esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Organic and Conventional Production on the Quality of Lemon \"Fino 49\"
## 2 Materials and Methods
### Plant Material
Treatments to control pests and diseases at the organic and conventional orchards are in Table 1.  
Regarding fertilization, the fertilization formula in the two farms was as follows: 257 kg ha\({}^{-1}\) of N, 47 kg ha\({}^{-1}\) of P\({}_{2}\)O\({}_{5}\) and 160 kg ha\({}^{-1}\) of K\({}_{2}\)O. The fertilizers used in the organic orchard were:  
* Solublack H 87 (70% humic acids and 15% fulvic acids). * Brotolim eco N-AA (6% N + 12% AA + 30% MO). * Ourpizca (CaO 6%). * Solured Mn-2.5. * Solured Zn-2.6. * Unicquel (Iron chelate 6%). * Haifa sop bio (K\({}_{2}\)O 53% + SO\({}_{3}\) 40%). * Agrosol fluid 2-4-6 + AA. * Copper shuttle (Cu 6.13%). The fertilizers used in the conventional production were:  
* Brotolim primavera + Ca + Mg (10-2-5 + 3.5 CaO + 1.5 MgO). * Unicquel (Iron chelate 6%). * Novatec fluid engorde (7,5-2,5-6,8). * Brotolim Ca AC (CaO 8%). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **Pests \& Diseases** & **Treatments** \\ \hline Organic & & \\
12-April & _Prays citri_ & _Bacillus thuringiensis_ Var. Kurstaki (PB-54 Strain) 32 \(\times\) 106 \\
05-May & _Prays citri_ & _Bacillus thuringiensis_ Var. Kurstaki (PB-54 Strain) 32 \(\times\) 106 \\
01-October & _Plytophthora_ & 35\% cupric hydroxide \\ Conventional & _Aonidiella aurantiti \&_ & \\
15-February & _Tetranychus urticae_ & 83\% paraffin oil \& 25.87\% Hexitiazox \\
12-April & _Prays citri_ & _Bacillus thuringiensis_ Var. Kurstaki (PB-54 Strain) 32 \(\times\) 106 \\
05-May & _Prays citri_ & _Bacillus thuringiensis_ Var. Kurstaki (PB-54 Strain) 32 \(\times\) 106 \\
24-May & _Aonidiella aurantiti \&_ & 83\% paraffin oil, 25.87\% Hexitiazox, \\  & _Tetranychus urticae_ & Abametcin 1.8\% and Spirotetramat 10\%. \\
01-October & _Plytophthora_ & 35\% cupric hydroxide \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Treatments to control pests and diseases at experimental orchards. * Brotolim engorde + Ca (8-2-6 + 2,5 CaO). * Lumbusol (organic matter 30%). The sampling of the fruits took place in the second half of October 2021. For each orchard (organic and conventional) 100 lemons from 10 trees (10 fruits per tree) were hand-harvested (from the middle part and the entire perimeter area of each tree) at physiological maturity, and immediately transported to the laboratory. Once in the laboratory, a selection of the 75 most homogeneous fruits (size, shape, and color) was made and 50 of these were separated for the physical-chemical, organic acids, sugars, and volatile compounds determinations, and the other 25 fruits were used for the sensory analysis.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 2 Methodology
### Data  
The studies were conducted based on raw data (National Marine Electronics Association NMEA records) from AIS obtained from the Polish Maritime Administration. Vessel traffic was analysed using data from March to May 2019. The three most common vessel groups, general cargo (GC), oil product tankers (OPT), and passenger vessels (PAS), were considered the most frequent in a given area. Ships longer than 50 m were included in the study. AIS raw data were processed using the IWRAP MK2 application [25], a modelling tool recommended by IALA for maritime risk assessment. The traffic patterns in Figure 2b are shown in a density plot, which helps to identify the location of shipping routes. Statistical analysis was performed using Statistica 10.0.  
A cross-section of the leg was made, and a histogram was prepared for each direction; the mathematical representation was prepared with various probability functions. The data were divided into classes of vessels, and each crossing line was examined. The study mainly consisted of matching the distribution of traffic with respect to the axis and obtaining the mean and standard deviation of the lane for three groups of vessels. Traffic characteristics, such as speed and course distribution, ship types, and dimensions, were determined. The main parameters of the traffic stream and their changes through the subsequent sections/legs were analysed.  
The ship's crossings show the actual behaviour of navigators and determine the influencing parameters on vessel routes. The correlation between the mean, standard deviation of spatial distribution, and distance to safe isobaths, leg length, width [\(B\)], and ships' draught [\(T\)] were verified.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning
### Deep Reinforcement Training Phase  
During the training phase, the weights are subject to iterative modifications employing the Stochastic Gradient Descent (SGD) technique. SGD represents a variant of the gradient descent method, wherein a differentiable (or sub-differential) function is optimised through stochastic approximation. This is achieved by substituting the actual gradient, computed from the complete dataset, with an estimated gradient derived from a randomly selected minibatch of the dataset. By adopting this approach, particularly in optimization problems characterised by high dimensionality, the computational burden is mitigated, resulting in accelerated iterations.  
Figure 9: Structure of the DQN, with two hidden layers of dimension H.  
During the training phase, the weights \(\varphi_{t}\) are subject to modification following each iteration of time, achieved through the utilisation of the SGD method on a selected mini-batch of data. It starts with an initial value of \(\varphi\), which is chosen at random, and iterates and updates this value using an \(\eta>0\) step size as follows:  
\[\varphi_{t+1}:=\varphi_{t}-\eta\
abla L_{t}(\varphi_{t}) \tag{4}\]  
The training is supported by replaying the experience from a replay buffer \(\mathcal{D}\). \(\mathcal{D}\) stores the different experiences \(i_{t}\) from different episodes at each time step. \(i_{t}\) is defined in (5)  
\[i_{t}:=(s_{t},a_{t},w_{s,s^{\prime},a}[t;q],s^{\prime}_{t}) \tag{5}\]  
Subsequently, random samples were drawn from the dataset \(\mathcal{D}\) and organised into minibatches. The training process is then conducted on these minibatches of data. This approach offers stability and mitigates the risk of convergence to local minima. As a result, the parameters used to generate the sample from \(\mathcal{D}\) differ from the current parameters of the deep neural network.  
The state-action value function \(Q^{*}_{\pi}(s,a)\) estimated by the deep Q-network (DQN) is defined by Equation (1).
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
###### Abstract
The shape parameters describe the degree of similarity in the life distribution between similar products of different models; [17] gives test results of momentum wheel life tests under conditions of high-temperature edge lubrication, seasonal changes, temperature, and micro-vibration; [18] established the network topology between the shaft temperature, current and momentum wheel component failure modes, and used the Bayesian network to model the reliability of the momentum wheel. Refs. [19; 20] used the multi-source information fusion to estimate the residual life and useful life of a momentum wheel in a satellite, respectively. Ref. [21] collected 450 original frictional torque data of satellite momentum wheel bearings by experiment to predict its dynamic reliability. Ref. [22] conducted the finite element analysis under multiple coupling operating conditions and frictional heat on momentum wheel bearings and established the reliability model of bearings based on stress-strength interference. Ref. [23] proposed a custom detector based on the requirements specific to attitude control, which can autonomously monitor motor health to detect degradation and failure for real-time on-orbit recovery to maximize attitude control lifetime. To date, the reliability modeling of momentum wheel has been mainly based on physics of failure (PoF). However, because the current momentum wheel failure mechanism model is not yet clear, the traditional PoF-based reliability model is not accurate enough to some extent. With the rapid development of prognostics health management (PHM) in recent years, system reliability modeling and evaluation are often combined with PHM. For a momentum wheel, it is more convincing and closer to engineering practice to conduct a reliability analysis through monitoring data such as degradation data. Therefore, based on performance degradation data of the momentum wheel, and considering that there is more than one performance degradation inside the system, this research proposes a reliability modeling and analysis of multi-degradation of a momentum wheel. Moreover, according to actual experience and failure analysis of the momentum wheel, it can be confirmed that there is a correlation between different performance degradations; therefore, it is necessary to quantify the correlation to accurately conduct reliability research on momentum wheels. The structure of this paper is organized as follow. In Section 2, key factors affecting the life of the momentum wheel are analyzed, and the correlation between different performance degradations is explained. In Section 3, the modeling method based on Wiener process is introduced. In Section 4, the Copula-based multi-degradation reliability modeling method is presented. In Section 5, a case study on reliability modeling and analysis of a momentum wheel is conducted. In Section 6, some conclusions are obtained.
## 3 Results and Discussion
### Comparing Different Measures for Classification Success
Using a one-vs-one configuration [36; 38], as recommended by tutorials of many software packages, an AUC score is then computed for each of these binary classifiers, and finally an average is computed to obtain a final AUC score for the entire set of \(n\) classes. For an actual binary classifier that classifies poorly, the miss-classifications will reflect in significant enough values of false positives and false negatives to give us a low true positive rate and high false positive rate as per Equations (9) and (10). This will result in a low AUC score. In the multi-class scenario with one-vs-one configuration, we observe that a classifier distributing miss-classifications sparsely across several classes leads to a small number of false positives and small number of false negatives for these artificially assumed binary classifiers. One should note that this will happen even if the classifier fairs poorly i.e., miss-classifies with a high rate. An example for this can be seen in Figure 8, which shows a confusion matrix for a classification run with 20 species. It can be seen that the miss-classifications are spread throughout the rows and columns of the confusion matrix. Consequently, less numbers of false positives and false negatives will amount to a high true positive rate and low false positive rate for individual binary comparisons and, therefore, a high AUC score (refer to Equations (9) and (10)). This is exactly what is reflected on averaging the individual AUC scores to compute the total AUC score for \(n\) classes. Figure 7: The precision, recall, and average precision for individual species classified in a subset composed of 10 randomly selected species are compared. The largest and the smallest value of each performance measure indicate a relatively large variation and strong dependence on the particular species under study. The classifier is distributing the false predictions sparsely across several classes and the one-vs-one generalization is unable to capture the actual performance of the model. This leads us to the conclusion that ROC is not a suitable performance measure for multi-class classification tasks. Especially in cases where the miss-classifications are distributed rather evenly among several classes, it is very likely to obtain overestimated AUC scores.
## 1 Introduction
Consequently, the control of interference becomes a significant research concern within the field of D2D communication networks. Despite the improvements in cellular networks enabled by D2D communication, many concerns should be addressed. Conventional cellular networks are significantly affected by the interferences imposed by D2D communications due to the sharing of cellular user resources. These concerns have a substantial effect on the performance of cellular user communications and could impact the future development of D2D technology. Therefore, it is essential to figure out efficient strategies for the purpose of allocating resources to D2D communication pairs without maximizing the complexity of the network's topology. Specifically, effective allocation of resources for D2D communications is an important challenge in the cellular network. Following the mode selection procedure, optimal sub-channel resource allocation is necessary to achieve the goal of D2D technology [10]. Power control and interference mitigation should be considered through the resource allocation process. Additionally, to enhance EE and guarantee QoS for both D2D pairs and CUs, appropriate power allocation approaches need to be designed. Blindly implementing power control to D2D communications in a cellular network could reduce system efficiency. D2D transmitters should optimize their transmission power to enhance EE, SINR demands, and system performance. Machine Learning (ML) has been demonstrated to be highly beneficial in multiple areas due to its ability to precisely predict future scenarios and address complex problems with huge datasets [11]. Artificial intelligence approaches have been indicated to be an excellent technique for tackling complicated non-convex optimization problems in communication networks. In the field of wireless communication networks, reinforcement learning (RL) based on ML techniques has been utilized to solve the issue of power allocation [12].
## 2 Materials and Methods
### System Optimization and Commissioning  
#### 2.2.1 X-ray Light Flux Calibration  
The maximum X-ray beam flux in the downstream optical path of Slit 2 is not an exact correspondence with the minimum signal difference between the left and right blade-edge voltage signals, as shown in Figure 4. This is because the signal of the gold mesh may not accurately represent the X-ray beam flux at the sample position, depending on its mesh structure and the illuminated position. Therefore, a PD detector was installed in the STXM end station, which directly measures the X-ray intensity; this was used to determine the difference between the maximum flux and the flux when \(\mathrm{S_{RL}}\) = 0, and to calibrate \(\mathrm{S_{RL}}\) through offset if the difference was big enough. Several calibration experiments were performed at energies of 540 eV (shown in Figure 6), 700 eV and 920 eV, and the offset values of \(\mathrm{S_{RL}}\) for the maximum PD light flux were calculated as \(-\)2.677%, \(-\)9.356%, and \(-\)13.5%, respectively. When the offset was set to zero, the light flux values of \(\mathrm{S_{RL}}\) = 0 at 540 eV, 700 eV and 920 eV were 99.12%, 96.88%, and 98.50% of the maximum PD flux, respectively. The maximum light flux deviation was smaller than 3%. Therefore, although the offset may be bigger than 10%, the flux of offset = 0 was almost the max flux on the sample, and the offset was to zero in the automatic feedback procedure.  
Figure 5: The logic of each tunning procedure. When the server gets a tunning request with threshold and offset values, it will try to read out \(\mathrm{S_{L}}\), \(\mathrm{S_{R}}\), \(\mathrm{S_{U}}\) and \(\mathrm{S_{D}}\) and calculate \(\mathrm{S_{v}}\). If \(\mathrm{S_{v}}\) is bigger than the threshold, the M2 motor will move one step (200 nm); otherwise, it will jump directly to the end of this tunning and waiting for the next tunning call.
## 4 Simulation Results  
After applying the AdaBoost algorithm, a performance comparison of different modulation techniques such as BPSK and QPSK is carried out. AdaBoost is used to improve the SNR of these techniques. The Algorithm is first applied to the coherent systems to improve the BER of the used modulation techniques for a Gaussian channel. It is next used to improve the BER of the noncoherent detection of the used modulation techniques in the AWGN and Rayleigh environments. A flowchart of the proposed system is shown in Figure 3.  
In the following section, the MATLAB simulation of each technique is illustrated, which proves the improvement made by applying the AdaBoost to the recovered signal at the receiver. The recovered signal, still contaminated with some noise will pass through the AdaBoost algorithm for tuning of its parameters.
## 1 Introduction  
Major depressive disorder (MDD) is known as the most debilitating mental health issue with significant morbidity and mortality [1]. Depression is a mental disorder affecting both mental and physical health. It is a heterogeneous group of pathological conditions with different etiologies that, according to DSM-5-TR, might include symptoms such as depressed mood, loss of enjoyment, difficulty concentrating, lack of energy, sleep and appetite disturbances, feelings of worthlessness, guilty feelings, and suicidal thoughts [2]. MDD is diagnosed when an individual exhibits a minimum of five symptoms over the course of two consecutive weeks, denoting a substantial deterioration in functioning. At least one of these symptoms must be a depressed mood or an absence of interest or pleasure in activities. The prevalence of major depression worldwide has been estimated at over 300 million patients, representing 4.4% of the world's population. Globally, as populations grow and more people reach the age when depression and anxiety are most common, the number of people affected is increasing, particularly in low-income countries [3]. According to [PERSON] et al. [4], there was a 49.86% increase in the number of cases of depression worldwidebetween 1990 and 2017. Major depression is considered a syndrome that results from psychological, environmental, and genetic factors. Often, a family history of it, major changes in lifestyle, chronic conditions, various medications, or substance abuse may cause it. Despite the growing number of studies, the exact mechanisms underlying MDD remain unclear [5].  
Current psychiatric guidelines recommend several antidepressants and cognitive-behavioral psychotherapy as first-line treatments for MDD. In addition, even new medications produce side effects that reduce tolerability and increase the risk for patients despite advances in psychopharmacology. Psychotherapy, in turn, is expensive, time-consuming, unsuitable for all patients, and unavailable in underdeveloped parts of the world [6]. Alternative noninvasive therapies such as electrical and magnetic brain stimulation are increasingly being researched, but their usefulness is still debated.  
The present study has been designed to evaluate the therapeutic potential of two noninvasive interventions for MDD. The interventions under scrutiny are transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (tMS). A scoping review of the extant evidence from both animal models and clinical studies has been undertaken to determine the efficacy of these interventions as standalone treatments and in combination with conventional therapies such as antidepressants and psychotherapy.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 5 Conclusions  
As ETSI proposes the NFV architecture using virtualization technologies, the performance degradation of virtualization is a challenge to meet the SLA of the telecom industry. This paper attempts to conduct the performance effects of EPA techniques for the NFV cloud. The EPA techniques adopted in our testbed include CPU pinning, CPU isolation, hugepages, OVS-DPDK, and SR-IOV. This paper further proposes a test framework for the comprehensive verification and evaluation of the cloud system under test in terms of functionality, performance, and application. The major advantages of this framework are threefold. First, this framework is based on open-source projects to eliminate the expenditure on system verification and evaluation. Second, the framework is easy to implement and expandable. The testing procedures are triggered on the test host with the test containers for different benchmarking tools. Third, for the performance testing, the verification not only compares the impacts of different network techniques, but also considers the length of packet path, different VM sizes, and performance tuning of packet processing, e.g., lcore and pmd-cpu.  
Results show that all the cases in functional tests are passed. The test cases in Rally_Sanity also fulfill the requirement of SLA. Throughputs of OVS-DPDK are up to 8.2 times as high as that of generic OVS networking. Regarding the performance at the application level, the SCR of vIMS is improved by up to 14% with OVS-DPDK. Our experiments further obverted that the throughput is limited when the packet size is small. This deterioration happens when the virtual switch cannot process a huge number of packets. The throughput declines when the packets are dropped. Regarding the end-to-end scenarios, the length of a traffic flow in PVP or PVVP has different impacts on the throughput with different networking techniques. The results and findings of this work are valuable for further reference in workload scheduling, resource placement, or load-balancing mechanisms. To extend this work, deploying a feature cloud and verifying the SUT automatically are interesting works. Further research will be to model the specific application performance based on the proposed testing method and the enhanced technologies, which are also left for future work.  
Conceptualization, W.-C.C.; Funding acquisition, W.-C.C.; Methodology, W.-C.C. and Y.-H.W.; Software, Y.-H.W.; Supervision, W.-C.C.; Validation, Y.-H.W.; Writing--review & editing, W.-C.C. and Y.-H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of Taiwan, under Grants MOST108-2218-E-033-008-MY3 and MOST111-2221-E-033-033.  
The authors declare no conflict of interest.