esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results and Predictions on Test Pictures
The test accuracy indicator obtained from each case evidenced an enhancement as the dataset size increased, as shown in Figure 6. This trend suggests that by increasing the size of the training set, a more accurate model could be achieved. To perform a comparison between the best models found for SVM and deep-learning, the following indicators were defined. \[\text{Precision}=\frac{\text{tp}}{\text{tp}+\text{fp}} \tag{2}\]  
\[\text{Recall}=\frac{\text{tp}}{\text{tp}+\text{fn}} \tag{3}\]  
\[\text{F}_{1}=2\ \frac{\text{precision-recall}}{\text{precision}+\text{recall}} \tag{4}\]  
where tp is the number of true positives, fp the number of false positives and fn the number of false negatives. The deep-learning model has a better average result on accuracy, recall and F\({}_{1}\) score, whereas the SVM model presents a better average precision, as shown in Table 4. This means that the deep-learning model is less restrictive, generating a lower number of false negatives, which leads to a 5.16% higher recall. However, as it is less restrictive, more false positives are also found, which lowers the precision to 2.19%. Taking into account the accuracy and F\({}_{1}\) score, it can be determined that the best model is the deep learning one. Moreover, the percentages of the test pictures that have the best result with each model have also been obtained for each indicator and presented in the 'Best model' row of Table 4. In this case, the best result is achieved by the SVM model except for recall. However, as it can be seen in the following row, the percentages of best model vary significantly in favour of the deep-learning model if it is considered to be the best model with a higher result or up to 3% lower. In contrast, if we proceed in the same way by favouring the SVM model with the same percentage, the results are not improved so substantially. These improvements derived from the 3% favouring in each case are summarised in the last row of Table 4. The enhancement in the percentage of test pictures that obtain their best result for each model, on average for all indicators, would be 15.18% for SVM and 42.86% for deep learning. These results serve as an argument to define the deep-learning model as a priority when it comes to improving it in future work, since a small improvement in the indicators (3%) would lead to a significant improvement in the comparative results with the SVM model, in terms of percentage of best model (42.86%). Figure 6: Deep-learning test accuracy vs. number of training images. In Figure 7, an example of the image segmentations made by the models for a test picture is shown. The segmentation mask of the model is overlaid on the original picture, assigning the green and yellow colours to \"leaf\" and \"non-leaf\" classes, respectively. The mask of the model's errors is overlaid on the original picture, assigning blue to the false positives (they are not leaves, but have been classified as such) and red to the false negatives (they are leaves, but have not been classified as such).
## 2 Materials and Methods
### Multivariate Analysis  
Partial least squares regression (PLSR) was used to develop tomato quality prediction models using the Pirouette software (Infometrix, Bothell, WA, USA). The NIR spectra were normalized and mean centralization. The second derivative and orthogonal signal correction were applied to correct baseline shifts, decrease noise regions and evidence specific absorption bands. The PLSR models were built by using the reference values as dependent variables and the NIR spectral data as independent variables. To calibrate, validate (cross-validation), and predict (external validation) the PLSR model's data were randomly separated in two sets, 80% of samples were used in the calibrate and validate (cross-validation) step and the remaining 20% to predict and validate the performance of models externally. Cross-validation by using leave-one-out algorithm was applied to do an internal validation in each calibration model. In the leave-one-out cross validation approach, each spectrum is dropped from the data set, and the remaining spectra are used for prediction of left-out data point until every spectrum is left-out once. The prediction set consisted of a blend between tomatoes analyzed at the laboratory and samples by which spectra were collected in the field.  
Model performances were reported in terms of correlation coefficient of cross validation (r\({}_{\text{v}}\)), the correlation coefficient of prediction (r\({}_{\text{pred}}\)), the standard error of cross validation (SECV), and the standard error of prediction (SEP) that shows the magnitude of error expected when the concentration of the independent/unknown samples are predicted, and number of factors.
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WWAN-Based IoT  
First of all, we will review energy-conserving solutions for IoT using a representative WWAN, _i.e._, 3 GPP LTE. These solutions include: overload control for the access and core networks and energy-efficient radio resource (time, frequency, and transmit power) allocation, paging cycle or DRX cycle optimization, MTC-specific energy conserving, resolving gateway-device synchronization, and handling multiple heterogeneous radio interfaces.
## 2 Materials and Methods  
The method used to analyze the results of the EEG signal output is power spectrum density (PSD). The dimension used by PSD is power per Hz. PSD is usually shown for spectrum continuity [52; 53]. The frequency domain is based on the approximate spectrum density of the signal power and is calculated using a periodogram or parametric methods [54]. A short-time Fourier transform (STFT) is used to convert EEG signals into spectrum domains. After converting a signal to the frequency domain using a STFT, the power spectrum density (PSD) can be calculated. PSD is an important signal quality in the frequency domain that represents the contribution of each frequency component to the overall signal segment's intensity [55]. Since the noise type and level affect how noise impacts brain signals and cognitive function, this study focused on two types of noise, namely continuous noise and intermittent noise, with noise levels below 85 dB, namely 55 dB, 65 dB, 70 dB, 75 dB, 80 dB, and 85 dB levels. In this study, the determination of noise level as an independent variable is based on a previous study that categorized noise levels into three categories: low (55 dB, 65 dB), medium (70 dB, 75 dB), and high (80 dB, 85 dB) [16; 25; 35; 49].  
A schematic overview of this experiment is presented in Figure 1. The experiment was followed by a paper-based survey to assess the overall experiment and identify any difficulties faced during the experiment. The whole experiment lasted for an average of 210 min per participant. Participants were allowed to stop and leave the experiment in the case of any difficulties encountered while experiencing distress.
# Analysis and Optimization of Output Low-Pass Filter
###### Abstract  
In this study, the design of output low-pass capacitive-inductive (CL) filters is analyzed and optimized for current-source single-phase grid-connected photovoltaic (PV) inverters. Four different CL filter configurations with varying damping resistor placements are examined, evaluating performance concerning the output current's total harmonic distortion (THD), the power factor (PF), and power losses. High-frequency harmonics are effectively attenuated by a second-order CL filter with the damping resistor placed parallel to the filter inductor. In addition, this filter type achieves the best performance by minimizing power loss. A systematic design methodology using filter normalization techniques allows to determine the optimum filter parameters based on the specified cut-off frequency (500 Hz), power loss (5% of rated power), and target THD (<5%). The analysis, simulations, and experiments show that under various operating conditions, this approach meets the grid connection standards (current THD < 5%, power factor between 0.8 leading and 0.95 lagging) while improving efficiency.  
low-pass filter; photovoltaic cells; pulse-width modulation inverters; solar power generation +
Footnote †: journal: _Applied Sciences_
## 2 Methodology
### Evaluation Metrics
#### 2.5.1 Viewed Snow Coverage Percent  
The first metric is the snow coverage percent within a certain interval of time, i.e. 1-day, 3-day, and 30-day periods. The three different periods represent time to respond to the daily, synoptic scale, and seasonal variations, respectively [54]. To investigate the viewing effectiveness, as a single sensor or part of a sensor constellation, we calculated the normalized snow coverage percentage that is viewed (\(P_{stow}\)) over the northern hemisphere terrestrial environment as:  
\[P_{stow}=\frac{\bigcup_{i=1}^{n}[A(\vec{x},t)_{viewed,i}\cap A_{snow}(\vec{x },t)]}{\int A_{snow}(\vec{x},t)d\vec{x}}\times 100\%, \tag{2}\]  
where \(A_{snow}(\vec{x},t)\) refers to the dynamic snow-covered terrestrial area during the study period (defined by IMS), \(A(\vec{x},t)_{viewed,i}\) is the terrestrial area that is viewed by the sensor \(i\), and \(\int A_{snow}(\vec{x},t)d\vec{x}\) is the total snow-covered area for a given day in space (\(\vec{x}\)) and time (t).  
The symbol \(\bigcup_{i=1}^{n}\) denotes the union of areas viewed by satellites from 1 to n that compose a given constellation. The symbol \(\cap\) represents the intersection.
# New Miniature Narrow Band Microstrip Diplexer for Recent Wireless Communications
## 5 Conclusions and Future Work  
In this paper, a miniaturized microstrip diplexer with dual channels is developed. This diplexer is built with a linked junction and two BPFs that operate in different frequency bands. Each filter is made with UIR, SIR, and feed lines. To determine the diplexer's response, a Sonnet EM solver was used. The diplexer's Tx/Rx channel resonances are 2.84 and 4.08 GHz, providing at least 35 dB of isolation between the two channels. The proposed diplexer has excellent electrical characteristics, negative group delay values, and a small size of 24 \(\times\) 18 mm\({}^{2}\), making it suitable for use in various wireless systems. The bandwidths magnitudes are 40 and 90 MHz for Tx and Rx bandpass filters, respectively, which are highly narrow bandwidths. These narrow frequency responses are effective in preventing interferences in adjacent bands. This research can be extended as future work to take up one of the following suggestions. One, the diplexer presented in this study could be upgraded to triplexers in order to support three frequency bands. Two, two extra channels can be added with dual bands for each channel to get four different frequency ranges for a wireless system.  
Methodology, Y.S.M.; Software, Y.S.M.; Validation, Y.S.M.; Resources, K.A.-M.; Writing--original draft, Y.S.M.; Visualization, K.A.-M.; Supervision, K.A.-M.; Project administration, K.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 5 Technologies for Autonomous Water Basin Mapping and Monitoring
### Issues Related to Robotic Systems
#### 5.2.4 Operation and Maintenance  
Even if FPV maintenance and operation costs are still not well assessed [127], technologies for monitoring water quality and preventing ecological problems are specifically necessary for floating solar projects [134]. As [135] reminds us, further improvements in FPV technologies are needed, in particular in the design of floating structures, instrumentation, and monitoring systems, the most important challenges concerning safety and standardization issues, such as the type of body structure.  
Regarding monitoring in general, swarm robotics deserve a mention. This concept could be applied to underwater environments, allowing multiple small robots to work collaboratively. These systems could cover large areas efficiently and could provide redundancy in data collection. A swarm of robots needs an additional control layer to determine their geometric configuration. This requires robots to know each other's distances using both acoustic and optical techniques [136; 137]. Ad hoc methodologies to minimize the exchange of information between robots have been developed [138].  
More generally, for operations of monitoring and maintenance performed by ASVs, ROVs, AUVs. and UAVs, a combination of them is desirable, but this is not so easy to achieve. As mentioned by [139], with the increasing complexity of underwater operation tasks, in fact, it is difficult for a single underwater drone to perform complex tasks, especially in an often-unknown underwater environment. However, more and more, AI and machine learning together enable adaptive sampling strategies, allowing robots to adjust their sampling locations and rates based on real-time data and environmental conditions. Predictive modelling could be used to anticipate potential water quality issues or failure issues, enabling the implementation of robots to better investigate the environment. Machine learning algorithms, for example, could be implemented to detect anomalies in water quality data, triggering specific autonomous investigation missions with ROVs, AUVs, or other types of drones or a combination of them.  
Looking ahead, future studies should explore and develop automated data monitoring and recording systems for FPV installations in order to improve the efficiency and accessibility of data collection [135].
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 4 Photocatalytic Properties  
### Photocatalytic Activity of Methyl Orange  
The PCA of the synthesized Cr-doped ZnO samples was examined using UV-photode gradation of MO as a function of time. Figure 6 displays the variations in the concentration ratio (C/Co) as a function of time during the PCA reaction. Here, C and Co are the concentrations of the MO molecule at different times of reaction (t) and t = 0, respectively. This ratio refers to the photodegradation of the absorption band of MO positioned at \(\sim\)464 nm [11]. The ZnO-Cr\({}_{1}\) sample exhibited the best catalytic activity with degradation of MO, where it reached 99.8% after 100 min of the reaction. The degradation of MO was about 77.5%, 58.97% and 50.1% after 120 min for the samples ZnO-Cr\({}_{0}\), ZnO-Cr\({}_{3}\) and ZnO-Cr\({}_{5}\), correspondingly. Here, the high PCA observed for the ZnO-Cr\({}_{1}\) sample could be assigned to a possible high-density defect in the nanorods' structure [49; 50]. This increases the density of the photoinduced electron traps [51].  
Figure 5: PL spectra of undoped and Cr-doped ZnO nanorods using an He-Cd laser of 325 nm excitation wavelength.  
The photodegradation time is quite long even for the ZnO-Cr\({}_{1}\) sample (100 min to obtain 99.8% degradation). We studied the PCA reaction without adding any promotion factor for the reaction such as the addition of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) or even adjusting the pH value of the reaction. Indeed, the use of H\({}_{2}\)O\({}_{2}\) could increase the \(\bullet\)OH radicals in the reaction where they react with dye molecules (i.e., MO) to become a major factor in the degradation process [19]. In addition, controlling the acidic level (pH value) has a significant effect on the degradation process parameters. It provides a fast decomposition of the H\({}_{2}\)O\({}_{2}\), and also removes the formed inorganic elements derived from the decomposition of the dye molecule [22]. Here, we limited our study to the effect of photocatalytic nanorods, without adding any catalytic factors, i.e., H\({}_{2}\)O\({}_{2}\) and pH level, on the photodegradation reaction. This allowed us to avoid any hazardous effect on the environment. Thus, we designed sample prototypes that could be used in eco-friendly photocatalytic reactions.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 2 Methods
### Loss Function  
Velocity modeling is approached as a regression problem. As such, the loss function used in this paper is the Mean Squared Error (MSE) function. Its mathematical formula is expressed as follows:  
\[m(y,\widetilde{y})=\frac{1}{n}\sum_{i=1}^{n}\left(y^{(i)}-\widetilde{y}^{(i)} \right)^{2} \tag{5}\]  
Figure 1: Architecture of the modified Attention U-Net neural network.  
where \(n\) represents the sample size, \(y\) represents the predicted results, and \(\widetilde{y}\) represents the true values. A smaller loss function indicates greater proximity between the predictions and the true values.
## 5 Grasp
### Lifetime GRASP
#### 5.2.1 Construction Phase  
As it has been said, the construction phase for the lifetime model follows the exact same approach as the latency model. Algorithm 2 is, therefore, also valid for this model. The reason behind such decision relies on the fact that the more hops a solution contains, the more energy is consumed and, thus, the lifetime is not as high as it could have possibly been. For calculating the overall network lifetime, let us consider a node \(i\), with maximum battery capacity of \(3J\). Let \(f_{ij}\) be the amount of connections that node \(i\) redirects to node \(j\). Since every node only generates its own connection, it can be rapidly seen that \(f_{ij}-1\) is the amount of connections that node \(i\) receives. The following formula calculates the lifetime, or amount of time steps \(t\) that node \(n\) can hold up without rendering out of energy:  
\[Lifetime=\frac{3*10^{9}}{922*(f_{ij}-1)+T_{ij}*f_{ij}}, \tag{3}\]  
where \(922\,n\,l\) stands for the amount of energy needed for receiving one packet and \(T_{ij}\) makes a reference to the amount of energy needed to transmit from node \(i\) to node \(j\) according to their distance, as specified in Table 1.  
The formula is divided into two parts: energy needed to receive and energy needed to retransmit. Since the energy needed to receive can be seen as an overhead, we concluded that a network with minimum hop count holds the lowest possible retransmissions and thus the additional overhead paid for having to receive packets is minimal. In some cases, this statement could be wrong: according to the energy and range values seen in Table 1, in very specific scenarios, it can be calculated that the amount of energy for doing two hops plus the energy needed to receive allows the network to reach the same distance with lower energy consumption than one single hop. However, this constructive algorithm provides a very good starting point and the local search is responsible for improving it by correcting such scenarios.  
The overall network lifetime is the minimum among all the node lifetimes since this value represents the amount of time steps until the first node exhausts its battery.
## 3 WBAN Requirements
### Security  
WBANs must incorporate adequate levels of security and privacy to protect the sensitive nature of personal and medical related data. WBANs are vulnerable to the same forms of attacks as other wireless networks such as eavesdropping and Denial of Service (DoS). These threats can expose a WBAN to serious security vulnerabilities and affect its performance at the physical, data link, network, and transport layers [18]. Patient data must be stored locally and securely transmitted to the server side in the context of transmitting data to a remote health professional. WBAN designers need to ensure that the processes for system setup and day to day operation do not introduce security risk as these tasks will be undertaken by non technical personnel. Efficient cryptographic techniques need to be used to mitigate the risk of security breaches but must not add significant overhead to packet size or transmission times.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Rst-Flare
#### 2.3.1 Flaring Sites Detection
Finally, it is worth stressing that, in this paper, given the spatial resolution of MODIS data (i.e., 1 km\({}^{2}\)), with the term \"flaring site\" we do not refer to individual single gas flare but to a zone affected by the presence of oil processing facilities, that may possibly include a single or a group of flare systems.
## 5 Results and Discussion
### Spatial Analysis  
#### 5.3.1 Spatial Distribution  
We visualized the EB estimated crash frequency map in Figure 5(a) Since the range of the crash frequency estimated by EB (CF-EB) varies from 0.13 to 13.25, we divided it into 14 intervals, assigning a unique color to each interval. To facilitate comparison, we created a parallel visualization in Figure 5(b), which ranks the CF, assigning a lower rank to grids with lower CF. The ranks are then divided into ten intervals (each encompassing 10% of the grids), with each interval given a unique color.  
Figure 6 reveals two prominent patterns. First, grids encompassing linkage roads of bridges and tunnels exhibit a higher CF-EB, a finding that echoes previous research [64]. This is likely due to the complexity of the road network at these locations, with more frequent merging and diverging points leading to more frequent lane changes by drivers. This increased complexity can augment the potential for driver errors, contributing to higher safety risks in these areas. Additionally, significant traffic disruptions at these locations may heighten the likelihood of conflicts among motor vehicles, pedestrians, and bicyclists [64]. Second, high-risk grids predominantly line the avenues from 5 th Ave to 8 th Ave. These avenues are characterized by high pedestrian activity due to the concentration of commercial establishments, public transit access points, and other urban amenities. The heightened pedestrian activity, coupled with vehicular traffic, can escalate the potential for conflicts and safety risks.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### Resource Consumption  
In this experiment, we compare the total allocated resources by the placement algorithms in terms of CPU and memory.  
Analyzing Figures 9 and 10, we realize that our approach outperforms the other approaches by allocating less CPU and memory to meet a given number of SFC requests. Again, the VNF sharing mechanism allows our approach to be a resource-allocation efficient.  
This observed behavior of our proposed placement strategy also corroborates with the previously presented experiment. It shows that, besides being energy-efficient in terms of less nodes being used, our approach is also energy-efficient in terms of less resource consumption of edge nodes.