esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Soil-Borne Plant Pathogens and Microbiota Determine Disease Suppression
### 3.1. Case Studies of Soil-Borne Pathogens and Diseases
#### 3.1.2 Contribution of Pathogenic Bacteria  
Continuous monoculture affects composition and taxonomic structure of soil microbiota. Disturbance of the bacterial community may also be determined by the CC systems, where microbiota are often related to the occurrence outbreaks of bacterial wilt disease that may cause damages to plant health and yield [114; 115]. For instance, bacterial wilt disease can reduce potato yield drastically [116]. Some studies confirmed that the microbial communities in the healthy rhizosphere were more rich and diverse in term of species than in the diseased rhizosphere, suggesting that microbiome-rich soil may exclude pathogens from the infection sites by restricting their ecological niches [117; 118]. In this regard, authors investigated microbial communities of the healthy and diseased cotton fields at the different plant growth stages during consecutive monoculture [119]. These authors reported that microbial communities in the healthy rhizosphere were more rich and diverse than in the diseased cotton field. In fact, the highest evenness of the microbial communities in diseased cotton plants was often observed, so suggesting the existence of relationships between microbial community composition and soil sickness. In particular, diseased cotton plants grown in the mono-cultured soil showed a higher abundance of the genera _Deinococcus_, _Thermus_ and _Bacillus_. Other authors showed that diseased soil showed more of a reduction of the alpha-diversity of the microbial communities in ginseng monoculture than in healthy soil [120] and investigated the bacterial communities in tobacco monoculture [121]. They found that in the bacterial alpha-diversity as the observed operational taxonomic units (OTUs), Chao1 richness, Shannon and Simpson diversity were reduced, the evenness was increased. However, abundance of the _Ralstonia_ spp. was positively correlated with bacterial wilt disease in tobacco monoculture. In another study, authors investigated the relationship of nitrogen (N) application and bacterial wilt on the bacterial community in CC of sesame, showing that both N-addition and wilt disease altered the bacterial composition and its structure [122]. This was likely due to fungi, although CC may promote soil-borne bacterial diseases with time, and more research to elucidate the impact on soil-borne bacterial diseases is needed.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 3 Methodology
### Network Model Setup, Simulation, and Network Scenarios for Data Collection
#### 3.2.3 SN-Inherited (WHA) Attack Model Simulation  
Two network models were designed for the SN-inherited attack simulation based on: (1) the number of nodes and (2) the state of nodes, each of them further divided into two use cases. The first use case of the first scenario comprises twenty nodes in the network, where two nodes are malicious and eighteen nodes are normal. In the second case of the first scenario, the network comprises fifty nodes, where two nodes are malicious and forty-eight are normal with one sink node in each case. Similar to the protocol-specific attack network model, the second scenario also depends on the state of the node, which is either static or mobile. The first instance of the second scenario contains all nodes in a static disposition, while the second use case consists of a network with partially mobile nodes.  
The rank attack is simulated from the protocol-specific attack category. The rank attack network model is cumulatively illustrated in Figure 2, where three nodes, 10, 19, and 20, broadcast the decreased rank and favorable characteristics through DIO messages to attract the network traffic. In the attack model, malicious nodes are placed near the root node strategically for the victim nodes to select them as parents. Algorithm 1 depicts the simulation of rank attack in the RPL-based IoT network in the Cooja simulator.
###### Abstract  
Elastocaloric cooling is recognized as a promising alternative to modern vapor-compression cooling systems, which often rely on environmentally hazardous refrigerants. Natural rubber (NR), a well-known renewable resource, stands out among elastomers exhibiting elastocaloric behavior due to a peculiar combination of nontoxicity, low cost, softness, long-life fatigue and high caloric power. Despite these properties, research on the refrigeration potential of NR is still in its early stages, and several aspects require attention. This work investigates, for the first time, the effect of crosslinking density on the elastocaloric properties of NR. Samples with three different crosslinking densities (2.9, 4.0 and 5.2 mol\(\cdot\)10\({}^{-4}\)/cm\({}^{3}\)) were produced by internal compounding and hot pressing, and thermo-mechanically characterized. The assessment of the elastocaloric effect of the produced samples revealed that reducing the crosslinking degree significantly enhanced the elastocaloric properties. To compare the cooling capacity of the samples, a qualitative coefficient of performance (COPmat) was evaluated as the ratio between extracted thermal energy and deformational work per unit volume. The results highlight that the least crosslinked sample achieved the higher COPmat, equal to 2.4. These results underscore the significance of crosslinking density as one of the primary factors to be considered to enhance the refrigeration potential of NR.  
natural rubber; vulcanization; crosslinking density; elastocaloric effect; solid state cooling +
Footnote †: journal: applied sciences
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 3 Applications in Food Microbiology
### Section Summary  
To sum up, the applications of MALDI-TOF MS in the detection and identification of various foodborne pathogens are summarized in Table 2. It includes information about the food matrices in which MALDI-TOF MS is used, the target microorganisms, the role of MALDI-TOF MS in the diagnostic process, and the corresponding references.  
The MALDI-TOF MS method is widely used for rapid identification of bacterial strains, detection of contamination in food products, and epidemiological studies, including outbreak investigations and strain tracking.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 3 Results
### Crown Segmentation from LiDAR Data  
The 0.23 m resolution of the TIR images made it difficult to confirm the edges of the canopy and to locate individual affected trees, particularly when canopies containing several trees were adjacent. Therefore, this study used LiDAR point clouds to distinguish nearby trees.  
Segmenting the crown of a single plant is the first step in extracting CST to obtain the temperature characteristics. Unfortunately, due to the coarser resolution of the TIR band compared with the optical bands for the UAV-based platform, the degeneration of the canopy boundary made it difficult to accurately extract the CST (Figure 5a). Because of the mixed radiation from the canopy and surrounding objects, the degeneration of pixels at  
Figure 4: The temperatures of different ground features in two plots (leaf region). The visible images of each feature (right region) are cropped from the high-resolution visible image obtained by the unmanned airborne vehicle.  
the canopy boundary occurred in the TIR image. To segment the canopy using TIR data, it was necessary to determine the temperature threshold to calculate the canopy boundary. The canopy boundary of individual trees can be extracted from high-resolution UAV-based optical images. However, the accurate separation of overlapping crowns is impossible from optical images without using the vertical information.  
The multiple point clouds in LiDAR data provide additional 3D information on a canopy structure, allowing its edge to be demarcated in detail. To extract the canopy boundary, the canopy was segmented by LiDAR using the PCS algorithm, on the basis of the 3D information of the canopy (Figure 5b). The canopy segmentation of an individual tree had a good accuracy according to LiDAR data for the two plots (Table 3).
## 4 Experimental Validation
### Feature Selection  
Feature, attribute, or variable subset selection involves the computation of the most productive feature subset from the data.  
Forward Feature Selection (FFS) [21] is a popular method for feature selection in machine learning. It is a greedy search algorithm that starts with an empty set of features and then iteratively adds the most relevant feature to the subset at each step. The relevant feature is determined by evaluating the model's performance on a validation set using a metric such as accuracy or F1 score. The algorithm stops when the desired number of features is reached or when no further improvement can be made by adding more features.  
Sequential Feature Selection (SFS), a derivative of FFS, starts with all the features and iteratively keeps removing the least important features until a specific number of features is reached (25 in this paper) or any other stopping criteria are met. The importance of  
Figure 4: Scatter plot of some random features. (**a**) Scatterplot of first two features; (**b**) Scatterplot of 10 th and 100 th features; (**c**) Scatterplot of 200 th and 400 th features; (**d**) Scatterplot of 300 th and 600 th features.  
each of the features can be determined using different methods, e.g., mutual information, chi-squared test or by using any classifier and evaluating it on a validation set. We used classifier-based feature selection and employed a Decision Tree for this task.  
Decision Tree classifiers are commonly used for feature selection because they are simple to understand and interpret and provide a clear measure of feature importance. The feature importances calculated by a DT classifier can be used to identify the most relevant features for a given problem. This can help improve the model's performance by reducing overfitting and increasing generalization.  
In summary, the FFS algorithm combined with a DT classifier can be an effective approach for feature selection in machine learning. It allows us to identify the most relevant features in the data, which can improve the model's performance. The approach starts with the computation of the most informative individual feature and stops when the desired number of selected features (25 in our case) is reached.  
In Figure 5a,b, we illustrate the selected features and their score for malware detection and malware categorization scenarios, respectively. The X-tick on the X-axis shows the index of the features; e.g., F618 means Feature number 618, and on the Y-axis, we show its obtained score. For example, F618 and F323, when evaluated alone, yielded validation scores of 0.937 and 0.831 for malware detection and categorization scenarios, respectively. It can be seen that the maximum accuracy has already been reached using these 25 features, so we stopped SFS.  
Figure 5: List of selected features and their scores. (**a**) SFS features for malware detection; (**b**) SFS features for malware categorization.
## 4 Experimental Results and Discussion
### Different Amounts of Data Used  
We recorded the changes in the mAP when models were fed with different amounts of data. In the field of deep learning, usually, the more the deep learning model is trained with training data with the right distribution for the same task, the higher is the model performance. Nevertheless, in our study related to nighttime detection, we found that this type of correlation was not explicit.  
Figure 13 illustrates no explicit increase in performance despite the use of additional training data. VGG16 [1] may lack capacity of modeling the nighttime data because its performance dropped a little when additional data were used, which is an ordinary and reasonable result in this kind of convolutional neural network with the traditional architecture. The expanded data sets produced no significant increase in performance, possibly because of the high similarity of gloomy nighttime images, which is expected for the data collected under nighttime conditions. However, the capability of localizing objects can be enhanced if additional training data are used but the localizing capability cannot be reflected by the measurement of the mAP.  
Figure 14; Figure 15 illustrate the performance of the Faster R-CNN [5] with VGG16 [1] and ResNet101 [2] trained using different amounts of data (500, 5500, 7659, and 9003). The model trained with 500 randomly selected nighttime images had a lower capability of localizing objects than the one trained with 9003 nighttime images. Although the performance of mAP in Figure 13 shows a stable and non-increasing trend, the visual comparisons in Figure 14; Figure 15 show the increase of the amount of our nighttime data can boost the ability of localization, which is related to the recall and cannot be measured by the mAP. Directly evaluating the model with recall as a metric is an unsuitable method because of the complex behavior required for object detection. Determining whether a bounding box is actually capturing one single predefined object is difficult. For example, determining the performance is difficult when two bounding boxes are lying on different parts of a single object, which should be bounded by one large bounding box. Knowing an object's existence and approximating the areas takes priority over inserting an exact number of bounding boxes in an image, especially for the tasks of autonomous driving and traffic surveillance.  
A new metric related to recall and the IOU is required for our specific model of nighttime vehicle detection systems. This metric will be published in one of our future works. For this study, judging the ability of localization and capturing objects directly through visualization is a suitable strategy (Figure 14; Figure 15).  
Figure 13: Changes in mean average precision (mAP) with different amounts of our nighttime data.  
Figure 14: Faster R-CNN [5] with VGG16 [1] trained using different amounts of our nighttime data.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method
### The Dataset  
Our study used an HAI dataset specifically designed for ICS security and augmented with hardware-in-the-loop (HIL) simulations. Figure 3 shows a model that simulates a real industrial environment. This dataset accurately reflects the operation of a real ICS environment, including key components such as the boiler, turbine, water treatment system, and HIL simulation. The boiler operation promotes heat exchange between water at various pressures and temperatures, while the turbine mechanism uses a rotor kit testbed to replicate the operation of a rotating machine. These components are seamlessly integrated through the HIL simulator to ensure they match the speed of the steam generator. The water treatment module emulates hydroelectric power generation by pumping water into an upper reservoir and then discharging it into a lower reservoir.  
Figure 2: Proposal process overview.  
Data were collected from 59 sensors every second for four days, during which 28 artificial attacks were launched against the system, targeting various system points such as control outputs and parameters. For our research, version 22.04 of the HAI dataset consisted of six train\(\backslash\)#.csv files, each containing 87 variables (devices). Table 2 is part of the Train1.csv file. We merged these files into a single dataset for model development. We then removed the \"attack\" column, which was deemed irrelevant to our study, resulting in a dataset of 86 operational variables.
## 2 Optical Transmission Characteristics of Seawater
Based on this assumption, taking the four-state protocol under the pure sea water channel as an example, we depict the light intensity distribution diagrams of the signal light at different transmission distances, as shown in Figure 2. Figure 2a represents the three-dimensional view of the light intensity distribution of the initial signal light at the sender, and Figure 2d is the corresponding plane view. Figure 2b,c illustrates the intensity distribution of the signal light at the receiver after transmitting through 8 and 20 m pure sea water, respectively, while Figure 2e,f presents their corresponding plane views. The three-dimensional views can describe the intensity variation of the signal light more vividly, while the plan views can show the divergence of the beam more intuitively. As can be seen in the figure, the energy of the beam is mainly distributed in the central focus position, and the farther it goes, the lower is its intensity. With the increase of the propagation distance, the intensity of signal light becomes weaker and the beam spot becomes larger at the reception plane. These are the obvious effects of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Types** & \(a\) **(m\({}^{-1}\))** & \(b\) **(m\({}^{-1}\))** & \(c\) **(m\({}^{-1}\))** \\ \hline Pure sea water & 0.0405 & 0.0025 & 0.043 \\ Clear ocean water & 0.114 & 0.037 & 0.151 \\ Coastal ocean water & 0.179 & 0.219 & 0.398 \\ Turbid harbor water & 0.366 & 1.824 & 2.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attenuation coefficients of four types of seawater at 520 nm wavelength.
## 6 Simulation Results
### Performance Evaluation
#### 6.2.1 Scenario 1  
Under the circumstances of random movement of the vehicles, Figures 11-13 depict the network behaviors, considering the transmission range, the vehicle speed and the cone angle, respectively. Assume that 250 nodes are deployed in the network. Figure 11 (bottom left) shows that the group size of HDTC is smaller than those of the distance method [9] and the cone-based method [8]. This is because the proposed time-slot method filters out the vehicle nodes that move away from the source/relay vehicle. Moreover, referring to Figure 11 (bottom right), a smaller transmission range (e.g., 110 m) leads to a smaller group size, which results in a larger number of hops for sending the message to the destination. In contrast, given a larger transmission range (e.g., 150 m), the number of hops of these three schemes are close, since the routing procedure can expedited by a larger communication coverage and more appropriate relay vehicles. Because of the random movement, Figure 11 (top) shows that there is no significant differences among the three schemes with regard to the connection stability (Figure 11 (top left)) and the frequency of link up/down (Figure 11 (top right)). However, with varying the vehicle speed, Figure 12 (top) shows that with the distance/angle information, the proposed HDTC scheme owns better group stability compared with other two schemes in terms of connection time (Figure 12 (top left)) and the frequency of the link up/down (Figure 12 (top right)). Observe that given a cone angle and a transmission range, the vehicle speed does not have a signified impact on the topology control (Figure 12 (bottom left)) and the routing task (Figure 12 (bottom right)) with respect to the three schemes.  
Considering the impact of the cone angle and random movement, Figure 13 shows that a smaller cone angle leads to a smaller group size (Figure 13 (top left)), a smaller connection time (Figure 13 (top right)) and a larger number of hops for information routing (Figure 13 (bottom right)). In contrast, a larger cone angle improves connection stability and group formation. Thus, reasonable values of the cone angle may be applied in order to maintain the local topology structure. For Scenario 1, \(60^{\circ}\leq\theta\leq 75^{\circ}\) may be an appropriate range for the proposed scheme.
# Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations
## 2 Modeling and Analysis
### Multi-Fidelity Design Benchmark Problems
Note that all responses and total derivatives can be computed analytically using 2 forward solves (cruise, maneuver) and 5 adjoint solves (lift, drag, and pitching moment at cruise and lift and stress at maneuver), in addition to structural mass and center of gravity sensitivities (computed without adjoint solves). When not including structural sizing, this would require 1 forward solve and 3 adjoint solves. It should be noted that, in reality, treating the load factor and moment constraints (i.e., _g2-g4_) as equality constraints may be of greater interest. In the present work, this was not done for several reasons. The primary reason is to avoid potential difficulties by an optimizer. In addition, the optimum should, in theory, yield active load factor constraints because of tradeoffs between aerodynamic performance and feasibility (for example, fuel burn and load factor tend to trend in the same direction with increasing angle of attack). Finally, if atmospheric conditions remain fixed, the load factor and moment will change throughout a mission as fuel is expended, so enforcing equality constraints would still only yield feasibility at one point in the mission. Although the model tool chain was set up to handle inequality constraints directly, the BFGS-based multi-fidelity optimizer in this work is inherently an unconstrained optimization method. As such, when using that particular optimizer, quadratic penalty functions are used to enforce the inequality constraints as  
\[f_{\text{penalty}}=f+\mathbf{P}_{\text{factor}}\times\max\left(\mathbf{g}+ \mathbf{P}_{\text{offset}},\mathbf{0}\right)^{2}, \tag{26}\]  
where each constraint's penalty factor and offset (to help avoid slightly violated constraints) were somewhat arbitrarily set based on estimated values. The resulting values are given in Table 4. Note that this incurs an additional offset for the moment constraint; serving different purposes, this simply ensures that an optimizer with direct constraint handling would enforce a similar constraint as a penalty-constrained one.
# SACT: A New Model of Covert Communication Based on SDN
## 3 Materials and Methods
### Watermark Conversion Analysis
#### 3.1.1 Watermark Modulation Analysis
Only the _L-bit_ string in the code group is affected when an error occurs during decoding. Suppose that the sender sends two packets in the order of \(a\) and \(b\), and the sending time is \(t_{a}\) and \(t_{b}\), then the time interval between the two packets is \(T=t_{b}-t_{a}\). Each _L-bit_ watermark string is mapped to \(N\) packets interval arrival delay \((T_{1},T_{2},\ldots,T_{3})\) in the _L-Bits to N-Packets_ coding. The value of \(T_{i}\) comes from the set \(E=(T:T_{min}+n\cdot\Delta,n=0,1,\ldots)\), \(T_{min}\) represents the minimum interval delay in the interval delay of packet, and \(\Delta\) represents the step of arrival delay between adjacent packet, e.g., in the _2-Bits to 1-Packets_ coding scheme, taking \(T_{min}=60\) ms and \(\Delta=20\) ms, the binary string '10' is mapped to the interval arrival delay \(T_{1}=60\) ms. The binary strings 01, 11, and 00 are mapped to \(T_{1}=80\) ms, 100 ms, and 120 ms respectively. The results are shown in Table 1. The length of the code table is appropriate, thus the encoder is simple to implement--it does not take up too many resources. Therefore, the encoding length is reduced and the channel capacity is increased. Figure 2: Method of the convolutional code encoder.
## 3 Results
### PLS-DA Classification Models
For the biceps site, the PLS-DA model highlights important spectral peaks at 1900 nm (strongly related to water absorption [24]), 2200-2290 nm (associated with N-H and O-H combination bands), and 2400 nm, which corresponds to C-H overtone and combination bands [23]. For the triceps site, significant peaks appear at 1000 nm (linked to second overtone C-H vibrations [23]), 1800 nm (first overtone of C-H stretching, related to lipid content [23]), 1950 nm (associated with water combination bands [23]), 2200 nm, and 2390 nm (covering combination bands of C-H, N-H, and O-H [3,23]), representing key molecular absorptions relevant to the classification.