esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme  
### Link Loss Model  
Analyzing and simulating a reliable data transmission scheme requires a realistic link loss model. In this paper, we use the PRR as the link quality between two nodes. The PRR ranges from 0 to 1 and is calculated using the following link loss model [20, 21]:  
\[PRR(d)=(1-\frac{1}{2}e^{-\frac{\gamma(d)}{2}\frac{1}{0.64}})^{16f-8l}, \tag{1}\]  
where, \(d\) is the distance between two nodes, \(\gamma(d)\) is a _signal to noise ratio_ (SNR) for \(d\), \(f\) is the length of a frame, and \(l\) is the length of a preamble. This model considers various radio parameters, such as a _path-loss exponent_ (\(\eta\)) and a _log-normal shadowing variable_ (\(\sigma\)). In the simulated environment, \(\eta\) and \(\sigma\) are 3.0 and 3.8, respectively. Equation (1) reflects MICA2 modes that use the NCFSK modulation method and Manchester Encoding. Other detailed information follows the MICA2 specifications [22].  
Recent researches [3, 4, 8, 20, 21] have shown three distinct data reception regions: connected, transitional, and disconnected. In the connected region, nodes can transmit packets reliably in the absence of congestion since PRR is always 1. In the transitional region, some links have high PRR, while others have the opposite. Therefore, when we design a routing scheme, we should consider this region. In the disconnection region, no links or very weak links exist. Figure 1 depicts the link layer model, where the observed connected region is from 0 to 8 \(m\), the transition region is from 8 to 33 \(m\), and the disconnected region is from 33 \(m\).
## 1 Introduction
### The Study  
The present research could be considered an initial phase of the design and development of a symbiotic system that can detect an increase in the user's MWL and, on this basis, adapt its operating to reduce the task demand. As the first step toward such purpose, this study's main objective was to define a set of implicit and explicit measures that could be exploited to monitor variations of the MWL experienced by the users due to the manipulation of the task difficulty. A second purpose was to investigate if participants were aware of the different levels of mental load. The assignment was carried out in an ad hoc built ecological setup, similar to a real assembly line, to increase the outcomes' potential generalizability. Indeed, this ad hoc system (described in Section 2.3, Equipment and Material) allowed us to simultaneously observe, and subsequently compare, all the measures under investigation in the present study.  
Based on the two main limits of symbiotic systems (i.e., data interpretability and system transparency), the study explored the following research questions:  
RQ1. Do the implicit and explicit measures reflect the variation in the MWL due to the different experimental tasks in the same fashion?  
RQ2. Do the implicit and explicit measures reflect the subjective perceptions of participants regarding the MWL imposed by the different tasks?  
For research question 1, we expected that both implicit (i.e., ocular, cardiac, and electrodermal activity), and explicit (i.e., task accuracy) measures would be affected, in a highly ecological paradigm, by different levels of MWL elicited through the manipulation of task difficulty.  
For research question 2, we predicted that the findings related to the implicit and explicit measures (e.g., eye tracking and task accuracy) would match participants' subjective perception (e.g., NASA-TLX scores) in reflecting the different levels of mental workload.
# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 4 Lung Images' Segmentation and Registration
### Segmentation Process
#### 4.1.1 Thresholding-Based Segmentation  
The most basic and easy to implement is a thresholding-based method. We can set a certain global threshold value for the whole gray-scale image. The picture will be transformed into a binary region map, with ones where pixel values are above or equal to the threshold level and zeroes where they are below. Although the algorithm is simple, the most difficult step is to automatically find the threshold level for a specific image. Having lung CT images taken by different machines, the values of pixels representing lungs may differ. Generating a histogram from an image can help to choose the threshold values.  
Unfortunately, even choosing a proper threshold does not guarantee that the desired object will be acceptably segmented. Sometimes, images have different lightning in different areas, and applying a simple threshold would give unacceptable results. That is why an adaptive threshold algorithm was introduced. It calculates the threshold \"adaptively\" for each region of an image. Thanks to this solution, the output is more accurate. It takes into consideration the different brightness of the pixels of the same object, which may be caused by lightning or a faulty camera [17]. The main advantages of this method are the calculation speed and simplicity of implementation. However, it works efficiently only when there is a large contrast between separated objects (which usually is not true in  
Figure 5: Example of images classified as _false positive_ by all tested networks.  
\begin{table}
\begin{tabular}{c c c c} \hline Real: Lung & 33 & 913 & Accuracy & 0.9763 \\ \hline Real: Not lung & 650 & 5 & Sensitivity & 0.9651 \\ \hline \multicolumn{4}{c}{Predicted: Not lung} & Predicted: Lung & Specificity & 0.9924 \\ \hline \end{tabular}
\end{table}
Table 5: Results for VGG16-2000 applied to the testing data.  
the case of lung CT images). It is very difficult to obtain a satisfying result if there is no significant value difference in the gray levels [18; 19].
## 4 Discussion
### Influence of Tillage Practices on Herbicide Degradation  
The observed variations in degradation rates highlight the combined effects of pesticide molecular properties and soil environmental conditions. Even under identical tillage treatments, significant variations were observed in the dissipation rates among the selected herbicides. Conversely, individual pesticides exhibited differing persistence levels when exposed to distinct soil management systems. These findings underscore the complex interplay between pesticide physicochemical properties and soil environmental conditions in governing degradation behavior.  
The observed differences in degradation rates can be primarily attributed to the inherent molecular characteristics of each pesticide, including solubility, volatility, adsorption capacity, and susceptibility to microbial transformation. Additionally, tillage-induced modifications in soil structure, moisture content, organic matter availability, and microbial community composition further modulate the biotic and abiotic processes responsible for pesticide dissipation.  
The findings underscore the importance of integrating soil management strategies into pesticide risk assessment frameworks. Appropriate tillage methods can help reduce environmental contamination by enhancing herbicide degradation without compromising crop productivity.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 2 Literature Review
### Temperature  
As in the case of humidity, temperature impacts both air properties and insulation properties. Referring to the former, an increase in temperature inversely affects the air density, decreasing the electric breakdown strength of air [38; 40]. Additionally, [PERSON] et al. [39] and [PERSON] et al. [26] explain that the electron mean free path is increased with a decrease in air density. Consequently, electrons gain more energy and generate electric discharges by crossing a greater distance between collisions [39]. As a result, an increase in temperature decreases the PDIV [26; 38; 39; 40; 41].  
Moving to the insulation properties, [PERSON] et al. [38] observed that an increase in relative permittivity with temperature (in the range 25-230 \({}^{\circ}\)C) decreased the PDIV. Different insulation materials (Polyamide-imide (PAI) and Polyetheretherketone (PEEK)) with different permittivity-temperature relationships that (increase the relative permittivity with temperature) are analysed. It is shown that not only are changes in air density dominant but also the evolution of permittivity with temperature must be considered. These conclusions are in good agreement with [42]. It must be kept in mind that, depending on the material, the relative permittivity can increase or decrease with temperature. Therefore, depending on the relative permittivity evolution, the PDIV will increase or decrease with the temperature. On the contrary, [PERSON] et al. [26] did not notice a considerable dependency on PAI in the range of 25-140 \({}^{\circ}\)C. They explained that the analysed temperature is well below the glass transition temperature (275 \({}^{\circ}\)C), so the dependency of the relative permittivity on the temperature is limited. In insulation materials with lower glass temperatures, the temperature dependency could be enhanced.  
Considering these studies, it is clear that there is an inverse relationship between temperature and PDIV due to air electric breakdown voltage decrease. However, in view of relative permittivity, the PDIV evolution can be analogous or inverse to the temperature. Thus, when the relative permittivity decreases with the temperature, both factors, air and permittivity, have an opposite effect on the PDIV. In this case, it is necessary to decide whether air properties or relative permittivity is the predominant factor to define the evolution of the PDIV with temperature.
# Online Streaming Feature Selection via Conditional Independence
###### Abstract  
Online feature selection is a challenging topic in data mining. It aims to reduce the dimensionality of streaming features by removing irrelevant and redundant features in real time. Existing works, such as _Alpha-investing_ and Online Streaming Feature Selection (_OSFS_), have been proposed to serve this purpose, but they have drawbacks, including low prediction accuracy and high running time if the streaming features exhibit characteristics such as low redundancy and high relevance. In this paper, we propose a novel algorithm about online streaming feature selection, named _ConInd_ that uses a three-layer filtering strategy to process streaming features with the aim of overcoming such drawbacks. Through three-layer filtering, i.e., null-conditional independence, single-conditional independence, and multi-conditional independence, we can obtain an approximate _Markov blanket_ with high accuracy and low running time. To validate the efficiency, we implemented the proposed algorithm and tested its performance on a prevalent dataset, i.e., _NIPS 2003_ and _Causality Workbench_. Through extensive experimental results, we demonstrated that _ConInd_ offers significant performance improvements in prediction accuracy and running time compared to _Alpha-investing_ and _OSFS_. _ConInd_ offers 5.62% higher average prediction accuracy than _Alpha-investing_, with a 53.56% lower average running time compared to that for _OSFS_ when the dataset is lowly redundant and highly relevant. In addition, the ratio of the average number of features for _ConInd_ is 242% less than that for _Alpha-investing_.  
Keywords:streaming feature; feature selection; conditional independence; markov blanket +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Related Work
These efforts, that are available on the market, include solutions covering the supply chain from field to retailer, namely GR-LIVE (Food Logistics; Waukesha, WI, USA) [54] and FoodLogiQ's Track + Trace (LogiQ; Charlotte, NC, USA) [55], from the packaging plant to retailer, namely iApp (ORBCOMM; Rochelle Park, NJ, USA) [56] and AutoSense (Emerson; St. Louis, MO, USA) [57], and from the packaging plant to consumer, namely HarvestMark (iFoolsD and HarvestMark; Seattle, WA, USA) [58] and iris (Frequentz; San Ramon, CA, USA) [59]. A general remark for these systems is that they deal with limited parts of the supply chain and do not cover the entire range of it, i.e. from field to consumer, while they do not provide an open architecture.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
###### Abstract  
Low-cost and flexible radio frequency identification (RFID) tag for automatic identification, tracking, and monitoring of blood products is in great demand by the healthcare industry. A robust performance to meet security and traceability requirements in the different blood sample collection and analysis centers is also required. In this paper, a novel low-cost and flexible passive RFID tag is presented for blood sample collection tubes. The tag antenna is based on two compact symmetrical capacitive structures and works at the ultra-high frequency (UHF) European band (865 MHz-868 MHz). The tag antenna is designed considering the whole dielectric parameters such as the blood, substrate and tube. In this way, it operates efficiently in the presence of blood, which has high dielectric permittivity and loss. Measurement results of the proposed tag have confirmed simulation results. The measured performance of the tag shows good matching in the desired frequency band, leading to reading ranges up to 2.2 m, which is 4.4 times higher than typical commercial tags. The potential of this tag as a sensor to monitor the amount of blood contained in clinic tubes is also demonstrated. It is expected that the proposed tag can be useful and effective in future RFID systems to introduce security and traceability in different blood sample collection and analysis centers.  
antenna; blood tracking; healthcare; radio frequency identification (RFID); passive sensor +
Footnote †: journal: Sensors  
1
## 3 System Model and Problem Formulation
### Drl Energy Consumption Model  
In the context of cloud computing, the significant performance advantages of cloud-based operations outweigh the energy consumed in transmitting and aggregating model parameters. Accordingly, this study concentrates solely on the energy expenditure involved in training at the edge, encompassing the local training energy consumption and the energy consumption associated with the transmission of model parameters at the edge. As demonstrated in the research conducted by [32], the computational burden is approximately proportional to the data volume in a nearly linear fashion. Specifically, assuming that a base station (BS) trains a single data sample, it necessitates \(c_{n}\) CPU cycles per bit. Taking into account the training data volume \(D_{t}\), the total energy consumption of \(BS_{n}\) during the \(t\)-th training epoch is expressed in accordance with [33,34]:  
\[E_{t,n}^{cmp}=L_{n}\mathbb{E}_{t,n}^{cmp}=L_{n}\mathbb{\xi}_{n}c_{n}D_{t,n}fre _{n}^{2} \tag{8}\]  
\[\begin{array}{l}\text{{subject to :}}\\ L_{n}\geq 1\\ n\in\mathcal{N}_{1}\\ \mathcal{N}_{1}\subset\mathcal{N}\end{array}\]  
The effective capacitance coefficient of the computing chip group of \(BS_{n}\) is denoted as \(\zeta_{n}\), while \(L_{n}\) represents the count of local training iterations conducted by \(BS_{n}\). To compute the local model, \(BS_{n}\) is required to perform CPU cycles. Based on Equation (8), it is apparent that the energy expenditure associated with local training can be appropriately regulated by adjusting the number of local training iterations \(L_{n}\). Assuming that \(P_{n}\) signifies the transmission power of \(BS_{n}\), the energy consumption involved in uploading the model can be derived as follows:  
\[E_{t,n}^{up}=P_{n}\frac{\wp_{t,n}}{\wp_{a}} \tag{9}\]  
\[\begin{array}{l}\text{{subject to :}}\\ n\in\mathcal{N}_{3}\\ \mathcal{N}_{3}\subset\mathcal{N}\end{array}\]  
Here, \(\wp_{n}\) represents the model size in \(BS_{n}\). The total energy consumption of the system in the \(t\)-th epoch is  
\[E_{t,system}=\sum_{n\in\mathcal{N}_{1}}E_{t,n}^{cmp}+\sum_{n\in\mathcal{N}_{2 }}E_{t,n}^{up} \tag{10}\]  
\[\begin{array}{l}\text{{subject to :}}\\ \mathcal{N}_{1}\subset\mathcal{N}\\ \mathcal{N}_{3}\subset\mathcal{N}\end{array}\]  
In this context, \(\mathcal{N}_{1}\) denotes the group of BSs that necessitate training during the \(t\)-th epoch, while \(\mathcal{N}_{3}\) signifies the set of BSs that require model aggregation during the same epoch. The selection of \(\mathcal{N}_{1}\) and \(\mathcal{N}_{3}\) shall be performed utilizing a dynamic and energy-efficient federated approach, which will be elaborated comprehensively in Section 4.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### ML Methodology  
Several studies have developed and investigated effective ML techniques for cyberattack detection and mitigation. Figure 6 illustrates the generalized methodology of an ML algorithm applied to ML-based IDS. The workflow includes several phases corresponding to dataset collection, data preprocessing, features selection and extraction, ML model training, hyperparameter tuning, and model testing and validation. The first step is the availability of a dataset, which can be balanced (using of an equal number of samples for each attack type in addition to normal class samples) or imbalanced (consisting of an unequal distribution of the classes in the dataset). The next step is data preprocessing, which involves several stages: class rebalancing and sample size reduction, missing value imputations, cleaning or feature removal, data normalization, and transformation (i.e., encoding labeled data). The advantage of balancing the dataset before using it in training is to avoid bias towards the majority class. This is followed by feature selection, which involves determining an optimal set of features to help reduce dataset dimensionality, especially when considering a large dataset that may have irrelevant, redundant, erroneous, and correlated features. A lower number of dataset dimensions lead to less computational and training time being required. The reduced dataset is then utilized to train the ML model. Optimal hyperparameter values can be obtained by applying efficient tuning techniques. The final step is testing and validation, which entails using several evaluation metrics to assess ML model performance, such as the probability of detection \(P_{d}\), probability of false alarm \(P_{fa}\), probability of misdetection \(P_{md}\), positive prediction value PPV, accuracy (ACC), F1-score, root mean square error RMSE, and receiver operating characteristics (ROC).
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 2 Methodology
### Ship Collision Avoidance Behavior Recognition
#### 2.2.1 Analysis of Ship AIS Trajectory Features  
After preprocessing the ship's AIS data, the basic recognition accuracy has been ensured. However, the quality of AIS data is susceptible to interference from various factors. For instance, the ship's position or heading may exhibit minor fluctuations due to the influence of wind, waves, and currents during navigation, or errors may occur during the transmission of AIS data, both of which reduce the efficiency and accuracy of data mining, as shown in Figure 3a.  
Ships navigating in open waters typically follow a planned course and maintain consistent speed and course, with their trajectories generally not exhibiting significant abrupt changes during straight-line navigation. When a ship's operator performs a collision avoidance maneuver, they typically follow the \"early, large, wide, and clear\" principle, turning sharply to avoid collisions as early as possible and then resuming the original heading after traveling a certain distance along the new course, as shown in Figure 3b.  
Figure 3: Characteristics of ship AIS trajectories under different conditions. (**a**) AIS Trajectory of Ships with Data Disturbances. (**b**) AIS Trajectory of Ships Taking Collision Avoidance Maneuvers. (**c**) AIS Trajectory of Ships Taking Turning Maneuvers.  
When the operator performs a turning maneuver, the ship continues to sail along the new heading once the turn is completed, as illustrated in Figure 3c. Therefore, determining whether a ship's turning behavior is a passive avoidance maneuver or an active turn based on the planned route can be approached by analyzing the trajectory changes over a period of time. However, local fluctuations in the trajectory, shown in Figure 3a, caused by wind, waves, currents, or transmission issues are difficult to resolve through data preprocessing alone and thus require a more detailed analysis of the trajectory changes.
# Study on the Coupling Relationship of Low Temperature Fluidity and Oxidation Stability of Biodiesel
## 3 Experiment Results and Discussion
### Oxidation Stability Experiment Results  
#### 3.2.1 Effects of Antioxidants on OIT of Biodiesel  
The OIT of biodiesel is influenced greatly by antioxidant, and the antioxidant effects of different antioxidants are also different. Commonly used antioxidants include BHA, BHT, TBHQ and PG. In the case of biodiesel without oxidation, the OIT was 3.72 h. After the addition of four antioxidants, BHA, BHT, TBHQ and PG, the OIT significantly increased, and both reached the national standard requirement of more than 6 h. Among the four antioxidants, BHA has the worst antioxidant effects, and TBHQ has the best antioxidant effects.