esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fuzzy Logic Model (FLM)
#### 2.3.1 Inputs
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Parameter**} & \multirow{2}{*}{
\begin{tabular}{c} **Universe of** \\ **Disclosure** \\ \end{tabular} } & \multirow{2}{*}{**Linguistic Term**} & \multirow{2}{*}{**MF \({}^{*}\) Type**} & \multicolumn{4}{c}{**MF \({}^{*}\) Parameters**} \\  & & & & **a** & **b** & **c** & **d** \\ \hline \multirow{6}{*}{pH} & \multirow{6}{*}{0–14} & Extra acidic & Trapezoidal & 0 & 0 & 4.9 & 5.1 \\  & & Very acidic & Trapezoidal & 4.9 & 5.1 & 5.4 & 5.6 \\  & & Moderately acidic & Trapezoidal & 5.4 & 5.6 & 5.9 & 6.1 \\  & & Slightly acidic & Trapezoidal & 5.9 & 6.1 & 6.4 & 6.6 \\  & & Neutral & Trapezoidal & 6.4 & 6.6 & 7.2 & 7.4 \\  & & Alkaline & Trapezoidal & 7.2 & 7.4 & 7.9 & 8.1 \\  & & Very alkaline & Trapezoidal & 7.9 & 8.1 & 14 & 14 \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & \multirow{2}{*}{0–50} & Cold & Trapezoidal & 0 & 0 & 15 & 20 \\  & & Optimal & Trapezoidal & 15 & 20 & 25 & 30 \\  & & Hot & Trapezoidal & 25 & 30 & 50 & 50 \\ \hline \multirow{2}{*}{Humidity (\%)} & \multirow{2}{*}{0–100} & Dry & Trapezoidal & 0 & 0 & 65 & 75 \\  & & Optimal & Trapezoidal & 65 & 75 & 85 & 95 \\  & & Wet & Trapezoidal & 85 & 95 & 100 & 100 \\ \hline \multirow{2}{*}{Electric conductivity (\(\text{uS/cm}\))} & \multirow{2}{*}{0–1500} & Low & Trapezoidal & 0 & 450 & 550 \\  & & Medium & Trapezoidal & 450 & 550 & 950 & 1050 \\  & & High & Trapezoidal & 950 & 1050 & 1500 & 1500 \\ \hline \multirow{2}{*}{Nitrogen (mg/kg)} & \multirow{2}{*}{0–300} & Low & Trapezoidal & 0 & 106.6 & 126.6 \\  & & Medium & Trapezoidal & 106.6 & 126.6 & 177.5 & 197.5 \\  & & High & Trapezoidal & 177.5 & 197.5 & 300 & 300 \\ \hline \multirow{2}{*}{Phosphorus (mg/kg)} & \multirow{2}{*}{0–20} & Low & Trapezoidal & 0 & 4.2 & 4.8 \\  & & Medium & Trapezoidal & 4.2 & 4.8 & 8.8 & 9.4 \\  & & High & Trapezoidal & 8.8 & 9.4 & 20 & 20 \\ \hline \multirow{2}{*}{Potassium (mg/kg)} & \multirow{2}{*}{0–180} & Low & Trapezoidal & 0 & 0 & 44.1 & 54.1 \\  & & Medium & Trapezoidal & 44.1 & 54.1 & 111.6 & 121.6 \\  & & High & Trapezoidal & 111.6 & 121.6 & 180 & 180 \\ \hline \hline \end{tabular}  
* Parameter ranges were obtained from different authors, pH crisp ranges are retrieved from [44], NPK values from [45], temperature from [46], electric conductivity from [47], and humidity from [48]. \({}^{*}\)MF is the abbreviation of membership function.  
\end{table} 
Table 1: Fuzzy model inputs.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 2 Related Work
Intruder detection systems that use federated learning might be susceptible to backdoor attacks, as stated in [22]. A backdoor attack manipulates model inputs to provide erroneous predictions, a kind of poisoning attack. An autoencoder known as the variational autoencoder (VAE) is used in the IoT for anomaly detection. The experiments showed backdoor attacks may evade modern poisoning precautions in federated learning. The attacker secretly introduced malicious traffic into the training dataset for white box poisoning. Thus, the model misclassified malicious network activity as innocuous. The author of [23] used CNN to construct anomaly-based IoT intrusion detection models. Multidimensional (1D, 2D, and 3D) network architectures were used to build the CNN model. CNN and transfer learning were used for multi-class and binary classification on the BoT-IoT, IoT network intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion datasets. The study [24] introduced federated transfer learning for mobile edge computing intrusion detection. The federated learning method employs transfer teaching to speed up model training, reduce processing costs, increase communication, and categorize. The CNN model architecture was used for binary classification. The model was trained in the source domain using NSL-KDD and the target domain using UNSW-NB15 to assess performance. Researchers in [25] propose a CNN-LSTM hybrid model for DDoS attack classification. Extreme gradient boosting (XGBoost) was used to select the top 10 characteristics. This model was evaluated using DDoSA and benign data from CICDDoS2019. The experimental findings are 99.5% accurate. The author of [26] used LSTM and the CICDDoS2019 dataset to identify DDoSA. The recommended method classified DDoSAs and regular traffic using binary classification with a 98.6% accuracy. Table 1 describes the crux of the existing literature.
## 2 Data and Methods
### Penetration Electric Fields, Equatorial Electrojet, and Current Intensity
Subsequently, after subtracting the baseline values from the D component, we calculated the hourly deviations that are approximately equal to the variation in \(\Delta\)D components. The \(\Delta\)D is a good indicator of CI, and we selected magnetometer stations located close to the middle-latitudes, one in the northern hemisphere, and the other in the southern hemisphere, for each sector. Finally, we computed the daily values of \(\Delta\)D = max (\(\Delta\)D) \(-\) min (\(\Delta\)D) for the northern and southern stations, between 06:00 and 18:00 LST, from 10 February to 28 February. Both \(\Delta\)D current intensities in the northern and southern hemispheres are positive, and their sum gives a rough estimate of the total solar-quiet (Sq) CI [24].
## 2 Materials and Methods
### Antimicrobial Activity Test  
Gram-positive bacteria (_Staphylococcus aureus_ 6538P ATCC) and Gram-negative bacteria (_Escherichia coli_ 25922 ATCC and _Pseudomonas aeruginosa_ ATCC 27835) were from the Collection of Microorganisms of the Department of Microbiology and Biochemistry (Murmansk Arctic University, Russia), frozen at \(-\)70 \({}^{\circ}\)C in nutrition broth (State Research Center for Applied Microbiology and Biotechnology, Obolensk, Russia). _S. aureus_ and _E. coli_ were grown in Mueller Hinton broth (MHB, Obolensk, Russia) and agar (MHA, Obolensk, Russia); _P. aeruginosa_ was grown in brain heart infusion (BHI, Obolensk, Russia) and agar (BHIA, Obolensk, Russia).  
The broth dilution method was employed to determine the minimal inhibitory concentrations (MIC) for NADESs [28; 29]. Briefly, twofold serial dilutions of the substances ranging from 0.25 up to 125 mg/mL were prepared in threefold repetitions. An equivalent volume of the bacterial suspension with the density of 1 \(\times\) 10\({}^{6}\) CFU/mL (prepared using the McFarland standard) was added to each well. The tubes were incubated at 37 \({}^{\circ}\)C for 24 h. The lowest drug concentration, which inhibited the visible bacterial growth, was accepted as MIC. Gentamicin (0.03125-1024 g/mL) was used as the reference antibiotics (positive control). Tubes containing inoculated and non-inoculated media were prepared as growth and purity controls simultaneously. The MIC values were determined as the lowest concentrations that inhibited bacterial growth compared with the compound-free control and are expressed in mg/mL. Minimum bactericidal concentrations (MBC) were determined after seeding of the samples treated from 1/2 \(\times\) MIC up to the highest concentration on Petri dishes with agar [30]. These samples were incubated at 37 \({}^{\circ}\)C for 24 h. MBC was defined as the lowest drug concentration reducing colony growth of the initial bacterial inoculum by \(\geq\)99.9%. All tests were performed as three independent experiments, each carried out in triplicate, and the results were presented as median/modal values. According to the widely accepted norm in MIC testing, the mode and median were used for the final value calculation when triplicate endpoints were within the two- and three-dilution ranges, respectively.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### Kilpatrick's Criterion  
One of the most useful attempts to understand the law governing sparks in a vacuum was made by [PERSON] [110] in the 1950s. [PERSON] analyzed rf and DC breakdown data and found the conditions that lead to breakdown-free operation. Later, [PERSON] [111] conveniently expressed the [PERSON] results in the formula \(f=1.64\cdot E^{2}\cdot e^{-8.5/E}\), where \(f\) is the rf frequency in megahertz and \(E\) is the surface electric field in megavolts per meter. This is known as the Kilpatrick's criterion. This criterion has often been and is still used as a basis for evaluating the peak surface electric field limit, especially at frequencies below GHz where systematic data on breakdown characteristics are scarce. However, at higher frequencies this criterion is rarely used for modern designs as it is being replaced by the more precise and practical parameters described in this article.
###### Abstract  
Software defined radio (SDR) is a commonly used platform for its ease of operation and cost-effectiveness for the development and testing of real wireless communication systems. By supporting high transmission rates and enabling fast and cost-effective deployments, mainly in millimeter-wave (mmWave), the co-operative 5G network has been standardized by 3 GPP Release 16. In this paper, a decode-and-forward (D&F) co-operative hardware network is proposed as one of the key technologies for future 5G/6G wireless networks. The proposed system consists of an emulated base station processing unit (gnodeB), a D&F protocol and the user equipment (UE). In particular, the design of the D&F relay node is based on an MIMO layer 2 relay technology. A testbed based on an SDR platform and MatlabTM software, in which the physical broadcast channel (PBCH) transmission, physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), and downlink shared channel (DL-SCH) for transport channel coding, according to the 3 GPP standardized 5G downlink signal, has been designed. The key performance indicators (KPIs), namely EVM, BER, and throughput, were measured for 5G signals with 64-QAM and 256-QAM modulation schemes. The obtained results show that the D&F co-operative 5G network achieves substantially improved KPIs in the communication between the gnodeB and the UE in an outdoor-to-indoor scenario. Furthermore, it has been demonstrated that the D&F protocol presents a good performance and behavior being compared to one commercial equipment.  
decode-and-forward; co-operative 5G network; 5G-software defined radio; KPIs +
Footnote †: journal: Sensors
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Revisiting the Back-Projection Algorithm
The difference between the original HR map, \(H\), and the reconstructed HR map, \(H_{1}\), is mapped to obtain another LR map, \(L_{2}\). Finally, the two intermediate LR maps (\(L_{1}\) and \(L_{2}\)) are summed together to obtain the output: \(L_{H}\). DBPN utilizes convolution and deconvolution to implement downsampling and upsampling operations. The outputs of the up-projection and downprojection units are concatenated for the reconstruction of HR images. Unlike the original DBPN, we fuse multiple feature maps from different layers with various scales, which has been proven to be helpful for underwater image enhancement [8; 9; 10; 11]. Inspired by the back-projection algorithm, this paper proposes a multiscale back-projection feature fusion mechanism to restore the missing information of low-scale layers, effectively using the features of nonadjacent layers to improve the blur and color bias while ensuring the sharpness of the image.
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 3 Operation Principle  
The operation principle of the proposed buck-boost inverter can be explained well by the switching patterns illustrated in Figures 2 and 3. Assuming that the desirable waveform of the output voltage \(v_{c}\) is a sinusoid, which pulsates at fundamental frequency. Under this assumption, the operation of semiconductor devices will be dependent on the sign of \(v_{c}\). For the case of a positive half-wave of \(v_{c}\), \(S_{1}\) and \(S_{4}\) remain in the off-state, as shown in Figure 2. As it can be observed from Figure 2a, when \(S_{2}\) and \(S_{3}\) turn on, the dc power supply charges the inductor \(L_{f}\), and hence, the inductor current \(i_{L}\) increases linearly. Meanwhile, \(S_{6}\) remains in the off-state and the capacitor \(C_{f}\) releases its energy to supply the load \(R_{o}\). In contrast, it is clear from Figure 2b that the inductor \(L_{f}\) transforms its energy to the capacitor \(C_{f}\) and load \(R_{o}\) when \(S_{2}\) and \(S_{3}\) turn-off, together with \(S_{6}\) switched on. For the case of a negative \(v_{c}\), the operations of \(S_{2}\), \(S_{3}\), and \(S_{6}\) are replaced by \(S_{4}\), \(S_{1}\), and \(S_{5}\), respectively. From the above analysis, the proposed buck-boost inverter operates very similar to the conventional dc/dc buck-boost inverter, except for its bipolar output voltage. As a consequence, it enables both the operation of voltage step-down and boost.  
Figure 1: Schematic diagram of the proposed single-stage transtormerless buck–boost inverter system.  
Figure 2: Switching patterns for the positive half-wave of \(v_{c}\): (**a**) Inductor charged, (**b**) Inductor discharged.
## 3 Design Considerations of C-PCL System  
For a real-world situation, in a remotely located C-PCL system, three key issues are important:  
* Power of transmitted sounding signals versus their power level at the PCL antenna aperture (i.e., radio link loss at the distance from the C-PCL to the PCL);  
Figure 4: Principle of operation of proposed C-PCL system.  
Frequency conversion efficiency (IMD generation) of the low-noise transistor related to its input;
* Power of the IMD products emitted back by the PCL antenna and received by the C-PCL antenna (i.e., radio link loss at the distance from the PCL to the C-PCL).  
The first issue listed above is related to the design and calculation of the transmitted power and antenna gain for the C-PCL transmitter.  
The second issue is related to the nonlinear effect in a low-noise transistor during the two-tone excitation at enough input power. In a real case, there are external signals that are continuously received by the LNA which are emitted by transmitters of opportunity and are further processed in the PCL circuitry. When sounding signals appear at the input of a PCL receiver, multi-tone excitation occurs and preferably the sounding tones' power should be dominant. The efficiency of IMD generation is power-dependent. That is why the power of the sounding signals entering the LNA input is so important. The type of transistor in a real situation is unknown; therefore, the only way to ensure that the IMD are to be generated is to emit an adequate, previously predicted, amount of power.  
The third issue is related to the design and calculation of reverse radio link efficiency. The PCL antenna serves now as a transmitting antenna and the only way to have a dimension of freedom in the C-PCL design is to assume that there is another receiving antenna matched to receive IMD frequencies. The gain of that antenna poses an additional variable of the considered design.  
Yet another issue is to ensure that the frequency of transmitted sounding signals corresponds to the input band of a PCL that is planned to be detected. In the case of a lack of preliminary knowledge about the type of PCL, the solution is to use frequency-swept sounding signals. However, this technique may require a wider bandwidth of the receiver, which results in more noise power received.  
The design and simulation of the C-PCL performance gives as a result the requirements for the transmitted power, antenna gains and distance of detection of a PCL.  
The analysis of the sounding signals power and antenna gains may be performed only after gaining the knowledge of the IMD generation efficiency.  
This issue was verified experimentally in this work with the use of a dedicated measurement setup.
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 2 Seismic Behaviour of Infilled RC Structures: Definition of Damage Type
### Representative Damage Type Observed in RC Buildings Due to Earthquakes
#### 2.1.9 Damage Type 9: Damage in Secondary Elements (Cantilevers, Stairs, etc.)  
After the last major earthquakes, the damage observed in the infilled RC structures was also reported and linked to secondary elements such as stairs and/or cantilevers. In the case of stairs, some partial collapses were observed and were related to the absence of proper design and/or poor detailing combined with constructive errors and no connection of the stairs to the primary structural system. Examples of this type of damage are shown in Figure 17a,b. Concerning the balconies and cantilevers, some collapses and excessive deformation were caused by the vertical accelerations (Figure 17c,d).  
Finally, it is vital to highlight the poor performance and collapse of masonry parapets, as shown in Figure 18. [PERSON] et al. [15] reported that the failure of these elements was the primary cause of fatalities in the Lorca (Spain) earthquake, since the parapets fell from the top of the buildings during the quake, hitting people that were in the streets. It was pretty common to observe parapets not linked to the structure, which resulted in their high out-of-plane (OOP) collapse vulnerability. This problem could be mitigated by designing an effective parapet connection to the structure and preventing its collapse.  
Figure 16: Damage type 8—example of bounding effect after the Lorca, Spain 2011 earthquake: (**a**) adjacent buildings with different heights; (**b–d**) cumulative stresses and cracking in common points among adjacent buildings.
## 4 Discussion
### Correlation Between Maximal and Submaximal Performance  
Our third hypothesis, which expected a positive correlation between performance in the maximal and submaximal zones, was largely supported by the data. H\({}_{0}\) was rejected, as strong positive correlations were seen for parameters like posterior tilt, NAH forward rotation, AH down tilt, and NAH down tilt (coefficients \(\geq 0.83\)). AH forward rotation showed a moderate correlation (0.70), while anterior tilt had the weakest but still a fair correlation (0.31), which is consistent with guidelines for interpreting correlation coefficients [31]. This consistency shows that performance in submaximal conditions can predict performance in maximal conditions, which is important for creating effective rehabilitation plans. These results align with studies such as [PERSON] et al. (2020), which demonstrated that both maximal and submaximal exertions impose a comparable neuromuscular burden, supporting the idea that performance remains stable across strength levels [24]. Similarly, [PERSON] et al. (2022) found no significant performance decline between submaximal (around 50% MVC) and lower weight conditions in crate-lifting tasks which is consistent with our findings that submaximal assessments can predict maximal performance in functional tasks [25]. Further, one study suggests that individuals adjust their neuromuscular responses according to anticipated loads, even in submaximal, non-fatiguing conditions, leading to movement patterns similar to those observed in maximal lifts, further corroborating our results [32]. This study reveals for the first time the relationship in performance between maximal and submaximal effort in a bimanual lifting task. These findings largely reject our H\({}_{0}\) hypothesis, indicating that monitoring performance in submaximal conditions can reliably predict performance under maximal conditions for most directions measured. In clinical implementation, the strong positive correlations for most positioning parameters suggest that submaximal performance can reliably predict maximal performance. Assessing submaximal movements in patients could provide valuable insights into their capacity for maximal levels of force control.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Stages in Pre-Processing
#### 5.3.1 The Step of Filtration  
Filtration removes unnecessary or useless material from the data, making the datasets simpler to use and comprehend. It distributes the remaining data and reorganizes it into classified groupings.
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy
### Routing Metrics  
There are five main routing metrics used for selecting routes in FANETs:  
* Mobility metrics, such as speed, distance, and angle of arrival. This metric helps to achieve two main objectives in FANETs, namely enhancing route stability (O.1) and enhancing routing performance and QoS (O.3) because nodes with similar speed or angle of arrival are selected as part of a route to prolong the route lifetime.
* Link expiration time, which depends on the distance between two UAVs. Longer link expiration time increases the link lifetime and, hence, the route lifetime. This metric helps to achieve two main objectives in FANETs, namely enhancing route stability (O.1) and enhancing routing performance and QoS (O.3) because (a) nodes with a higher number of neighbor nodes, which tend to have higher link expiration time, are selected as part of a route to increase route stability and (b) a new route can be reestablished before a link expires based on the link expiration time to ensure smooth network operation.
* Geographical location can be obtained from GPS. In general, nodes are spaced far apart, and so, physically closer neighbor nodes are selected as part of a route; however, this may increase the number of hops of a route. This metric helps to achieve two main objectives in FANETs, namely enhancing route stability (O.1) and enhancing routing performance and QoS (O.3)because physically closer neighbor nodes can increase route lifetime and reduce route updates, discovery, and maintenance.
* Residual energy can be used to reduce routing overhead incurred in route discovery and maintenance caused by link disconnections and network partitions, contributing to lower energy consumption. This metric helps to achieve three main objectives, namely enhancing route stability (O.1), enhancing network coverage (O.2), and enhancing routing performance and QoS (O.3) (e.g., higher throughput with reduced route discovery and maintenance) because neighbor nodes with higher residual energy are selected as part of a route to improve network connectivity among UAVs.
* Node identity, such as node ID, can be used to select intermediate nodes, which receives packets and forwards them towards the destination, as part of a route in a random manner. This metric can be used along with the aforementioned routing metrics to achieve routing objectives.
## 3 The Proposed Approach
### Adjustment of the Reliability Model
#### 3.1.2. Improvement Factor Parameterization  
The preventive maintenance action performed at time \(T_{j}\) with intervention level \(s_{j}\) promotes a reduction in system failure intensity for all maintenance cycles. To represent the improvement factor, an exponential function that expresses the factor value regarding the intervention level of the maintenance is used, defined as Equation (6):  
\[a\big{(}s_{j}\big{)}=1-EXP\big{(}-s_{j};\theta\big{)},\ j=1,\ldots,c \tag{6}\]  
So, the adjustment of the reliability model is performed by obtaining the value of the parameters \(\theta\), \(\lambda\), and \(\beta\), as defined above. Here, the traditional maximum likelihood estimation method has been adopted, as described in the following subsection.