esa-sceva/satcom-chunk-collection huggingface dataset:
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 2 Materials and Methods
### The Proposed Approach
#### 2.4.3 The Ensemble Model Classifier  
As previously mentioned, the proposed approach uses an ensemble method to boost the performance of a set of estimators. In particular, a soft-voting strategy has been applied and assessed on ensembles of Inception-V1 and Inception-V3 models. It should be pointed out that generally, soft voting is applied to homogeneous ensembles. For heterogeneous ensembles, the class probabilities generated by different type of learners cannot be compared directly without a careful calibration [24]. Starting from this consideration, ensemble classifier models have been separately built on Inception-V1 and Inception-V3.  
The individual models have been built tuning a single hyperparameter of the networks. Each model has been built to be able to analyze a 3D CT scan to generate a distribution probability according to three possible classification labels (i.e., Normal, NCP and CP) (see Figure 7).  
Then, the ensemble classifier model combines the output of each individual model through a decision fusion layer to provide the final output (see Figure 8). In particular, each ensemble consists of five models obtained tuning the dropout value of the last (fully connected) layer of the networks. Dropout [28] refers to a technique to prevent overfitting issue in neural networks. Basically, it works by ignoring a set of randomly selected neurons during the training phase. In so doing, it forces a neural network to learn more robust  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Total** & **Train** & **Val.** & **Test** \\ \hline Normal & 966 & 696 & 77 & 193 \\ CP & 1523 & 1093 & 122 & 308 \\ NCP & 1534 & 1107 & 123 & 304 \\ Total & 4023 & 2896 & 322 & 805 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of chest CT scans used for the training set, validation set and test set for patients without lung lesions (i.e., Normal), with common pneumonia (i.e., CP) and with novel coronavirus pneumonia (i.e., NCP). The last row reports the overall size of the dataset.  
features that are useful in conjunction with many different random subsets of the other neurons [29]. The hyperparameter used to control the dropout is used to specify the probability that neurons at a given layer are dropped. The five models of each ensemble have been built setting this hyperparameter to 0.0 and ranging it from 0.3 to 0.6 with a step size of 0.1.
## 1 Introduction
However, environmental concerns must be taken into account while deciding on the best strategy to prevent worsening the problem rather than solving it. Life cycle assessment is a cradle-to-cradle or cradle-to-grave analysis approach to evaluate the associated environmental impacts with all products' life stages, from raw material extraction to processing, production, usage, and disposal [15]. The inputs and outputs for each activity within a stage are computed and then agglomerated over the life cycle using energy and material balances drawn over the system boundary [16]. The associated environmental impact of a particular AC varies since activated carbon can be made from a variety of carbonaceous materials through chemical or physical activation or by both processes. [PERSON], [PERSON] et al. studied an LCA of an AC produced from cocoa pods in which they used KOH as an activation agent. It was reported that the main contributor to environmental impacts in the laboratory steps is electricity, with an average contribution of almost 70% [17]. [PERSON] et al. also studied the AC production from olive-waste cake by using H\({}_{3}\)PO\({}_{4}\) as an activation agent. It was reported that using H\({}_{3}\)PO\({}_{4}\) as an impregnating agent has the highest environmental impacts of most of the tested indicators [18]. Similarly, [PERSON], [PERSON] et al. also investigated the AC production from corn pericarp by utilizing KOH as an activation agent. They found that the main contributor to the environmental impact of AC production is fossil depletion, which is related to the KOH production used as an activation agent [19]. The primary objective of this study is (1) to estimate the possible ecological burdens of the olive tree-based AC production, (2) to examine the environmental impacts of the AC production by using H\({}_{3}\)PO\({}_{4}\) activating agent, and (3) to analyze the ecological footprint of each process involved in the production of the AC from olive tree. To the best of our knowledge, no study on the life cycle assessment of olive trees and _Lantana canara_ has been done. Therefore, this article could be beneficial for researchers and industrialists for the preparation of activated carbon from olive trees and _Lantana canara_.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 2 Wind Farm Contribution in Frequency Regulation
Coordination between wind farms and conventional power plants in terms of frequency control is also investigated in order to reach more efficient frequency response through wind farms. In References [39; 40], it is shown that the provided inertial response from DFIG wind farms is only efficient for low level of penetration. Authors have proposed an inertial coordination strategy between the wind farms and conventional power plants in a high level of penetration, which has shown improvement in frequency performance compared to uncoordinated strategy. In Reference [41], it is shown that the inertial response coordination between conventional power plants and wind farms can help the system to reach efficient frequency response in terms of smoothing tie-line fluctuations, reduced peak frequency excursion and settling time. The literature shows also that the wind farms can contribute in the frequency regulation of the power systems by different strategies and at different stages of the frequency regulation. Most of the abovementioned studies covered by the inertial response and primary control of DFIGs to contribute in primary frequency control of power system. However, the secondary frequency control or LFC as a part of AGC is not well addressed for the wind farms.
## 4 Conclusions  
Dark fermentation proved capable of producing valuable products from two solid waste streams, used DNs and EFPs. The distribution of the substrate chemical energy was affected by the process pH, presenting the opportunity of favoring the most preferred product through pH adjustment. While maximum hydrogen production was observed at pH 6 (4.05 \(\mathrm{NLH_{2}}/\mathrm{L_{reactor}}\)) and VFAs at pH 7 (13.44 g/L), the cost of NaOH had a significant impact on the net economic potential, with the best results at pH 4.5, with minimum NaOH addition (181 EUR/ton VS of substrate, 138 EUR/ton VS of substrate, and 296 for valorization of volatile fatty acids through microbial fuel cell, biodiesel production, and anaerobic digestion respectively), and pH 6 with moderate NaOH addition and increased amounts of VFAs and hydrogen gas (191 EUR/ton VS of substrate, 142 EUR/ton VS of substrate, and 339 EUR/ton VS of substrate for valorization of volatile fatty acids through microbial fuel cell, biodiesel production, and anaerobic digestion respectively).  
Conceptualization, D.Z. and K.T.; methodology, D.Z., M.P. and K.T.; validation, D.Z., K.T. and P.T.; formal analysis, D.Z. and K.T.; investigation, D.Z., K.T. and P.T.; resources, M.K.; data curation, D.Z., M.P. and K.T.; writing--original draft preparation, D.Z., K.T. and P.T.; writing--review and editing, D.Z., K.T. and M.K.; visualization, D.Z. and K.T.; supervision, C.Z. and M.K.; project administration, C.Z.; funding acquisition, C.Z. and M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Project WASTE4 THINK (H2020-GA 688995) \"Moving towards Life Cycle Thinking by integrating Advanced Waste Management Systems\".  
Not applicable.  
Data Availability Statement: Data is contained within the article, in Section 3.  
The authors declare no conflict of interest.  
Figure 4: Economic potential per ton VS of substrate, recoverable through dark fermentation followed by different VFAs exploitation methods.
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 1 Introduction
The approach utilizes the robust propagation characteristics of the spatially incoherent light beam comprising the speckle field and exploits the properties of the speckle field to encode and transmit the OAM modes with various topological charges. A spatial light modulator (SLM) is employed for the sequential encoding of various modes, and the information of the OAM modes is recovered from the correlation of spatial intensity fluctuations of the speckle field at the detector plane. Moreover, a simultaneous detection of amplitude and phase of the OAM modes is achieved by adopting the holography scheme by superposing an independent reference random field with the speckle field of the spatially incoherent source. The complex field recovery of OAM modes provides an additional advantage of determining the sign of the topological charge along with the amplitude, which makes the potential realization of the approach in the applied domains of optical storage and communication. Applicability of the proposed technique is theoretically investigated and experimentally demonstrated by recovering the complex correlation functions bearing various OAM modes.
###### Abstract
In this research, a new optimization method named remora optimization algorithm (ROA) [26], with a low number of control parameters, is applied for MPPT solving. The reasons for using the ROA to solve the MPPT problem are the high power of the ROA in preventing trapping in the local optimal, high accuracy in achieving the global optimal, and also its simplicity. In addition, the low number of control parameters of this algorithm is another reason for using this method in MPPT solving (see Table 1). In this study, a new meta-heuristic algorithm named the ROA is applied to track the PV system GMPP in different PSCs. The studied system includes the PV configuration, the DC/DC boost converter, and the load and MPPT control system. Its output power is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Control Item** & **Value** \\ \hline \multirow{3}{*}{Particle swarm optimization [18]} & Social parameters & (C1 = 2, C2 = 2) \\  & Weights of inertia & 0.1–0.9 \\  & Velocity & 10\% of dimension value \\ \hline Multi-verse optimizer [25] & Exploitation accuracy (p) & 6 \\ \hline Grey wolf optimizer [20] & Convergence control, a & From 0 to 2 \\ \hline \multirow{2}{*}{Bat algorithm [19]} & Q\({}_{\text{min}}\) and Q\({}_{\text{max}}\) & 0 and 2 \\  & A and r & 0.5 and 0.5 \\ \hline Flower pollution algorithm [21] & Probability parameter, p & 0.8 \\ \hline Crow search algorithm [3] & p\({}_{\text{a}}\) & 0.25 \\ \hline \multirow{2}{*}{Moth flame optimizer [10]} & a & \([-2,-1]\) \\  & b & 1 \\ \hline \multirow{3}{*}{Manta ray foraging optimizer [25]} & a & \([0,1]\) \\  & w & \([0,1]\) \\  & r1, r2 and r3 & \([0,1]\) \\ \hline \multirow{3}{*}{Atom search optimization [23]} & Mining capability p & 1/6 \\  & Random parameters r1, r2, r3 \& r4 & \([0,1]\) \\ \cline{1-1}  & Contrast parameter H & 0.5 \\ \hline \multicolumn{2}{c}{Remora optimization algorithm (ROA)} & Remora factor C & \([0,0.3]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Control parameters of different algorithms. calculated based on the sampled voltage multiplied by the calculated PV current at each time and in proportion to its difference with the global peak power, the DC/DC converter duty cycle is optimally determined by the ROA to extract power in these conditions and transfer the PV maximum power to the load. In this research, the superiority of the ROA is examined and compared with the manta ray foraging optimizer (MRFO) [25] and particle swarm optimization (PSO) in different standard and PSCs conditions. The simulation results include the current-voltage and power-voltage characteristics of the PV system drawn in each of the PSCs as well as the PV power and voltage variations based on the proposed method in the different PSCs. In Section 2, PV cell and PSC is modeled. The proposed MPPT method based on the ROA is formulated in Section 3. The simulation results are explained and discussed in Section 4, and finally, conclusion is described in Section 5.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed DBSCAN-Based Alternative Model's Parameters
#### 3.4.2 Sensitivity Analysis of the Outlier Proportion Parameter  
The impact of the outlier proportion parameter \(\alpha\) on the proposed DBSCAN-based alternative model is analyzed using \(\varepsilon=0.250\) m, \(\lambda=0.250\) m, \(\delta=0.110\) m, and \(\tau=0.001\). As shown in Figure 23, the model achieves a recall of \(63.0\%\) when \(\alpha=0.0\). Recall rapidly increases to \(81.1\%\) at \(\alpha=0.001\), with the highest value of \(89.3\%\) observed at \(\alpha\) ranges between \(0.025\) and \(0.026\), \(0.033\) and \(0.035\), and \(0.051\) and \(0.063\), with a median of \(0.055\). Beyond these ranges, performance slightly declines, stabilizing at \(87.4\%\) when \(\alpha=0.430\).  
Figure 23: Sensitivity of the proposed DBSCAN-based alternative model to outlier proportion parameter \(\alpha\) using \(\varepsilon=0.250\) m, \(\lambda=0.250\) m, \(\delta=0.110\) m, and \(\tau=0.001\)—case study.  
Figure 22: Sensitivity of the proposed DBSCAN-based alternative model to the proximity threshold \(\varepsilon\) using \(\lambda=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.080\), and \(\tau=0.001\)—case study.
## 3 Performance Analysis and Simulations
### Irradiance Uniformity vs. Optical Loss
#### 3.1.4 Discussion  
Using the SOE, the proposed CPV unit added an additional optical loss to the system. However, the optical loss of 5.86% caused by the SOE found in the simulation is small and can be compensated for using the Si low-cost cell to capture diffuse solar radiation, which is discussed in the next section. Meanwhile, by using the SOE, the proposed CPV unit significantly improved the irradiance uniformity compared to the conventional CPV unit without the SOE, resulting in the elimination of hot spots and increasing the performance and lifetime of the solar cell.  
Additionally, using an existing simple plano-concave lens as the SOE resulted in a small reduction of the acceptance angle of the proposed CPV system. The problem can be resolved by designing a new  
Figure 10: The angular transmission curve vs. incident angle for both conventional and proposed CPV units.  
Figure 9: Ray-tracing at incidence angle of \(1.2^{\circ}\): (a) the conventional CPV unit; and (b) the proposed CPV unit.  
SOE type in order to improve not only the irradiation uniformity, but also the acceptance angle of the proposed CPV system.
###### Abstract
The sender has access to the secret information via authorized or unauthorized access. Figure 2 is a conceptual view of inside-SoC covert channel communications. In this figure, the sender and the receiver use the same code, the receiver can use a precise measurement of the information in the covert channel before decoding it to obtain the secret information sent by the sender. Many methods to create inside-SoC covert channels can be found in the literature, and most use previously presented physical side channels including the SoC power management and power distribution systems [7; 8; 9]. In this paper, we address the possibility of performing unauthorized intra-SoC communication using the access time of share cache memory (also called cache-base covert channel communication). To perform a cache timing attack (using a cache timing analysis or cache-based covert channel communication), an attacker (malicious third-party hardware IPs or software applications) has to fulfill two main conditions. The first condition is to distinguish a cache miss from a hit to understand if targeted data or an instruction are present in the cache memory or not [10; 11; 12; 13]. Indeed, as presented in Figure 3, the attacker wants to know if the victim process (or the sender process in the case of a covert channel) has access to specific information stored in the main memory. To be able to distinguish a cache miss from a hit, the attacker can measure access time to the targeted data in the memory system or use a performance counter unit, like the performance monitor unit (PMU) in ARM processors. The second condition is to be able to evict (flush) cache lines. Indeed, the  
Figure 1: Conceptual view of an inside-SoC side-channel analysis. Figure 2: Conceptual view of an inside-SoC covert channel. attacker has to periodically flush part of the cache to be sure to detect specific access to main memory data or an instruction by the victim process (or the sender process in the case of a covert channel). Modern heterogeneous SoC-FPGAs (such as Xilinx Zynq or Intel Cyclone V) are equipped with a cache coherency port called the accelerator coherency port (ACP) that connects the master interfaces of the hardware accelerator with the cache memory system. This article presents the methods used to fulfill the two previously mentioned conditions required to perform a cache timing attack from the programmable logic part of an SoC-FPGA. This paper starts, in Section 2, by reviewing related works. Section 3 presents the technical background required to understand and implement the attacks presented in the article. Section 4 presents a method to measure the time needed to access consistent data from the programmable logic part of an SoC-FPGA and a method to evict a cache line from the same part. Mastery of these two methods is the sine qua non-condition for the implementation of a cache timing attack. Finally, Section 5 provides proof of the practicality of the cache attacks.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.6 Predicting Disasters  
Telco Cloud enables the early prediction of disasters such as earthquakes, volcanoes, and floods through the accurate and real-time processing of data obtained from sensor groups distributed in various locations such as the seas and underground or carried on drones in the air and connected to the Telecommunications network where these sensors work to collect data. It provides continuous reports on the surrounding environments [208]. As a result, there is the possibility of taking preventive measures before a disaster occurs.
## 4 Results and Analysis
### Analysis of the Subsidence Characteristics of the Main Section  
Analysis of the main section is essential for the monitoring of mining subsidence and relies on robust line analysis at the discrete monitoring points. To this end, the section lines on the four subsidence DEMs were extracted from the main sections of the strike and dip of the subsidence basin along the working face at 0.5 m intervals to ensure that the start and end coordinates of the section lines were the same, and the values were graphed, as shown by the black scatters in Figure 7. The direction of the strike and dip profiles is from left to right and from top to bottom, with lengths of 1600 m and 910 m, respectively.  
The Savitzky-Golay smoothing denoising method was used to fit the scatter data. This method is based on the use of a polynomial low-pass filter. As can be seen in Figure 7, the low-frequency signal changes slowly and the waveform is smooth, while the high-frequency signal changes quickly and abruptly.  
The instability in altitude during flight leads to more jump points in the elevation in the UAV data, therefore, Savitzky-Golay smoothing was suitable for reducing the mutation noise in elevation. Finally, the time series subsidence curve for the main section was produced, as is shown by the red curves in Figure 7.  
It can be seen from Figure 8 that the maximum subsidence values for the strike and the dip increased regularly, and the subsidence velocity of the last data was reduced significantly compared with that of the previous three periods; also, the subsidence value was close to the maximum value under the geological mining conditions. In the time-series strike subsidence curve, the area of maximum subsidence tended to move to the right, which was consistent with the direction of mining of the work face. The comparison between the maximum subsidence value of the fitting curve and the measured value is shown in Table 5. The relative error between the fitted and measured value was not more than 20%, and the minimum value was 0.7%. Use of UAV photogrammetry to monitor the mining subsidence can reflect comprehensively the range of influence of mining subsidence, and a high level of accuracy for monitoring the maximum subsidence value can be realized.  
Figure 7: Curve fitting of subsidence and scattered points on the profile line: (**a**) strike 07.20\(-\)06.14; (**b**) strike 09.07\(-\)06.14; (**c**) strike 11.15\(-\)06.14; (**d**) strike 07.31\(-\)06.14; (**e**) dip 07.20\(-\)06.14; (**f**) dip 09.07\(-\)06.14; (**g**) dip 11.15\(-\)06.14; and (**h**) dip 07.31\(-\)06.14.  
Figures 7 and 8 are the comparison of the main section profiles of strike and dip extracted from the subsidence DEM in different periods, showing the dynamic development process of the subsidence curve in the form of line, which conforms to the development law of the mining subsidence curve and verifies the effectiveness of the curve fitting and subsidence monitoring methods.