esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 2 Materials and Methods  
The methods developed in this article focus on the calculation of the relative transformation between systems. The Kabsch [35] algorithm, also known as the Kabsch-Umeyama [36] algorithm, is used to find the rotation and translation between two sets of points in the 3D space. The main idea is to use reference points in space with known positions relative to the reference systems of the camera and the LiDAR, respectively. A rectangular polygon with known dimensions is used to implement this idea.  
The proposed methods are divided into the following parts:* Calculation of the fisheye camera calibration parameters by means of the _toolbox_ presented in [37].
* Obtaining a set of 3D points belonging to the calibration pattern in LiDAR coordinates (Section 2.1).
* Extraction of a set of 3D points belonging to the calibration pattern in camera coordinates (Section 2.2).
* Calculation of the transformation between both reference systems using the Kabsch algorithm.  
Two different methods are used for the LiDAR and the camera in order to obtain the 3D coordinates of the corners of the rectangle. The 3D coordinates of the corners relative to the camera cannot be obtained in a direct way, unlike in the case of the LiDAR. The process to find the 3D coordinates of the points with respect to the camera is explained in Section 2.2.
## 4 Fluid Dynamic Performance
### Performance Assessment  
From the combination of some of these parameters, it is possible to calculate the drag force due to the viscoelastic flow around the model. The drag force can be described in terms of the wall shear stresses, due to viscous effects and normal stresses due to the pressure. Both wall shear stress and pressure vary in magnitude and direction along the surface. Thus, it is useful to know the detailed distribution of shear stress and pressure over the surface of the body to calculate the drag force, which is given by the following equation (Equation (3)):  
\[F_{D}=\int_{A}dF_{x}=\int_{A}pcos\theta dA+\int_{A}\tau_{w}sin\theta dA. \tag{3}\]  
This implies that, apart from knowing the wall shear stress, it is important to know the pressure distribution along the surface of the body. The calculation of this pressure, as it was mentioned previously, is sometimes difficult due to the experimental limitations, and a good approximation would be the calculation of the pressure difference before and after the object. Nevertheless, CFD simulationsare, in this case, an excellent tool to determine the pressure surface distribution around the object, and, subsequently, to calculate the drag force combining experimental and numerical techniques.  
In order to propose a complete _performance parameter_, a combination of the result of these experimental techniques is crucial. Therefore, it is expected that a low pressure drop around the microbot, small length of the wake behind the object, low disturbance of the velocity field and low stress field would be the key combination for the maximum efficiency.  
To theoretically conceive the morphology of the prototypes to get the optimum design as a function of the maximum performance, optimization numerical techniques, able to combine the characteristics of the object and the fluid flow conditions, are also available to determine the morphology able to diminish the drag and flow disturbance [85; 86].  
[PERSON] et al. [77] also proposed an _efficiency parameter_ that, in terms of energy dissipated in the flow by the presence of the object, allowed for classification of better efficiency of different microbot prototypes. Nevertheless, the drag force experienced by the prototypes was not reported.
## 5 Comparative Analysis Results and Discussion
### Hyperparameter Finetuning Results
#### 5.3.4 BERT Classifier Layer  
The BERT model consists of an embedding layer, whose operation is based on calculating the sum of the embeddings of the tokens, segmentation, and positions, respectively. For the BERT base model, the embedding matrix is followed by 12 encoders stacked on top of it, plus an additional pooling layer. The special classification token (CLS) is the aggregation of the last hidden state weights after the final encoder, and is used for sequence representation. The CLS token is an input for the next-sentence prediction (NSP) layer. This NSP layer is the first layer after the encoders, and is used to determine what relationship exists between neighboring sentences [6]. The NSP layer then is reshaped to be connected to the convolutional layer (Figure 4) to build the feature map.  
An artificial neural network is capable of collecting different levels of syntactic structure information from different layers, based on the defined downstream task, such as a text classifier [5]. For this paper, we researched the impact of different convolution and dense layers linked to the output of the stacked BERT encoders. For each evaluated configuration we then trained and tested the model in order to select the highest-performing model for our given dataset.  
For this paper, we next focused on adapting different hyperparameters, including the different filter sizes for convolution layers, the drop-outs, and the number of neurons for  
Figure 9: Activation function effect on training time and accuracy when fine-tuning BERT with different number of convolution and dense layers.  
each dense layer. We achieved the best results for two convolutions with 256 and 128 filter sizes, respectively. Both convolution layers are followed by a Max pooling layer, which capture the largest element of the feature map based on the kernel size. A fully connected neural network with a 64 neuron dense layer is followed by a drop-out layer with rate 0.5. This feed forward network is followed by a final dense layer with the \"softmax\" activation function for the classification task. Table 7 illustrates the results we obtained for different configurations of convolution and dense layers.
# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
### Our Contributions  
Our contributions are as follows:  
* A hybrid framework that enables CC and DCs to handles long- and short-lifetime data, which represents the freshness (or recency) of data, in order to ensure the availability of unexpired data for the local task (i.e., vertical clustering) and the global task (i.e., vertical routing performed over a clustered network) in FANETs under 5G network scenarios.
* A DQN-based vertical routing over a clustered FANET that selects routes across different network planes (or network cells) to enable inter- and intra-plane communications while improving network lifetime, as well as reducing energy consumption and link breakages. Our proposed scheme focuses on route selection, rather than signaling protocol and message structure, in 5G access networks.
## Results and Discussion
### Anticorrosion Mechanism Analysis  
Based on the above discussion, the nano-PDA@GO-TiO\({}_{2}\) composites can significantly improve the corrosion resistance of waterborne epoxy coatings. Its anti-corrosion mechanism is shown in Figure 12. First, DA enhances the interface binding force between PDA-GO-TiO\({}_{2}\) and epoxy resin by self-polymerizing and depositing on the surface of GO to form a polydopamine (PDA) layer, and the active metabody (beramand and midamin) of the DA and PDA can react with the epoxy substrate of the epoxy resin. In addition, PDA and the oxygen-containing functional group interaction on the surface of GO, so that graphene oxide reduction peeling and anchor between its sheet structure, increase the surface area and enhance the compatibility of the GO in the water-based epoxy resin dispersion. The uniform dispersion of nano-PDA-GO-TiO\({}_{2}\) composites in water-based epoxy resin layer overlay, which forming a dense physical barrier layer. The GO and TiO\({}_{2}\) have synergies, by the filling resin cured after the micro-cracks and pores to achieve healing effect, and in the coating-metal interface caused by strong shielding and locking effects. Making the diffusion penetration path of the corrosive medium more complex, which effectively improves the anti-penetration ability of the coatings.  
Figure 12: Diagram of the path of corrosive media (O\({}_{2}\), H\({}_{2}\)O, Cl) through pure waterborne epoxy coating (WEP) and PGT/WEP coating.
## 1 Introduction
This paper focuses on an analysis of the MTF decrease derived from the complex image motion, such as linear motion, low-frequency sinusoidal motion, and high-frequency sinusoidal motion, and builds a calculation model for it. First, we propose a general MTF expression for the mixed image motion of linear motion and sinusoidal motion based on the average intensity analysis of a sine-wave response, and the definition of modulation contrast (MC). On this basis, to solve the problem that trigonometric functions are hard to integrate, we introduce the summation of infinitely many terms involving Bessel function to replace the integral of the trigonometric functions, so as to obtain the analytic expression of MTF. In order to calculate the numerical results of MTF, only a finite order of Bessel functions can be used for the calculation, and we also calculated the truncation error. We further propose a simulation method based on interactive programming for MATLAB and ZEMAX, to verify the calculation results, and some experiments were carried out. The simulation results and experimental results agreed well with the calculated MTFs, indicating the validity of the proposed MTF calculation model. This study is of great significance for the design and image restoration of remote-sensing imaging, and has been applied in the design of airborne remote-sensing camera. The remainder of this paper is organized as follows: the analytical MTF expression for the mixed image motion of linear motion and sinusoidal motion is presented in Section 2. The truncation error caused by replacing the infinite sum with a finite sum approximation is analyzed in Section 3. The formula analysis in Section 3 shows a strong connection between the frequency of sinusoidal motion and the MTF. In Section 4, the simulation method is proposed to simulate the variation of MTF. The experimental results are presented in Section 5. Section 6 gives the conclusions.
## 3 Study Area and Data
### Data Cleaning  
The original dataset provided by Mobike was filtered to meet the research objective. As the study focuses on bike-sharing and its connection with rail transit, those trips not related to subway stations are outside the scope of this study. Thus, the term \"access trip\" is introduced here, defined as trips whose endpoints (origin or destination) coincide with the traffic zone where the subway station entrances are located. These access trips were filtered and the other data were discarded. In consideration of the rail network density and critical accessing distance for a cyclist to rail transit [33], the data with excessively small (less than 100 m) or large (greater than 6 km) distances and those with both origin and destination located in the vicinity of subway station entrances (less than 5%) were regarded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Data Description** & **Amount of Data** & **Data Sources** \\ \hline \multirow{3}{*}{GPS data} & ordered, userid, bikeid, & \multirow{3}{*}{2,466,817 records} & Open data of Mobike \\  & biketype, starttime, & & Co., Ltd., Beijing, China \\  & geohashed\_end\_loc; & & \\ \hline \multirow{2}{*}{POI data} & name, latitude, longitude, & \multirow{2}{*}{723,154 records} & \multirow{2}{*}{Autonavi map} \\  & address, type & & \\ \hline \multirow{2}{*}{Track data} & OD of each path, direction, & \multirow{2}{*}{879,158 records} & \multirow{2}{*}{Baidu map} \\  & distance, trip time & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description and sources.  
as abnormal data and discarded. In addition, extreme weather can significantly affect the possibility of commuters choosing cycling mode, so data on the days with extreme weather conditions were also removed. Thus, the data for 17 May was discarded to avoid the influence of stormy weather. Finally, 879,158 GPS data records of Mobikes remained, accounting for 36% of the original data.
# Closed-Loop Robotic Arm Manipulation Based on Mixed Reality
## 1 Introduction
Among the key findings of this research work, safety hazards also contain ergonomics issues. Moreover, collision avoidance and mitigation are among the key topics providing fertile ground for further research. Interestingly, safety assurance in collaborative environments, according to [PERSON] et al. [19], requires (i) integration of recent Industry 4.0 technologies in order to adequately acquire data and (ii) the development of suitable algorithmic approaches for processing these data and constantly adapting system parameters in order to ensure that humans and robots can safely co-exist. According to the OECD (Organization for Economic Co-operation and Development), 14% of jobs in OECD countries are at risk of automation [20], owing to a lack of meaning, increased repetitiveness, or a high risk of injury [21]. Based on the abovementioned challenges, the identified challenges as well as the limitations of the investigated key relevant publications in the field of HRC are summarized in Table 1, below. Therefore, in this paper, the design and development of a framework for near real-time remote navigation of robotic arms is presented. Furthermore, the proposed framework is facilitated with the integration of MR. Additional functionalities, such as safety zones, robot reachability zones, etc., are implemented in the framework in an attempt to upscale the user experience. The rest of the paper is structured as follows. In Section 2, the most pertinent literature on AR-based robotic manipulation interfaces is reviewed. In Section 3, the proposed system architecture and its modules are discussed in detail. Then, in Section 4, the implementation steps are presented. Section 5 presents the case study, and Section 6 summarizes the results and the discussion. Finally, conclusions and future steps are set out in Section 7. \begin{table}
\begin{tabular}{c c c c c} \hline
**A/A** & **Ref. ** & \multicolumn{2}{c}{**Challenges**} & **Limitation** \\ \hline \multirow{6}{*}{1} & & \(\bullet\) & Limitation of a robot’s interface design & \multirow{6}{*}{Proposed framework is a proof of concept} \\  & & \(\bullet\) & Bottleneck caused by a combination of & \\  & & & multimodal control commands for intuitive & \\  & & & control of the robot & \\  & & & Typical challenge of Human–Robot & \\  & & & Collaboration (HRC) assembly: HRC assembly & \\  & & & and adaptive responses to commands and & \\  & & & correct triggering of the relevant controls & \\ \hline \multirow{6}{*}{2} & & \(\bullet\) & Confusions exist surrounding the relationships & \multirow{6}{*}{Low acceptance of the human–robot combination} \\  & & \(\bullet\) & Lack of standards & \\  & & \(\bullet\) & Lack of safety solutions & \\ \hline \multirow{6}{*}{3} & & \(\bullet\) & Limited investigation of behavior of motion; & \\  & & & primitive learning in the presence of & \\  & & & multiple demonstrators & \\  & & & & No investigation of the higher order model for \\  & & & behavior will be a key skill to allow & \\  & & & collaborative robots to operate and become & \\  & & & useful in the human environment & \\  &
## 3 Problem Description and Mathematical Models
### Mathematical Model
\begin{cases}0&\text{ ; }min\left(t_{i}^{\sim}\right)>b_{i}\\ \frac{\left(\left[b_{i}-max\left(t_{i}^{\sim}\right)\right]-min\left(\widetilde {u_{ij}\overline{\xi}}\right)\right)^{2}}{interval_{ij}^{\sim}*\left(\text{mid} \left(\widetilde{u_{ij}\overline{\xi}}\right)-min\left(\widetilde{u_{ij} \overline{\xi}}\right)\right)}&\text{ ; }max\left(t_{i}^{\sim}\right)>b_{i}\text{ and }min\left(t_{i}^{\sim}\right)<b_{i} \text{ and }\left|b_{i}-max\left(t_{i}^{\sim}\right)\right|\leq mid\left( \widetilde{u_{ij}\overline{\xi}}\right)\\ 1-\frac{\left(max\left(\widetilde{u_{ij}\overline{\xi}}\right)-\left|b_{i}-max \left(t_{i}^{\sim}\right)\right|\right)^{2}}{interval_{ij}^{\sim}*\left(max \left(\widetilde{u_{ij}\overline{\xi}}\right)-\text{mid}\left(\widetilde{u_ {ij}\overline{\xi}}\right)\right)}&\text{ ; }max\left(t_{i}^{\sim}\right)>b_{i} \text{ and }min\left(t_{i}^{\sim}\right)<b_{i}\text{ and }\left|b_{i}-max \left(t_{i}^{\sim}\right)\right|>mid\left(\widetilde{u_{ij}\overline{\xi}}\right) \\ \frac{b_{i}-\max\left(t_{i}^{\sim}\right)}{interval_{ij}^{\sim}}&\text{ ; }max\left(t_{i}^{\sim}\right)<b_{i} \text{ and }\frac{b_{i}-max\left(t_{i}^{\sim}\right)}{interval_{ij}^{\sim}}\leq 2\\ 2&\text{ ; }\frac{b_{i}-max\left(t_{i}^{\sim}\right)}{interval_{ij}^{\sim}}>2 \end{cases}  \tag{23}\]  
12. Time window constraints: \[t_{i}\leq JIT_{i},(i=1,2,\ldots,n)\] (24)  
13. Advanced time for materials: \[t_{i}\leq JIT_{i},(i=1,2,\ldots,n)w_{ie}=JIT_{ie}-t_{i}(i=1,2,\ldots n;\ e=1,2, \ldots,E_{i})\] (25)  
14. Penalty time: \(w_{kmin}\) indicates the minimum advance time of the material required for station \(i\), which is delivered by the \(kth\) AGV. \(w_{k}\) indicates the total penalty time of each AGV. Equation (28) indicates that the departure time from the raw library of the \(kth\) AGV is delayed for \(w_{kmin}\) to satisfy the smallest demand time of the material required for workstation \(i\) delivered by the \(kth\) AGV in time. \(N_{k}\) indicates the set of stations distributed by the \(kth\) AGV. \[w_{i}=\sum_{\epsilon=1}^{E_{i}}w_{ie}(i=1,2,\ldots n)\#(26) \tag{26}\]  
\[w_{kmin}=\min\{w_{ie}\},(i\in N_{k},e\in E_{i}) \tag{27}\]  
\[w_{k}=\sum_{i=1}^{N_{k}}(w_{i}-w_{kmin}) \tag{28}\]  
15. Decision-making variables: \[x_{ijk}=\left\{ \begin{array}{l}1,\ kth\ AGVis\text{ \emph{responsible}}\ \emph{for}\ \emph{the distribution}\ \emph{from}\ \emph{workstation}\ \emph{i}\ \emph{to}\ \emph{j}\\ 0,\ \emph{other}\end{array} \right.\] (29)  
\[y_{ik}=\left\{ \begin{array}{l}1,\emph{Delivery}\ \emph{service}\ \emph{for}\ \emph{station}\ \emph{i}\ \emph{is}\ \emph{completed}\ \emph{by}\ \emph{the}\ \emph{kth}\ \emph{vehicle}\\ 0,\emph{other}\end{array} \right. \tag{30}\]  
Thus, the JIT-RMOVRPTW can be summarized by three objectives defined by Equations (1)-(4), subject to constraint conditions defined by Equations (5)-(30).
## 3 Methodology
### Fuzzy Image Classification
#### Building Classification
The smallest size for a building in this study is 3 m \(\times\) 4 m and the biggest size is 25 m \(\times\) 20 m. Regarding the image spatial resolution, area limit for building segments will be around 50 to 2000 pixels, within which, area from 100 to 1500 is the most probable range (membership degree equals to 1) and the remaining regions are the fuzzy areas. In Figure 8, the _medium_ fuzzy function is used for delineating buildings; the _low_ fuzzy function will also be used for vehicle detection in Section 3.2.5. **Figure 9.** Fuzzy functions for area, _medium_ fuzzy function is used for building detection; _low_ fuzzy function is used for vehicle detection. Equations related to low and medium lingual values for area. _low_ Area \[\mu(x)=1-\frac{|T_{1}-x|}{T_{1}},\text{for }0\leq|T_{1}-x|\leq T_{1}\ ;\mu(x)=0\] (15) _medium_ Area \[\mu(x)=\min\left\{2-2\left(\frac{|\alpha-x|}{\lambda}\right),1\right\},\text {for a}-\lambda\leq|\text{a}-x|\leq\text{a}+\lambda;\ \mu(x)=0\] (16)  
where, \(\text{a}=\frac{T_{2}+T_{3}-2T_{1}}{2}\) and \(\lambda\ =\ T_{1}\); also in this project \(T_{1}\), \(T_{2}\), and \(T_{3}\) equal 50, 100, 1500, respectively. In this research, Rectangular shapes with moderate area which are neighbors to shadows in the direction specified by sun azimuth angle are classified as buildings. The resulting fuzzy rule for building detection is:  
If **Rectangular fit** is _high_ and **elliptic fit** is _fairly high_ and **Area** is _medium_ and **southern/western-neighbor** is _shadow_, then segment is **Building**.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Experimental Configuration and Methodology  
The experimental setup employs a derivative-based PPM generation system utilizing a voltage-controlled oscillator (VCO) with a monostable multivibrator for modulation. The VCO represents the most noise-susceptible component due to inherent capacitive and inductive characteristics, making it the logical injection point for noise analysis. The system parameters were standardized with a 5-Hz sinusoidal input signal at 1 Vpp amplitude, while the VCO operated at a base frequency of 20 Hz with frequency sensitivity of 1 Hz per volt. Both the modulation and demodulation monostable multivibrators employed identical threshold settings of 0.5 V for consistent comparison.  
The demodulation process utilizes the Quasi-FM-PWM technique described in Section 3.2.3, selected for its implementation simplicity and computational efficiency. The demodulation chain consists of a monostable multivibrator followed by a sixth-order Butterworth low-pass filter with an 8 Hz cutoff frequency, providing adequate selectivity while maintaining signal integrity.
# Forecasting Multivariate Time Series
## 3 Experimental Results and Discussion
### Experiment 1: Univariate Series
A total of 892 models were trained and evaluated, with Bidirectional LSTM combined with GAIN imputation emerging as the top performer, achieving an RMSE of 10.028 and MAE of 9.0879. Among statistical techniques, autoregression (AR) showed promise when paired with cubic interpolation, ranking at 693. Conversely, the CMA-based model with spline interpolation and a fifth-degree polynomial exhibited the poorest performance, with an RMSE of 9344.1381 and MAE of 7431.54. Notably, tree-based machine learning algorithms performed suboptimally, with the decision tree regressor and random forest models ranking at 703 and 729, respectively, as shown in Table 2. Performance graphs of the best and worst models are provided in Figure 8.  
Figure 6: (**a**) Comparison of actual and forecasted data generated by ARIMA (7,1,2) imputed by CMA. (**b**) Comparison of actual and forecasted data generated by CMA imputed by polynomial interpolation of degree 9. Figure 7: (**a**) Decomposition graph of closing prices of Bitcoin (**b**) Data distribution: the graph shows normal data distribution of Bitcoin’s closing price in USD. The results indicate that Pretimize performs well across different types of time series data. For instance, the Vanilla LSTM model showed the best performance on stock market data, with an RMSE of 0.0294, while ARIMA models with various imputation techniques performed best on datasets with missing values, highlighting the framework's versatility and effectiveness.
## 2 Introduction to IPv6
### Transition Methods
#### 2.3.2 Dual Stack  
Dual IP layer or Dual Stack is defined in RFC 4213 ([46], p.1) as a \"technique for providing complete support for both Internet protocols--IPv4 and IPv6--in hosts and routers.\" At least in part, dual stacking will occur in any transition from IPv4 to IPv6. It is further expected that dual-stacked networks will exist for a long time also after IPv4 depletion. A complete dual stack is probably the best way to avoid security issues involved with IPv4-IPv6 interaction. On the other hand, it does increase administration workload by adding complexity and literally doubling the configuration overhead. Most other transition techniques of the categories tunneling and translation require some kind of dual stacking.