esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Unsupervised Analyses of Cinnamon by SPME/GC-MS
#### 2.2.1 Analysis of the Cinnamon Authenticated Drugs and Commercial Preparations VOCs Dataset  
HCA dendrogram of the whole analyzed dataset revealed that 14 out of 27 groups were clustered mainly among two major classes. Although many biological samples were not clustered together, the CA and CT demonstrated to be clustered with CP-1 and CP-3, while CI, CV, and CVM clustered along CP-2 and CP-4 were in the second main cluster. CA and CI appeared to be unique to segregate separately as sub-clusters within the main first and second clusters, respectively, Figure 3A. These findings were in agreement with and confirmed by heatmap analysis (Supplementary Figure S1), where metabolites of low variance values were excluded.  
In addition, the PCA score plot (Figure 3B) showed weak discrimination between investigated cinnamon samples, with PC1 accounting for 35.03% of the total variance, i.e., 58.31%. However, authenticated drugs CA and CT (negative side) were found to be segregated from other drugs, i.e., CV, CVM, and CI clustered on the other positive side along PC1. Moreover, CP-1 and CP-3 were also separated from CP-2 and CP-4 likewise along PC1. The overlap of CV and CVM specimens confirmed that the different geographical origin, i.e., Malaysia and Pakistan, of _C. verum_ did not affect their VOCs profile and provided model validation by clustering of closely related specimens as expected.  
Analysis of the PCA loading plot (Figure 3C) revealed that coumarin (P61), (_Z_)-3-hexen-1-ol (P6) and 3-hexen-1-ol isomer (P9), and cinnamaldehyde (P22) were the major discriminators to account for specimens' segregation, with coumarin found to be related mainly to CA and CT, while hexenol to the commercial products CP-1 and CP-3. (_E_)-Cinnamaldehyde as a major component appeared enriched in CV, CVM, and CI, in addition to the commercial CP-2 and CP-4 products, and suggestive that the later products were derived from _C. verum_ posing that model to predict unknown samples origin.
## 5 Conclusions
The authors would like to express their appreciation to the anonymous reviewers for their help and meaningful comments on this paper. **Conflicts of Interest:** The authors declared that there is no conflict of interest with respect to the research, authorship, and/or publication of this article. **Nomenclature**  
\begin{tabular}{l l} \(a\) & Adjust factor of fatigue life of DACBB, \(a=1.3\) \\ \(C_{k}(k=\mu,v)\) & Basic rating dynamic load \\ \(c_{1}\) & The coefficient used to adjust the sign of deformation \\ \(c_{2}\), \(c_{3}\) and \(c_{4}\) & The index related to the configuration of DACBB \\ \(D\) & Ball diameter, mm \\ \(d_{m}\) & Pitch diameter of DACBB, mm \\ \(d_{c}\) & Axial distance between two rows, mm \\ \(E\) & Young's modulus, MPa \\ \(F_{0}\) & Axial preload, N \\ \(F_{r}\) & External radial load, N \\ \(F_{a}\) & External axial load, N \\ \(f_{i,l}\)/\(e\) & Coefficient of inner and outer raceway groove curvature \\ \(K_{n}\) & Contact stiffness coefficient of DACBB, N/mm1.5 \\ \(L\) & Distance between the center of DACBB and the center of rolling ball, mm \\ \(L_{m}\) & The axial distance from the center of inner ring groove curvature to r-axis, mm \\ \(L_{10}\) & Fatigue life, Rev \\ \(L_{i,l}\)/\(L_{e}\) & Basic rating life of inner and outer raceway \\ \(l_{0i}\)/\(\omega_{0c}\) & The initial distance between the inner/outer groove centers and ball centers, mm \\ \(l_{i,l}\)/\(e\) & The distance between the inner/outer groove centers and ball centers, mm \\ \(M\) & Moment load, Nmm \\ \(O_{L}\)/\(O_{R}\) & Inner ring groove curvature centers of the left and right row of DACBB. \\ \(Q_{m}\) & Contact load between ball and raceway, N \\ \(Q_{mr}\)/\(Q_{mz}\) & Decomposed component of contact load in radial and axial, N \\ \(Q_{k}(k=\mu,v)\) & Equivalent contact load of ball-inner/outer raceway \\ \(R_{m}\) & Distance between the inner raceway groove curvature center and \(z\)-axis, mm \\ \(r_{i}\) & Radius of inner raceway groove curvature, mm \\ \(r_{e}\) & Radius of outer raceway groove curvature, mm \\ \(u_{mr}\)/\(u_{mz}\) & Radial and axial displacement of inner groove curvature centers \\ \(v_{mr}\)/\(p_{mz}\) & Radial and axial displacements of rolling ball center \\ \(Z\) & Number of ball \\
**Greek Symbols** \begin{tabular}{l l} \(a_{0}\) & Unloaded contact angle, rad \\ \(\alpha\) & Actual contact angle, rad \\ \(\gamma\) & Misalignment angle, rad \\ \(\delta_{m}\) & Total contact deformation, mm \\ \(\delta_{r}\) & Radial displacement, mm \\ \(\delta_{a}\) & Axial displacement, mm \\ \(\delta_{\gamma r}\)/\(\delta_{2\gamma}\) & Deformation components caused by angular misalignment, mm \\ \(v\) & Poisson's ration \\ \(\delta_{0}\) & Axial deformation, mm \\ \(\varphi_{i}\) & The azimuth angle of rolling ball, rad \\ \(\Delta_{ij}\Delta_{e}\) & The clearances between ball and inner/outer raceway, mm \\ \end{tabular}
# Iterative Oblique Decision Trees Deliver Explainable RL Models
## 3 Methods
### DT Training Methods
#### 3.4.2 Bounding Box (BB)  
Detailed investigations of the failures of DTs trained on episode samples for the pendulum environment show that a well-performing oracle only visits a very restricted region of the observation space and oversamples specific subregions. A good oracle is able to swing the pendulum in the upright position quickly and keeps the system in a small region around the corresponding point in the state space for the rest of the episode's duration.  
To counteract this issue, we investigated an approach based on querying the oracle at random points in the observation space. The algorithm consists of three steps:  
1. The oracle is evaluated for a certain number of episodes. Let \(L_{i}\) and \(U_{i}\) be the lower and upper bound of the visited points in the \(i\)th dimension of the observation space and \(\text{IQR}_{i}\) their interquartile range (difference between the 75 th and the 25 th percentile).
2. Based on the statistics of visited points in the observation space from the previous step, we take a certain number \(n_{s}\) of samples from the uniform distribution within a hyperrectangle of side lengths \([L_{i}-1.5\cdot\text{IQR}_{i},U_{i}+1.5\cdot\text{IQR}_{i}]\). The side length is clipped, should it exceed the predefined boundaries of the environment.
3. For all investigated depths \(d\), OPCTs are trained from the dataset consisting of the \(n_{s}\) samples and the corresponding actions predicted by the oracle.  
It should be noted that this algorithm has its peculiarities. Randomly chosen points in the observation space might correspond to inconsistent states. While this is evident for the example of Pendulum (the observations contain \(\cos\theta\) and \(\sin\theta\) (see Table 1), which was solved here by sampling \(\theta\) and generating observations from that), less apparent constraints between observables may occur in other cases. We cannot guarantee that we will always generate samples that meet the constraints. We found Algorithm BB to still work if constraint-violating samples are added, but the oracle may be evaluated at points that cannot occur in actual episodes. Such evaluations may lead to performance degradation of BB. This limitation exists only for algorithm BB, not for EPS or ITER, and was, in fact, one of the main reasons for developing the ITER algorithm, which we describe in the next section.
Thermographic images are the most used when the heating and the cooling dynamics of the weld acquired during the welding process can reveal anything important for the defect detection and/or classification. IR thermography has been used for different kinds of applications, such as to monitor and control the weld geometry [31; 32], to detect defects as lack of penetration and estimate of depth of penetration [33], and to perform seam tracking, bead width control, and cooling rate control to ensure acceptable weld quality with artificial neural networks [34]. The thermographic images have largely been studied with DL architectures [35; 36; 37], since they provide a gold standard for NDT methods. As an example, deep architectures have been employed to detect cracks [38] and characterize defects in a carbon-fiber-reinforced plastic specimen [39; 40] by automatically analyzing thermal images and videos. To the best of the authors' knowledge, there are no studies that solve the presented problem with the specific setup; hence, a detailed introduction is required to understand the conceived approach. In this study, according to the industrial requirements of the Comau(r) S.p.A., the authors propose several automatic classification frameworks capable of detecting a defective laser weld based on the electrical properties, i.e., the electrical resistance. As reported above, the analyzed industrial task considers the welding of different lithium-ion cells to build a battery pack for electric vehicles. The studied process concerns the welding of nickel-plated aluminum tabs representing the cell's electrode and a copper busbar. Busbars are solid metal bars used to carry current and place in connection the different cells; their technical attributes make busbars ideal for some high-voltage connections in electric vehicles. The diagnostic intelligent frameworks developed in this study are able to classify, in real time, a weld joint as either _defective_ or _good_ depending on electrical properties of welded joints. The processed data consist of a single sequence of thermographic images acquired with IR camera during the welding and the cooling processes. A large set of features were extracted from the IR image sequence, and different classifiers are proposed, trained, and cross-validated, also by following three proposed workflows characterized by different feature reduction and selection procedures. Finally, a study and discussion of the execution time of the training and inference procedures of all evaluated classifiers is also reported. The remainder of the article is organized as follows: Section 2 describes the materials and methods used during the study; the outcomes of the experimental studies are provided and discussed in Section 3; finally, the conclusive remarks and future perspectives are presented in Section 4.
## 4 Discussion
venusta_ soil addition in sunflower, observing a significant improvement in plant growth, an increment in chlorophylls, and, moreover, a protein amount correlated to the production of bacterial auxin (IAA). [PERSON] et al. [42] showed that _B. sonorensis_, a very similar species to M21 and M23 strains, produced IAA (4.3 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)), siderophores, ACC deaminase, and solubilized phosphate, but they also observed, with the inoculation of this bacterium, improved plant growth. Moreover, it is reported in the literature [43] that _B. licheniformis_, another species phylogenetically similar to M21 and M23, produces IAA and siderophores, solubilizes phosphate, and inhibits the growth of dangerous pathogenic fungi, such as _Botrytis cinerea_, _Colletotrichum gloesporioides_, and _Pluytophthora nicotianae_. According to [PERSON] et al. [44], this _Bacillus_ species can be used in fruit orchards to control fungal diseases and increase fruit production. Similarly, M21 and M23 produce IAA, siderophores, and large amounts of ammonia. Despite the above evaluation of NaCl's effects on PGP features, little is known, and only few studies on _Bacillus_ spp. have been reported. Moreover, to our knowledge, no information about _Halomonas_ spp. is at present available. In fact, these studies are focused on IAA production, and only small amounts of are available on ammonia production. Here, we showed that QH24 produces significant amounts of IAA, even in the presence of high concentrations of NaCl (600 mM), but in the absence of this salt, no production is observed. QH23, M21, and M23 produce IAA with or without the addition of NaCl; however, unlike QH24, in these strains, salt exerts a negative effect on its production. All bacterial strains analyzed in our study produced ammonium at both 75 and 600 mM NaCl, and we also highlighted how salt sees different regulation in _Halomonas_ strains compared to those of _Bacillus_. In QH23 and QH24, the ammonium production was directly positively associated with the NaCl concentration, whereas in M21 and M23, NaCl's addition to the growth medium strongly reduced its production. Finally, for M23 and QH24 strains, it is also possible to highlight, only for 600 mM NaCl, relevant production of siderophores. Therefore, all our isolates exhibit PGP features that could enhance plant growth and development even under salinity stress conditions. However, further analyses are necessary to verify these potentialities, such as in in vivo tests and thorough experiments in the field involving direct bacterial inoculation of plants.
## 2 Materials and Methods
### Quality Analysis
#### 2.3.3 Colour Measurement  
Colour measurements were taken using a reflectance colorimeter (CR-400 Minolta Co, Ltd., Chiyoda, Japan) with a 2\({}^{\circ}\) observer that closely matched the CIE 1931 standards observer [32]. A standard white plate with D65 illumination (\(\gamma\) = 93.7, x = 0.3158, and y = 0.3324) was used to calibrate the instrument prior to the measurements. The measurements were taken in triplicate and randomly on different maize kernels spread on a plate by placing the instrument on top of the kernel. The colour parameters were expressed in terms of L* related to the lightness (L* = 0 for black and L* = 100 for white), a*, which represents the intensity in green-red (a* < 0 for green and a* > 0 for red), and b*, which describes the intensity in blue-yellow (b* < 0 for blue and b* > 0 for yellow). From the a* and b* values, the chroma and hue were computed. Chroma (C*) indicates the colour saturation, which is proportional to its intensity and is calculated using Equation (6):  
\[\mathrm{C}^{*}=\sqrt{\mathrm{a}^{*2}\mathrm{+}\mathrm{b}^{*2}} \tag{6}\]For the hue, an angle of 0\({}^{\circ}\) indicates a red hue, while the angles of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) indicate yellow, green, and blue hues, respectively. To obtain the hue, Equation (7) was used.  
\[\mathrm{h^{o}} =\arctan\left(\frac{\mathrm{b^{*}}}{\mathrm{a^{*}}}\right) \tag{7}\]  
Colour alterations were estimated and expressed as the total colour difference \(\Delta\mathrm{E^{*}}\) of the samples before and after drying using Equation (8):  
\[\Delta\mathrm{E^{*}}=\sqrt{\Delta\mathrm{L^{*2}}+\Delta\mathrm{a^{*2}}+\Delta \mathrm{b^{*2}}} \tag{8}\]
## 2 BiCA-LSTM-Informer Framework
### Dual-Branch Encoder  
#### 2.2.1 Bidirectional LSTM Branch  
To effectively capture local dependency features in time series, the proposed model incorporates a bidirectional long short-term memory network (BiLSTM) as the foundational encoding module. This design aims to fully exploit both forward and backward temporal dynamics, thereby enhancing the completeness of feature representation.  
The standard LSTM addresses the vanishing and exploding gradient problems commonly encountered in traditional recurrent neural networks (RNNs) by introducing gating mechanisms. To further enhance the model's ability to capture dynamic information from both past and future contexts, we employ a BiLSTM architecture. Unlike unidirectional LSTMs, BiLSTM separately encodes the input sequence in two directions: forward (from \(t=1\) to \(T\)) and reverse (from \(t=T\) to \(1\)), and then concatenates the resulting hidden states.  
At each time step \(t\), the BiLSTM regulates information flow through three key gates: the input gate, forget gate, and output gate. First, the forget gate determines which information to retain or discard from the memory cell \(c_{t-1}\):  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{1}\]  
where \(x_{t}\) is the current input, \(h_{t-1}\) is the previous hidden state, \(W_{f}\) and \(U_{f}\) are learnable weight matrices, \(b_{f}\) is the bias term, and \(\sigma\) denotes the Sigmoid activation function.  
Next, the input gate controls how much new information from the current input \(x_{t}\) should be added to the memory cell. A candidate memory state is generated using the following hyperbolic tangent function:  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{2}\]  
\[\tilde{c}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{3}\]  
The memory cell is then updated by combining the results of the forget and input gates:  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t} \tag{4}\]  
where \(c_{t-1}\) denotes the memory state at the previous time step, and \(\odot\) represents element-wise multiplication (Hadamard product).  
Finally, the output gate determines which part of the memory cell \(c_{t}\) should be passed to the hidden state \(h_{t}\):  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{6}\]After processing the entire sequence in both directions, the final BiLSTM hidden state at time step \(t\) is obtained by concatenating the forward and backward outputs:  
\[h_{t}^{(b)}=[h_{t}^{(\rightarrow)};h_{t}^{(\leftarrow)}] \tag{7}\]  
where \(h_{t}^{(\rightarrow)}\) and \(h_{t}^{(\leftarrow)}\) denote the hidden states from the forward and backward passes, respectively, and \([\cdot]\) indicates vector concatenation.  
Through this bidirectional structure, the BiLSTM branch can simultaneously capture contextual information from both past and future time steps, significantly enhancing its capability for temporal dependency modeling. The resulting hidden states serve as input to the subsequent cross-modal attention module, where they are fused with global features extracted by the Informer encoder.
## 4 Results and Discussion
### Validation of the Behavior of the Motion Intention Switcher (MIS)
The system without the MIS quantified the meaning of \"far\" as 42 cm by considering the default perceptive distance and the robot moved to location '\(c_{B}\)'. The quantified output of the system with MIS was 218 cm since it considered the \(D_{a,max}\) as \(D\) since the evaluated gesture indicated a request to change the default intention. Figure 6: The view of the robot and the third person view of sample scenarios are shown with the corresponding case (**a**–**e**) given in Table 2. The tracked skeletons of the users are also superimposed with the RGB view of the robot for better clarity. In case (d), the initial location was '\(d_{1}\)' and it was commanded, \"move far forward\". The quantified output of the system without the MIS was 70 cm and the robot moved to location '\(dg\)'. In the system with the MIS case, \(D\) should be altered to \(D_{a,min}\) since \(D_{gesture}<\delta_{min}D_{r}\). However, in this situation \(D_{a,min}\) and \(D_{r}\) were the same. Therefore, \(D\) was not altered and the quantified output is the same as the system without MIS. Therefore, the robot moved to location '\(d_{A}\)' which was almost the same as '\(dg\)' (due to navigational errors there is a very small different in position coordinates). In this case, the intention of the user was to express his intention of navigating the robot to a location that is in between the obstacle and the robot without altering the default intention. Moreover, the proposed system is capable of successfully handling such situations. In case (e), similarly to the case (b) the robot with MIS switched the intention by identifying the actual intention of the user by analyzing the instructions conveyed from pointing gestures given along with voice instructions. Similarly, the behavior of the MIS was found to be capable of effectively switching the intention of the robot according to the actual intention of the user in all the test cases. An explanatory video that shows the behaviors of the two systems in a similar kind of experimental scenario is provided as a supplementary material. It shows the video footage from a third person's view along with the traced location of the robot within the navigation map. Furthermore, parameters used in the interpretation process of the commands are also given with annotated explanations. Figure 7: The initial and final positions of the robot during the experiment for identifying the behaviors of the proposed method are marked on the map with corresponding case letters. The shaded areas represent the objects in the environment. The map is drawn to a scale. However, it should be noted that the markers do not represent the actual size of the robot.
## 4 The Monte Carlo Simulation and Experimental Results
### Statistics Regarding Monte Carlo Simulation  
Our study derives Table 3 based on the results of the 30-run simulation which can be calculated by the Equations (1) to (8). It shows that the average rate for reducing number of vehicles is 9.79 in First In First Out (FIFO) and 15.56 by our approach. Additionally, the standard deviation is 2.27 for FIFO, 7.96 for our approach. In addition, the average rate and the standard deviation for traffic jam amount is 1.35 and 0.08 in FIFO, and 0.34 and 0.09 in our approach. It can be seen that the conventional traffic queuing problem can be solved in an efficient manner by using our approach.
## 3 Self-Triggered Control for Islanded Microgrids under DoS Attacks
### Stability Analysis
This is because otherwise, \(V(t)\) would become negative which is in contradiction with the positive semi definiteness of Lyapunov function. From Equations (12)-(14), we can conclude that in both the cases \(\left|dev^{ij}\big{(}t_{l}^{ij}\big{)}\right|<\varepsilon\) and \(\delta^{ij}\big{(}t_{l}^{ij}\big{)}=0\), the control input \(u^{ij}(t)\) is set equal to \(0\) and stops to update. Then, \(u^{ij}(t)=0\) caused by the persistence of the jamming period can be excluded. According to the model of DoS attacks, the adversaries cannot permanently destroy the transmission capacity of communication links. This means that there must exist a successful triggering time instant \(t_{\beta_{l}^{ij}(t):0\to 1}\), which can be ensured by the proposed ACK-based monitoring mechanism. Thus, \(u^{ij}(t)=0\) caused by the persistence of the jamming period can be excluded. It can be concluded that for all \((i,\ j)\in\Xi\) and \(t\geq T^{*}\), there must exist \(\left|dev^{ij}(t)\right|=\left|x_{j}^{AVE}(t)-x_{i}^{AVE}(t)\right|<\varepsilon\). Since each pair of neighboring agents differs by at most \(\varepsilon\) and the graph \(G\) is undirected and connected, the state variable \(x_{i}^{AVE}(t)\) can converge to a point \(x^{*}\) belonging to the set defined as follows  
\[x^{*}\in\left\{\left|x_{j}^{AVE}(t)-x_{i}^{AVE}(t)\right|<(N-1)\varepsilon,\ \forall(i,j)\in V\times V\right.\right\} \tag{21}\]  
where \(N\) is the total number of agents. By choosing the appropriate value of \(\varepsilon\), the range of the final consensus region can be small enough to improve the control accuracy of the proposed algorithm. Figure 4 shows the comparison of our algorithm and the method applied in [32]. **Remark 5**: _Due to the distributed property, the proposed self-triggered control scheme is reliable and cost-efficient, and suitable for scalability and flexibility of microgrids. Continuous monitoring of the state variable is not required in our method, and each linked pair of agents can exchange information independently, which improves computation efficiency while reducing communication requirements. Compared with the method used in [32], our algorithm allows the agents to achieve consensus regardless of the frequency of DoS attacks. As illustrated in Figure 4, the minimal interval between any consecutive communication attempts for agents \(i\) and \(j\) can be  
Figure 4: Comparison of our algorithm and the method in [32]. represented as \(\Delta\) in [32]. However, this method becomes invalid in the case that the frequency of DoS attacks is larger than \(1/\Delta\), because all information exchanges are blocked on communication link \((i,\ j)\in\Xi\). By introducing the ACK-based monitoring mechanism, a new triggering condition is proposed to ensure the successful data transmissions when the attack shifts to a sleep period. Thus, even under high-frequency DoS attacks, the agent's consensus can still be achieved by our algorithm._