esa-sceva/satcom-chunk-collection huggingface dataset:
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 1 Introduction
Because of their lack of an intrinsic immune function and impenetrable nature, nails are a particularly challenging tissue to treat. Individuals with onychomycosis can experience very long-lasting disease, especially in the absence of effective treatment [18]. Therefore, given the poor efficacy of various forms of therapy, there is strong interest in exploring new methods for evaluating antifungal treatments. The first tests focused on the evaluation of activity against dermal infections dating back to the 1960s, where guinea pigs were used to induce dermatophytosis through occlusion of an established area [19; 20]. However, recent studies demonstrated that occlusion does not offer any advantages in the establishment of skin infections [21]. Among the most-used methods to evaluate the antimicrobial efficacy against fungal growth is the agar zone of inhibition. It is similar to the disk diffusion susceptibility test, though it does not quantify the antimicrobial efficacy [22; 23]. To obtain a quantification of the efficacy on a small surface, the best experimental designs are based on a microbial suspension, in which an inoculum of microorganisms suspended in a nutrient medium is exposed to a test sample and incubated following specific parameters [24]. [PERSON] et al., in trying out a topical triazole, produced experimental intea unguiium and tinea pedis through the inoculation of _Trichophyton metagrophyte_ between the toes of the hind paws of guinea pigs [25]. [PERSON] et al. [26] tested the effectiveness of amorolfine against dermatophyte nail infections using an in vitro model based on the employment of a nail powder and a drug incubation period of 4 weeks. Therefore, the aim of this work was to develop a new protocol, using hood membranes as an in vitro model to evaluate the capability of a product claiming to prevent fungal adhesion, which is the first step of the infection. Bovine hoof keratin membranes have been chosen as the in vitro model since they are widely used in several fields of nail-related research [27; 28]. The novelty of this work is that of seeing not only the antifungal effect, which can be evaluated with a more traditional assay to quantify the minimum inhibitory concentrations (MICs) of the tested drugs, in addition to the antiadhesive effect (AE) of a specific substance on the nail, using hood membranes as a model of the human nail. In this work, two specific and representative fungal strains, _Trichophyto rubrum_ and _Candida albicans_, were used, and a pharmaceutical product and a base coat were tested as positive and negative controls to set up the in vitro model.
## 3 Application  
### Experiment Setup and a Call-for-Maintenance Case  
An in-lab experiment was performed to construct a plug-in or add-on fixed, charge-coupled device (CCD) camera monitoring system for the inspection of WHRA performance degradation by recording the deviation from the center of the wafer (red cross wafer mark in Figure 3) and measuring the circle peripheral (the white circle in Figure 3) in the vertical view each day. Figure 3 illustrates the vision of CCD. In [1], the potential failure mechanism was the wear of the belt teeth according to the test profiles; thus, this was the cause of an eccentricity problem during wafer transfer. Figure 4 presents a picture of the WHRA, cassette, and CCD camera used in [1]; the same setup was used in this study.  
Figure 4: Picture of the WHRA and add-on charge-coupled device (CCD).  
Figure 3: Illustration of the Wafer-Handling Robot Arm and charge-coupled device camera field of view.  
In this experiment, 2,819,678 data were recorded by the CCD camera and categorized as circle radii \(C_{R}\) or eccentric positions measured along the x-axis (\(C_{X}\)) or y-axis (\(C_{Y}\)), as presented in Figure 5a with a moving average by 16 points and with original data plot in Figure 5b. Each of these positioning deviations represents a symptom of a problem requiring a call for maintenance. Figure 6 indicates that the synergy-deviated positioning by Equation (11) grows monotonically.  
\[A\] = \[\sqrt{C_{X}{}^{2}\] + \[C_{Y}{}^{2}\] + \[C_{R}{}^{2} \tag{11}\]  
Figure 5: (a). Data recorded trends for the circle radii \(C_{R}\), eccentric positions measured along the x-axis (\(C_{X}\)) and y-axis (\(C_{Y}\)) by every 16 points moving average. (b). Data recorded for the diameter of the circular image (\(C_{R}\)) with eccentric position along the x-axis as shown on CCD (\(C_{X}\)) and eccentric position along the y-axis as shown on CCD (\(C_{Y}\)).  
In this study, the X and Y position deviation and the enlargement of the circle diameter of the CCD constituted an index of the possibility of necessary run-to-failure maintenance during the RUL. Thus, the synergy-deviated position A in Equation (11) is used as a guide for WHRA degradation. The movement cycle of the WHRA from the cassette to the destination, for the CCD's monitoring, takes approximately 11 s. The positioning data for each of \(C_{X}\), \(C_{Y}\), and \(C_{R}\) were stored as approximately 7800 records per day. Far fewer than 7800 records per day were recorded for 38 days due to possible temporary power shutdowns in the laboratory. The data log for the 38 days was deleted, and the data of 331 days remained for the analysis. Thus, the raw data formed a \(331\times 7800\) matrix.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 5 Conclusions  
In this paper, we proposed a novel algorithm of a rotated anchor box and oriented target matching. The algorithm took advantage of the high matching accuracy of a large number of anchor boxes with different angles, calculated the maximum inclined IOU between all anchor copies and the target for each oriented anchor and determined more accurate RRoIs in the matching stage to improve the recall rate of high aspect ratio targets. In the experiment, we used the remote sensing image datasets DOTA and HRSC2016, labeled by rotating bounding boxes. For target categories with a high aspect ratio, our model can achieve higher mAP and show a higher recall rate under the condition of consistent accuracy. Based on the existing literature and experimental phenomena, we proposed an improved method in the multi-angle anchor matching part. This method did not need to improve the structure of the existing rotating anchor frame matching network nor does it depend on the optimization of the loss function. It can be directly applied to the existing rotating anchor frame matching network. Our method provided more accurate RoIs for the subsequent coordinate regression and improved the recall rate of the algorithm detection. In the follow-up work, we will try to apply the algorithm to networks under other frameworks except for RetinaNet and further explore other possibilities to separate matching and regression anchors.  
Conceptualization, K.W. and X.W.; methodology, K.W. and B.C.; software, K.W. and B.C.; validation, Y.L.; formal analysis, K.W. and X.W.; writing--original draft preparation, B.C. and X.W.; visualization, B.C.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the China Postdoctoral Science Foundation (2013M540735); in part by the National Nature Science Foundation of China under Grant 61901388, 61301291, 61701360, 61502367, 61501346, 61571345, 91538101, 61801359, 61401337; in part by the 111 Project under Grant B08038; in part by the Yangtze River Scholar Bonus Schemes; in part by the Ten Thousand Talent Program; in part by the Fundamental Research Funds for the Central Universities; and in part by the Youth Innovation Team of Shaanxi Universities.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Recall rates of targets from different angles. The ordinate is different recall rates with the same accuracy. (**a**) DOTA. (**b**) HRSC. Our method can effectively improve the recall rate of the target when the target is different from the anchor. When the target angle is the same as that of the anchor, it is basically the same under the dataset, and it is still improved under the HRSC2016 dataset.
###### Abstract  
The security of the space environment is under serious threat due to the increase in space debris in orbit. The active removal of space debris could ensure the sustainable use of the space environment; this removal relies on detumbling technology. According to the characteristics of the mechanical impact-type active detumbling method, this paper discusses a method to accurately identify the impact force using a pressure sensor. In this work, the impact force between a flexible impact end-effector and the space debris was analyzed theoretically and experimentally considering the pressure change during impact. Firstly, a nonlinear impact force model was established for the impact between a flexible end-effector and space debris. Secondly, impact experiments were performed and the friction model was modified. Finally, the effect of detumbling was verified through simulation experiments. The results showed that the identification error of normal impact force was less than 6.7% and the identification error of tangential friction force was less than 6.9%. Therefore, this identification method of impact force met the requirements of space debris detumbling, which has important guiding significance for the active removal technology of space debris.  
Keywords:detumbling; mechanical impact; flexible end-effector; impact force +
Footnote †: journal: Applied Physics Letters
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 5 Conclusions  
This paper presented a comparative study of exact (KNN) and approximate (ANN-LSH) neighbour search algorithms applied to agricultural recommendation systems. The innovation lies in adapting these machine learning techniques--traditionally used in domains such as e-commerce or document retrieval--to simulate farmer-to-farmer recommendations within a structured, domain-specific dataset.  
A synthetically generated dataset of farming profiles, supported by literature-grounded attribute ranges, served as the foundation for testing and validating algorithmic performance. This approach ensured experimental control while maintaining real-world relevance.  
This work makes two primary contributions. First, it demonstrates that both KNN and ANN-LSH are capable of generating meaningful and explainable recommendations for farmers. Second, it quantifies the performance trade-offs between accuracy and scalability. While KNN achieves high precision, its execution time increases considerably with larger datasets. By contrast, ANN-LSH provides a fast and scalable alternative, delivering approximate results with substantially lower computational cost.  
This study demonstrates how similarity-based decision support systems can facilitate peer-informed agricultural decision-making, leading to improved yield optimisation, reduced waste, and enhanced sustainability. The adaptation of ANN-LSH to agricultural contexts represents a novel contribution to the field and sets a foundation for future research in hybrid or real-time recommender architectures.  
As the agricultural sector continues its digital transition, such systems can serve as critical enablers of precision agriculture--linking farmers not only to data but also to each other in intelligent, actionable ways.  
Conceptualization, I.B. and O.M.; methodology, R.E.; software, I.B.; validation, I.B. and R.E.; formal analysis, H.V.; investigation, I.B.; resources, R.E.; data curation, I.B.; writing--original draft preparation, I.B.; writing--review and editing, R.E.; visualization, O.M.; supervision, H.V.; project administration, H.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting the reported results are publicly available at [[https://github.com/[PERSON]/agrirecsys-](https://github.com/[PERSON]/agrirecsys-)]([https://github.com/[PERSON]/agrirecsys-](https://github.com/[PERSON]/agrirecsys-)) (accessed on 20 March 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Differential Volatile Aroma Screening and OPLS-DA Analysis of VOCs during Ripening of R. roxburghii
), three alcohols (1-octanol, iso-geraniol, and (3E)-3-hexen-1-ol), 10 esters (such as (E)-3-hexenyl acetate, hexyl acetate, (E)-2-hexenyl acetate, etc. ), one acid (octanoic acid), two types of ketones (4-methoxy-2,5-dimethyl-3 (2H)-furanone and ethyl vinyl ketone), and seven alkenes ((Z)-\(\beta\)-ocimene, \(\beta\)-caryophyllene, \(\alpha\)-caryophyllene, (E)-\(\beta\)-ocimene, etc. ), along with one other compound (4-allylanisole). Notably, 2-(E)-hexenal, 1-nonanal, and hexanal, which were the principal aldehyde components, persisted and significantly contributed throughout the maturation process. 1-Octanol served as the primary volatile aroma component among the alcohols. Hexyl acetate, (Z)-3-hexenyl acetate, and ethyl caprylate were the most critical substances among the esters, with hexyl acetate and (Z)-3-hexenyl acetate being particularly prominent. Octanoic acid was identified as the main component of acids, while \(\beta\)-caryophyllene and \(\alpha\)-caryophyllene were recognized as key volatile components of olefins. In summary, these differential compounds were predominantly ester compounds, which played a crucial role in the aroma formation of _R. roxburghii_ fruit. Combined with the OPLS-DA results, a total of 21 differentially volatile aromas were identified that met the criteria of VIP > 1 and ROAV > 1. These included 2- (E)-hexenal, (Z)-3-hexenyl acetate, (E)-2-octenal, (Z)-3-hexenyl butanoate, hexyl acetate, ethyl caprylate, ethyl Hexanoate, (Z)-\(\beta\)-ocimene, caprylic acid methyl ester, hexanal, (3E)-3-hexen-1-ol, octanal, 1-nonanal, (E)-\(\beta\)-ocimene, 4-allylanisole, 1-octanol, \(\alpha\)-Caryophyllene, \(\beta\)-Caryophyllene, 4-methoxy-2, 5-dimethyl-3 (2H)-furanone, ethyl vinyl ketone, and octanoic acid. These compounds were considered as the key volatile constituents influencing the aroma of R. roxburghii fruit at the ripening stage and played a significant role in assessing the aroma characteristics across different ripening phases.
# Graph Planarity by Replacing Cliques with Paths +
## 1 Introduction
### Our Results  
A graph \(G\) is _planar_ if it admits an embedding in the plane where no two edges cross; this embedding is a _planar embedding_ of \(G\). A planar graph with an associated planar embedding is said to be an _embedded planar_ graph, or a _plane_ graph.  
In the version of _h_-Clique2 Path Planarity that we study, the input graph \(G\) is a _simple topological graph_. A _topological_ graph is embedded in the plane so that each edge is a Jordan arc connecting its end-vertices. A topological graph is _simple_ if a Jordan arc does not pass through any vertex, and does not intersect any arc more than once (either with a proper crossing or sharing a common end-vertex); finally, no three arcs mutually cross at the same point.  
Our main goal is to investigate the complexity of _h_-C2 PP in relation to the well-studied class of _k-planar graphs_, i.e., those that admit a drawing in which each edge has at most \(k\) crossings [9; 10; 13; 14]. With a slight abuse of notation, we use the term _embedding_ also for non-planar graphs, where we interpret each crossings as a dummy vertex. In particular, a _k-planar_ graph together with a _k_-planar embedding is a _k-plane graph_.  
A _geometric graph_ is drawn in the plane so that each edge is a straight line segment. The version of _h_-C2 PP in which the input graph \(G\) is a geometric graph has been recently studied by [PERSON] et al. [15], who called it the _partition spanning forest problem_. They proved that 4-C2 PP for geometric graphs is NP-complete, which immediately implies the NP-completeness of 4-C2 PP for simple topological graphs.  
We strengthen this result by proving that 4-C2 PP is NP-complete even for simple topological 3-plane graphs. On the positive side, we prove that the _h_-C2 PP problem for simple topological 1-plane graphs can be solved in linear time for any value of \(h\). We finally remark that the 2-SAT formulation used in [15] to solve 3-C2 PP for geometric graphs can be easily extended to solve 3-C2 PP for any simple topological graph.
## 2 Materials and Methods
### Optimal Allocation of Water Resources
#### 2.2.4 Establishment of Optimal Allocation Model of Water Resources
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Crop Type**} & \multirow{2}{*}{**Crop Yield**} & \multirow{2}{*}{**Crop Price**} & \multirow{2}{*}{**Planting Cost**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} \\  & & **(kg/ha)** & & **(USD/kg)** & & **(USD/ha)** & & **Planting Area** \\ \hline Spring wheat & 6750 & [0.33, 0.36] & 1442 & 2122 & 9583 \\ Corn & 11,250 & [0.22, 0.25] & 1545 & 3275 & 14,055 \\ Flax & 4050 & [0.76, 0.82] & 364 & 211 & 2871 \\ Potato & 45,000 & [0.11, 0.14] & 1580 & 393 & 2080 \\ Melon & 37,500 & [0.14, 0.16] & 2404 & 152 & 1126 \\ Goji berry & 7500 & [1.10, 1.24] & 3640 & 351 & 3339 \\ Forest fruits & 30,000 & [0.27, 0.34] & 2060 & 2239 & 5180 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Crop basic parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Crop Type**} & \multirow{2}{*}{**Mar.**} & \multirow{2}{*}{**Apr.**} & \multirow{2}{*}{**May**} & \multirow{2}{*}{**Jun.**} & \multirow{2}{*}{**Jul.**} & \multirow{2}{*}{**Aug.**} & \multirow{2}{*}{**Sep.**} & \multirow{2}{*}{**Oct.**} & \multirow{2}{*}{**Total**} \\  & Spring wheat & 14.7 & 63.1 & 194.6 & 161.3 & 22.1 & 0.0 & 0.0 & 0.0 & 455.8 \\  & Corn & 0.0 & 46.8 & 129.7 & 149.7 & 162.9 & 130.4 & 68.2 & 21.1 & 708.8 \\  & Flax & 17.3 & 119.2 & 187.2 & 184.5 & 74.4 & 20.3 & 0.0 & 0.0 & 602.9 \\  & Potato & 0.0 & 2.3 & 76.6 & 143.8 & 156.6 & 122.8 & 44.3 & 0.0 & 546.5 \\  & Melon & 25.1 & 121.6 & 181.3 & 154.0 & 36.4 & 0.0 & 0.0 & 0.0 & 518.3 \\  & Goji berry & 0.0 & 1.2 & 44.2 & 79.9 & 101.2 & 64.6 & 17.5 & 0.0 & 308.6 \\  & Forest fruits & 22.5 & 83.0 & 132.7 & 130.8 & 142.4 & 113.9 & 29.5 & 2.0 & 656.7 \\ \hline \hline \end{tabular} (3)  
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Return Flow (10\({}^{6}\) m\({}^{3}\))**} & **Irrigation** & **Return Flow** \\  & & & **Diversion (10\({}^{6}\) m\({}^{3}\))** & **Coefficient** \\ \hline
2018 & 46.571 & 137.859 & 0.338 \\
2019 & 47.691 & 147.839 & 0.323 \\
2020 & 44.406 & 162.358 & 0.274 \\ \hline \hline \end{tabular} (4)  
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Return Flow (10\({}^{6}\) m\({}^{3}\))**} & **Irrigation** \\  & & & **Diversion (10\({}^{6}\) m\({}^{3}\))** & **Coefficient** \\ \hline
2018 & 46.571 & 137.859 & 0.338 \\
2019 & 47.691 & 147.839 & 0.323 \\
2020 & 44.406 & 162.358 & 0.274 \\ \hline \hline \end{tabular} (5)  
\end{table} 
Table 5: _Cont._(5) Other parameters of the model  
See Table 8 for the total cultivated land area, irrigation water price, and irrigation water utilization coefficient in the study area.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 4 Experiments
### Classification Model Building  
We opted to apply various prominent classification algorithms for training purposes that best suit text classification. The dataset consists of TF-IDF based features computed from \(n\)-gram terms in the tweets and all the tweets have pre-assigned class labels. Seven different classifiers are considered; SVM, naive Bayes', decision tree, random forest, KNN, and ensemble methods (with bagging and boosting). Each classification model is trained in a supervised fashion using tweets that are labeled either extreme or neutral.  
The ensemble classification techniques (with bagging and boosting) are applied to build a strong model by combining a set of weak learners to achieve better performance. Where, individual learners are trained on the same dataset and the prediction task is performed using a combination approach. In order to get better predictive ability, individual members of the classifiers should be accurate and diverse. We, therefore, used different learning algorithms in our ensemble model that were SVM, decision tree, and naive Bayes', etc. All experiments were performed using complete set of features extracted from our Twitter dataset that consists of TF-IDF feature set and PCA-based transformed reduced features obtained through the original TF-IDF features in order to find the most effective algorithms in terms of classification accuracy. For the evaluation of the classification model, the data is usually split into training and test set. The training data is used to train model and the test set is used to evaluate the performance of the model. This approach of using one part of data as a training set and the other part of data as a test set does not give reliable accuracy as the accuracy with another test set may vary. We, therefore adopted, to use a \(K\)-fold cross-validation method [52; 53] where we split data into \(K\) parts. In each fold, K-1 parts are used for training and the remaining one part is used for testing purpose and this process is repeated K times with varying training and test sets. This helps in getting generalization performance of the classification model for the dataset and more reliable results on an unseen test set. All the classification results presented in this paper are based on 10-fold (K = 10) cross-validation though experiments with K = 5 were also performed, however, predictive accuracy was not much different.
## 2 System Model
### Stackelberg Game Model  
The Stackelberg game involves a leader and a follower, where the follower acts according to the strategy chosen by the leader. In our case, the PU or the source will act as the leader, while CJs will act as the followers. The PU's optimal strategy (\(\alpha^{*},\beta^{*}\)) would maximize its secrecy rate \(R_{s}\), while the power level of the \(i^{th}\) jammer \(P_{CJ_{i}}^{*}\) is selected to maximize its utility rate \(U_{CJ_{i}}\), as is illustrated in Figure 3. The PU's optimal strategy and the CJ's power choice are jointly referred to as the Stackelberg equilibrium. We assume that CJs are honest and trustworthy, where they do not deviate from transmitting jamming signals based on a power ratio of \(\beta\) determined by the PU, and they do not turn malicious. All PUs and CJs are also assumed to be selfish and rational, which mimic a non-altruistic behavior.  
Once the PU has decided the optimal strategy (\(\alpha^{*},\beta^{*}\)), the CJs will respond with a set of power choices \(P_{CJ_{i}}^{*}\), where \(\mathcal{J}\subseteq\{1,\ldots,N\}\) is the set of chosen jammers. Since there are multiple CJs in our scheme, all chosen \(\mathcal{J}\) CJs will need to compete among themselves. The outcome of the competition is a set of power choices that can be described by the game-theoretical concept of the Nash equilibrium (NE), where any unilateral deviation in the player's strategy would not produce any gain [57]. (The NE considered in this paper is based on [48], which considers all the values of \(\alpha\) and \(\beta\) for its calculation and hence gives the best results. In comparison, iterative algorithms for NE have been presented in [25; 26],  
Figure 2: System model for cooperative jamming.  
which will give optimum results, as they incur lower computational complexity.) The power control game of [48] was modified and adapted to our relaying-based cooperative jamming system, as will be described in the following section.