esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Analysis of the Performance of the Traditional PI Control Method in Current Harmonic Suppression  
To evaluate the effectiveness of the traditional PI control method in suppressing current harmonics, experiments were conducted under three operating conditions: low speed, medium speed, and high speed. The harmonic components of the current and the torque ripple were analyzed for each condition.  
Figure 9 shows the experimental results for the PMSM operating at a low speed (set speed of 450 r/min, motor current fundamental frequency of 30 Hz). As shown in Figure 9a, when using the traditional PI control method, the fifth harmonic component of the stator current accounts for 24.22%, the seventh harmonic component accounts for 1.03%, and the total harmonic distortion (THD) is 24.38%. Figure 9b indicates that the maximum torque ripple is 0.7%. Figure 10 demonstrates the frequency spectrum of the phase current at low speed.  
Figure 8: The motor drive test platform.  
Figure 9: Phase current and load torque waveforms at low speed (450 r/min). (**a**) Phase current waveform. (**b**) Load torque waveform.  
Figure 11 presents the experimental results for the PMSM operating at a medium speed (set speed of 1125 r/min, motor current fundamental frequency of 75 Hz). As shown in Figure 11a, when using the traditional PI control method, the fifth harmonic component of the stator current accounts for 24.85%, the seventh harmonic component accounts for 1.66%, and the THD is 25.34%. Figure 11b shows that the maximum torque ripple is 1.05%. Figure 12 demonstrates the frequency spectrum of the phase current at medium speed.  
Figure 13 provides the experimental results for the PMSM operating at a high speed (set speed of 2250 r/min, motor current fundamental frequency of 150 Hz). As shown in Figure 13a, when using the traditional PI control method, the fifth harmonic component of the stator current accounts for 25.11%, the seventh harmonic component accounts for 1.59%, and the THD is 25.66%. Figure 13b shows that the maximum torque ripple is 1.13%. Figure 14 demonstrates the frequency spectrum of the phase current at high speed.  
Figure 11: Phase current and load torque waveforms at low speed (1125 r/min). (**a**) Phase current waveform. (**b**) Load torque waveform.  
Figure 12: Frequency spectrum of the phase current at medium speed.  
Figure 10: Frequency spectrum of the phase current at low speed.  
The comparative analysis of the experimental results reveals that the traditional PI control method results in high harmonic content in the phase current under low, medium, and high-speed operating conditions. The sinusoidal quality of the current waveform is low, with noticeable waveform oscillations and significant distortions, which further exacerbate torque ripple.
## 2 Materials and Methods
### Super-Resolution Implementation  
There are many models [1; 2; 3; 4; 5] used in SR studies. In deep learning-supported applications, the process that started with linear learning continued with residual learning, and has continued with recursive learning, adversarial learning, and attention structures. In a number of studies, the advantages of different learning strategies have been used together. Global and local residual learning have been applied separately as residual learning, then evaluated together [42; 43]. With the modifications made in the design of the models and hyperparameters, higher performance values supported by visual interpretation can be obtained, and higher performance values can be achieved with lightweight models. CNN-based SR studies, which started with SRCNN in 2014, have achieved high performance values both visually and metrically with the available data. Regardless of the approach  
Figure 4: Training image example in dataset-1 (1600 \(\times\) 1600) and dataset-2 (512 \(\times\) 512).  
being considered, almost all studies make comparisons with SRCNN, as it is a turning point for SR. Because the aim of this study is to reveal the improvability of historical aerial photographs and overcome their limitations rather than to show the superiority of one model over another, the SRCNN model was used directly. No changes were made to the hyperparameters to achieve better results. Dropout, batch normalization, or regularization techniques such as L1 or L2 were not included in the model to prevent overfitting or underfitting. It is thought that it is more realistic to use the model as provided in order to minimize the constraints arising from the dataset. In addition, the ability of the proposed dataset design to cope with the smoothing effect, which is frequently mentioned for SRCNN and similar models, was determined. The simplified architecture of the SRCNN model is presented in Figure 5.  
The SRCNN model transforms an LR input image into an HR output image using learned weights. SRCNN generally consists of three main layers. The input layer accepts a low-resolution input image. The feature extraction layers apply various convolution and activation operations to extract features from the LR input image. These layers use learned filters to capture features in the image. The reconstruction layer creates a HR output image using feature extractions [6]. This process allows the LR image to gain more detail and clarity. As the activation function, ReLU was used in the first two layers and linear was used in the last layer. The filter numbers were 128, 64, and 1, respectively, while the kernel sizes were set to 9, 3, and 5. The training process was conducted for a scale factor of 4, whereas the input sizes for the HR-LR pair were set to 256 and 64. ADAM was used as the optimization algorithm with a value of 0.01. Early stopping was used during training to prevent overfitting and to make the training process more effective. Implementation and testing of the model were carried out in the Google Colab environment.
# Numerical Coupling between a FEM Code and the FVM Code OpenFOAM Using the MED Library
## 2 The Numerical Platform Environment
### The MED and MEDCoupling Library from the SALOME Platform  
The best strategy to exchange heterogeneous fields between different codes with different data structures is to use an intermediate representation that is common to all codes. The MED and MEDCoupling libraries have been developed to offer a very rich set of functionalities, and they were used in this work to manage the data exchange between OpenFOAM and FEMuS.  
The coupling procedure was developed, using the MED and MEDCoupling libraries, with the idea to combine the best features of the FEM and FVM approaches. The MED library is a module of the SALOME platform for retrieving, processing, and sharing data at the memory level, avoiding the use of external disk files. This library is a low-level implementation of an abstraction layer for data structures that can be manipulated and stored in HDF5 format. The MEDCoupling library, on the other hand, is one of the available modules of the SALOME platform environment and the main library on which the coupling work depends. It uses MED communication for the exchange format and implements field distribution and interpolation algorithms.  
SALOME is a numerical platform developed by CEA (Commissariat a l'energie atomique et aux energies alternatives) and EDF (Electricite de France) to provide open-source software for computer-aided engineering (CAE) [22]. This platform offers several modules, such as GUI, Geometry, Mesh, Fields, YACS, JobManager, and ParaViS, that may manage every stage of a computational simulation performed by multiple external standalone codes. The software implements tools for parametric CAD modeling, tetrahedral and hexahedral mesh generation, code supervision, data analysis, and postprocessing [15]. In particular, the supervisor can generate simulation workflows that connect different computational units with the support of the FIELDS and MEDCoupling libraries. This manages data communication by manipulating the inputs and outputs of the simulations. It can also perform transfer, modification, and analysis of data. Among its features, the most relevant for this work are reading/writing from/to files, the aggregation and exchange of data, interpolation between different grids, format conversion, and renumbering or partitioning of data for multiprocessor frameworks. In the following, for simplicity, we will refer to the MED and MEDCoupling libraries only by the name MED.
## 4 Avinomics Assessment
### Control
#### 4.1.1 Navigation  
Assuming that a UAV has its path already determined (i.e., concerning its mission, purpose of usage, etc.), its navigation involves the real-time process of guiding it along that path. It can therefore be perceived as the constant monitoring of the UAV's position, velocity, and orientation with respect to the planned trajectory. As a short general description, under the control subset of a UAV, navigation refers to the ongoing execution and adjustments of the UAV's movement along its planned path. Other related reviews in the field mainly classify UAV navigation techniques as either indoor (i.e., used in closed areas where GPS signals, for example, can be weak or unavailable), or outdoor environments (i.e., for aerial surveying, crop monitoring, and rescue missions). Indoor navigation methods, on the one hand, can be achieved using a packet loss-based approach [84]. In GPS-denied environments, a drone is capable of estimating its current position based on the wireless packet loss from a reference point packet transmission. Specifically for indoor corridor environments, a UAV can be navigated through a Convolutional Neural Network (CNN), resulting from the combination of ResNet and DenseNet networks [85]. The CNN analyses the images captured by the UAV's monocular camera and estimates the position/orientation of the UAV relative to the environment. A precise indoor UAV location estimation can also be obtained by means of ultrasonic acoustic signals with a three-stage localization scheme [86]. Outdoor UAV navigation methods, on the other hand, mainly rely on GPS [87], Inertial Navigation System (INS) [88], Global Navigation Satellite System (GNSS) [89], integrated INS/GNSS [90], and others [91]. This manuscript, however, conducts another approach for reviewing UAV navigation techniques, namely, classified as navigation strategy, path planning/obstacle avoiding, and localization.
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 2 Background and Related Work  
### Overview of OLSR  
The OLSR [14] protocol is a proactive routing protocol for MANETs. It is an optimized version of the classical link state routing protocol that reduces control message overhead. The nodes in a MANET select an MPR node to provide maximum coverage for their two-hop neighbors and reduce the number of control messages. With a minimum number of MPR nodes, a node can reach its neighbors through a small number of transmissions without any duplicated messages, as shown in Figure 1. MPR nodes are responsible for relaying both control messages and data messages.  
The OLSR node maintains the topology information for the network through periodic exchanges of control messages. There are two main types of messages that describe the topology information of the network, namely the HELLO message (described in Figure 2) and topology control (TC) message (described in Figure 3). Every node includes its neighborhood information (one-hop neighbor nodes) in a HELLO message and broadcasts it to all one-hop neighbors. Subsequently, each node collects the topology information of the network until reaching two-hop range. TC messages are advertised by MPR nodes. These messages include a list of MPR selectors that have chosen the broadcasting nodes as their MPR nodes. Every routing node acquires the network topology by utilizing hello and TC messages. Finally, the shortest route to a destination node is calculated and data packets are forwarded efficiently.
# Insights into Organizational Security Readiness: Lessons Learned from Cyber-Attack Case Studies
## 4 Results and Findings  
Based on the factors we interpreted through the case study evaluation depicted in Table 2, we wanted a map to discover any frequent patterns across case studies. We utilized Association Rule Mining (ARM) to quantify our findings, as shown in Table 3 below. The rules which had a strong co-occurence are indicated with metrics of confidence and lift. We ran ARM on the case study data we gathered in Table 2 that match with the associations of cyber threats intuitively, as well. Association rules essentially help quantify frequently co-occurring patterns in the data. Our aim here was to see if there are certain factors which implied policy or human factors with a high frequency [49]. All of these association rules indicate a high confidence close to 1, denoting that the left-hand side is strongly linked to the right-hand side factors. Moreover, the high lift values greater than 1 show a high correlation between the factors. We next used connections between these association rules to link various factors. These were visualized and evaluated to represent the factors impacting cyber-attacks using ARM and connections between these rules. We next illustrate several example observations from this data. 1. _Denial of Service (DOS)_ attacks [50] usually occur when an environment has a weak configuration and cybersecurity policy despite training and the related cyber awareness, as shown in Figure 4 below. Strong cybersecurity policies and strong configuration in the IT infrastructure [51] are a necessity for a secure environment because training and cyber awareness are not good enough without implementing them properly. The credibility and the reputation of the organization are impacted heavily by this kind of incident. The DOS attack [52] is related to the circular impactful attributes that imply the loss of credibility. 2. _APT:_ In Figure 5, we notice that APT attacks are linked to the lack of technology adoption, with not much training, as well as minimal security policy. They may cause medium to high financial loss. Cyber awareness is not enough if we do not have the proper training and technology adoption for cybersecurity. 3. _Social Engineering_: We see a lack of awareness and training with a lack of technology adoption. As a result, there is no corporate culture for security and no corporate investment for cybersecurity. The outcome from this may lead to significant financial loss. Ignorance is the main human factor for this kind of attack, as shown in Figure 6.
# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## 1 Introduction  
### Significance of African Landscape Fires  
Landscape fires modify the physical and radiative properties of the surface, and the chemical composition of the atmosphere [1; 2; 3; 4]. Africa contributes on average almost half of all carbon emitted by landscape fires [5], around 1.0 (\(\pm\)0.22) Pg C yr\({}^{-1}\) annually [6], though the continent is generally considered a small carbon sink [7; 8], because assimilation of carbon by the growing vegetation is of a similar magnitude to the respiration, and because of its low fossil fuel emissions (4% of global total). Satellite remote sensing is key to better understanding the magnitude of fire on African landscapes, and we here use this technique to investigate the temporal dynamics of fuel consumption across sub-Saharan Africa, and its relationship to the available fuel load.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 2 The Proposed Method of Spoofing Detections' Fusion
### The Model of Detections' Fusion
It is necessary to add a rule with Equation (4) in such a case as follows:  
\[m(A)=m(C)=0.5\qquad\text{when}\qquad\kappa=1\]  
In what follows, they are denoted as Equation (4) together. After the fusion, a final decision must be made to determine the authenticity of the signal. The decision rule is given as follows:  
\[\left\{ \begin{array}{l}\text{Determine }\,A\,\,\,\text{when}\,\,\,m(A)\geq m (C)\\ \text{Determine }\,C\,\,\,\text{when}\,\,\,m(A)<m(C)\end{array} \right. \tag{5}\]  
As \(m(A)+m(C)=1\), the rule of Equation (5) determines \"\(A\)\" if \(m(A)\geq 0.5\) and determines \"C\" if \(m(A)<0.5\). That is, 0.5 is the threshold of the final decision after detections' fusion. Figure 1 shows the diagram of the detections' fusion. Detector \(D_{i}\) points at \(x_{i}\), which could be any feature of the GNSS signals or measurements, such as signal power, Doppler shifts, code delay, pseudoranges, the results of positioning, etc. It should be noted that not the different detection methods, but the different signal features are the key of fusion. The evidence of the spoofing attack is collected from the detectors and fused according to the rule of Equation (4). The fusion makes the result more credible if the evidence from different detections is coincident, while it would give a compromise result according to all evidence if it is conflictive. Two examples are provided in Table 1 as follows to demonstrate the combination of coincident and conflictive evidence. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Coincident Evidence**} & \multicolumn{3}{c}{**Conflictive Evidence**} \\ \hline  & \(m_{1}\) & \(m_{2}\) & \(m_{1}\oplus m_{2}\) & & \(m_{1}\) & \(m_{2}\) & \(m_{1}\oplus m_{2}\) \\ \hline A & 0.8 & 0.2 & 0.903 & A & 0.8 & 0.2 & 0.632 \\ C & 0.2 & 0.3 & 0.097 & C & 0.3 & 0.7 & 0.368 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples for combination rule. Figure 1: Diagram of the fusion of multiple spoofing detectors.
## 3 Fault-Tolerant Output Regulation Scheme
Therefore, if (4) is available from measurements for all \(t\geq 0\) and considering the exosystem (5), the output regulation problem for unknown signals consists in computing a control law:  
\[u(t)=K(x(t)-\Pi w(t))+\Gamma w(t), \tag{6}\]  
where \(K\in\mathbb{R}^{m\times n}\) is such that \(A+BK\) is Hurwitz, and the mappings \(\Pi\in\mathbb{R}^{n\times(p+d)}\) and \(\Gamma\in\mathbb{R}^{m\times(p+d)}\) such that  
\[0 =A\Pi+B\Gamma+D-\Pi S-\Pi S_{H}Q_{w}, \tag{7}\] \[0 =C\Pi-Q_{ref},\]are satisfied; these are variations of the so-called Francis equations [2]. Hence, the closed-loop system guarantees that the tracking error  
\[e(t)=y(t)-y_{ref}(t)=Cx(t)-Q_{ref}w(t), \tag{8}\]  
goes asymptotically to zero. Several approaches exist for finding \(K\), such as ensuring that \((A+BK)\) is Hurwitz. The LMI framework allows adding performances such as decay rate, input, and output constraints; to do this, consider the Lyapunov function candidate \(V(x)=x^{T}Px\), \(P=P^{T}>0\), which leads to LMIs  
\[X>0,\quad AX+BM+XA^{T}+MB^{T}+2\varphi X<0, \tag{9}\]  
where \(M=KP\), \(X=P^{-1}\) and \(\varphi>0\) is related to the maximum speed convergence. The previous LMIs guarantee that \(\dot{V}\leq-2\varphi V\). In order to avoid actuator saturation, let us impose a control input bound \(\|u(t)\|\leq\beta\), with \(\beta>0\), through the following LMIs  
\[ \begin{bmatrix}1&x^{\top}(0)\\ x(0)&X\end{bmatrix} \geq 0,\text{ and } \begin{bmatrix}X&M^{\top}\\ M&\beta^{2}\end{bmatrix} \geq 0, \tag{10}\]  
where \(x(0)\) is the initial condition. If LMIs (9) and (10) are feasible, then the stabilizing gain is computed as \(K=MX\)[18].
## 3 Results and Discussion
### Optimization of Harvesting Conditions for M. Oleifera Seed Powder  
#### Effect of pH  
The success of harvesting microalgae by flocculation depends on many parameters, such as the chemical structure of the flocculants, their molecular weight, the way the flocculant interacts with the microalgae, the pH of the algae culture, the algae species, and the cell size [37]. Therefore, several of these parameters were further investigated to optimize the harvesting efficiency of M. Oleifera seed powder for the used algal suspension. It has been reported in previous studies that pH is a significant parameter when using MP as a flocculation agent [18; 38]. Therefore, a range of pH values varying from acidic to basic was tested to assess its effect on the harvesting efficiency of MP, and the results are shown in Figure 2. The initial pH of the algae sample was 7, and it was used as a control without any additions during the experiment.  
At the end of 60 min, MP was effective in both acidic and alkaline conditions, with harvesting efficiencies ranging from 74% to 89% at pH values between 3 and 11 and no significant difference between the tested conditions (\(p>0.05\)). This can be explained by the high isoelectric point of _M. Oleifera_, which only shows anionic properties at pH > 11 [39; 40; 41]. However, previous studies have reported different results with _M. Oleifera_ seed powder. For instance, [PERSON] et al. [18] observed significantly higher harvesting efficiencies at pH > 7, and [PERSON] & [PERSON] [23] found maximum efficiencies at a pH value of 9.5. This discrepancy may be attributed to the auto-flocculation properties at acidic pH values observed for the algal culture used in the present study. As shown in Figure 2,when the pH decreased from 7 to 6, a significant increase in harvesting efficiency was observed even in the absence of MP. While [PERSON] & [PERSON] [23] did not report the harvesting efficiency of their algal culture in the absence of flocculants, [PERSON] et al. [18] did not observe any auto-flocculation of the used _Chlorella_ suspension at acidic pH values, which can explain their lower harvesting efficiencies at lower pH values. Even though in the present study both _Desmodesmus_ sp. and _C. vulgaris_ were present, _Desmodesmus_ was dominant in the cultures used for the flocculation experiments. It has been shown that _Scenedesmus_, a closely related genus to _Desmodesmus_, has auto-flocculation properties at acidic pH values due to the neutralization of its cell wall charges by protons [42], which can explain the observed results in the present study.  
At pH values of 7 and 8, which is the common pH range used when growing algae on digestate, MP addition increased the harvesting efficiency by 36% and 50%, resulting in a final harvesting efficiency of around 85%, indicating that M. oleifera is an adequate flocculant for the proposed application in this study without the need for pH adjustment.
## Abbreviations  
The following abbreviations are used in manuscript:  
3 GPP & Third-generation partnership project  
5G & Fifth-generation mobile communication network  
5G-A & 5G-Advanced  
ADS & Advanced design system  
CAD & Computer-aided design  
CMOS & Complementary metal oxide semiconductor  
CW & Continuous-wave  
EM & Electromagnetic  
FBW & Fractional bandwidth  
FoM & Figure of merit  
FR & Frequency range  
GaAs & Gallium arsenide  
GaN & Gallium nitride  
GaN-on-Si & Gallium nitride-on-silicon  
HB & Harmonic balance  
HEMT & High-electron-mobility transistor  
IMD3 & Third-order intermodulation distortion  
IMN & Input matching network  
ISMN & Interstage matching network  
ITR & Impedance transformation ratio  
MMIC & Monolithic microwave integrated circuit  
mmWave & Millimeter-wave  
MN & Matching network  
OID & Optimal impedance domain  
OMN & Output matching network  
OPG & Operating power gain  
PA & Power amplifier