (21) and (22) in Table 5.  
Figure 12: Comparison between the deduced K\({}_{\rm KC}\)\(-\)rock brittleness index B9 relationships, i.e., Equation (13) in Table 3, and the relationship from [24], i.e., Equation (18) in Table 5.  
\(\sigma_{\rm c}\) were relatively close between the data in this paper and [18] and [69], rock types with higher \(\sigma_{\rm c}\) were considered in this paper compared to [24]. The second reason may be the KIC test method effect. This paper used data collected from different references, where different KIC test methods were used (Table 1). Meanwhile, Equations (18) and (20)-(22) in Table 5 were proposed using the data obtained, totally or mostly, using the CB test method, i.e., Equations (18), (20) and (21), or using the SR test method, i.e., Equation (22). In order to examine the effect of the KIC test method more closely, the relationships between KIC and each of the indices B3, B4 and B9 were examined using data in Table 1 considering only the data from the CB test. Most of these data, around 70%, were obtained from [18], i.e., the data used to obtain Equations (20) and (21). However, these data were completely different from those used to obtain Equations (18) and (22). The results for B3, B4 and B9 are shown in Figures 13-15, respectively. Considering only KIC data from CB test produced significant correlations between KIC and B3 in contrast to the weak correlations in Figure 3. Furthermore, the best correlations between KIC and each of the B4 and B9 indices had a higher R\({}^{2}\) (0.8176 for power models) compared to R\({}^{2}\) = 0.5044 and 0.5035 for B4 (exponential model) and B9 (linear model), respectively (see Table 3). It should be noted that there were no changes in the correlation significance between KIC and each of the B1 and B2 indices when only CB test data were considered. The increased correlation significance, particularly that between KIC and B9, indicated that there was a significant effect of the KIC test method on the correlation between KIC and strength-based indices B3, B4 and B9. Furthermore, the test method's insignificant effect on the correlation between KIC and indices B1 and B2 confirmed that these indices were not good predictors for KIC. Much research has reported significant differences in KIC results obtained from different test methods for the same rock. Six different KIC test methods, CB, SECRBB, SCB, SNSRB, BDT and Flattened Brazilian Disk (FBD), were investigated in [33]. It was concluded that CB was the optimum KIC test method due to its stable crack growth, which results in creating a sharp and narrow crack at the top of the notch, and the small FPZ, which satisfies the LEFM assumption. Furthermore, boundary conditions did not have
## 7 Conclusions  
We have calculated the propagation of photon states through non-Hermitian linear dimers with Gaussian gain and losses. As a practical example, we studied propagation in the different experimentally feasible configurations of the \(\mathcal{PT}\)-symmetric dimer and show that each and every configuration presents a different signature in the propagation of vacuum, single and two-photon states. These signatures go beyond the mean photon number at the waveguides, which can be split into spontaneous and stimulated generation components, and can be found, for example, in the second order two-point correlation of the photon state propagating through the dimers.  
First, let us focus on the mean photon number signatures. They show that the propagation of vacuum and single photons through dimers in the \(\mathcal{PT}\)-symmetry regime might provide us with directional coupler devices controllable by the propagation length. Furthermore, devices that provide full single photon switching can only be designed using dimers in the passive-loss and loss-loss configuration. Outside the \(\mathcal{PT}\)-symmetric regime, the asymptotic stability of the dimers in any given configuration suggest their use as symmetric intensity sources at the Kato exceptional point and, in the broken symmetry regime, they might provide asymmetric intensity sources where the intensities ratio can be controlled by the linear properties of the material.  
On the other hand, second order two-point correlation signatures can help us choose configurations depending on the type of state needed for a particular application. For example, if spatially separable states are needed, choosing a configuration showing photon bunching comes naturally. Spatially entangled states are provided by passive-loss and loss-loss configurations where the initial two-photon state is delocalized in the waveguides.  
We want to note that our description of linear media is far from complete. Real world linear materials saturate; this induces further restrictions on the model that we do not consider in this manuscript and could provide further avenues of research. Furthermore, the addition of new interactions, like introducing a two-level system to create more complex hybrid devices [20], requires a first principle reformulation in order to recover adequate effective models.  
[PERSON] acknowledges financial support from CONACYT #294921 PhD grant.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Photon bunching and anti-bunching shown in terms of the \(q^{(2002)}(\zeta)\) parameter for different realizations of the effective \(\mathcal{PT}\)-symmetric dimer. The first row, (**a–c**), shows balanced gain-loss, the second row, (**d–f**), shows gain-gain, the third row, (**g–i**), shows gain-passive, and the fourth row, (**j–l**), shows both passive-loss and loss-loss configurations in the \(\mathcal{PT}\)-symmetric regime, the first column with \(|\gamma|=0.5\), the Kato point with \(|\gamma|=1\), the second column, and the broken symmetry regime, the third column with \(|\gamma|=1.2\).
# The Electromagnetic Shielding of Optoelectronic Devices by Mesh Structures
## 5 Conclusions  
In this article, the shielding properties of mesh structures with various parameters were studied. It was shown that the shielding effectiveness increased in the decimeter/centimeter/millimeter wavelength range with decreases in the mesh period and the aperture size. A comparison of various theoretical calculations of the shielding properties and the experimental transmission coefficient was carried out. It was found that one-mode calculation of the shielding properties is effective [13]. For the first time, a full transmission spectrum of the mesh structure on K108 glass from the UV (0.185 um) to decimeter wavelength range (12 cm) was obtained. It is found that a high-pass filter on K108 glass acquires a band pass spectrum with two transmission bands--in the visible/near-IR range of 0.35-3 um and in the THz frequency range of 0.6-3 mm. With the recorded attenuation in the microwave range, it was possible to provide transmissions of 75% in the visible wavelength range for a 90 um \(\times\) 10 um \(\times\) 5 um mesh structure and of 85% for a 280 um  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Attenuation, dB**} \\ \cline{3-4}  & & **4 GHz** & **12 GHz** \\ \hline \multirow{4}{*}{Mesh structures} & 280 μm \(\times\) 20 μm \(\times\) 5 μm & 38.9 dB & 34 dB \\ \cline{2-4}  & 90 μm \(\times\) 10 μm \(\times\) 5 μm & 34.2 dB & 23 dB \\ \hline Carbon nanotubes (Ref. [7]) & 10 nm nanotube diameter & 30 dB & 25 dB \\ \hline Contiguous rings (Ref. [10]) & Element size = Period = 321 μm linewidth = 3.52 μm & 20 dB \\ \hline Petal-shaped mesh (Ref. [11]) & Element size = Period = 250 μm linewidth = 5 μm & 31 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of shielding surface attenuations at frequencies of 4 and 12 GHz.  
\(\times\) 20 \(\upmu\)m \(\times\) 5 \(\upmu\)m mesh structure, and for those at 1.55 \(\upmu\)m, the transmissions were 78% and 86%, respectively. Depending on the customer's requirements, the obtained data provide a choice of mesh parameters to vary a ratio between the microwave attenuation and transmission of OED in the operating frequency range.  
Conceptualization, M.K.K.; Formal analysis, M.K.K.; Funding acquisition, G.I.K.; Investigation, V.V.B., A.A.S. and V.S.S.; Methodology, A.A.S. and V.S.S.; Project administration, G.I.K.; Software, V.V.B.; Supervision, G.I.K.; Visualization, V.V.B.; Writing--original draft, M.K.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Development of a Combined Orchard Harvesting Robot Navigation System
## 2 Materials and Methods
### Overall Design of the System
#### 2.1.1 Structure and Design of the Master-Slave Navigation System  
An overall framework of the orchard master-slave navigation system, including the automatic navigation system of the orchard master robot and slave robot, is depicted in Figure 2. The autonomous navigation system for the master-slave robot is the same basically, mainly including the positioning unit, the central control unit, and the bottom control unit. The positioning unit consists of an RTK-GNSS (includes GNSS plug in the radio, a GNSS receiver, external radio antennas, and GNSS base station), LIDAR, and odometer to provide positioning and operational status information to the central control unit of the robot. The central control unit streams its status data with the central control units of other robots via Wi-Fi, makes judgments based on the status information of other robots and the information provided by the positioning unit itself, selects the navigation mode applicable to the current working environment, and sends control commands to the underlying control unit.