esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
###### Abstract  
Spectral decision making is a function of the cognitive cycle. It aims to select spectral opportunities within a set of finite possibilities. Decision-making methodologies, based on collaborative information exchanges, are used to improve the selection process. For collaborative decision making to be efficient, decisions need to be analyzed based on the amount of information. Using little data can produce inefficient decisions, and taking a lot of data can result in high computational costs and delays. This document presents three contributions: the incorporation of a collaborative strategy for decision making, the implementation of real data, and the analysis of the amount of information through the number of failed handoffs. The collaborative model acts as a two-way information node, the information it coordinates corresponds to the Global System for Mobile Communications (GSM) band, and the amount of information to be shared is selected according to five levels of collaboration: 10%, 20%, 50%, 80%, and 100%, in which each percentage represents the total number of users that will be part of the process. The decision-making process is carried out by using two multi-criteria techniques: Feedback Fuzzy Analytical Hierarchical Process (FFAHP) and Simple Additive Weighting (SAW). The results are presented in two comparative analyses. The first performs the analysis using the number of failed handoffs. The second quantifies the level of collaboration for the number of failed handoffs. Based on the obtained percentage ratios, the information shared, and the average of increase rates, the level of collaboration that leads to efficient results is determined to be between 20% and 50% for the given number of failed handoffs.  
cognitive radio; collaborative models; decision making; failed handoffs; multi-criteria techniques +
Footnote †: journal: Applied Physics Letters
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 2 Materials and Methods
### Experimental Setup of Our HPPS
#### 2.1.2 Air Compressor and Air Tank  
The compressor used in our system is an oil-injected, rotary-screw compressor. It has a power range of up to 11 kW, maximum acceleration of 8500 rpm, maximum flow rate of 1.2 m\({}^{3}\)/m, and functions under a pressure of 15 bar. An air storage tank is used for storing compressed air to maintain a stable compressed flow, reduce pressure fluctuations, and allow for the braking components to perform smoothly. In our HPPS, the tank capacity is 110 L, with compressed air pressure that can reach up to 15 bar.
## 4 Lunar Antenna Types
### Planar Antennas  
In recent decades, planar antennas have gained significant attention in lunar/space research. These antennas offer several advantages, making them an attractive option for a variety of applications. One of their primary advantages is their cost-effectiveness compared to waveguide-based antenna technology as well as their lightweight and compact design, which is crucial for many commercial applications like those for handset antennas or base stations. Moreover, planar antennas offer an advantageous platform for deploying expansive arrays, facilitating the seamless integration of supplementary electronics like amplifiers and phase shifters. These electronic modules play a pivotal role in various critical areas, including electronic warfare tactics, satellite communication systems, millimeter-wave imaging technologies, and radar functionalities. Furthermore, planar antennas are highly advantageous for various applications due to their flat shape. Specifically, they are ideal for situations where shape and size are crucial, such as using conformal printed antennas on aircraft fuselages. The ease of incorporating microwave or millimeter-wave circuit elements renders planar antennas a favored option among both designers and scientists for utilization in lunar and space communication applications. Among the planar antennas, the most common types used in lunar communication are patch and resonant slot antennas due to their exceptional parameters with miniature form factors [43].
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 4 Experiment and Analysis
### Experiment Setting
#### 4.1.2 Evaluation Metrics  
In order to measure the effectiveness of LogBD in anomaly detection, the \(F_{1}\) value and AUC were used as evaluation indicators. The \(F_{1}\) value combines the precision and recall of the models. A higher \(F_{1}\) value denotes a better performance of the classification model. Precision represents the percentage of real anomalies in all detected anomalies, recall represents the percentage of anomalies detected in the dataset, and the \(F_{1}\) value is calculated as follows:  
\[F_{1}=2\frac{Precision\times Recall}{Precision+Recall}. \tag{8}\]  
The formulas of precision and recall are as follows:  
\[Precision=\frac{TP}{TP+FP}. \tag{9}\]  
\[Recall=\frac{TP}{TP+FN}. \tag{10}\]  
The AUC is the area under the ROC curve. The closer the area is to 1, the better the model effect is. It is mainly used to measure the performance of a binary classification model. The AUC calculation formula is as follows:  
\[AUC=\frac{\Sigma_{\text{ins}_{i}\in\text{positirectlass}}rank_{\text{ins}_{i}} -\frac{M*(M+1)}{2}}{M\times N}, \tag{11}\]  
where \(\text{rank}_{\text{ins}_{i}}\) is the ordinal number of the i-th sample, M and N are the number of positive and negative samples, respectively, and \(\Sigma_{\text{ins}_{i}\in\text{positirectlass}}\) is the sum of the ordinal numbers of all positive samples.  
The value range of the \(F_{1}\) value is 0 to 1. A higher value denotes a better performance of the classifier and a better model effect, which can comprehensively describe the detection performance of the model. The value interval of AUC is 0.5 to 1. A higher value denotes a better performance of the model. AUC is not sensitive to abnormal thresholds.
## 3 Numerical and Simulation Results
This is because for each interfering signal from six co-channel BSs in the RIS system, the combined interference signal's power is much higher than the useful signals, thereby reducing SINR as in Equation (3) in the high SNR region and as \(q\) tends to approach 1 (fully loaded system). Figure 3: Ergodic capacity vs. SNR with varied number of RIS element, N.  
Figure 6 shows that as the number of RIS elements N increases from N = 10 to N = 100, the EE performance for RIS also increases, accordingly. This is because the power consumption from RIS elements is very low due to the passive use of power for the RIS metasurface. As per EE in Equation (15), the denominator is largely dominated by the base station power rather than the RIS passive elements power. Thus, the effect of RIS hardware power consumption is still relatively low, even when the number of RIS elements increases to N = 100. In Figure 7, for FRF = 3 for the cell edge, we can see that the partially-loaded system has lower ASE than a fully-loaded system, for both RIS and DAS, since the ASE increases as the active channels in the systems increase. From (16) and (25), although the individual channel achieved higher rates in a partially loaded system due to the lower CCI; this effect is offset by the fact that there are fewer active channels \(q\) in the system. By comparing the two systems, we can see that the RIS system provides better ASE or data rates supported per cell-edge in bits/s/Hz/km\({}^{2}\) than the DAS system for different values of active channels in a cell, \(q\). The results show that the channel efficiency per unit area for RIS is better than DAS, mainly due to the better average capacity of RIS caused by the summation factor of  
Figure 4: Ergodic capacity vs. SNR with varied path loss exponent, \(\beta\). Figure 5: Ergodic capacity vs. SNR with varied traffic activity factor, \(q\). the number of antennas N in nominator in (2), and less CCI from other RISs located in \(k_{th}\) cells (in comparison to DAS). Figure 7 also shows that, in a high SNR region, the gap in ASE performance between both systems narrows by about 0.005 bits/s/Hz/km\({}^{2}\) while at 0 dB or less, the gap in ASE between the two systems widens to 0.03 bits/s/Hz/km\({}^{2}\). From design perspective, both systems have the highest ASE when all available channels for the cell-edge is being utilized. Based on Figures 3-7, it is evident that our analytical method using MGF work almost perfectly and was validated using \(10^{5}\) Monte Carlo simulations.
## 4 Implementation
### Computational Resource  
The computational experiments were conducted on a high-performance GPU server provided by Gachon University's Smart-city Lab. The server was equipped with an Nvidia A6000 GPU, which offers substantial computational power to handle complex model training involving large datasets and deep neural networks. The system was also supported by 64 GB of RAM, ensuring efficient handling of large-scale data operations and simultaneous processes without bottleneck issues.
# A Deeper Look at Sheet Music Composer Classification Using Self-Supervised Pretraining
## 4 Discussion
### Effect of Data Augmentation  
The first analysis is to characterize the effect of the training and test time augmentation strategies. Recall that the bootleg scores are shifted by up to \(\pm K\) shifts during training, and that shifted versions of each query up to \(\pm L\) shifts are ensembled at test time. Figure 6 shows the fragment classification performance of the GPT-2 model with IMSLP pretraining across a range of \(K\) and \(L\) values. Figure 7 shows the performance of the same models on the full-page classification task.  
There are two notable things to point out in Figures 6 and 7. First, higher values of \(K\) lead to significant increases in model performance. For example, the performance of the GPT-2 model with \(L=0\) increases from 0.67 to 0.88 macro F1 as \(K\) increases from 0 to 3. Based on the trends shown in these figures, we would expect even better performance for values of \(K\) greater than 3. We only considered values up to 3 due to the extremely long training times. Second, test time augmentation helps significantly but only when used in moderation. For most values of \(K\), the optimal value of \(L\) is 2 or 3. As \(L\) continues to increase, the performance begins to degrade. Combining both training and test time augmentation, we see extremely large improvements in model performance: the fragment classification accuracy increases from 57.3% (\(K=0\), \(L=0\)) to 84.2% (\(K=3\), \(L=2\)), and the full-page classification performance increases from 0.67 macro F1 (\(K=0\), \(L=0\)) to 0.92 macro F1 (\(K=3\), \(L=2\)).  
The takeaway from our first analysis is clear: training and test time augmentation improve the model performance significantly and should always be used.  
Figure 6: Effect of data augmentation on fragment classification performance (fragment length 64). \(K\) specifies the amount of training data augmentation and \(L\) specifies the amount of test time data augmentation. Within each grouping, the bars correspond to values of \(L\) ranging from 0 (leftmost) to 4 (rightmost).
# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 4 Experiments
### Experimental Performance and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Task** & **DANN** & **OSDAFD** & **BWAN** & **DAN-COSFD** & **Ours** \\ \hline J01 & 95.96 & 75.22 & 74.15 & 79.52 & 90.52 \\ J02 & 70.65 & 64.92 & 69.06 & 72.30 & 87.66 \\ J03 & 64.22 & 67.58 & 57.74 & 82.00 & 89.24 \\ J04 & 64.31 & 70.39 & 61.09 & 55.35 & 86.76 \\ J05 & 65.76 & 74.55 & 84.44 & 93.02 & 87.40 \\ J06 & 69.38 & 56.87 & 72.39 & 85.32 & 94.28 \\ J07 & 70.60 & 57.85 & 65.98 & 87.59 & 95.52 \\ J08 & 53.85 & 64.63 & 73.95 & 75.46 & 91.42 \\ J09 & 66.00 & 68.43 & 77.53 & 90.73 & 94.19 \\ J10 & 55.16 & 67.95 & 69.17 & 85.71 & 95.18 \\ \hline Avg & 67.56 & 66.84 & 70.55 & 80.70 & 91.22 \\ \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy (%) of the JNU dataset. Figure 8: The accuracy of various methods applied to tasks in the JNU dataset. Figure 7: The accuracy of various methods applied to tasks in the CWRU dataset. Figure 9: The accuracy of various methods applied to tasks in the VMS dataset.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Uniform Distribution of Events  
We also tested the proposed method without a forest fire scenario (stable condition) in terms of the metrics of the average residual energy and packet delivery ratio (PDR) for various sink locations. In other words, across the entire network, events are uniformly distributed in each round of the simulations. According to the obtained results for the uniform distribution of events, the last node dies in the proposed method at about 1850 to 1900 rounds, which is superior to the state-of-the-art method [29], where this occurs at about 150 to 200 rounds. Up to 1900 rounds of the simulation, as shown in Figure 14, our protocol experiences the maximum average residual energy and consequently enhances the network lifetime compared to other methods.  
Unlike the forest fire scenarios (Figure 7), with the changing position of the sink, there is no obvious difference in the efficiency of the algorithms. However, in order to evaluate the quality of clustering links and improvements in the packet transmission, we performed the average packet delivery ratio (PDR) analysis in Figure 15 based on various sink positions. Figure 15 clearly shows that the proposed method has the highest average PDR compared to other methods.  
Figure 13: Packet delivery ratio (PDR) for various sink locations in forest fire scenarios and 40 rounds of the simulation.
## Biomedical Applications
### Antibacterial Activity  
An antibacterial compound may be used to control the growth of harmful bacteria. This action aims to prevent the spread of disease and infection, eradicate the infected host, and prevent decay and destruction of a material by bacteria. Bacterial resistance to pharmaceutical drugs is increasing; hence, there is the urgent need to screen new drugs from natural resources. Seaweed from the marine ecosystem is an important source of bioactive compounds and one of the main subjects for screening of various pharmaceutical drugs [124; 125]. Several plants have antibacterial activity, one of which is _U. lactuca_.  
Several studies have examined the antibacterial potential of ulvan, with a range of results, including high, moderate, and weak potential. Ulvan has been tested for its antibacterial against a wide range of toxic bacteria (Table 1) [76; 77; 85; 92; 93; 94; 95; 96; 97]. Ulvan has shown a moderate percentage of inhibition against _Staphylococcus_ sp. _Streptococcus_ sp., and _Bordetella_ sp. and a weak percent inhibition against _Bacillus subtilis_, _Proteus_ sp., _Salmonella_ sp., and _Bacillus cereus_, among others.  
Endophytic bacteria isolated from _U. lactuca_, identified as strains of _B. subtilis_, are active against _Staphylococcus aureus_ (1.6 \(\upmu\)g/mL) and _Enterococcus faecalis_ (0.2 \(\upmu\)g/mL). In vivo testing of the strain _Enterobacter cloacae_ is active against _S. aureus_ and _Klebsiella pneumonia_ at 0.4 \(\upmu\)g/mL [97]. Several factors influence antibacterial activity, such as the extraction procedure, the solvent used, and the physiological conditions of _U. lactuca_ itself. The antibacterial effect is also influenced by the harvest period; an antibacterial effect is particularly prominent when the plant enters the third harvest [93].  
Antibacterial testing of ulvan and _U. lactuca_ has produced variable results, with weak to moderate inhibition in some strains of bacteria. This variability may depend on internal factors such as the content of metabolites and endophytic bacteria present in the ulvan and external factors, namely the extraction procedure and harvest time.
# Self-Learning Power Control in Wireless Sensor Networks
## 4 Method  
We adopt Q-learning for Transmission Power Control (QL-TPC), where the agent is the transmitter of a point-to-point communication and the environment is the wireless channel. The WSN is modelled as an MDP, single agent system, when there is only one transmitter in the network, and as Dec-POMPD, multi-agent system when there are more transmitters.
# Numerical Calculation of Slosh Dissipation
## 2 SPH Method: The SPH-Flow Solver
### \(\delta\)-LES-SPH Scheme: Evaluation of the Energy Dissipation  
Following the analysis performed in [30; 35], the energy balance for the particle system is extended to the \(\delta\)-LES-SPH equations. For the sake of brevity, only the main terms are briefly reported in this section. The \(\delta\)-LES-SPH energy balance can be written as:  
\[\left\{\begin{array}{l}\dot{E}_{K}\,+\,\dot{E}_{P}-\mathcal{P}_{NF}=\mathcal{ P}_{V}+\mathcal{P}_{V}^{turb}+\mathcal{P}_{N}\\ \\ E_{K}(t)\,=\,\frac{1}{2}\,\sum_{i}\,m_{i}\,u_{i}^{2}\,,\quad E_{P}(t)\,=\, \sum_{i}\,m_{i}\,g\,y_{i}\,,\quad\mathcal{P}_{NF}\,=\,\sum_{i}\,m_{i}\,(\, \boldsymbol{f}_{NI}\cdot\boldsymbol{u}_{i})\end{array}\right. \tag{6}\]  
where on the left-hand side, \(E_{K}\) and \(E_{P}\) are the kinetic and potential energy of the particle system, and \(\mathcal{P}_{NF}\) is the power linked to the non-inertial forces. In Equation (6), \(y_{i}\) is the vertical position of the generic \(i\)-th particle.  
Conversely, the right-hand side of the energy balance (6) contains the dissipation terms due to the real viscosity \(\mathcal{P}_{V}\), to the turbulent viscosity \(\mathcal{P}_{V}^{turb}\), while \(\mathcal{P}_{N}\) takes into account the effect of the density diffusion and the particle shifting \(\delta\boldsymbol{u}\).  
The elastic potential energy linked to the compressibility of the liquid is negligible within the weakly-compressible assumption; hence, it is not considered in the energy balance.  
The power related to the viscous forces is directly evaluated through the expressions (5) as:  
\[\mathcal{P}_{V}+\mathcal{P}_{V}^{turb}=\frac{K}{2}\sum_{i}\sum_{j}(\,\mu+\mu_ {ij}^{T}\,)\,\,\pi_{ij}\,\boldsymbol{u}_{ij}\cdot\
abla_{i}W_{ij}\,\,V_{i}\, \,V_{j} \tag{7}\]  
where the quantity \(\mathcal{P}_{V}^{turb}\) refers to the viscous dissipation of the modeled sub-grid scales, whereas \(\mathcal{P}_{V}\) refers to the resolved scales.  
Finally, the energy dissipated by the fluid can be evaluated by integrating time into Equation (6)\[\left\{\begin{array}{l}E_{K}(t)\,+\,E_{P}(t)-W_{NF}(t)=E_{diss}(t)\,,\\ \\ W_{NF}(t)\,=\,\int_{t_{0}}^{t}\left(\sum_{i}\,m_{i}\,f_{NI}\,\cdot\,u_{i}\right) dt\,,\qquad E_{diss}(t)\,=\,\int_{t_{0}}^{t}\left(\mathcal{P}_{V}+\mathcal{P}_{V}^{ turb}+\mathcal{P}_{N}\right)dt\end{array}\right. \tag{8}\]  
where \(W_{NF}\) is the work performed by the non-inertial forces on the fluid. The first equation of (8) gives two ways for the evaluation of the energy dissipated by the fluid: (i) the first is using the left-hand side, while (ii) a second way is to use the second equation in which the three dissipation terms are integrated in time. Both these approaches were adopted in the SPH simulations in order to verify that the present model is able to close the energy balance accurately.
# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 3 The Hybrid Ontology-based Approach  
This section presents the approach used to build the recommendation system and as previously mentioned, combines the application of ontology and the KNN algorithm. Figure 1 shows the overview of our hybrid ontology-based approach, which starts with creating a user profile that contains, among other attributes, the categories and respective products bought by the user.  
In order to recommend products to the user, it is necessary to find other users (Neighbours) that at least have bought one common product, and select only the ones that, respecting the previous criteria, have also bought other products. These are the products that have the potential to be recommended to the user.  
When the user and their Neighbours' profiles are created, we apply the KNN algorithm to find the nearest Neighbours of the user. This procedure allows the extraction of products that the user has not yet purchased. The last step, is to apply the KNN algorithm to find the nearest products according to our metrics, which include the overall rating given by the respective neighbor, the price of the product for knowing whether it is between the lowest and highest value spent by the active user. We also include a text review of each product because it is a huge source to extract important information [20].  
In the following subsections, we provide a detailed explanation about each step of the proposed approach. This includes the dataset used, the ontology described, and the application of the KNN algorithm.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### High Reliability  
With the continuous increase in performance and reliability requirements of RF systems, developing high-performance switches that can meet complex communication demands has become particularly important. However, the structural characteristics of RF MEMS switches make them prone to failure [73]. In commercial applications, lifespan and long-term reliability have become major limiting factors [74]. Especially in satellite applications, where devices need to maintain stability over many years, the failure time prediction of RF MEMS switches under long-term excitation is critical [75].  
Over the past few decades, extensive research has been conducted to address these reliability issues, covering aspects like dielectric charging, mechanical creep, and contact degradation. Different failure mechanisms can affect the performance of switches, which can be categorized into three main types: (1) electrical [76; 77; 78], including charge trapping and electrostatic discharge; (2) mechanical [79; 80; 81], mainly involving degradation and creep of contact surfaces; (3) environmental mechanisms [76; 82], mainly involving the effects of humidity, environmental particles, and contaminants. Measures such as reducing the impact of shocks, lowering actuation voltages, and improving the materials and structure of contacts can significantly minimize mechanical and electrical degradation. Meanwhile, employing good packaging techniques can effectively control problems related to the environment.