esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Differential VOCs at Various Ripeness Levels of Rice Grains  
The value of variable importance in projection (VIP) reflects the contribution of each VOC to groups. Based on the VIP values from the OPLS-DA model, we screened for significant variances using the following criteria: VIP \(\geq 1\) and \(p\)-value \(<0.05\). This analysis identified 212, 186, and 148 significantly different compounds in the maturation of YX430, MXZ2, and YJSM2 cultivars, respectively (Tables S2-S4). It is mainly composed of ester, heterocyclic compounds, hydrocarbons, and terpenoids. The variety of these substances was abundant in the metabolic profile we examined. A Venn diagram was made to illustrate the shared differential VOCs among YX430, MXZ2, and YJSM2 during grain ripening (Figure 3). Across three pairwise comparisons, 115 overlapping differential VOCs were identified as key markers for rice grains (Tables S5), including esters (**23**), heterocyclic compounds (**21**), terpenoids (**16**), hydrocarbons (**10**), alcohols (**10**), ketones (9), aldehydes (**8**), aromatics (**7**), acids (**3**), amines (**2**), phenols (**1**), nitrogen compounds (**2**), sulfur compounds (**1**), and other compounds (**1**). These substances may be used as markers for different ripening stages of rice grains.  
Figure 3: **(A) Venn diagram of different VOCs among YX430, MXZ2, and YJSM2 during grain ripening. Red means YX430, blue means MXZ2, and green means YJSM2. (B) Scatterplot of rOAV odor activity values. The horizontal coordinate represents different groups, and the vertical coordinate represents the rOAV value of VOCs. These points represent all VOCs.**  
Figure 2: OPLS-DA score plots of YX430 (**A**), MXZ2 (**C**), and YJSM2 (**E**). Two hundred permutation tests of YX430 (**B**), MXZ2 (**D**), and YJSM2 (**F**). (**G**) OPLS-DA score plots of brown rice in 30 d. (**H**) Two hundred permutation tests of brown rice in 30 d.  
Additionally, a total of 39 differential VOCs were altered exclusively in YX430 and MXZ2, but not in YJSM2. Among these, 24 VOCs were annotated as contributing to aroma (Table S6). These include five heterocyclic compounds (succinimide, 3-acetyl-2,5-dimethylfuran, 2-hexylthiophene, 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone, and 2-isobutylthiarole), five esters (butyl hexanoate, 2-methylbutyl hexanoate, ethyl 2-ocenoate, ethyl sorbate, and 2-methylbutyl 2-methylpropanoate), five terpenids (Paramenthatriene, carvone, 2,6,6-trimethyl-2,4-cyclohexptadien-1-one, L-menthol, and geraniol), four aldehydes (hexanal, cis-3-hexanal, nonanal, and (E)-cinnamaldehyde), three aromatics (4-allyphenol, o-xylene, and styrene), one nitrogen compound (2-nonenonitrile), and one sulfur compound (3-(methylthio)propanoic acid). These compounds may serve as markers to distinguish between aromatic and non-aromatic rice grains at different ripening stages.
## 2 Methods
### PMT-Based Time-Gated Measurement  
The system utilizes a pulsed nanosecond light source and operates the PMT detector in gated mode to perform time-gated imaging. Upon emission of a laser pulse, the PMT is triggered to acquire data for the corresponding duration determined by gating. By introducing a temporal delay to synchronize the return light signal with the electronic gate of the detector, we enable imaging of light returning from a predetermined range. In this context, it is important to note that the PMT serves as an intensity sensor capable of reconstructing images with varying grayscale intensities.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 6 Results of Random Topology
### Convergence Time  
#### 6.1.1 Convergence Time of 20 Nodes  
As shown in Figure 6, it is obvious that the original algorithm is the slowest in building up the DODAG because the listen-only period of the original algorithm is too long; after the listening period, the communication between nodes starts. Moreover, as displayed in Figure 5, the best convergence time is reached by the elastic hop count algorithm rather than by the dynamic algorithm, and after that by the e-trickle algorithm. Focusing on the elastic hop count algorithm, the convergence time takes almost 80% less time than the original algorithm in building DODAG because the listen-only period was divided into intervals depending on the hop count. On the other hand, we note that when RX values decrease, the convergence time increases because external influences may play a larger role in transmission success, which makes the convergence time in building a DODAG longer.  
Figure 5: Scenarios of randomly placed nodes (20 and 40).  
Figure 6: Convergence time for 20 nodes with various RX values.
# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 6 Experimental Results and Performance Evaluations
### Parameters for Classifiers  
As explained in Section 5.6, 5-fold cross validation is used to obtain the results. During training, the F1-score is maximized to optimize each classifier. The best learning model is extracted using the tuning of the learning rate (\(\alpha\)) and the regularization parameter (\(\lambda\)) for LR. A set of nearest neighbors (100, 200..., 1000) is experimented with to obviate bias and overfitting. For the case of traditional decision tree and TDOST, pre-pruning is exploited to prevent overfitting with maximum splits. Considering SVM, Gaussian kernels were used for optimization.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 4 Multi-UAV Collaborative Search Algorithm  
This section concerns the multi-UAV collaborative search algorithm part of our methodology, which is shown in Figure 10. In this section, we describe our proposed multi-UAV collaborative search algorithm in detail.  
Figure 9: Moore curve route map.  
We denote the grid cells on the path as \(P=[P_{0},P_{1},P_{2},\ldots,P_{mn-1}]\), where \(P_{i}\) denotes the \(i\)th cell on the path. The Euclidean distance between the centers of two neighboring cells \(a\) and \(b\) is denoted as \(dist(a,b)\). The nearest cell and the farthest cell, close to the departure side, can be found by:  
\[j^{-}=\underset{j}{\text{argmin}}\quad dist(S,C_{0,j}) \tag{2}\]  
\[j^{+}=\underset{j}{\text{argmax}}\quad dist(S,C_{0,j}) \tag{3}\]  
According to the rules of different routes, \(C_{0,j^{+}}\) or \(C_{0,j^{-}}\) is chosen as the position of the first target cell for the UAV to reach. When the UAV reaches a cell, it will take photos at the cell's hovering time \(T\), and, once all sensing is finished, it will return to its beginning location \(S\). The UAV travels at the pace \(V\) specified by the route. In the working process of the UAV, we do not consider the acceleration and deceleration of the UAV when hovering in each target cell, and the default speed \(V\) is a constant value. Our algorithm is based on the assumption that the working structure is on a two-dimensional plane. In the algorithm, we assume that all the UAVs are at the same altitude and the atmospheric pressure is not taken into account. We do not consider 3D structures or other realistic constraints and only focus on the route in the plane; that is, the UAV path changes only along the horizontal axis.  
The following is an expression for the amount of tracking time needed for a UAV to travel through each compartment. It should be mentioned that the tracking time reduces if more than one UAV is used. The utmost surveillance period is therefore the time after that.  
\[\begin{split} Maxtime&=\frac{dist(S,P_{0})+dist(S,P_{ mn-1})}{V}\\ &\quad+\sum_{i=0}^{mn-2}\frac{dist(P_{i},P_{i+1})}{V}+mnT\end{split} \tag{4}\]
# In Vitro Cultures and Volatile Organic Compound Production in _Chiliadenus montanus_ ([PERSON].) [PERSON]  
1 Department of Horticulture and Crop Science, School of Agriculture, The University of Jordan,  
Amman 11942, Jordan; [EMAIL_ADDRESS] (D.A.-D.); [EMAIL_ADDRESS] (R.S.)1 Department of Agriculture Biotechnology and Genetic Engineering, Faculty of Agriculture Technology,  
Al-Ahiyya Amman University, Amman 19328, Jordan  
1  
[PERSON]  
1 Department of Horticulture and Crop Science, School of Agriculture, The University of Jordan,  
Amman 11942, Jordan; [EMAIL_ADDRESS] (D.  
to treat renal troubles, diarrhea, stomachaches, and chest diseases [8; 9]. Phytochemical studies of _C. montanus_ identified monoterpenes, sesquiterpenes, diterpenes, triterpenes, triterpenes, sterols, and flavonoids as major compounds in its natural extracts [10; 11; 12; 13]. The chemical composition of its essential oils showed high scavenging activity against peroxyl radicals [14]. Furthermore, the plant was found to exhibit anticancer [15; 16], antioxidant [17], anticholestatic [18], and antimicrobial activities [19] as well as antiatherogenic [20] and anti-diabetic efficiency [21; 22]. Therefore, _C. montanus_ is considered one of the most valuable medicinal plants in the region and it has great potential as a therapeutic agent against multiple diseases [23].  
The _C. montanus_ plant is considered a vulnerable neglected herb that grows in marginal habitats and it is subjected to threats of extinction due to overgrazing, urbanization, and climate change [1; 24]. Additionally, it is considered naturally endophytic of many fungi and bacteria, which makes it very challenging to establish a successful micropropagation system for this plant [25]. Sexual propagation of _C. montanus_ was reported previously and was associated with limited seed viability and production because it only blooms for two months during the growing season [26]. To date, the establishment of in vitro tissue cultures for the _C. montanus_ plant and the utilization of such systems for the production of secondary metabolites have not been reported. Hence, a micropropagation protocol for the _C. montanus_ plant through in vitro techniques is considered a promising approach for its multiplication and conservation and the ex-situ production of volatile organic compounds (VOCs).  
The main objectives of this study were to establish in vitro tissue cultures for _C. montanus_ using different explant types, media compositions, and growth conditions and to identify VOCs produced from _C. montanus_ callus and microshoot cultures. Moreover, in vitro cultures of _C. montanus_ were evaluated for their VOCs contents and compositions using the headspace solid-phase microextraction (HS-SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS) analysis.
## 3 The Proposed Method
### Decoding of a QR Code  
A QR Code is 2D square matrix in which the dark and light squares (modules) represent bits 1 and 0. In fact, each such a module on the pixel level is usually made up of cluster of adjacent pixels. In a QR Code decoding process, we have to build a 2D matrix that has elements with a value of 1 or 0 (Figure 15).  
Figure 14: Perspective transformation.  
Figure 15: Transformation of the QR Code into the binary grid.  
The pixels, where the brightness is lower than the threshold, are declared as 1 and the others are declared as 0. When analyzing such a cluster of pixels (module), the central pixel of the module plays a decisive role. If the calculated position of the central pixel does not align to integer coordinates in the image, the brightness is determined by bilinear interpolation.  
Once the binary matrix of 1 and 0 is created, the open-source ZBar library [24] can be used to start the final decoding of the binary matrix and to receive the original text encoded by the QR Code.
## 3 TRM Technique
### Virtual Time-Reversal Mirror  
In the VTRM technique, the signals are one-way transmission in the UWA channel [24]. VTRM uses the received detection signal to estimate the channel and reduces the complexity of communication systems. The diagram of the VTRM is shown in Figure 8, and the algorithm of VTRM is shown in Algorithm 1. After passing through the UWA channel, the received detection signal is used to get the CIR \(h^{\prime}(t)\) with the appropriate channel estimation method at the receiver. Then, the convolution of the received signal and the estimated time-reversal channel is completed.  
The transmitted signal which passes through the UWA channel can be expressed as  
\[s_{r}(t)=s(t)\otimes h(t)+n(t). \tag{18}\]  
After VTRM treatment, the signal obtained is as follows  
\[\begin{array}{ll}r(t)&=s_{r}(t)\otimes h^{\prime}(-t)\\ &=[s(t)\otimes h(t)]\otimes h^{\prime}(-t)+n(t)\otimes h^{\prime}(-t)\\ &=s(t)\otimes[h(t)\otimes h^{\prime}(-t)]+n(t)\otimes h^{\prime}(-t).\end{array} \tag{19}\]  
In (19), the matched channel \(h(t)\otimes h^{\prime}(-t)\) can be regarded as an effective channel, through which the signal is transmitted and is basically the correlation between the CIR of the actual UWA channel and the CIR of the estimated UWA channel. When the UWA channel is estimated accurately and the time delay of each multipath is independent, the energy of the direct path is much higher than that of other paths in the matched channel. VTRM realizes the energy in-phase superposition of the multipath signals, which can suppress the inter-symbol interference caused by multipath effect and enhance the output SNR.  
Figure 8: Diagram of VTRM.  
Figure 7: Multipath channel with equal time delay differences. (**a**) Original channel; (**b**) matched channel.  
Equation (19) can also be regarded as the convolution of received multipath signals \(s(t)\otimes h(t)\) and the time-reversal channel \(h^{\prime}(-t)\), which is equivalent to the superposition of received multipath signals when the direct path is taken as reference, to achieve energy aggregation. Besides, the white noise \(n(t)\) is not correlated with \(h^{\prime}(-t)\), so the energy of \(n(t)\) is not improved even after TRM treatment. Therefore, the output SNR is improved.  
```
1:Input: The received signal
2: Intercept the detection sequence and the information sequence from the received signal which passes through the UWA channel.
3: Use the detection sequence to estimate the UWA channel with an appropriate method.
4: Make time-reversal of the estimated channel.
5: Make the convolution of the information sequence with the estimated time-reversal channel.
6:Output: The information signal after VTRM
```  
**Algorithm 1** Algorithm of VTRM.
## 2 Suggested Techniques
### Adjacency Check
#### 2.3.2 Movement Path Generation  
The process of generating movement paths is iterated for each training data point. The process begins by selecting a random cell and sampling the data points within that cell. This is followed by a random walk in the neighbor matrix of the current cell. A random walk samples the data points contained in a cell at a specific location. If this cell is the last one in the path to be generated, the corresponding coordinate values (x, y) are used as the label. The algorithm for this is a refinement of Algorithm 3 from our previous paper [12], expressed as Algorithm 5. Algorithm 5 is a movement path data generation algorithm. It takes a dataset D, the state transition matrix P from the previous paper, the length of the movement path, cell information, and cell adjacency matrix A as inputs. The algorithm produces an output dataset defined as \(D^{\prime}\). It collects the data points \(x\) contained within cells and uses the cell number of the last point. Separate datasets are created for training and testing purposes. Accordingly, the output data \(D^{\prime}\) and each element t are defined by Equation (7).  
\[D^{\prime}=\{t_{1},t_{2},\ldots,t_{k^{\prime}}\},t_{i}=\Big{(}\Big{(}x_{1},x_{ 2},\ldots,x_{path}\Big{)},y\Big{)},y\in\{1,2,\ldots,r\} \tag{7}\]
```
Input:dataset \(D=\{X,Y\}\),  state transition matrix \(P\),  Path length path,  number of path to create \(k^{\prime}\),  cell information set \(\mathcal{C}=\{c_{1},...,c_{r}\}\),  cell adjacency matrix \(A\) Output:sequence data \(D^{\prime}\) \(D^{\prime}\leftarrow\{\}\); foriter\(\leftarrow\) 1 to \(k^{\prime}\)do \(\textit{cellidx}\leftarrow\textit{idx}\in\{i|a_{i,j}=1,i=1,2,...,r\}\); \(x^{\prime}\leftarrow\{\}\); \(x^{\prime}\leftarrow\{\}\); \(x^{\prime}\leftarrow\{x^{\prime}\cup\{x\in\{x_{i}|c_{cellidx,l}\leq Y_{i,l} \land Y_{i,l}\leq c_{cellidx,l}+c_{cellidx,m}\land C_{cellidx,m}\leq Y_{i,l} \land Y_{i,l}\leq c_{cellidx,m}+c_{cellidx,h},l=1,2,...,k\}\}\); forpathiter\(\leftarrow\) 2 to pathdo \(\textit{cellidx}\leftarrow\textit{idx}\in\{i|a_{i,j}=1,i=1,2,...,r\}\); \(x^{\prime}\leftarrow x^{\prime}\cup\{x\in\{x_{i}|c_{cellidx,l}\leq Y_{i,l} \land Y_{i,l}\leq c_{cellidx,l}+c_{cellidx,w}\land C_{cellidx,m}\leq Y_{i,l} \land Y_{i,l}\leq c_{cellidx,m}+c_{cellidx,h},i=1,2,...,k\}\}\);  end for\(D^{\prime}\gets D^{\prime}\cup\{(x^{\prime},cellidx)\}\); End
```  
**Algorithm 5 Movement Path Generation Algorithm [12]**
## 2 The Range of Accuracy and Effect Size in the Assessment of Laboratory Tests  
Experimental evidence indicates a significant impact of the lack of parallelism on the quantification of neurofilaments, a well-established biomarker for neurodegeneration [5,6]. The FDA and the EMA approved the use of two novel drugs based on laboratory results quantifying neurofilaments. A state-of-the-art randomised controlled trial (RCT) demonstrated a reduction in neurofilament blood levels as proof of efficacy for a noveldisease-modifying treatment in multiple sclerosis [7], and another RCT [8] lead to rapid FDA approval for the antisense oligonucleotide tofersen to treat amyotrophic lateral sclerosis. However, neither study considered the possibility of partial non-parallelism of neurofilaments. Although not currently relevant in studies with large effect sizes, such as [7; 8], non-parallelism becomes more pertinent in studies with smaller effect sizes, such as those encountered in the large number of trials on Alzheimer's disease which employ biomarkers as an outcome measure.  
Accepting that parallelism is a vital factor in the evaluation of laboratory tests for biomarkers, it needs to be acknowledged that parallelism is just one among several other factors influencing test reliability [9]. [PERSON] emphasised that analytical and clinical specificity and sensitivity are additional critical factors [2]. Various biological and technical factors, such as matrix effects, variations in biomarker metabolism, or variations in laboratory test procedures, can also influence the accuracy of laboratory tests for biomarkers. A large international consortium underscored the importance of using high-quality samples [10]. Furthermore, prospective experimental evidence highlighted that the inter-laboratory reproducibility and technician skills are other key factors affecting test outcomes [11].  
Assessing the range of accuracy of laboratory tests for biomarkers is a complex task that depends on multiple factors in addition to parallelism. It is crucial to be aware of these factors and to critically evaluate laboratory tests to determine their suitability as diagnostic tools and trial outcome measures in medicine.
## 3 Methodology
### Data Processing Methods
#### 3.3.3 Feature Selection Using REFCV  
The features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) method. The Recursive Feature Elimination (RFE) method is a recursive process. It ranks the features according to their importance to the algorithm [40; 41]. The pseudo-code of the algorithm is shown in Algorithm 1.  
```
Inputs: Training set \(T\), Set of \(p\) features \(F=\left(f_{1},\ldots,f_{p}\right)\), Ranking method \(M(T,F)\)
```  
**Outputs:** Final ranking \(R\)  
```
1:for\(i\) in (1:\(p\))do
2: Rank set \(F\) using \(M(T,F)\)
3:\(f^{*}\leftarrow\) last ranked feature in \(F\)
4:\(R(p-i+1)\gets f^{*}\)
5:\(F\gets F-f^{*}\)
6:endfor
```  
**Algorithm 1**RFE algorithm--Pseudo-code for the RFE algorithm  
Based on the RFE algorithm, the RFECV method cross-validates different feature combinations. By calculating its weight coefficient, finally, we select the best combination of features.
## 4 Methodological Conception and Execution
### Aggregated Gradient Performance Mechanism (AGPM)  
Differential Privacy (DP) and Secure Multi-Party Computation (SMPC) generate critical privacy protections when performed in federated learning, yet they cause some issues, including greater computational overhead and a drop in model utility. In order to alleviate  
Figure 2: Privacy preservation.  
these disadvantages, we introduced an Aggregated Gradient Performance Mechanism (AGPM) which aims at maximizing the trade-off between privacy and model performance. The AGPM improves the learning process by smartly controlling three factors: (i) noise addition in DP, (ii) the hierarchical aggregation of multiple levels, (iii) dynamic privacy budget allocation depending on the gradient variance.  
Each client computes its local gradient \(G_{i}^{t}\) at round t. To ensure \((\epsilon,\delta)\) through Differential Privacy, Gaussian noise is added before transmission:  
\[\widetilde{G}_{i}^{t}=G_{i}^{t}+\mathcal{N}\left(0,\sigma^{2}\right) \tag{6}\]  
Here, \(\mathcal{N}\left(0,\sigma^{2}\right)\) represents Gaussian noise with zero mean and variance:  
\[\sigma^{2}=\frac{2\Delta G^{2}log(1.25/\delta)}{\epsilon^{2}} \tag{7}\]  
where \(\Delta G\) is the L2-sensitivity of the gradient function, the federated architecture follows a hierarchical model with regional and global aggregators. First, perturbed gradients from clients within a region are aggregated as  
\[\overline{G}_{region}^{t}=\sum_{i=1}^{n}w_{i}\widetilde{G}_{i}^{t} \tag{8}\]  
where \(\mathrm{w_{i}}\) denotes the weight assigned to client i, typically based on local dataset size or reliability score. Then, the global server aggregates across all regional servers:  
\[\overline{G}_{global}^{t}=\sum_{r=1}^{R}w_{r}.\overline{G}_{i}^{t} \tag{9}\]  
To further improve model utility, the AGPM includes a feedback-driven adjustment of the privacy budget \(\epsilon\). At each round, the variance of gradients is monitored. If the variance is below a predefined threshold \(\tau\) indicating convergence or low update sensitivity, the AGPM reduces the added noise by increasing \(\epsilon\):  
\[if\ Var\left(G_{i}^{t}\right)<\tau,\ \ \ \ \ \epsilon\leftarrow\epsilon \times\alpha,\ \ \ \ \ \ \ \textit{where}\ \alpha>1 \tag{10}\]  
On the other hand, in the event of high variance, the privacy budget can be reduced to increase privacy. This dynamic adjustment maintains model performance at a certain level without compromising data protection, particularly in later rounds where models have stabilized.
## 2 Results
### Metabolite Class Analysis
#### 2.4.3. Polyketides, Prenol Lipids and Fatty Acyls  
In the leaves, polyketides (PKL) presented a rapidly increased production at 6 hpc (3.4 fold), decreasing to 1.7 fold at 48 hpc. A negative value (\(-2.4\) fold) was observed at 72 hpc (Figure 7E). In the roots, polyketides (PKR) were over-produced at 6, 12, 48, and 72 hpc, with fold change values of 1.6, 2.5, 3.7, and 4.8, respectively.  
In the leaves, prenol lipids (PRL) decreased to negative values (\(-1.9\) fold) at 24 hpc, increasing immediately to 2.1 fold at 48 hpc, and decreasing again to 0 at 72 hpc. In the roots, prenol lipids (PRR) were down-produced, with fold change values of \(-2.9\) and \(-2\) at 6 and 48 hpc, respectively, increasing at 72 hpc to a positive value of 1.6 fold (Figure 7E).  
In the leaves, fatty acyls had a fold change maximal value of 6.7 at 6 hpc, decreasing to negative values from 24 to 72 hpc (Figure 7F). An inverse profile was observed for fatty acyls in the roots, with negative values (\(-2.9\)) at 6 hpc, increasing thereafter at 12 hpc (2), 48 (2.3), and 72 (2.1) hpc (Figure 7F).  
Figure 7: Number of putative metabolites of _Pilomis purpurea_ over- and down-produced in leaves and roots (**A**) and their fold change profiles along the time schedule after challenging with _Phyophthora cinnamomi_. Terpenoids (**B**); taurine (**C**); and imidazole-4-acetaldehyde (**D**) in leaves; polyketides (PK) and prenol lipids (PR) in leaves (L) and roots (R) (**E**); fatty acyls (**F**); alkaloids (**G**); sub-classes of alkaloids in roots (**H**); flavonoids (**I**); phenylpropanoids (**J**); and vitamins and other phytochemicals in leaves (**K**).