esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Sparse SLMS Algorithms  
By incorporating sparsity-aware function into the cost function of the standard SLMS in Equation (6), sparse SLMS algorithms could be developed to take advantage of sparse structure information, to mitigate S\(\alpha\)S noise as well as to reconstruct channel FIR. First of all, this section proposes five effective sparse SLMS algorithms with different sparse constraints. These proposed algorithms are SLMS-ZA, SLMS-RZA, SLMS-RL1, SLMS-LP, and SLMS-L0. Later, performance analysis will be given to confirm the effectiveness of the proposed algorithms.
## 3 Results and Discussion
### Application  
#### 3.2.1 Vertical Surface Deformation  
On the basis of our inverted source model, we used a method proposed by [PERSON] (1992) [PERSON] (1992) to calculate the surface uplift (Figure 6) and compare it with the corresponding InSAR results by [PERSON] et al. (2021) [PERSON] et al. (2021).  
The major vertical displacements presented in Figure 6 were seemingly centered northwest of the Meinong earthquake's hypocenter. According to [8], this indicates that  
Figure 5: Slip distribution on the indicated fault with distance used as a reference.  
Figure 6: Predicted vertical surface displacements calculated using the proposed inverted model. The red star is the hypocenter, and the small yellow points represent aftershocks. Areas with high displacement values located northwest of the hypocenter indicate that the fault rupture extended from the hypocenter in the northwest direction along the seismogenic fault.  
the fault rupture extended from the hypocenter in the northwest direction along the seismogenic fault. Compared with the results derived from InSAR data, our results could better detect areas with high vertical displacement values, but our derived maximum vertical displacement result was 5 cm, which is smaller than the maximum displacement result derived using InSAR data (13 cm). [PERSON] et al. (2021) [26] used the source model of [3] to calculate surface vertical displacement. They obtained a maximum surface displacement of approximately 5 cm, and the areas corresponding to high values were slightly farther west compared with observations from InSAR data-derived results. According to [26], rather than only coseismic fault slip, some other mechanisms may underlie the surface pop-up. [PERSON] et al. (2021) [9] offered clear explanations of such mechanisms, including responses of aftershocks in soil liquefaction areas and activities associated with the Xinhua fault by the Meinong event. In this study, the earthquake model was assumed to be a half-space one, as in the study of [25], and differences between estimated and observed surface deformations were expected; surface deformation was affected by the local geologic properties. Therefore, the calculated results based on the source model can provide a valuable reference for investigating the factors contributing to actual observations.
# An SSI-Based Solution to Support Lawful Interception
## 2 Background
### Self-Sovereign Identity
#### 2.2.2 Issuers, Holders, and Verifiers  
Issuers, holders, and verifiers are the three roles involved in the SSI system, also known as the Trust Triangle. The issuers are the source of VCs, and they are usually some authorities such as universities or companies. The holders are the subjects of the credential that are responsible for storing the VCs in a digital wallet. The holders can be individuals but also organizations or objects. Lastly, the verifiers are the entities that offer a benefit to the holder after the execution of a verification process of one or more credentials. The verifiers can be individuals or organizations. The three actors are involved in several interactions as request, issuing, and verification of the VCs. One of the most relevant interactions between SSI roles is the verification of one or more VCs presented by the holder to the verifier. The verifier must define an access control policy for each benefit they offer. In the SSI world, this policy can be seen as the set of VCs that the holder must present to obtain access to the service. After the presentation process, the verifier starts the verification, which is the union of several steps:  
* VC's state verification: the verifier must check that the VC is still valid so that it has not been revoked.
* VC's claims verification: the verifier must check that the information contained in the VC satisfies the access control policy.  
* VC's issuer verification: the verifier must check the authenticity of the VC's issuer. The VC includes the issuer's Decentralized Identifier (DID), allowing the verifier to retrieve the issuer's public key from the blockchain to validate the VC's digital signature.
# A Single-Launch Deployment Strategy for Lunar Constellations
###### Abstract  
Satellite constellations can provide communication and navigation services to support future lunar missions, and are attracting growing interest from both the scientific community and industry. The deployment of satellites in orbital planes that can have significantly different inclinations and right ascension of the ascending node requires dedicated launches and represents a non-trivial issue for lunar constellations, due to the complexity and low accessibility of launches to the Moon. In this work, a strategy to deploy multiple satellites in different orbital planes around the Moon in a single launch is examined. The launch vehicle moves along a conventional lunar escape trajectory, with parameters selected to take advantage of gravity-braking upon encountering the Moon. A maneuver at the periselenium allows the transfer of the spacecraft along a trajectory converging to the equilibrium region about the Earth-Moon libration point \(L_{1}\), where the satellites are deployed. Providing a small \(\Delta V\), each satellite is transferred into a low-energy trajectory with the desired inclination, right ascension of the ascending node, and periselenium radius. A final maneuver, if required, allows the adjustment of the semimajor axis and the eccentricity. The method is verified using numerical integration using high-fidelity orbit propagators. The results indicate that the deployment could be accomplished within one sidereal month with a modest \(\Delta V\) budget.  
lunar constellation; low-energy trajectory; single-launch deployment; satellite constellation deployment; small satellites +
Footnote †: journal: Appl. Sci.  
0000-0002-2800-78200]Cited for updates [PERSON], [PERSON] A Single-Launch Deployment Strategy for Lunar Constellations
# Biophotonics in Dentistry
## 1 Introduction  
In recent decades, the role of photonics in medical and biomedical applications has significantly increased, and it is as crucial as its applications in optics and optoelectronic devices. The role of photonics in medical applications, such as sensing [1, 2], diagnostic imaging [3], therapy [4, 5], drug delivery [6, 7] and laser surgery [8], is as significant as its application in optics and electronics, such as a high-quality single photon sources [9], semiconductor technologies [10] and fibre optics telecommunications [11]. In dentistry, laser and light amplification by stimulated emission of radiation (LASER) plays a crucial role in soft tissue and periodontal applications [12]. Figure 1 illustrates dental applications of laser in the modern era in accordance to their respective speciality (oral surgery [13, 14], prosthodontics [15, 16], periodontitis [13], endodontics [17, 18, 19] and orthodontics [13]).  
Medical applications of laser in surgery date back to about 40 years ago, when CO\({}_{2}\) laser was invented by [PERSON] in 1964 [20; 21]. In fact, reducing the operating time and complications of surgeries are two main advantages of utilising laser in surgery [17]. Specifically, in dentistry, in addition to toxic issues of conventional procedures [22], treatment is very time-consuming and can be stressful to the patients [23]; alternatively, lasers can overcome all these disadvantages of the typical procedures [18; 24; 25; 26; 27; 28].  
In this review paper, historical events and currents advancements are discussed initially, followed by many applications in dentistry, such photoelasticity, interferometry techniques (moire interferometry), optical coherence tomography, lasers (types of lasers, applications of lasers, peri-implantitis and regenerative laser periodontal therapy), photodynamic therapy (management of symptomatic oral lichen plants, management of chronic periodontitis in smokers and anticancer therapy) and quantum dots.
## 1 Introduction
### Innovative Contributions
#### 1.1.1 Detecting Cloning Attacks and Mitigation  
Our study is centered on the mitigation of cloning attacks, particularly addressing replay attacks, etc. The model employs unique algorithms during the data processing phase to identify irregularities that may indicate cloning attempts. These algorithms analyze challenge-response pairs (CRPs) and flag bit packets and utilize specific parameters, such as C, R to detect deviations from regular behavior. The model suggests a new approach to thwarting cloning attempts by introducing a lightweight Cipher PRESENT algorithm. This encryption method ensures secure communication and data exchange, adding an additional layer of protection against cloning attacks.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 4 Experiments
### Results
#### 4.2.2 7-Scenes Results  
Zero-shot generalization experiments were performed on the 7-Scenes dataset. The model trained on NYU Depth V2 was directly tested without being fine-tuned. The generalization results of our SPDepth and P\({}^{2}\)Net are compared in Table 2.  
In terms of the average values in the last row, SPDepth achieved similar generalization performance with P\({}^{2}\)Net. In the three scenes Heads, Pumpkin and RedKitchen, SPDepth outperformed P\({}^{2}\)Net. And in the remaining four scenes, SPDepth generalized not that well. It can be noticed that there was a significant performance gap between two methods in the scene Stairs. In order to analyze the cause of the gap, we select a representative test image from Stairs. The predicted depth of the image is visualized in Figure 6.  
In Figure 6, the regions framed in black and green should have obvious depth changes theoretically. P\({}^{2}\)Net correctly predicted the depth difference. However, SPDepth could not distinguish the difference clearly. The results reveal that P\({}^{2}\)Net is still good at distinguishing the depth of the entire plane from neighboring regions and SPDepth could incorrectly propagate depth from planar regions to neighboring areas with similar appearance. The results show that the propagation could make some mistakes.
# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 2 Convolutional Neural Networks (CNNs)
We apply convolutional, ReLU and pooling operations repeatedly. In the first convolutional layer, the kernels are used to determine low-level features (edges), but the kernels in the subsequent layers are designed to determine more abstract features. If many convolutions, ReLU and pooling layers are used, more detailed features are determined. The last layer of CNN is a classifier known as the dense layer. This layer is called a fully connected layer which performs the classification of features. Individual features obtained from the convolutional layers are applied as an input to the fully connected layer. For this purpose, the output of the convolutional part of the CNN is converted into a one-dimensional feature vector and used for the classification. Conversion of features to a one-dimensional (1D) vector is called flattening. In the fully connected layer, all neurons of the previous layer are connected to the neurons of the current layer. At this time, all the neurons of the current layer are connected to the neurons of the next layer. Softmax function is used for classification in the fully-connected network. The output of a fully connected network will be clustering results. \[y_{i}^{(l)}=f(z_{i}^{(l)})\text{ with }z_{i}^{(l)}=\sum_{l=1}^{m_{i}^{(l-1)}}w_{i,j}^{(l-1)}y_{i}^{(l-1)} \tag{4}\]  
where \(w_{i,j}^{(l)}\) are the weight coefficients between the neurons of the fully connected layers, \(f\) is a transfer function representing the non-linearity. Training of the CNN starts after determining the output signals. During training, the loss function is determined in the output of the CNN. This loss function is propagated back so as to change the network's interconnections. Training is performed using the RMSprop learning algorithm presented in [53]. This technique is an extension of the stochastic gradient technique attempting to solve the rapid decreasing (vanishing) learning rate issue. The algorithm solves the problem by normalizing the gradient itself and using the average of the squared gradient. The update of the network's interconnections is based on the reduction of the loss function. \[\theta_{j}=\theta_{j}-\epsilon\frac{\
abla_{\theta}L(\theta)}{\sqrt{g_{t+1}+1} e^{-5}};\quad g_{t+1}=\alpha\cdot g_{t}+(1-\alpha)\
abla_{\theta}L(\theta)^{2} \tag{5}\]  
where \(\theta_{j}\) are network parameters, \(L(\theta)=\frac{1}{N}\sum_{i=1}^{N}l\left(\theta;yd^{(i)},y^{(i)}\right)\) is the loss function calculated at the output of the network, \(\alpha\) is the decay rate, \(\epsilon\) is the learning rate, \(yd\) and \(y\) are the desired and current values of the network output, and \(N\) is the number of training pairs. During training, CNN parameters are determined.
## 5 Methodology
### Experimental Setup  
The physical system employed in this study is shown in Figure 4. The robot used for the experiments is a UR3 (Universal Robots A/S, Odense, Denmark). This robot arm has a six-revolute-joints anthropomorphic geometry. The joints are actuated by servo motors via harmonics drive reduction; two encoders are used in each joint. A magnetic encoder monitors the motor position, and an optical encoder monitors the link position. A force sensor is mounted on the end effector of the robot.  
The robot has a sampling frequency of 125 Hz or, in other words, a sampling period of 0.008 s. The force/torque sensor is type HEX-EB165 (OnRobot A/S, Odense, Denmark), with a force range from 0 N to 200 N and a torque range from 0 Nm to 10 Nm. The sensor has a maximum sampling frequency of 500 Hz and is directly connected with the robot controller. All the variables, such as position, velocity, torques, and forces, are sent via ethernet to a computer, where the data are received every sampling period by an acquisition software implemented in Labview (version 2017, National Instruments, Austin, TX, USA). Afterward, these data are processed with Matlab (version 2019, MathWorks Inc., Natick, MA, USA).  
The preliminary experiments were made using an aluminium probe with a spherical tip attached directly to the force sensor to avoid vibrations. The definitive experiments were made with a common polishing tool.  
Two materials were considered as workpiece--aluminium and a polymer plate--with the aim to evaluate the force control with different stiffness of the environment.  
The tool chuck was driven by a Dremel model FortiFlex 9100 (Dremel Europe, Breda, The Netherlands) with a flexible rotary shaft. It was installed in the end-effector of the robot through a custom machined coupling. The final tool was a commercial polishing tool of 130 mm of diameter. It was composed of a rubber base and a buffing pad.
## 4 Outline and Methodology
### Evaluation of Consistency
#### 4.4.3 Accuracy Verification  
Accuracy verification in the present study consists of the producer accuracy (\(p_{A_{i}}\)), user accuracy (\(p_{u_{i}}\)) and overall accuracy (\(OA\)) in the confusion matrix [53]. The \(p_{A_{i}}\) is a measure indicating the probability that the classifier has labeled an image pixel into Class \(i\) given that the ground truth is Class \(i\). (Equation (4)). The \(p_{u_{i}}\) is a measure indicating the probability that a pixel is Class \(i\) given that the classifier has labeled the pixel into Class \(i\) (Equation (5)). The \(OA\) is calculated by summing the number of pixels classified correctly and dividing by the total number of pixels (Equation (6)). The formulas used to calculate the \(p_{A_{i}}\), \(p_{u_{i}}\) and \(OA\) are as follows:  
\[p_{A_{i}}=p_{ii}\ /\ p_{+i} \tag{4}\]  
\[p_{u_{i}}=p_{ii}\ /\ p_{+i} \tag{5}\]  
\[OA=\sum_{i=1}^{\infty}p_{ii}\ /\ p \tag{6}\]  
The Kappa coefficient (\(K\)) measures data coincidence via a discrete multivariate technique [54]. Moreover, considering the possibility of accidental consistency between two groups of data sets, the \(K\) reflects the classification accuracy of land cover products more exactly (Equation (7)).  
\[K=-\frac{N\sum_{i=1}^{r}p_{ii}-\sum_{i=1}^{r}(p_{i+}p_{+i})}{N^{2}-\sum_{i=1} ^{r}(p_{i+}p_{+i})} \tag{7}\]  
where \(p_{ii}\) is the constituents in which the type \(i\) of the classification results of MCD12Q1 is consistent with the type \(i\) of GlobeLand30, _i.e.,_ the number of the correctly classified pixels; \(P\) is the sum of all pixels in the GlobeLand30 classification result; \(p_{i+}\) is the sum of the type \(i\) in the classification result of MCD12Q1 in line \(i;p_{+i}\) is the sum of the type \(i\) in the classification result of GlobeLand30 in column \(i\); and \(N\) is the number of the pixels used for the accuracy evaluation.
# Generating Datasets for Real-Time Scheduling on 5G New Radio
## 1 Introduction
Because the optimal scheduling results of the systems with many tasks cannot be found, the supervised learning model is only trained by small-size systems. Therefore, creating training datasets is the primary step for supervised-learning-based scheduling. For 5G NR scheduling, there are no public datasets and related generating algorithms. Therefore, in this paper, we will propose algorithms to generate datasets. Because the solutions in training datasets directly affect the scheduling performance of supervised learning models, to improve the scheduling performance, we will find optimal solutions to constitute datatsets. However, our 5G NR scheduling problem is NP-hard [9]. There are no polynomial-time optimal algorithms. Although some researchers have found optimal scheduling solutions for 5G NR [10; 11], their 5G techniques and models are different from ours. Therefore, we propose a method to formalize our 5G NR scheduling problem, and  
Figure 1: A 5G-based industrial system. then, based on the formalization, invoke a solver to find optimal solutions. The main contributions of this paper are as follows:  
* First, according to the requirements of 5G NR scheduling, we formulate the problem into an optimization-modulo-theories (OMT) problem, which reduces variables by increasing the formalization of the relationship between them. * Second, to further reduce the difficulty of solving the problem, we transform the OMT problem into an satisfiability-modulo-theories (SMT) problem and then reduce the solution space by tightening the upper and lower bounds of the scheduling objective. * Third, we use the SMT-based method to generate a large number of optimal solutions and then constitute the dataset for 5G NR scheduling. Compared to existing work, our proposed method reduces the solution time by 74.7%. * Finally, we use the generated dataset to train a pointer network model, and the evaluation results show that the model can achieve better scheduling performance than existing polynomial-time algorithms. The rest of the paper is organized as follows: Section 2 reviews related work. Section 3 details the 5G NR scheduling model. Section 4 introduces the corresponding OMT problem. Section 5 transforms the OMT problem into an SMT problem. Section 6 evaluates our methods. Section 7 concludes this paper.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 2 Network Architecture and System Model
### System & Channel Model  
The received signal-to-noise ratios (SINRs) at the base station (BS) from the jth, kth, and lth user equipment are given by Equations (1)-(3), respectively. Let 'x' be a binary value which takes the value of 1 when two UEs are allocated the same resource block.  
\[x=\left\{\begin{array}{l}1,\textit{sameRB}\\ 0,\textit{otherwise}\end{array}\right.\]  
\[\gamma_{j}=\frac{P_{jx}\,h_{j}}{\sigma^{2}+P_{k\,x}\,h_{k\,j}+P_{l\,x}\,h_{l \,j}}\forall x\in\chi,j\in J \tag{1}\]  
\[\gamma_{k}=\frac{P_{k\,x}\,h_{k}}{\sigma^{2}+P_{j\,x}\,h_{j\,k}+P_{l\,x}\,h_{l \,k}}\forall x\in\chi,k\in K \tag{2}\]  
\[\gamma_{l}=\frac{P_{l\,x}\,h_{l}}{\sigma^{2}+P_{j\,x}\,h_{j\,l}+P_{k\,x}\,h_{k \,l}}\forall x\in\chi,l\in L \tag{3}\]  
Table 2 shows the different channel power gains including the desired and the interference power gains. We assume a log-normal shadowing distribution for all UEs. In [16], channel modeling in vehicular communications has been presented. Table 3 shows the channel model used to model the three different UEs. We used different path loss models for each type of UE. The channel is modeled as a Rayleigh fading channel with different shadowing standard deviation. The channel power gain between \(a\) and \(b\) for a VUE is given by Equation (4a). The channel power gain is in turn-dependent on the path loss and shadowing given by Equation (4b), where \(PL\) is the path loss, \(Y\) is the log-normal shadowing component, \(\ell\) is the distance between transmitter and receiver, and \(\gamma\) is the decay exponent.  
\[h^{a,b}=\alpha^{a,b}g^{a,b} \tag{4a}\] \[h^{a,b}=PL\times Y\times\ell^{-\gamma}\times g^{a,b} \tag{4b}\]
# Reduction of Water Color in a Spinning Disc Reactor
###### Abstract  
In this study, spinning disc (SD) technology was successfully applied to a synthetic water to remove its color. The preliminary data performed in a regular mixing system using a potential adsorptive material, i.e., double-layered hydroxide of a ZnAILDH type, did not provide a significant decrease (no more than 10-15%) in the water color content. Thus, ZnAILDH (2 g/L) was added to the synthetic water containing 50 mg/L Rosso Remazol RB dye that was subsequently fed onto the spinning disc. The SD efficiency was investigated at four different water-supplying flow rates (5.76, 6.00, 7.44 and 8.16 L/h) and four different disc rotational speeds (100, 250, 500 and 800 rpm). The best color removals of 44.39%, 41.14% and 42.70% were obtained at 6 L/h and 250 rpm, 6 L/h and 500 rpm and 5.76 L/min and 800 rpm, respectively, in only a 50 min working time period. In addition, for a relatively low color concentration in water (\(\sim\)30 mg/L dye) and at the lowest electric power consumption, Fenton oxidation was performed in the SD setup for a more advanced color removal of 62.54% within a 50 min time period. Furthermore, two other materials, titanium and aluminium oxides, underwent similar investigations in the SDR setup, and the obtained results were comparatively discussed. The FTIR spectra of each solid material before and after the SD technology application were used to appreciate the dye-retention performance of each material used. The obtained results indicated that the spinning disc technology correlated with the tested materials could significantly improve the water color (over 40% color reduction), this level of color reduction being higher than that obtained following a coagulation-flocculation test (20-28% color reduction), an ion exchange (25-30% color removal) or a sand filtration step (15-20%) applied to the same dye-based water sample. A further increase in color removal could be achieved by using an additional oxidative step (more than 65% color reduction).  
color removal; ZnAILDH; titanium and aluminium oxides; spinning disc reactor; Fenton oxidation; adsorption efficiency