esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sensing Per Crop
### Sensing Tomato and Cucumber Diseases  
Fusarium wilt is caused by the _F. oxysporum_ fungi. Symptoms of _Fusarium_ diseases are rots, leaf spots, blights or wilts [186] and once the soil is infested, the pathogen is difficult to eliminate since it survives in the soil for long periods [7]. The optimum temperature for growth of _F. oxysporum_ is 28-29\({}^{\circ}\)C. There are no symptoms of infection if the soil temperature is below 20\({}^{\circ}\)C or above 30\({}^{\circ}\)C [187].  
Rhizopus rot is a fungal soft rot requiring injuries caused by insects, hail, or cracking for infection to occur. The early appearance of the fungal mycelium is as a fluffy white mass [7]. Rot progression is temperature related with rapid fungal growth at 27\({}^{\circ}\)C, and no spore germination at 4\({}^{\circ}\)C. To minimize the incidence of Rhizous rot, fruit has to be carefully handled to avoid wounds while keeping clean storage containers, warehouses, and hydrocooling water (Table 1). Rhizous rot is more likely to be a problem when fruit is allowed to fully ripen on the plant and when poor sanitary conditions are found on field bins and at the packinghouse. Other diseases that attack tomato plants are anthracose, early and late blight, Septoria leaf spot.  
Downy mildew of cucurbits is a devastating disease, especially in temperate regions of the world [188] where humid conditions favor disease spread; infection by zoospores requires free water on the lower leaf surface for at least 2 h, and production of zoosporangia in the dark occurs at an RH of \(>\)90% for at least 6 h [189]. After penetrating the leaf through stomata, this pathogen rapidly colonizes the mesophyll of its host cell producing intercellular hyphae and intracellular haustoria for the uptake of nutrients [190]. First symptoms on leaves are small, slightly chlorotic to bright yellow areas on the upper surface without loss of vitality in plant cells.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 6 Discussion  
### Alternative ICN Underlays  
Our solution is applied to the ICN-based architecture defined in [5] but it can also be applied in other ICN-based solutions. We now examine how our solution can be used with two other ICN systems.  
Named-data networking (NDN) [37] is probably the most popular ICN architecture. The main difference between NDN and our underlying architecture is that in NDN, all routers participate in the ICN network as opposed to our underlay architecture, where only edge routers are aware of the ICN functionality. This means that in NDN, all routers handle advertisements and maintain routing state. NDN uses the solution defined in [36] to protect against fake advertisements. Since this solution requires the definition of \"rules\" in each router, it is applied only in edge routers (in order to decrease administrative overhead). Our solution does not require any configuration in routers; hence, if our solution was used in NDN, all routers could potentially verify advertisements.  
The publish-subscribe internet architecture (PSI) [39] is an alternative ICN-based system. PSI assumes an overlay \"rendezvous\" system which is responsible for handling advertisements, as well as for responding to content-lookup requests (hence, this rendezvous system is similar to DNS). PSI considers that each rendezvous node is responsible for handling a particular portion of the name space as well as \"controlling\" which entities can advertise that portion. In other words, each rendezvous node can hold the role of a registrar as defined in our system. Hence, our solution fits naturally to PSI.
## 1 Introduction
The EDA method has been applied to solve the classical tracking specification problem [12]. To the best of the authors' knowledge, the EDA method is not explored for the MIMO TES problem with the inversion feedforward, except the work on single input single output (SISO) case in Reference [13]. Consequently, the main technical contributions are summarized as follows. 1. A computationally efficient and simple method to solve the multivariable TES problem with inversion feedforward for the large number of uncertain parameter is proposed. 2. The proposed formulation converts the original MIMO uncertain system into a set of equivalent SISO systems using the EDA concept. By doing this, it works on the nominal plant of the equivalent SISO system and the TES bounds for each of the SISO systems are computed by solving the single inequality at each phase. 3. The proposed method is free of pairing requirement and does not require any tuning factors as in the case of existing methods such as [PERSON] et al. method [4]. Figure 1: The 2-degree of freedom (DOF) multi-input multi-output (MIMO) control structure in quantitative feedback theory (QFT). The proposed approach is applied to the benchmark MIMO magnetic levitation system and a comparative work with existing methods is undertaken to demonstrate some advantages. The rest of this paper is divided as follows. Section 2 states the problem statement. Section 3 discusses about the development of the proposed feedback controller design method for the model matching. The effectiveness of the new design approach is illustrated using a challenging and benchmark magley system in Sections 4 and 5 briefly concludes this work. **Notation**: The \((n\times n)\) plant transfer matrix is denoted as \(P\) belonging to uncertain set \(P\). The \((n\times n)\) diagonal feedback controller matrix is denoted as \(G\) and \(M\) that in turn denotes the \((n\times n)\) desired response model matrix. The notation \(G_{R}\) denotes the \((n\times n)\) tracking feedforward controller matrix. The nominal element of the uncertain system is denoted by the subscript notation \"0\". The notation \"diag\" represent the diagonal matrices. The \((i,j)\)th element of the plant and the desired response model are denoted as \(p_{ij}\) and \(m_{ij}\) respectively. The dependence on \(s\) and \(j\omega\) is not written explicitly for ease in notation in the rest of this paper. The terms such as model matching and tracking, the on-channel (on-diagonal) and control loop are interchangeably used in the rest of the paper.
## 5 Conclusions  
We tracked some stars at sea, achieving a pointing accuracy of less than 180\(\upmu\)rad. We acquired and tracked some Low-Earth-orbit satellites at sea, achieving a tracking accuracy of about 20\(\upmu\)rad. In this work, we have taken other sources of error into account, including position and alignment errors. The initial coarse orientation of the ATP was based on the satellite's predicted orbital position, with an uncertainty generally less than 200 m.A position error is less than 0.1 mrad at 2000 km. An alignment error of less than 0.05 mradcan be achieved between the beacon laser and tracking camera after adjustment. We can obtain a total initial open-loop pointing error of less than 0.324 mrad, including position error (less than 0.1 mrad at 2000 km), pointing error (less than 0.174 mrad), and alignment error between the beacon laser and tracking camera (less than 0.05 mrad). In accordance with two-dimensional Gaussian distributions, if the acquisition probability reaches 98.9%, the initial open-loop pointing error (RMS) of the shipborne ATP must be less than one-sixth of the beacon divergence angle. If the divergence angle of the ATP uplink beacon laser is greater than 2 mrad, the shipborne ATP system open-loop pointing beacon laser spot can irradiate the satellite. Therefore, by verifying the pointing accuracy of the shipborne ATP and selecting the appropriate divergence angle for the uplink beacon, optical links between the shipborne ATP and the satellite can be established. We achieved significant experimental results for the shipborne ATP acquisition and tracking that will contribute to the realization of laser communication in free space between satellites and the sea.  
Conceptualization, D.H. and Y.H.; funding acquisition, Y.T., G.R., B.Q. and Y.H.; investigation, Q.W. and Z.S.; methodology, D.H., Q.W., Z.S., J.R. and Y.C.; project administration, Y.T.; resources, X.L., Z.S., J.Z., Z.W., T.Z., J.R. and Y.C.; software, D.H., Q.W., X.L., C.G., T.Z. and X.Q.; supervision, G.R., B.Q. and Y.H.; validation, X.L., J.Z. and C.G.; visualization, Z.W. and X.Q.; writing--original draft, D.H.; writing--review & editing, D.H.  
This research was funded by the Natural National Science Foundation of China (NSFC), grant number (U1738204).  
Thanks to our colleagues in the Key Laboratory of Optical Engineering, Chinese Academy of Sciences. Thanks for the financial support from Youth Innovation Promotion Association, Chinese Academy of Sciences.  
The authors declare no conflict of interest.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.6. Harmony Search  
Harmony Search (HS) is a meta-heuristic search algorithm that mimics the process of musicians finding a pleasing harmony. It was first introduced in 2001 by [PERSON], [PERSON], and [PERSON] [53]. During the process of finding a pleasing harmony, musicians improvise and adjust notes in order to, eventually, create a pleasing sound. Similarly, harmony search iteratively generates a set of candidate solutions, evaluates their quality, and modifies them to identify the optimal solution. In more practical terms, each decision variable (musician) generates a value (note) with the intent of eventually finding a satisfactory solution (best harmony) [54; 55; 56; 57]. It is known for being easy to implement due to being both simple in concept and having few parameters [54; 55].  
The key steps involved in HS are the following [54; 55; 56; 57]:  
* Step 1: The diverse dynamic parameters involved in the algorithm are initialized. These include the harmony memory size, harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and the number of improvisations or iterations. The harmony memory is filled with randomly generated solution vectors. The number of musicians is set to match the number of variables.
* Step 2: A new harmony (solution) is improvised based on the algorithms state. This is carried out by having each musician choose a new note out of their possible domain. This choice is affected by the HMCR and the PAR.
* Step 3: After generating a new harmony, it is evaluated using the defined objective function. If the new solution is better than the worst one in the harmony memory, it replaces it. This ensures that the harmony memory gradually evolves to contain better solutions.
* Step 4: Steps 2 and 3 are repeated until a stopping criterion is reached, such as reaching a maximum number of iterations or attaining a satisfactory solution.
## 3 RF-EH Techniques
### RF-EH from Ambient RF Sources  
This source type is further subdivided into two categories:  
* Static sources, even though they are stable-power transmitters, are not simplified; the sensor device's power is supplied by modulating the signal (for instance, by modulating the frequency and transmitted power). Ambient sources, including broadcast radio, mobile base stations, and television, are examples of what is expected [33].
* Dynamic sources. Although these are transmitters that regularly broadcast in a manner that is not monitored by the internet of things' system for such sources to yield energy, an intelligent WEH is required to continuously monitor the channel for potential  
Figure 1: An RF energy harvesting systems conceptual block diagram.  
harvesting opportunities. Unknown ambient sources include Wi-Fi access points, microwave radio links, and police radios, to name a few examples [33].
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology
### Dynamic Bayesian Network Analysis
#### 2.3.4 Diagnostic Analysis  
Diagnostic analysis is instrumental in uncovering the most critical REs that contribute to the occurrence of the UEs [8; 22]. The identified critical REs constitute the weakest links in the GDS, thereby playing a pivotal role in informing risk-management decisions.  
To determine the most critical REs, two key importance measures are introduced: the Birnbaum measure (BM) and the risk reduction worth (RRW). Specifically, the indices forBM and RRW of each RE, denoted as \(I^{BM}(RE_{i})\) and \(I^{RRV}(RE_{i})\), respectively, can be computed by integrating Equations (18) and (19) with BN predictive analysis.  
\[I^{BM}(RE_{i})=P(UE=1\Big{|}RE_{i}=1)-P(UE=1\Big{|}RE_{i}=0),\,i=1,2,\cdots,n \tag{18}\]  
\[I^{RRW}(RE_{i})=P(UE=1)-P(UE=1\Big{|}RE_{i}=0),\,i=1,2,\cdots,n \tag{19}\]  
where \(P(UE=1\,1\,RE_{i}=1)\) and \(P(UE=1\,1\,RE_{i}=0)\) show the conditional failure probability of the GDS given the occurrence and non-occurrence of the \(RE_{i}\). The total weighting (\(R_{j}^{*}\)) of the \(j\)th RE is calculated as follows, and the critical REs can be obtained by ranking them.  
\[R_{j}^{*}=\left(\begin{array}{c}I_{j}^{BM}\\ \frac{n}{i=1}\,I_{j}^{BM}\end{array}+\frac{I_{j}^{RRW}}{\sum\limits_{i=1}^{n}I _{j}^{RRW}}\right)\times 100\% \tag{20}\]  
when near-misses or incidents are observed, the framework further employs the ratio of variation (RoV) metric (Equation (21)) to dynamically reassess critical REs [8]. The RoV quantifies the relative change in the probability of an undesired event (UE) before and after incorporating near-miss data. For example, if a near-miss indicates a malfunction in a pressure sensor, the RoV evaluates how significantly this observation alters the UE probability (e.g., gas leakage). REs with higher RoV values are deemed more critical, as their contributions to system risk are amplified by the near-miss. Engineers can then prioritize these REs for maintenance or redesign, thereby proactively reducing the likelihood of future accidents.  
\[RoV(X_{i})=\frac{\pi(X_{i})-\theta(X_{i})}{\theta(X_{i})} \tag{21}\]  
where \(\pi(X_{i})\) is the adapted probability of \(RE_{i}\); \(\theta(X_{i})\) is the non-adapted probability of \(RE_{i}\).  
BM/RRW are primarily used for baseline risk prioritization based on static or historical data. RoV complements BM/RRW by capturing the dynamic impact of near-misses, enabling real-time risk re-evaluation. Engineers can thus prioritize REs with both high BM/RRW values (inherent criticality) and elevated RoV (near-miss-driven sensitivity) for targeted interventions.
## 4 Multi-Objective Scheduling Optimization Model
### Multi-Objective Model Solution
#### 4.2.1 Payoff Table  
The payoff table mainly reflects the relationships between different objective functions [28]. The values of the objective functions were calculated when the MECS operated under other optimization objectives. Firstly, the optimal scheduling schemes were calculated as individual optimization functions. Then, the values of the other objective functions \(F_{i},i=1,\ldots,i-1,\ldots,i+1\ldots,I\) were calculated under the optimized objective functions \(F_{i},i=1,2,\ldots,I\). Assume \(F_{ij},j=1,2,\ldots,I\) is the values of the other objectives under \(F_{i}\), and \(F_{ii}^{s}\) is the optimal value of objective \(F_{i}\). Then, the payoff table can be gained. Table 1 is the payoff table of the proposed multi-objective model.  
According to Table 1, the minimum value and the maximum value can be gained for different objective functions. Correspondingly, the information needed to calculate the weight can be collected. Since different objectives have different magnitudes, dimension reduction is necessary before calculating the weight coefficients. The fuzzy satisfaction theory can be used to estimate the optimization degrees of different objective functions [29]. In general, the objective functions exist in two optimization directions, namely, the maximum and the minimum. Therefore, this paper applies the decreasing gradient half membership functions and increasing half line membership functions to handle the objectives.  
When the optimization direction of the objective function is in the minimal direction:  
\[f_{i}=\left\{\begin{array}{l}0,F_{i}\geq F_{i}^{\max}\\ \frac{F_{i}^{\max}-F_{i}^{\min}}{F_{i}^{\max}-F_{i}^{\min}},F_{i}^{\min}<F_{i} <F_{i}^{\max}\\ 1,F_{i}\leq F_{i}^{\min}\end{array}\right.. \tag{34}\]  
When the optimization direction of the objective function is in the maximal direction:  
\[f_{i}=\left\{\begin{array}{l}1,F_{i}\geq F_{i}^{\max}\\ \frac{F_{i}-F_{i}^{\min}}{F_{i}^{\max}-F_{i}^{\min}},F_{i}^{\min}<F_{i}<F_{i} ^{\max}\\ 0,F_{i}\leq F_{i}^{\min}\end{array}\right. \tag{35}\]  
where \(F_{i}^{\min}\) is the minimum value of the objective function \(F_{i}(\cdot)\). \(F_{i}^{\max}\) is the maximum value of the objective function \(F_{i}(\cdot)\). \(f_{i}\) is the membership function.
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Pharmaceuticals
#### 3.1.3 Pesticides  
The use of organophosphorus insecticides has increased agricultural output significantly. However, the accumulation of organophosphorus pesticide residues in the air, soil, food, surface water, and wastewater has become a substantial environmental hazard due to their widespread usage and moderate persistence [92; 93]. [PERSON] et al. [51] investigated the destruction efficiency of DBD plasma devices with different structures and discharge powers for pesticides (dimethoate and dichlorvos). The results showed that a smaller gap distance (5 mm) and a larger discharge power (85 W) could obtain a higher destruction efficiency. The authors also demonstrated the DBD reactor had a significant effect on pesticide degradation. More precisely, it was observed that when the initial concentration of organophosphorus pesticides rose, the rate of pesticide degradation fell. In another study by [PERSON] et al. [71], the identification of dimethoate intermediates and products was explored using GC-MS, UV-VIS spectrometry, and FTIR techniques. The corresponding DBD plasma degradation pathways of dimethoate were also proposed (Table 3). Additionally, different 2,4 dichlorophenoxyoxyacetic acid (2,4-D) intermediates were detected (Table 3), and the degradation pathway of 2,4-D in plasma treatment process was suggested in a study by [PERSON] et al. [74]. These observations have important implications for the development of wastewater treatment, particularly the use of organophosphorus pesticides. In addition, it is impossible to overlook the negative effects other pesticides have on the environment and human health. Pesticides may contain the carcinogenic ingredient pentachlorophenol (PCP), which is difficult to break down naturally [94]. [PERSON] et al. [67] used glow discharge electrolysis to show how PCP can be reduced to a low level in water. For process periods under 0.5 h, PCP reduction was achieved below detection limits (0.01 ppm). The highest rates of breakdown occurred when using air or O\({}_{2}\), indicating that O\({}_{3}\) chemistry was crucial. The study also discovered that this method's power cost was competitive with that of other traditional advanced oxidation techniques, such as UV/peroxide or UV/O\({}_{3}\).
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 4 Empirical Study
### Discussion  
[PERSON] et al. (2017) achieved a conclusion to some extent different from the conclusion in our experiment. Their results concluded that \(ER_{1}^{{}^{\prime}}\) is the best performer, \(ER_{5}\) is the worst in most cases, and the other three formula groups GP02, GP03, and GP19 perform similarly, but with GP03 being better than GP02 and GP19. The difference between their study and our study is probably because different metrics and experimental setups were used. [PERSON] et al. used the expense metric to measure the number of statements should be examined before the faulty statement is found. In this paper, we utilized the expected faulty location to measure the expected location of faulty statement in the faulty program. Additionally, [PERSON] et al. conducted an empirical study with five subject programs from SIR, including flex, grep, gzip, sed, and space. In our experiment, we performed the empirical study with 11 subject programs in which flex, grep, sed, and space were used as well. Nevertheless, their conclusion is slightly different to that of our study of \(ER_{1}^{{}^{\prime}}\dashrightarrow\) GP03\(\dashrightarrow\) GP02\(\dashrightarrow\)\(ER_{5}\dashrightarrow\) GP19. Most importantly, [PERSON] et al. and ourselves both observed that \(ER_{1}^{{}^{\prime}}\) is the best performer followed by GP03; that is, we have the same recommendation of using \(ER_{1}^{{}^{\prime}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Scenarios** & **Flex** & **Grep** & **Sed** & **Space** & **print\_tokens** & **print\_tokens2** & **Replace** & **Schedule** & **schedule2** & **tcas** & **tot\_info** & **Total** \\ \hline \(B_{(<)}A_{(>)}\) & 22 & 18 & 23 & 27 & 9 & 28 & 14 & 8 & 20 & 5 & 29 & 203 \\ \(B_{(<)}A_{(=)}\) & 6 & 2 & 2 & 3 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 15 \\ \(B_{(<)}A_{(<)}\) & 5 & 5 & 6 & 10 & 1 & 2 & 4 & 0 & 5 & 1 & 6 & 45 \\ \(B_{(=)}A_{(>)}\) & 6 & 7 & 6 & 2 & 5 & 9 & 4 & 3 & 0 & 1 & 12 & 55 \\ \(B_{(>)}A_{(>)}\) & 2 & 10 & 7 & 20 & 1 & 16 & 3 & 3 & 3 & 2 & 8 & 75 \\ \(R_{1}=R_{2}\) & 9 & 8 & 6 & 8 & 4 & 13 & 5 & 6 & 2 & 1 & 25 & 87 \\ \hline total & 50 & 50 & 50 & 70 & 20 & 70 & 30 & 20 & 30 & 10 & 80 & 480 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scenarios covered by individual subject program.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Comparison Pair (\(R_{1}\) vs. \(R_{2}\))** & \(R_{1}\)**Better** & \(R_{2}\)**Better** & \(R_{1}\)**Same as \(R_{2}\)** \\ \hline CP1 (N1 vs. W1) & 35 & 12 & 1 \\ CP2 (N1 vs. GP02) & 26 & 9 & 13 \\ CP3 (N1 vs. GP03) & 9 & 5 & 34 \\ CP4 (N1 vs. GP19) & 30 & 12 & 6 \\ CP5 (W1 vs. GP02) & 16 & 31 & 1 \\ CP6 (W1 vs. GP03) & 14 & 29 & 5 \\ CP7 (W1 vs. GP19) & 29 & 15 & 4 \\ CP8 (GP02 vs. GP03) & 11 & 25 & 12 \\ CP9 (GP02 vs. GP19) & 29 & 17 & 2 \\ CP10 (GP03 vs. GP19) & 27 & 12 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results as number of selected faulty versions that one formula is better than another.
## 5 Experiments and Results
### Validation
#### 5.3.2 Validation Through Improvement in Cp Values  
For post-optimization validation, stakeholders such as process engineers rely on process capability metrics to assess the industrial viability of the proposed solution. One of the most widely used metrics is the process capability index \(C_{p}\), which quantifies the potential of a process to produce outputs within specified tolerance limits [44]. It is defined as follows:  
\[C_{p}=\frac{USL-LSL}{6\sigma} \tag{13}\]  
where \(USL\) and \(LSL\) represent the upper and lower specification limits, respectively, and \(\sigma\) is the standard deviation of the process. A higher \(C_{p}\) value indicates lower process variability and better conformance to quality specifications.  
To demonstrate that the optimization is not only mathematically sound but also practically effective, this section presents the improvement in \(C_{p}\) values before and after optimization. An increase in \(C_{p}\) reflects enhanced process stability and manufacturing precision. In addition to identifying Pareto-optimal trade-offs, the RL-based solution resulted in a notable enhancement in process capability. For the pinion manufacturing dataset, the \(C_{p}\) value for thickness improved from 0.33 to 0.44, while the \(C_{p}\) for the pinion  
Figure 6: Contour representation of two continuous objective functions and their intersecting feasible half-spaces in a multi-objective optimization setting.  
radius increased from 0.32 to 0.38. Similarly, for the open-source continuous manufacturing dataset, an average improvement of approximately 30% in \(C_{p}\) values was observed across all measured target parameters, further validating the robustness and practical utility of the approach.