esa-sceva/satcom-chunk-collection huggingface dataset:
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
###### Abstract  
In this paper, a backstepping-based formation control of quadrotors with the state transformation technique is proposed. First, the dynamics of a quadrotor is derived by using the Newton-Euler formulation. Next, a backstepping-based formation control for quadrotors using a state transformation technique is presented. In the position control, which is the basis of formation control, it is possible to derive the reference attitude angles employing a state transformation technique without the small angle assumption or the simplified dynamics usually used. Stability analysis based on the Lyapunov theorem shows that the proposed formation controller can provide a quadrotor formation error system that is asymptotically stabilized. Finally, we verify the performance of the proposed formation control method through comparison simulations.  
backstepping method; formation control; quadrotor; position control; small angle assumption; state transformation technique; asymptotical stability Article
## 5 Conclusions
\begin{tabular}{l l} F & Prandtl's tip loss factor \\ \(\mathbf{F}_{Ext}\) & External loads on the tower \\ \(\mathbf{F}_{Gyro}\) & Gyroscopic load vector \\ \(\mathbf{F}_{Imb}\) & Load vector due to mass imbalance of blades \\ \(\mathbf{F}_{Su}\) & Thrust force on the rotor \\ \(\mathbf{F}_{sup}\) & Load vector on tower arising from TLP acceleration \\ \(\mathbf{F}_{Reac}\) & Reaction force node from tower to the TLP \\ \(\mathbf{F}_{Rot}\) & Load vector on rotor \\ \(\mathbf{F}_{wave}\) & First order wave forces on TLP \\ FN & Normal force on blade section \\ FT & Tangential force on blade section \\ \(\mathbf{FT}\) & Thrust force matrix \\ \(\mathbf{I}_{j\pm k}\) & Identity matrix with size j times k \\ \(\mathbf{J}_{dt}\) & Equivalent drive train mass moment of inertia \\ \(\mathbf{J}_{r}\) & Rotor mass moment of inertia \\ \(\mathbf{K}_{TLP}\) & Stiffness matrix of TLP \\ \(\mathbf{k}_{tow}\) & Tower stiffness matrix of single element \\ \(\mathbf{K}_{low}\) & Global tower stiffness matrix \\ \(\mathbf{m}_{b,imb}\) & Mass imbalance of blades \\ \(\mathbf{M}_{\chi}\), \(\mathbf{M}_{Pi}\),\(\mathbf{M}_{Ro}\) & Moments caused by the rotor around the nacelle \\ \(\mathbf{m}_{tow}\) & Tower mass inertia matrix of single element \\ \(\mathbf{M}_{tow}\) & Global tower mass inertia matrix \\ \(\mathbf{M}_{TLP}\) & Inertia matrix of TLP \\ \(\mathbf{m}\) & Vector with blade sections masses \\ \(\mathbf{n}_{x}\), \(\mathbf{n}_{y}\) & Direction vectors \\ NB & Number of blades \\ NS & Number of blade sections per blade \\ \(\mathbf{N}_{g}\) & high to low speed gear ratio \\ \(\mathbf{O}_{j\pm k}\) & Zero matrix with size j times k \\ \(\mathbf{\bar{q}}\), \(\mathbf{\bar{q}}\), \(\mathbf{q}\) & Acceleration, velocity and displacemnet of the TLP \\ \(\mathbf{r}_{b,em}\) & Distance from rotor center to blade mass center \\ \(\mathbf{R}\) & Retardaton function matrix \\ T\({}_{0}\) & Static drivetrain torque on low speed shaft \\ T\({}_{g}\) & Generatortorque on high speed shaft \\ T\({}_{r}\) & Rotor torque on low speed shaft \\ \(\mathbf{T}_{imb}\) & Matrix for the direction of the imbalance loads \\ \(\mathbf{T}_{Nac}\) & Transformation matrix which isolates the center node of the nacelle in FEM of tower \\ \(\mathbf{T}_{Reac}\) & Transformation matrix for the reaction loads from the bottom node of the tower to the TLP \\ \(\mathbf{T}_{totv}\) & Transformation matrix for the acceleration of the TLP into each node of tower \\ \(\mathbf{u}\) & Velocites of water particles \\ \(\mathbf{V}_{0}\) & Free stream wind velocity \\ \(\mathbf{V}_{19.5}\) & Wind speed 19.5 m above sea surface \\ \(\mathbf{V}_{rel}\) & Relative wind velocity \\ \(\mathbf{V}_{Rot}\) & Tangential velocity \\ \(\mathbf{V}_{TLP}\) & Velocity of TLP in surge \\ \(\mathbf{V}_{tow}\) & Velocity of tower top in surge \\ \(\mathbf{W}\) & Induction velocity \\ \(\mathbf{Z}\), \(\mathbf{Z}\), \(\mathbf{Z}\) & Tower global acceleration vector, velocity vector and displacement vector \\ \(\mathbf{\alpha}\) & Blade sections angle of attack \\ \(\mathbf{\alpha}_{d}\) & tower damping weighting parameter \\ \end{tabular}
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Thermal Ablative Therapies
#### 2.1.3 Laser Ablation  
Laser ablation (LA) requires the percutaneous insertion of an array of needles into the tumor that allows the passing of optic fibers that will emit near-infrared light. The method may be used for single or multinodular HCC with sizes usually up to 5 cm and yields a response rate of up to 97% and reported overall survival rates at 3 years of up to 68% [45; 46; 47]. LA commonly uses an Nd-YAG (neodymium: yttrium-aluminum-garnet) laser with either a shorter wavelength of 800-980 nm or a higher-tissue penetrating 1064 nm wavelength [48]. The released infrared energy induces heat in the area around the insertion. Similarly to RFA and MWA, cell death is obtained at temperatures above 60 \({}^{\circ}\)C, but exceeding 100 \({}^{\circ}\)C induces vaporization, which hinders the effectiveness of the method. Tissue carbonization,  
Figure 3: MRI examination of an HCC nodule in the right liver lobe, treated with MWA 2 months prior. (**a**) T1 weighted image with fat suppression; (**b**)—subtraction image; (**c**)—T1 WI with fat suppression in the hepatobiliary phase (at 20 min); (**d**)—T2 weighted image with fat saturation; (**e**)—diffusion weighted image; (**f**)—apparent diffusion coefficient map. Arrow in figure a points to an ellipsoid area of ablation, with MRI features of coagulative necrosis, but low signal intensity is observed on the T2* sequence (**g**), suggestive of chronic hemorrhage.  
which appears at over 300 \({}^{\circ}\)C, has even more significant effects in terms of limiting heat conduction [48] (Figure 4).  
Literature data regarding the histological effects of laser ablation on either liver explants or animal models is scarce. However, follow-up imaging studies describe similar findings to other thermal ablative therapies such as RFA and MWA [49; 50]. Ultrasound examination in the days following LA reveals a central zone of vaporization surrounded by a thin halo of carbonization and a peripheral thick rim of coagulation [45].  
Coagulation necrosis is the desired effect and its volume depends on the energy delivered [51]. Incomplete or partial necrosis may be observed in a variety of cases such as tumor subtype, growth pattern, and nodule size [52; 53]. Moreover, in vivo animal studies showed LA-induced heat stress-related apoptosis in HCC cells as well as hepatocytes via caspase-3/7 activation [54]. Interestingly, new information suggests that laser ablation might stimulate HCC growth due to heat stress via PI3K/mTOR/AKT signaling [55]. These data must be further analyzed and should also be sought in other thermal ablative methods.
# An Indirect Identification Method for Train Basic Resistance Parameters
## 4 Case Analysis
### Case Studies
#### 4.2.3 Online Identification Test  
Finally, we set up a set of simulation experiments on the TBRPs changing with the position of the line. This simulation simulates the scenarios of reduced friction caused by smooth track after rain and increased air resistance caused by increased external wind speed. The simulation conditions of TBRPs in different sections are shown in Table 4. We introduced random disturbances in the last two sections. In this simulation process, the TBRPs of the current section were identified online based on real-time operation data generated by the train. The sampling interval was 100 m, and the data capacity \(I\) was 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Section** & \(a\) & \(b\) & \(c\) & **Disturbance** \\ \hline
[0, 22] km & 0.79 & 0.002288 & 0.001368 & No \\
[22; 52] km & 0.49 & 0.001208 & 0.001368 & No \\
[52; 64] km & 0.79 & 0.002288 & 0.001368 & No \\
[64; 82] km & 0.79 & 0.002288 & 0.001368 & Yes \\
[82; 120] km & 0.79 & 0.002288 & 0.002557 & Yes \\ \hline \end{tabular}
\end{table}
Table 4: The simulation conditions of TBRPs in different sections.  
Figure 4: Parameter identification results. (**a**) Comparison between fitted curves and actual curves, (**b**) standard deviation changes with iteration, (**c**) TBRPs with iteration, (**d**) comparison between actual parameter characteristics and parameter characteristics obtained by [PERSON]’s method.  
Figure 5 illustrates the results of this simulation. The identification algorithm accurately predicted the basic resistance values of most positions, but significant deviations exist in the positions of parameter switching. Similar to the previous simulation case, in the section of no data disturbance, the TBRPs obtained by this method are basically the same as the actual values, but in the section of existing data disturbance, the identification results of parameter \(c\) are relatively close to the actual situation, and deviations exist in the identification results parameters \(b\) and \(c\).
## 3 Results and Discussion  
### Effects of Discharge Power and Flow Rate of Helium  
Without the plasma treatment, the contact angle of the rayon flock synthetic leather is 0\({}^{\circ}\). Figure 3 shows the contact angles of rayon flock synthetic leather after TMS plasma treatment. The results show that the contact angle can be decreased with an increase in the helium flow rate. Remarkable enhancement in contact angle was observed under all discharge powers. At a discharge power of 30 W, the contact angle fluctuates with respect to the flow rates of helium. The fluctuation is attributed to insufficient discharge power. The ionization of TMS is unstable without sufficient energy provided from discharge power. A discharge power of 50 W results in stability and the best improvement in contact angle (helium flow rate of 12.5 LPM). Further increase in power did not promote enhancement in contact angle. The ionization of the TMS might have occurred at a discharge power of around 50 W. It is suggested that adequate power is required for plasma deposition.  
Figure 2: Sliding angle measurement.  
Figure 4: SEM micrographs illustrate the surface morphology of rayon flock synthetic leather after TMS plasma treatment with different discharge power: (**a**) untreated, (**b**) 30 W, (**c**) 40 W, (**d**) 50 W, (**e**) 60 W (treatment parameters: amount of TMS = 0.2 mL; flow rate of helium = 12.5 LPM; jet distance = 10 mm; treatment time = 30 s).  
Figure 3: Contact angle of rayon flock synthetic leather after TMS plasma treatment (treatment parameters: amount of TMS = 0.2 mL; jet distance = 10 mm; treatment time = 30 s).  
The surface morphology of rayon flock synthetic leather modified with different discharge power was examined by SEMs (Figure 4). The untreated sample surface (Figure 4a) is relatively smooth while rubs and burrs appear on the surface of the plasma modified samples [22]. A small number of rubs and burrs are deposited on the sample surface with a discharge power of 30 W, as shown in Figure 4b. The number of rubs and burrs on the plasma-modified samples increase when the power is increased from 30 W to 50 W (Figure 4b-d). However, the number of rubs and burrs drops at a discharge power of 60 W (Figure 4e). The SEM images indicate that a discharge power of 50 W (Figure 4d) promotes the deposition of silicon compounds from TMS effectively, irrespective of the discharge power.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
Since it is assumed that the point cloud is rotationally aligned with the ground through accelerometers, only one rotational degree of freedom has to be solved. For the set of rotations, we calculate a transformation over the \(XY\) plane using template matching of floor plans. Figure 1 gives an overview of the localization steps. In Section 3.5, we will introduce methods for improving run-time efficiency by caching and updating parts of the matched templates over time.
## 2 System Model  
The secure visual MIMO communication system setup proposed for the security vehicular network (SVN) is illustrated in Figure 1. It uses an existing dashboard camera of the trailing vehicle and rear light of the leading vehicle as a receiver and transmitter, respectively. The leading vehicle transmits environmental data acquired from exteroceptive sensors, vehicles internal parameters, vital biological signals of the driver and influential person as an optical signal. Transmission of this optical signal demands LoS. To facilitate the LoS, the rear light is modified with the proposed QR inspired the LEA patterns, and the particle filter is used to track the spatially varying LEA pattern.  
In the transmitter section at the leading vehicle as illustrated in Figure 2, data is encrypted by stream ciphers using the proposed LFSR based random key generation. The keys are generated using  
Figure 1: Visual multiple-input multiple-output (MIMO) communication system setup for security vehicular network (SVN).  
the synthetic color image stored in the database. The encrypted data is converted into a parallel form and given to the proposed QR inspired LEA setup.  
At the receiver section, the proposed LEA setup is captured by the camera at the trailing vehicle and further processed as shown in Figure 3. The finder and alignment patterns of the QR-Code are detected to extract the region corresponding to the LEA. Geometric transformation is applied for rectifying the distortions, and the original image is reconstructed. Each LED from the LEA is segmented, and data is extracted from the LED using a threshold value. Then, the extracted data is given to the serial to parallel converter and decrypted using the proposed key generation algorithm. Finally, the original data is retrieved from the encrypted data.
## 2 Backgrounds and Methods
### NTK Interpolation  
Although Position Interpolation has shown some effectiveness in extending the context window, models fine-tuned using this method suffer performance degradation within the original context window [14]. The reason for this performance degradation is apparent: while position interpolation avoids the issue of RoPE rotation angle out-of-bounds at large indices, it compresses the distance between tokens, severely disrupting the model's local resolution. Given that language modeling heavily relies on local relationships, disrupting local relationships inevitably leads to inaccurate predictions.  
NTK [13] addresses the \"local distortion\" problem caused by stretching all dimensions equally in PI. They derive NTK Interpolation using Neural Tangent Kernel (NTK) theory as Equations (5) and (6):  
\[base^{\prime\frac{d-2}{d}}= s\cdot base^{\frac{d-2}{d}} \tag{5}\] \[base^{\prime}= \text{{base}}\cdot s^{\frac{d}{d-2}} \tag{6}\]  
where \(s=\frac{L^{\prime}}{L}\) represents the scaling factor, _base_ is the value used in RoPE during pre-training, _base_\({}^{\prime}\) is the new corresponding value used during fine-tuning, and \(d\) denotes the embedding dimension of the attention head. This adjustment avoids uniformly stretching all dimensions, instead dispersing interpolation pressure across multiple dimensions. After this adjustment, the lowest-frequency dimensions are scaled similarly to PI, while the highest-frequency dimensions remain unchanged (i.e., unscaled). It is noteworthy that this adjustment offers an ability to extend the context window without the need for fine-tuning. However, when used for fine-tuning, this method may lead to out-of-bounds values in some dimensions compared to the maximum rotation angles during pre-training, resulting in performance degradation.  
[PERSON] [14] proposes segment-wise adjustment of \(\theta\) values in RoPE based on the period of different dimensions for interpolation. This method addresses the issue of out-of-bounds values in some dimensions in NTK and achieves better results in extending the context window compared to NTK. However, this method introduces additional hyperparameters that need to be adjusted for different models to achieve particularly good results. Additionally, we argue that interpolation for certain high-frequency dimensions may lead to performance degradation in these dimensions, as we believe these dimensions have fully learned the \([0,2\pi]\) angle during pre-training and do not require interpolation.
## 3 Methodology
### Proposed LSTM-Attention-BiTCN Model
#### 3.3.2 Bidirectional Temporal Convolutional Networks (BiTCNs)  
In our study, we also delved into the application of Temporal Convolutional Networks (TCNs) [16], an architecture explicitly crafted for sequential data processing, which amalgamates the principles of causal convolutions and dilation. TCNs are distinguished by two principal features. Firstly, the causality inherent in their convolutional layers ensures that information from future time steps does not inadvertently influence earlier ones. Additionally, their architectural design enables the mapping of input sequences of any length to outputs of equivalent length.  
The cornerstone of TCNs is the implementation of dilated causal convolutions, which are instrumental in expanding the receptive field without necessitating an increase in the number of layers. The operation of dilated convolution on a one-dimensional input sequence is mathematically represented in Equation (1), incorporating a filter \(f=\{0,1,\ldots,k-1\}\to R\), where \(k\) is the filter size, \(d\) is the dilation factor, and \(s-d\cdot i\) indicates the prior data points.  
\[F(s)=\sum_{i=0}^{k-1}f(i)\cdot x_{s-d\cdot i} \tag{1}\]  
TCNs feature a residual block structure formed by stacking two convolutional layers. If there is a discrepancy between the lengths of the input and output, an additional one-dimensional convolution is integrated into the residual block to ensure output vectors match the input length. The construction of a TCN involves stacking a requisite number  
Previous studies [16; 17] have demonstrated TCNs' superiority over LSTM models in certain natural language processing (NLP) tasks. In our proposed model, we integrate two successive hidden layers of Bidirectional Temporal Convolutional Networks (BiTCNs). This choice is predicated on the ability of BiTCNs to thoroughly assimilate contextual information from the embedded vectors derived through word embedding techniques [18]. By processing both forward and backward sequences of words within sentences, BiTCNs adeptly capture the intricate dependencies and relationships between words, thereby enriching the model's interpretative capabilities.
# Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks
## 2 Theoretical Background
### Social Considerations
#### 2.2.2 Privacy, Data Protection, and Security  
Neural networks and machine learning algorithms rely on vast quantities of data for performance enhancement and training. However, this dependence raises privacy and security concerns. Personal information collected during data collection and processing may be susceptible to unauthorized access, misuse, or intrusions, which could result in privacy violations or identity theft.  
To safeguard privacy, developers need to adhere to stringent data protection practices, such as anonymization and encryption, and conform with applicable privacy regulations. Transparent data usage policies, user consent mechanisms, and data minimization strategies can enable individuals to make educated decisions regarding their data. Moreover, ensuring a secure infrastructure, conducting regular vulnerability assessments, and instituting robust access controls are essential for protecting sensitive data.
## 3 Results
### Question 4: How to Collect Data  
The procedure of data collection is essential for IEQ assessment. In most studies, both subjective and objective data were collected through on-site measurements and questionnaires. However, some studies evaluated IEQ performance using objective or subjective data alone. Additionally, few studies used the data obtained through simulations or chamber experiments to assess IEQ performance [57; 58; 74; 76].
## 4 Results
### Assessment of Discharge Simulations Using Multiple SPPs  
The aforementioned statistical evaluation of multiple SPPs against the ground precipitation data shows that 3B42 RT presents the best overall quality among all near-real-time products, and IMERG-F and 3B42V7 demonstrate a relatively higher accuracy than the other two post-real-time products. Therefore, 3B42 RT, IMERG-F, 3B42V7, and the gauge-based gridded precipitation data set were adopted to drive the GXAJ hydrological model for 3-hourly discharge simulations from 1 April 2014 to 31 December 2016 at the five hydrological stations in the Chindwin River basin. Hydrological simulations were performed under two scenarios: discharge simulations with rain-gauge-calibrated parameters and discharge simulations with satellite-product-specific parameters (detailed description in Section 3.3). The results under these simulation scenarios are discussed in the following subsections.
## 2 Theoretical Background
### Reliability Indices  
The ability to maintain continuous service of an electricity-supply system is an indication of the reliability [23]. The following reliability indices are incorporated to measure system performance [19; 23]. The failure rate is denoted by \(\lambda i\), the number of customers is given by \(Ni\), the annual unavailability is denoted by \(Ui\), and the average load connected to load point \(i\) is given by \(La(i)\)[19; 23]:  
System Average Interruption Frequency Index (SAIFI):  
\[\text{SAIFI }=\frac{total\ number\ of\ customer\ interruptions}{total\ number\ of\ customers\ served}=\frac{\sum\lambda iNi}{\sum Ni} \tag{10}\]System Average Interruption Duration Index (SAIDI):  
\[\text{SAIDI}\ =\ \frac{\text{\it sum of customer interruption durations}}{\text{\it total number of customers}}\ =\frac{\sum\text{\it U}\text{\it Ni}\text{\it Ni}}{\sum\text{\it Ni}} \tag{11}\]  
Consumer Average Interruption Duration Index (CAIDI):  
\[\text{CAIDI}\ =\ \frac{\text{\it sum of customer interruption durations}}{\text{\it total number of customer interruptions}}\ =\frac{\sum\text{\it U}\text{\it Ni}\text{\it Ni}}{\sum\text{\it Ni}\text{ \it Ni}} \tag{12}\]  
Energy Not Supplied Index (ENS):  
\[\text{ENS}=\text{total energy not supplied by the system}=\sum\text{\rm L}_{\text{a(i)}}\text{\rm U}_{\text{\rm i}} \tag{13}\]
# Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
## 3 Theoretical Model
### Model of the Helmholtz Resonant Chamber  
The Helmholtz resonant chamber is similar to a closed-ended pipe volume chamber of volume, as shown in Figure 3. From the equation of state of the fluid:  
\[K_{e}=-V\frac{dp}{dV} \tag{1}\]  
where \(K_{e}\) is the apparent bulk modulus, \(V\) is the volume of the capacitance chamber.  
According to \(dV=-qdt\), so there are:  
\[\frac{V}{K_{e}}\frac{dp}{dt}=-\frac{dV}{dt}=q \tag{2}\]  
Figure 3: HTPA schematic. 1 Pipeline A; 2 Helmholtz resonant chamber; 3 Three-way unit; 4 Pipeline B; 5 Throttle valve.  
Figure 2: Helmholtz Fluid Resonator. (**a**) Schematic view; (**b**) Equivalent Schematic view.  
The dynamic characteristic equation of the Helmholtz chamber is obtained by applying the Laplace transform to Equation (3):  
\[Q_{1}(s)=\frac{Vs}{K_{e}}P_{1}(s) \tag{3}\]  
The neck pipeline is equivalent to a short length of straight pipeline, and the dynamic characteristics equation of the neck pipeline unit is established by the lumped parameter method as follows [27]:  
\[\begin{bmatrix}P_{2}(s)\\ Q_{2}(s)\end{bmatrix}=\begin{bmatrix}1&\frac{\rho L}{A}(s+R_{v1})\\ \frac{A\rho L}{K_{e}}s&1\end{bmatrix}\begin{bmatrix}P_{1}(s)\\ Q_{1}(s)\end{bmatrix} \tag{4}\]  
where \(L\) is the length of the neckline, \(A\) is the cross-sectional area of the neckline, \(\rho\) is the density of the oil, and \(R_{v1}\) is the relative friction coefficient of the neckline.