esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 6 Numerical Experiments  
In this section, we describe how to carry out numerical experiments for several test problems to evaluate the efficiency of two iterative methods, called Algorithms 2 and 3, using CGLS for solving the new proposed TVL2 problem (4). Performance of Algorithms 2 and 3 is evaluated by comparing their numerical results with those of the existing fixed-point method called Algorithm 1 and the split Bregman methods called Algorithms 4 and 5.  
All numerical tests have been performed using Matlab R2016a on a personal computer equipped with Intel Core i5-3337 1.8 GHz CPU and 8 GB RAM. For numerical experiments, we have used three types of PSFs (point spread functions) which are Gaussian blur with standard deviation 9 and Average blur and Motion blur of size \(9\times 9\). PSF arrays \(P\) for Gaussian blur with standard deviation 9 and Average blur and Motion blur of size \(9\times 9\) are generated by the built-in Matlab function \(fspecial(^{\prime}Gaussian^{\prime},[9,9],9)\), \(fspecial(^{\prime}average^{\prime},9)\) and  
\[P=zeros(9);\ P(4:6,:)=fspecial(^{\prime}motion^{\prime},9,1),\]  
respectively. The blurred and noisy image \(f\) is generated by  
\[f=A\cdot vec(X)+vec(E),\]  
where \(A\) stands for the blurring matrix that can be generated by the PSF array \(P\) according to the reflexive boundary condition, and \(E\) is the Gaussian white noise with mean 0 and standard deviation 3 that can generated using Matlab function \(E=3\times randn(m,n)\), where \((m,n)\) denotes the size of true image.  
In order to illustrate efficiency of the proposed algorithms, we have used four test images with an intensity range of \([0,255]\) such as Cameraman, Lena, House, and Boat with pixel size \(256\times 256\). To evaluate the quality of the restored images, we have used the peak signal-to-noise ratio (PSNR) between the original image and restored image, which is defined by  
\[\text{PSNR}=10\ \text{log}_{10}\left(\frac{\max_{i,j}|u_{ij}|^{2}\cdot m \cdot n}{\|u-\bar{u}\|_{F}^{2}}\right),\]  
where \(\|\cdot\|_{F}\) represents the Frobenius norm, \(\bar{u}\) denotes the restored image of the original image \(u\) with size \(m\times n\), and \(u_{ij}\) stands for the value of the original image \(u\) at the pixel point \((i,j)\). In general, the larger PSNR stands for the better quality of the restored image.  
For all numerical experiments, an initial image was set to the blurred and noisy image \(f\), \(\kappa=1\times 10^{-6}\), \(maxit=150\), and \(tol\) is set to \(5\times 10^{-4}\) (for Algorithms 1 and 5), \(2\times 10^{-4}\) (for Algorithm 4) or \(1\times 10^{-3}\) (for Algorithms 2 and 3). For the CGLS method that is used to solve a linear system every iteration of Algorithms 1-5, the tolerance for stopping criterion is set to \(5\times 10^{-2}\) (for Algorithms 1, 4 and 5) or \(1\times 10^{-3}\) (for Algorithms 2 and 3), and the maximum number of iterations is set to 60.
## 3 System Model  
### Network Model  
Actually, the underwater network model and its corresponding communication are not the same in shallow water and deep water. In this paper, we only focus on the shallow water environments since most IoT-based applications are located in inland lakes and rivers, e.g., aquaculture and water quality monitoring. As mentioned earlier, UWSN is a typical heterogeneous wireless network, for which we take care of the energy heterogeneity factor. Assume that the three-dimensional network can be mapped into a two-dimensional network. Let \(n\) nodes be randomly deployed in a static \(M\times M\) shallow monitoring water area. All nodes can be regarded approximately in the same plane, and more characteristics are as follows:1. Initially, each node is equipped with the different energy over the interval of \([E_{0},(1+\lambda)E_{0}]\), where \(E_{0}\) is the lower bound (namely unit energy), and \(\lambda\) is a constant without upper bound to determine the value of the maximal initial energy, which satisfies \(\lambda>0\).
2. The sink is located at the center of the monitoring area, which is the only one not restricted by resources, such as energy, memory and calculation ability. Moreover, the sink can directly communicate with the gateway (base station).
3. Owing to the adoption of clustering mechanism, non-cluster-head nodes are permitted to communicate with the cluster-head through single or multiple hops, while they cannot directly send packets to the sink.
4. Each node is anchored at the specified area with a buoy, which means the network is relatively stable.
5. Assume that the surface and the bottom of the water are relatively smooth planes, where the influence of underwater transmission delay and success rate for each node are the same.
## 1 Introduction
They found that LED lights with higher values in the color quality scale (CQS), gamut area scale (Qg), CQS color preference scale (Qp), and gamut area index (GAI) were preferred and more accepted by subjects than LED lights with lower values in the Qg, Qp, and GAI. [PERSON] et al. [28] compared two LED lamps, blue-pumped LED (BP-LED) lamps and BP-LED lamps that had diminished yellow emission (YD-LED), to explore user preference and found that the YD-LED was preferred. Spatial brightness, which is defined as an attribute of a visual sensation, can be assessed on scales of _dim-bright_[29; 30; 31]. Some research has shown that brightness perception increases as the CCT of LED lights increases [32; 33], while other research has found that an increase in the CCT of LED lights does not necessarily increase brightness perception [34; 35]. These studies found that differences in brightness perception may occur at different CCT levels. Additionally, [PERSON] et al. [28] found that, at the same CCT levels, YD-LED lights were perceived to be brighter than BP-LED lights. [PERSON] et al. [16] also found that LED lights with higher Qg, Qp, and GAI values were perceived to be brighter than those with lower Qg, Qp, and GAI values. These results demonstrate that brightness perception can be different at the same CCT levels. LED lighting can provide a wide range of color aspects with different color rendition and CCT levels. This results in very different user acceptance responses. Therefore, this study investigated the effects of the CRI and CCT of LED lights on user acceptance and thereby proposes appropriate color attributes for HCL in offices. User acceptance factors were measured in terms of preference, visual comfort, readability, satisfaction, and self-reported productivity, all of which are related to work performance in offices. The illuminance recommended for offices by the IESNA [21] and the BAuA [22] is 500 lux, while that recommended by the Korean Agency for Technology and Standards (KATS) [36] is in the range of 150-300 lux. For LED office lighting, past psychophysical experiments were conducted under a single illuminance level between 150 and 300 lux [37; 38].
# Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
## 2 Literature Review
### Applications of Decision Trees in Motion Analysis
The results showed that the classification accuracy of the decision tree was as high as 99%, and the rate of accuracy in the identification of hits and misses was 95%. [PERSON] et al. [35] used the depth-sensing technology of Microsoft Kinect to obtain human-skeleton data from tennis players. Next, they used the KNN algorithm to define dimension weights for action classification. The research results showed that the accuracies of the KNN algorithm, that of the decision tree algorithm, and that of the convolutional neural network (CNN) algorithm were 92.4%, 92.81%, and 89.97%, respectively. To develop a teaching system with correct tennis posture through accurate classification, [PERSON] et al. [36] classified videos of shots into long shots, medium shots, close-up shots, and off-field shots, and they proposed a decision tree architecture for the shot-classification of outdoor-sports videos. Using the sports videos on YouTube for automatic classification, the results suggested that the classification accuracy improved by an average of 6.9%. [PERSON] and [PERSON] [37] proposed the use of an audio-stream-analysis, multi-label-classification algorithm, which was applied to the decision tree classification algorithm for classification. Using the continuous-time hidden Markov model (CHMM), the audio employed multiple labels for the branch-pruning and classification of the tree. They then used 72 shots and 56 tennis highlights as the verification material for the algorithm. The detection rate of the bottom-line hit event reached 92% and 88%, which are relatively high, while the detection efficiency of the ace-ball event and the tennis event, 84% and 80%, respectively, were not very good. Based on the abovementioned decision tree studies, it can be determined that, by using decision trees, collected data can be sequentially arranged into graphs via tree diagrams so that the results presented from different nodes can be clearly seen. Thereafter, the most advantageous option can be determined.
## 4 Numerical Study  
The effectiveness of the model developed in Section 3 is verified by means of numerical integration, examining formations operating in some scenarios of interest: (a) an ELINT formation with the chief satellite in a SSO with altitude of 530 km, (b) a SAR formation with the chief satellite in a circular LEO with altitude of 530 km and inclination of 52 degrees, and (c) a formation at support of a lunar navigation system with the chief satellite on a circular lunar orbit with altitude of 4300 km and inclination of 40 degrees. Similarly, the guidance strategy is validated by applying it on a space reconnaissance satellite approaching a space object in a medium inclination LEO; the satellite is transferred into a bounded orbit about the object to be observed.  
For all the cases, configurations invariant with respect to the zonal harmonic perturbation are identified in the Hamilton coordinates \((\mathbf{w},\mathbf{z})\) and are then transformed to position and velocity coordinates \((\mathbf{\rho},\dot{\mathbf{\rho}})\) in \(\mathcal{F}_{r}\) by applying the inverse of canonical transformations \(\mathcal{T}_{1}\)-\(\mathcal{T}_{7}\), considering the combinations of the zonal coefficients \(J_{n}\) selected as indicated in Appendix A.2.  
The initial states selected in this way are then converted to dimensional form using the following factors for distance and time (see Appendix A.3):  
\[\begin{cases}DU=\bar{a}\\ TU=1/n\end{cases} \tag{39}\]  
where \(\bar{a}\) and \(n\) are the chief orbit mean semimajor axis and angular velocity. The General Mission Analyisis Tool, developed by NASA's Navigation and Ancillary Information Facility at the Jet Propulsion Laboratory, is used to propagate the initial states, using the DE405 ephemeris model [34]. The gravity field of the Earth is described by the JGM-2 model [35], including all the harmonic coefficients up to order and degree 70. For the gravity field of the Moon, the LP-165 model was used [36], including all the harmonic coefficients up to order and degree 165.
## 4 Proposed Method
### Data Processing Flow  
The specific process is as follows:  
1. Obtain the original magnetic data from the OPM and preprocess the magnetic data, mainly filtering.
2. Obtain original current data from a current sensor and combine the direction cosine information of the geomagnetic field from fluxgate magnetometer to construct the current matrix of the compensation model.
3. Calculate the compensation coefficients using the least square algorithm (LS).
4. The modeling interference is calculated using the current matrix and compensation coefficients, and the modeling interference is subtracted from the filtered OPM data to generate the magnetic field data after compensation.  
The data processing flow of the compensation coefficients solution is shown in Figure 18, and the compensation flow of the magnetic interference caused by UAV electronic equipment is shown in Figure 19.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Model Architectures  
Several models were used for classifying Arabic meters, including ARBERT, MARBERT, CAMeLBERT, Arabic-BERT, AraELECTRA, BiGRU, and BiLSTM.
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 4 Numerical Assessment on the Performance  
In this section, numerical simulations are carried out to assess the performance of the proposed vibration control method. For comparison, connectors with/without a control effect are studied in terms of the frequency responses of a floating system five modules. The frequency response represents the characteristic of the 1-in-1000 amplitude of the extreme responses of the modules in a frequency domain. It describes the transmissibility from the wave excitation to the module response with the unit wave height. The stiffness-adjustable connector is marked as the novel model (named \"novel\") in order to compare it to two other types of connectors. One of these types is the flexible connector without the actuator involvement (named \"flexible\"). The other is the hinged connector (named \"hinged\") [32], which have been used by many scholars. The flexible connectors are mounted in the same positions, and the VLFS modules are also the same for the three types of connectors.
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 10 Numerical Analysis  
A numerical model of the beam has been prepared through the applied element method (AEM) to perform nonlinear static analysis and reproduce the laboratory observations up to collapse. The AEM is a rather new method, akin to the discrete element method, which is capable of predicting with a reasonable degree of accuracy the continuum and with a high degree of accuracy the discrete behavior of structures during the collapse. The AEM has been proved to track the structural collapse behavior passing through all the application load stages. Within the AEM, the structure is modeled as an assembly of 8-points hexahedral elements. Therefore, each element is assumed rigid (6 degrees-of-freedom) and has a 3-D physical solid shape. Two adjacent elements are assumed to be connected by one normal and two shear springs distributed around the elements' edges on the interface. Each group of springs represents the entire stresses and deformations of a certain volume. More details on the theoretical aspects related to AEM and its comparison with finite element method can be found in [PERSON] et al. [31].  
Figure 15: DIC (**top**) and FOS #2 (**bottom**) outcomes at loading steps #4 (top-left) and #8 (top-right) in the central area of the beam specimen (between the vertical loads—see Figure 2).  
The four-points bending test on the reinforced concrete beam has been reproduced through nonlinear static analysis in AEM. The results have been used to anticipate the structural behavior and to compare with the measurements [32]. Different AEM models have been developed by considering a sensitivity analysis to identify a reasonable discretization for the beam. Accordingly, materials have been assumed to be C20/25 and B450C for concrete and steel respectively, with nonlinear constitutive laws following the models by [PERSON] for steel and [PERSON] for concrete [33]. A 5 cm side-length element mesh has been selected for the comparison with SHM systems as the reasonable compromise between computational efforts and accuracy.  
Considering the global behavior (Figure 16a), the AEM model performs satisfactorily by reasonably anticipating the force-displacement response. Moving to the local behavior, the selected mesh results also are able to correctly trace the bending and shear cracks. Figure 16b, in particular, reports the development of a crack at step #8. The two regions where the AE and DIC techniques have been applied can be observed. The strain concentration due to crack opening, as detected by the DIC technique (Figure 15, top-right) in the central region and by the AE sensors at the extremity, is correctly reproduced by the AEM simulation. It is worth underlining how the screen effect of voids, generated by micro-fractures and distributed damage in the concrete matrix, does not allow the AE sensors to detect the cracks generated at the mid-span of the beam.
## 3 A Model of Deployment-Independent, Self-Organizing, Cyber-Physical Systems
### Cyber System  
A cyber system is a collection of _logical devices_ (or devices for short), each with the ability of connecting to other devices, called _neighbors_--generally, such connections (_logical neighboring links_) define a structure that can change over time. A device (Figure 2) is a logical entity with the following components:  
* A set \(\sigma\) of logical _sensors_.
* A set \(\alpha\) of logical _actuators_.
* A _state_\(\kappa\), representing the device's local knowledge (where we abstract the particular representation chosen for a state).
* A _communication component_\(\chi\) handling interaction with neighbors, holding information on the identity of neighbors and how to reach them, managing _input channels_ used to receive external messages into the device's state, and _output channels_ for emitting messages to all its neighbors (i.e., the output channel of a device is connected to the input channels of all its neighbor devices as per the neighboring relationship).
* A _computation_ function \(\beta\) modelling the device _behavior_, which maps the state of the device to (i) a new state, (ii) a \"prescriptive\" set of actuations to be performed, and (iii) coordination messages to be emitted.  
Each individual device of the cyber system performs a MAPE-like cycle that includes the following steps and that defines the interactions between the device's subcomponents as depicted in Figure 2 (with arrows denoting message flow).  
1. _Context acquisition._ In this step, the device retrieves information from context sources (sensors and communications) and stores them in the device state.
2. _Computation._ The behavior function is applied against (data inferred from) the device state; its (possibly processed) output is stored in the device state.  
Figure 2: A logical device, split into sub-components, and one of its neighbors.  
Coordination data propagationFrom the device state, coordination data is sent to all neighbors.
* _Actuation_. Actuators are activated to execute a set of actions inferred from the device state (these actions are, e.g., prescribed through the device behavior or mere sensor data-driven reactions).
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 9 Conclusions  
In this study, we first analyzed DoS, flood, SlowITe, malformed, and brute-force attacks in malicious MQTT traffic. Second, we proposed a novel feature extraction method using the source port index, TCP length, MQTT message type, keep alive, and connection ACK. Third, we proposed a Seq2 Seq model to classify the five attacks that demonstrated an accuracy of 99.97%, which is 7.33% higher than that of the previous methods. Therefore, we anticipate that our feature extraction method and the Seq2 Seq model can prevent malicious MQTT traffic.  
Conceptualization, S.C.; Methodology, S.C.; Software, S.C.; Validation, S.C.; Writing, S.C.; Supervision, J.C. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean Government, Ministry of Science and ICT (MSIT) (Implementation of Verification Platform for ICT Based Environmental Monitoring Sensor), under Grant 2019-0-00135 and partially supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government, Ministry of Science and ICT (MSIT) (Building a Digital Open Lab as open innovation platform) under Grant 2021-0-00546.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 18: Accuracy when a connection ACK is not used.
## 3 Power Converters Modelling
### Cuk Converter Average Model  
In order to include LED luminaries as final load, a driver based on Cuk converter is designed. Cuk driver is the interface between 400 W DC-DC full bridge converter and LED luminaire; Cuk converter schematic is shown in Figure 4.  
In Figure 4, two power devices, MOSFET (\(Q\)) and diode (\(D\)), can be distinguished. Switching functions \(S_{1}\) and \(S_{2}\) are defined for \(Q\) and \(D\), respectively. Again, if \(S_{1}=1\), \(Q\) is conducting, else, if \(S_{1}=0\), the device is off state. The same logic is used for function \(S_{2}\), and complementary operation is assumed. The following combinations are not allowed  
Figure 4: Cuk converter.  
Figure 3: DC-DC Full Bridge Converter.  
\(S_{1}=1\) with \(S_{2}=1\) and \(S_{1}=0\) with \(S_{2}=0\). By applying Kirchhoff laws, state equations in time domain are obtained from the Cuk converter switching equivalent circuit; then, the averaged model is obtained by considering the modulating signal \(u(t)\) as the average of the commutation function. The averaged model is described as:  
\[\frac{di_{L1}}{dt} =-\frac{(1-u)}{L_{1}}v_{\text{C1}}+\frac{V_{i}}{L_{1}}\] \[\frac{di_{L2}}{dt} =\frac{u}{L_{2}}v_{\text{C1}}+\frac{1}{L_{2}}v_{\text{C2}}\] \[\frac{dv_{\text{C1}}}{dt} =\frac{1-u}{C_{1}}i_{L1}+\frac{u}{C_{1}}i_{L2}\] \[\frac{dv_{\text{C2}}}{dt} =-\frac{1}{C_{2}}i_{L2}+\frac{1}{R_{o}C_{2}}v_{\text{C2}} \tag{3}\]  
Cuk output current flowing through LED array inside the luminary must be regulated to avoid unwanted lighting effects.