esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Real World Application  
In collaboration with the Australian Women's Hockey Team, BIT was implemented alongside their 2012-2013 training schedule, four sessions/week, 20 min/day. Immediate outcomes (i.e., following nine weeks) from the intervention saw 'high-risk' athletes reduce their peak valgus knee moments and subsequent knee and ACL injury risk predisposition by 29% (\(p=0.003\)) [37]. Following prolonged exposure (i.e., following 25 weeks) [37], the entire training group reduced their peak valgus knee moments and knee and ACL injury risk by 26% (\(g=0.30\)). Interestingly, in 2011, prior to the training intervention, there were 155 incidences of lower limb injuries, with three to the ACL. In 2012 and 2013, which was during of the intervention period, there were 124 and 48 incidences of lower limb injuries, respectively, with 0 to the ACL. An unexpected, but interesting, finding from their preseason training results following the first year of training was their athletic performance increased. Their endurance improved by 2.2% (\(p=0.002\)), their max speed test improved by 1.7%, while maintaining their maximum strength. The findings are important as there is a misconception in the literature that athletic performance suffers when injury prevention research is implemented.  
BIT was also implemented as a clinical control trial among community level female hockey players (two sessions/week, 15-20 min/day). Acute outcomes from the intervention (i.e., nine weeks) showed that the comparison group displayed moderate-to-large increases in both peak valgus (\(\Delta+27\%\), \(d=-0.36\)) and internal rotation (\(\Delta+38\%\), d \(=-0.56\)) knee moments, with negligible-to-no changes observed within the intervention group [38]. These training results suggest that adjunct BIT maintained an athlete's relative risk of lower limb and ACL injury over the training season, whereas the comparison group were at a greater risk of lower limb and ACL injury after a season of play. These training outcomes are also consistent with previous research [39; 40], suggesting even low volume training (two 15-20 min sessions per week) can be used to mitigate the potential deleterious effects of regular season community-based sport participation.  
From this pilot research, there is a rationale for the researchers to reconsider the training content and delivery methods of their prophylactic training interventions. Therein, researchers should shift their focus from the type of training prescribed (e.g., balance, glycoprotein, resistance) to the intended biomechanical focus of the training content (individual exercises), and incorporate established psychological and behavior change principles in their delivery methods to maximise training outcomes.
## 2 Methods
### Outcome Measures
#### 2.5.1 Neuropsychological Tests
The result represents the number of correctly recalled numbers written in the testing brochure. Reliability coefficients over time range between 0.66 and 0.89, depending on the length of the interval and the age of the individuals [56]. Digit Symbol Substitution TestThe Digit Symbol Substitution Test, initially introduced to measure information processing speed [55], is a component of the Wechsler Battery of Intelligence Tests [49]. This test assesses psychomotor abilities by presenting the individual with a line containing nine numbers and corresponding symbols, and serving as a key [56]. Each number is paired with a specific symbol. Initially, the test subject completes a trial section to familiarize themselves with the symbols associated with each number. Upon receiving a signal from the experimenter, the subject proceeds to fill in the blank fields with the appropriate symbols beneath each number. The objective is to fill in as many blank fields as possible within a limited time frame. In our study, we restricted the solving time to 60 s. The result is determined by the number of correctly written symbols within this time limit. The Digit Symbol Substitution Test demonstrates high reliability and stability over time [56; 57], with reliability coefficients ranging from 0.82 to 0.88. Furthermore, the test exhibits sensitivity in identifying dementia and monitoring its progression. Pattern ComparisonThe Pattern Comparison test [58; 59] assesses cognitive abilities related to perceptual speed. In this test, participants are presented with 30 pairs of patterns, each consisting of 3-, 6-, or 9-line segments. Within a time limit of 30 s per page, participants are instructed to indicate whether the patterns in each pair are the same (by writing \"S\") or different (by writing \"D\"). This test exhibits high reliability, with coefficients of 0.85 among students and 0.90 among older adults. Additionally, it is highly correlated with measures of perceptual speed [58]. For all the above-listed neuropsychological tests, the primary outcome measures were the number of correct responses.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
###### Abstract  
As avionics technologies have advanced, it is possible to perform many aerial applications which demand cooperative work with multiple Unmanned Aerial Vehicles (UAVs). Since one of the basic applications is reconnaissance, we focus on efficient cooperative reconnaissance. While random mobility models are useful for multi-UAVs reconnaissance, they suffer from overlapped reconnaissance problem that two or more UAVs reconnoiter a region at the same time. The overlapped reconnaissance also leads to imbalanced reconnaissance in which an area scanned by one UAV may be re-visited soon by the other UAV. Thus, we provide overlap avoidance schemes for the existing reconnaissance mobility models and enhance their performance. Throughout the simulations, we evaluate the effect of applying overlap avoidance in the existing models. The simulation results show that overlapped area is reduced by up to 20 times and 90%-coverage reaching time is improved by up to 19%.  
U
## 3 Results and Discussion
### Kinetics Parameters of Analysed Oils Oxidation under Rancinat Conditions
70%, which is resistant to oxidative changes. The constant of the oxidation rate shows the influence of temperature on the rate of fatty acid oxidation. Therefore, it is the most important parameter related to the kinetics of oil oxidation. The obtained results show the dependence in the fact that with the increase of the temperature used in the Rancimat test, the value of the constant rate of oxidation increases. The fatty acid composition may also influence the value of this constant [63]. Due to the high content of polyene fatty acids, the oils dominated by these acids have higher \(k\) values for the same temperature than the other oils. A particularly high value of the parameter \(\mathrm{k}\) at 120 \({}^{\circ}\mathrm{C}\) (compared to the others) was observed for basil seed oil--it amounted to 3.0679 h\({}^{-1}\). In the case of the remaining oils, the course of the oxidation reaction was slower, and it ranged from 0.1504 to 1.8889 h\({}^{-1}\). For linseed oils, [PERSON] et al. [4] obtained \(k\) parameter values in the range of 0.6537-0.7056 h\({}^{-1}\). In turn, the results obtained by [PERSON] [62] were more diversified--0.4934-9.8196 h\({}^{-1}\). The author reported a value of 1.0162 for parsley seed oil. This result was lower than that obtained for this type of oil analysed in this study (1.3340 h\({}^{-1}\)) but similar to that obtained for dill seed oil (1.0397 h\({}^{-1}\)). The obtained values of the constant rate of oxidation at the temperature of 120 \({}^{\circ}\mathrm{C}\) for the oils of fenugreek seeds, dill seeds, parsley seeds, and cress seeds were in the range of 1.0391-1.5873 h\({}^{-1}\), as obtained by [PERSON] et al. [65] for linseed oils tested at the temperature of 110 \({}^{\circ}\mathrm{C}\). The dill and coriander seed oils were characterised by the lowest \(k\) values at 120 \({}^{\circ}\mathrm{C}\). The coriander seed oil has a large proportion of unsaturated acids, but they are mainly MUFA acids (Table 4), and additionally, for this oil, a long oxidation induction time was determined in the Rancimat test (28.92 h) (Table 6). These factors may be the reason for a relatively low value of the \(k\) parameter for coriander seed oil. As the next part of the kinetics parameters, the oxidation reaction was the calculation of the analysed oils' enthalpy (\(\Delta H\)) and entropy (\(\Delta S\)). In the case of both enthalpy and entropy, CO had the highest values--97.6 kJ/mol and \(-\)85.2 J/mol K, respectively. On the other side, GCO had the lowest values of those parameters--75.8 kJ/mol and \(-\)121.8 J/mol K, respectively. According to [PERSON] et al. [66], negative entropy values indicate a more significant ordering of active complexes than reactant molecules. Higher entropy values mean a low probability of active complex formation and a slower oxidation reaction.
# Deep Learning-Based Portable Image Analysis System for Real-Time Detection of _Vespa velutina_
## 2 Materials and Methods
### Performance Evaluation of YOLOv5s  
YOLOv5s was trained on a PC equipped with an Intel i7-11700K 3.60 GHz CPU, DDR4 128 GB RAM, and NVIDIA GeForce RTX 3080 Ti 12 GB, using 1960 out of 2358 image data (83.1%). The hyperparameters used were as follows\" epoch: 300, batch size: 2, img_size: 1920, and optimizer: [PERSON]. With image data obtained from the image acquisition device in the image analysis part measuring 1920 \(\times\) 1080, the img_size was set to 1920. The predicted results of a model can be compared with the actual values using a confusion matrix. Thus, a confusion matrix is used to evaluate the performance of an object detection model. True positive (TP) represents the number of values correctly classified as the positive class, whereas false positive (FP) represents the number of values incorrectly classified as the positive class but actually belonging to the negative class. Likewise, true negative (TN) represents the number of values correctly classified as the negative class, whereas false negative (FN) represents the number of values incorrectly classified as the negative class but actually belonging to the positive class. The values in the confusion matrix allow us to evaluate three measures: accuracy, precision, and recall. Accuracy is the ratio of TPs and TNs to all samples and indicates how accurately the deep learning model predicts true and false samples (Equation (4)).  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{FP}+\text{TN}} \tag{4}\]  
The _V. velutina_ detection model trained in this study is designed to locate the actual object's location without separately detecting cases in which there is no object. Therefore, the TN item is not used. Precision is the ratio of TP to all predicted positives, indicating the prediction accuracy of the deep learning model (Equation (5)).  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]Recall is the ratio of TP to all actual positives, indicating how well the deep learning model detects objects of interest (Equation (6)).  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
Another method to evaluate performance is the mean average precision (mAP), which is the average of the AP values for each class. A higher mAP indicates a higher object detection accuracy. The precision-recall (P-R) curve displays the recall (X-axis) and precision (Y-axis) values based on the confidence threshold. The AP value can be obtained by calculating the area under the P-R curve, which shows how precision values change with respect to recall values. That is, the P-R curve allows us to check the precision value based on the change in the recall value, and the AP value can be obtained by calculating the area of the P-R curve.  
Finally, the F1 score is the harmonic mean of the precision and recall values, with the two measures evenly weighted (Equation (7)).  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{7}\]
## 4 Overview of the Type of Classifiers Used for MR
### Clustering Algorithms  
Clustering algorithms for modulation classification have been used for analogue communication in the literature, which provides a comparison among clustering algorithms for analogue MR [104]. Clustering algorithms, which belong to a class of unsupervised machine learning, are used for data classification. Clustering algorithms aim to find the similarities in the dataset, and the clusters are formed on the basis of the similarities. For instance, assuming a set of animals, such as dog, cat, parrot, sheep, and pigeon, we can create two clusters from the animal data set on the basis of whether the animal can fly or not [84]. Although clustering algorithms are not popular for AMR, few clustering algorithms, such as k-means [85], fuzzy c-means [83], and subtractive clustering [84; 105], have been used in the literature. An MQAM MR method was discussed in [106], thereby proposing a cyclic approximation method for estimating the carrier frequency and a method for subtractive clustering with a clustering radius. The simulation results demonstrated that such algorithms acquired 100% classification success rate for 4-QAM, 16 QAM, 32 QAM, and 64 QAM at 10 dB SNR. In [107], two threshold sequential algorithmic schemes and PR were proposed to identify QAM and PSK. Classification was conducted by utilizing the constellation of the received signal through fuzzy clustering, and hierarchical clustering algorithms were used for classification.
## 1 Introduction  
Cassava (_Manihot esculent_ Crantz) is one of the most important crops grown by smallholder farmers globally, mainly for human consumption and as a cash crop [1; 2]. The crop is consumed by around 800 million people globally, especially in Africa. Cassava has become an attractive crop for resource poor farmers due to its ability to withstand harsh growing conditions compared to other crops like maize, rice, wheat and sorghum. Thus, it is an ideal food security and industrial crop. biotic stresses have a negative effect on crop production and yields globally, as reflected by a plethora of reports in various journal articles. Molecular methods to unravel the response of host plants to these stresses have played a major role in understanding the plant-biotic stressor interactions.  
To develop control strategies for cassava, molecular mechanisms regulating the host plants' responses to biotic stress is crucial. The literature has provided information about cassava's response to insects and pathogens [3; 4; 5; 6]; however, a lot more work to unravel a myriad of various cassava defence mechanisms against many of these stressors is still required. Genes and transcription factors that are regulated by salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) signalling are either induced or repressed in response to biotic stress. These studies indicated that the expression of genes and transcription factors related to defence and hormone signalling were altered by biotic stressors.  
Recently, hyphenated techniques have become some of the utilised methods to unravel plant-biotic stressor interactions [7; 8; 9; 10; 11]. These stresses include plant-insect pests and diseases, among others. The use of some of these hyphenated techniques/methods may be costly; however, understanding the interactions between the host plant and biotic stressor has shown progress. The findings from several studies have assisted significantly in solving major diseases of economic importance globally,especially of staple crops grown for human consumption [3; 10; 11]. Due to its ability to withstand growth under poor environmental conditions, cassava is one of the promoted staple crops globally after maize, rice, wheat and sorghum.  
Understanding the interactions of cassava and its biotic stressors is important in resolving responses to the host plant and biotic stress. Although there are many reports in the literature on cassava responses to biotic constraints [3; 4; 5; 6; 12; 13], limited research has been carried out on hormone signalling in response to biotic stress. The hormone signalling response of two or more stress combinations still cannot be fully explained.  
However, the crops' productivity and yield are hampered by biotic stresses such as insect pests and diseases. These insect pests and diseases reduce cassava yields, especially in Africa, where cassava is an important food crop [14; 15]. Major cassava diseases include cassava mosaic disease (CMD), cassava bacterial blight (CBB) and cassava brown streak disease (CBSD) [3; 4; 5; 16], while the major pests are whiteflies, cassava green mites (CGM) and cassava mealypbugs (CMB) [6; 17; 18].
## 3 Preliminaries
### Long Short-Term Memory
#### 3.2.2 LSTM Architecture  
A typical LSTM model is illustrated in Figure 2. It comprises a cell, an input gate, an output gate, and a forget gate. Each of these components plays a crucial role in governing the flow of information within the network:  
* Cell State: This signifies the ongoing long-term memory of the network, retaining a list of past information.
* Previous Hidden State: This refers to the output from the preceding time point, akin to short-term memory.
* Input Data: This includes the input value of the current time step.  
By incorporating these elements, the LSTM effectively manages both short-term and long-term memory, enabling more sophisticated learning and decision processes in neural networks (See Figure 2).  
The recurrent part of LSTM is divided into three main gates (forget, input, and output), and each gate has the structure shown in Figure 3.  
A brief analysis of the different gates in the LSTM architecture is provided below. It is assumed that \(\mathbf{x}\in\mathbb{R}^{N_{x}}\) is the input of the system, and \(\mathbf{h}\in\mathbb{R}^{N_{t}}\) is the hidden state of the system.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 2 Theoretical Content  
### Typical Chaotic Map  
Logistic chaos is a mapping relationship of a typical chaotic sequence. As chaotic sequences have initial value sensitivity and parameter sensitivity, they are widely used in various encryption processes. Its mathematical formula is expressed as follows:  
\[x_{n+1}=\mu x_{n}(1-x_{n})\quad x_{0}\in(0,1) \tag{1}\]where \(\mu\) is the system parameter, and the value range is \(0<\mu\leq 4\). \(x_{0}\) is the initial value, and the range of the values is (0,1). The bifurcation diagram of Logistic chaos is shown in Figure 1.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Separation Formulation Based on Position Error  
The ICAO DOC9689 [26] provides considerations to calculate separation for manned aircraft separation, including air traffic characteristics, route structures, air traffic service capability, and the TLS. The document notes that aircraft collision avoidance capability should not be considered in separation. In contrast, the safe operation of UAVs heavily relies on collision avoidance ability, i.e., conflict detection and resolution (CDR) technology. Therefore, CDR mechanisms are considered in the establishment of separation for urban logistics UAVs. Except for the UAV position error caused by the track prediction, factors to be considered when calculating the separation required for the operation of urban logistics UAVs are as follows.
## 1 Introduction
The use of agro-industrial by-products and unconventional forages,rich in potentially anti-nutritional factors, can affect the quality of the raw milk and the dairy products made from it. Therefore, their inclusion should be carefully assessed to prevent any adverse effects on the quality and marketability of products like cheese and yoghurt. In this respect, the application of food industry by-products, such as sugar beet pulp, molasses, or tomato pomace, has affected the sensory characteristics, including the colour, firmness, and texture, as well as the fatty acid composition. However, these effects were neither consistent nor significant [15; 16]. Regarding citrus by-products in sheep ratios, no adverse effects on milk production or composition have been reported. For example, [PERSON] et al. [17] used dried citrus pulp to partially replace concentrate feed and found no significant effect on milk yield or composition. However, it did reduce the saturated fatty acids (C4:0-C10:0). In that study, the entire orange pulp, including seeds and peels, was dried after juice extraction without undergoing depectinisation or molasses extraction. The first step in processing dried citrus pulp involves adding lime to reduce its hydrophilic properties, which are attributed to the presence of pectin. Additionally, [PERSON] et al. [18] found that ensiled sliced oranges made from non-marketable oranges can be fed to lactating ewes without negative effects on their performance. However, the impact of such diets on the composition, physicochemical, and sensory characteristics of dairy products (cheese and yoghurt) has not been sufficiently documented. Produced through the fermentation of _Lactobacillus delbrueckii_ subsp. _bulgaricus_ and _Streptococcus thermophilus_, yoghurt is one of the most widely recognised and consumed cultured milk products globally. It has been a significant part of the human diet for thousands of years. In Greece, sheep milk yoghurt is particularly popular due to its firm, smooth texture, distinctive sensory qualities, and the abundant supply of milk from the country's large dairy sheep population [19; 20]. This study aims to evaluate the impact of incorporating orange peel as a feed ingredient on the quality of sheep milk yoghurt, contributing to sustainable dairy production.