esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 4 Material and Methods  
### System Model  
The system model of the mobile robot generally consists of the motion model and the observation model. For SLAM, the motion model and the observation model can be described using discrete nonlinear functions. Therefore, the more complex the nonlinear function, the greater the computational complexity in solving the SLAM problem. Relatively simple motion models and observation models are generally used in SLAM. In this paper, we adopt the wheeled robot model shown in Figure 2.  
The state vector \(x_{k}\) of the wheeled robot is usually composed of the robot's position information \(x_{k}\), \(y_{k}\), and course angle \(\phi_{k}\), i.e., \(x_{k}=[x_{k}y_{k}\ \phi_{k}]^{\mathrm{T}}\). The observation vector \(z_{j,\ k}\) of the wheeled robot is usually composed of the distance \(r_{j,\ k}\) between the \(j\)-th feature \(m_{j}\) of the map and the sensor, and the direction angle \(\theta_{j,\ k}\) between feature \(m_{j}\) and the sensor, i.e., \(z_{j,\ k}=\left[r_{j,\ k}\ \theta_{j,\ k}\right]^{\mathrm{T}}\). Therefore, the motion model and observation model of the wheeled robot are expressed as  
Figure 1: The flow chart of the MCSRUKF-SLAM algorithm.  
\[\mathbf{x}_{k}=\left[\begin{array}{c}x_{k}\\ y_{k}\\ \phi_{k}\end{array}\right]=\begin{bmatrix}x_{k-1}+\Delta T\cdot V_{k}cos(\phi_{k- 1}+\Omega_{k})\\ y_{k-1}+\Delta T\cdot V_{k}sin(\phi_{k-1}+\Omega_{k})\\ \phi_{k-1}+\frac{\Delta T\cdot V_{k}}{L}sin(\Omega_{k})\end{bmatrix}+\mathbf{w}_{k}, \tag{53}\]  
\[\mathbf{z}_{j,k}=\left[\begin{array}{c}\mathbf{r}_{j,k}\\ \theta_{j,k}\end{array}\right]=\left[\begin{array}{c}\sqrt{\left(x_{j,k}-x_ {k}\right)^{2}+\left(y_{j,k}-y_{k}\right)^{2}}\\ arctan\left(\frac{y_{j,k}-y_{k}}{x_{j,k}-x_{k}}\right)-\phi_{k}\end{array} \right]+\mathbf{v}_{k}, \tag{54}\]  
where \(V_{k}\) denotes the speed control amount of the robot, \(\Omega_{k}\) denotes the wheel steering angle control amount of the robot, \(\Delta T\) denotes the sampling interval, \(L\) denotes the wheelbase of the front and rear wheels, \(\left(x_{j,\ k},\ y_{j,\ k}\right)\) is the coordinate of the \(j\)-th feature in the world coordinate system, \(\mathbf{w}_{k}\) is the process noise, and \(v_{k}\) is the observation noise.
# A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments
## 3 Construction of the Orchard Environmental Identification Model  
In recent years, with the application of fully convolutional networks (FCNs), convolutional neural networks (CNNs) have been used to generate semantic segmentation charts of any size on the basis of feature diagrams, which can split images at the pixel level [19]. Based on this method, many algorithms have been derived, such as the deep-splitting network framework DeepLab series [20; 21], the scene resolution network PSPNet using a pyramid pooling module [22], and the U-Net network for medical image segmentation [23].  
The U-Net network can still achieve better model segmentation accuracy with few samples, and for the first time, used the skip connection to add encoded features to decoding features, creating an information propagation path that allows signals to spread more easily between low-level and advanced features; this not only facilitates backpropagation during training but also improves model segmentation accuracy. However, the U-Net network has insufficient ability to obtain contextual information from images, especially for complex scene data with large differences in category scales. Multi-scale fusion is usually used to increase the depth of the network to improve the U-Net network's ability to obtain contextual information from images. However, with increasing network depth, the accuracy of model recognition decreases rapidly after saturation rapidly declines, and the recognition error increases. To solve this problem, [PERSON] et al. [24] proposed a deep residual network that used identity mapping to obtain more contextual information. The error did not increase with the depth of the network, which solved the problem of training degradation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Number** & **R** & **G** & **B** \\ \hline Background & 0 & 0 & 0 & 0 \\ Road & 1 & 0 & 0 & 255 \\ Peak & 2 & 0 & 255 & 0 \\ Sundries & 3 & 210 & 210 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orchard environment category label information.  
Figure 2: Labeled map of the orchard environment.
## 6 Experiment
### Result and Discussion
#### 6.2.1 General Performance  
In the first part of the experiments, M-LSTM is trained and evaluated with two types of data preprocessing strategies, i.e., with a labeled time field and without a labelled time field. Figure 9 shows the results of the test set in _DatasetS1_, _DatasetS2_, and _DatasetS3_. In this question, there are two results, _(i)_ a sufficient training set, and _(ii)_ a lack of a training set.  
For the first result _(i)_, the model is sufficiently trained with data from 2017 and 2018 and evaluated in 2019 from _DatasetS1_ and _DatasetS2_. Figure 9 shows that the model achieves better performance in all three metrics with the labeled time field. The results are similar under the same settings for the other models. The details are provided in Table 2 and the line plot in Figure 10. Therefore, the models can learn and extract more valuable features if they are trained with the appropriate data preprocessing strategy.  
For the second result _(ii)_, the model is only trained with data from 2018 and evaluated in 2019 from _DatasetS3_. Figure 10 shows that the model performed well in predicting and matching the actual values, as evidenced by its superior fit line compared with the other models in Figures 9 and 10. These findings suggest that the proposed preprocessing method is effective, particularly in situations with a limited amount of training data available for model training.  
Figure 9: Comparison of M-LSTM trained with labelled time and without labelled time. (**a**) \(MAPE\) error in the scale of (0, 1), (**b**) \(NRMSE\) error, (**c**) \(R^{2}\)_score_. Note that, the higher \(R^{2}\)_score_ indicates the better model’s performance.
## 1 Introduction
The latest captured interferograms are then used to reconstruct the depth map using the Fresnel transformation method as proposed by [PERSON] et al. [16]. This methodology provides better noise cancellation in real-time compared to state-of-the-art methods. This ability enables us to monitor live cells without compromising their viability in real time. Using the proposed methodology, we were able to monitor the epithelial to mesenchymal transition of different cell cultures and report the morphological transformations experienced during this process. We used four different cell lines during the experiments: HCT-116, ONCO-DG-1, MDA-MB-231, and HUVEC. The observation of the cell cultures are done over the course of 48 h after treatment with the TGF-\(\beta\) growth factor. The TGF-\(\beta\) is known to induce the epithelial to mesenchymal transition process by acting as a tumor promotor. It has been shown in various studies that the application of TGF-\(\beta\) results in the delocalization, degradation, and downregulation of cell to cell junctions [17; 18; 19]. This in turn causes a loss in the epithelial functions and stimulates the metastasis process. A depth map of the sample is obtained every 6 h during this period and surface area, eccentricity and height parameters are calculated for each observation. We compare the changes in these parameters for each cell line. The primary motive of this study is to show the advantages of using quantitative phase imaging methods where the observation of morphological transformation of the sample is important. Obtained results indicate that cells become more spherical during this process which is attributed to the decrease in cells adhesive properties. The extraction of such morphological parameters will lead to a better understanding of the metastasis process. The proposed methodology allows such an observation for living cells in vitro.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### Monitoring System Layout  
The LEWS mainly consists of deformation measurement-systems installed on the surface (e.g., attached to existing infrastructure), shallow subsurface up to about 1 m depth (e.g., inclination sensor probes and linear extension and shear measurement systems placed in trenches) and at some points deep subsurface in drillings. The system is based on the following sensors/measurement devices:  
1. Horizontal Continuous Shear Monitor (CSM) measurement lines and extensometer (EXT);
2. Wireless LoRa sensor nodes;
3. Piezometers, extensometers and vertical CSM in drillings.  
These devices as well as the gateways and data management system are shown in a schematic layout of the whole EWS in Figure 1.  
Figure 1: Schematic layout of the Inform@Risk monitoring system [64]. Data from CSM (Continuous Shear Monitor) and extensometer (bottom), as well as LoRa (Long Range) Nodes (top right) are combined in the Inform@Risk network and cloud system (top left).  
The horizontal and vertical CSM lines provide spatially and temporally highly resolved measurements of shear deformation along coaxial measurement cables. The cables are installed horizontally across the slope in trenches (ideally on multiple levels throughout the slope) and vertically in boreholes, so that possible landslide movement will be oriented orthogonally to the cable axis, thus shearing the cable [27; 65]. If shear deformation occurs, the CSM measurements allow to detect the exact location of deformation and to estimate the amount of deformation [27; 65]. The wire-extensometers which are installed parallel to CSM provide additional extension measurements, which allow to observe large deformation amounts in the dm to m range. While the CSM and EXT provide seamless observations along the measurement lines, their application is limited by the required free space and the laborious installation procedure. To fill the gaps between the CSM and EXT lines and to make the implementation of a wide range of sensors possible, the system is complemented by LoRa sensor nodes in different setups. They all share the same basic design but differ by their placement and the number and types of sensors attached to them. As shown in Figure 1, there should be at least two LoRa gateways in the system because of redundancy. Ideally, these gateways are positioned at points where the horizontal CSM lines intersect and/or where drillings are located. This makes it possible for the different measurements systems to share the required infrastructure (e.g., enclosure, power, data connection) with the LoRa gateways. The connection from the gateways to the internet (\"Network-\" to \"Middle-ware\" -layer) is achieved using GSM modems and/or DSL via landline.  
In the following, as one of the most important new developments of the Inform@Risk project, the LoRa nodes are described in detail [20; 66].
## 2 Analysis of Modulation Process
### The Models of Antenna and Spinning Vehicle
The only difference between the antenna coordinate system in Figures 1 and 2 is that the origin of the coordinate system in Figure 2 is at the center of the vehicle, not the center of the antenna. However, \(\theta\) in Figure 2 can be approximately equal to the azimuth angle \(\theta\) in Figure 1, because the diameter of the vehicle is short enough to be ignored, as compared to the distance between the vehicle and the satellite, while \(\varphi\) in Figures 1 and 2, it is totally the same. So, the radiation pattern of the antenna can still be represented by (1) and (2) with angles (\(\theta\),\(\varphi\)) in Figure 2.  
As we can see from Figure 2, the \(i\)-th satellite is denoted by \(S_{i}\). The \(\alpha\), \(ob\) and \(\alpha\) are the projections of the satellite's line-of-sight (LOS) vector on \(y_{1}oz_{1}\), \(x_{1}oz_{1}\), and \(x_{1}oy_{1}\) surfaces, respectively. \(\alpha\) is the roll angle in surface \(y_{1}oz_{1}\) and \(\beta\) is the elevation angle in surface \(x_{1}oz_{1}\). When the vehicle rotates in the air, \(\alpha\) changes periodically from 0 to 360 degrees. To facilitate analysis of the spinning vehicle, (\(\theta\),\(\varphi\)) is replaced by (\(\alpha\),\(\beta\)). Figure 3 shows the variation of normalized amplitude received by a rotating antenna at different angles of \(\beta\). So, the power of receiver signals changes in the form of a sine wave, when a vehicle with a single-patch antenna mounted on the side of it, rotates. And the information of the roll angle is also modulated on the variation map of the power of receiver signals, which are called roll modulation signals.
## 5 Discussion
### Limitations in Satisfying Security Requirements for Offline Payment Function
#### 5.2.3 Additional Considerations to Strengthen the Security Requirements  
Despite the security of [PERSON] et al. [95] being reinforced through the methods mentioned above, there is still a fundamental problem related to the dependence on TEE technology. This problem is not limited only to the implementation of the security by [PERSON] et al. [95], but all offline payment systems have no other available options but to use terminals, such as cards or smart devices, which will inevitably encounter a problem. Any effort contributing to strengthening the security of the offline payment function can become futile through continuous attacks on the terminal itself by hackers.  
Figure 5: Flowchart of the method to sync every regular time to satisfy the security requirement of unforgeability.  
For this reason, even if the terminal used in the system operates in offline payment mode for a certain amount of time due to various environmental constraints, in other cases, transactional information that was operated in an offline payment mode must be recorded on the online server through periodic synchronization with the online server. Synchronization can facilitate the judicial process after a fraudulent action that breaks down all security elements. Although this method solves the problem from the ex-post point of view, it can reduce the motivation of hackers to attack.
## 3 System Dynamic Model for Urban Energy Consumption and CO2 Emissions  
### Overall Framework of the Model  
According to the modeling method of system dynamics, the modeling goal is to evaluate the impact of energy consumption on carbon emissions by analyzing the input, operation and output characteristics of urban carbon emissions system, and then establishing a causal feedback loop diagram. Therefore, the overall framework of the carbon emissions effect model of urban energy consumption takes population growth and energy consumption as the basic factors, and considers the relationship between industrial structure, economic growth and land expansion. The model defines five corresponding sub-models, namely, a residential sub-model (\(F_{\text{Res}}\)), a commercial service sub-model (\(F_{\text{Com}}\)), an industrial sub-model (\(F_{\text{Ind}}\)), a transportation sub-model (\(F_{\text{Tra}}\)) and a carbon sequestration sub-model (\(F_{\text{Ga}}\)). The overall frame and the feedback relationships among the factors are shown in Figure 2. In the model, the inputs include the consumption of fossil energies (e.g., coal, liquefied petroleum gas (LPG), gas, natural gas (NG) and electricity) through the five urban subsystems: residence, commercial services, industrial, transportation and carbon sink. The mutual conversions of materials consumption and energy between the subsystems are analyzed, and, finally, converted into carbon dioxide emissions. The outputs include carbon emissions and indices (such as the total CO2 emissions, the emissions intensity, the emissions per unit land, and the per capita emissions). The boundary of the model is the city's perimeter, and the period of concern for the dynamic model is from 1949 to 2016.  
Figure 2: A causal feedback loop diagram for the urban CO2 emissions model.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 3 System Performance
### BER Performance
Hence, it would be possible to obtain simpler expressions after some mathematical approximations following the approximation given in (51) involving an upper bound and a lower bound for the Gaussian Q-function that represents the behavior in terms of error probability of an ideal Gaussian channel, as shown in Equation (22), and based on series expansion. Additionally, and for a future work, we have planned to use a more generic model for the turbulence, the Malaga model (52) and its formulation from Generalized-K functions (53). Thus, as commented in (54), its pdf can be approximated by a Gauss-Laguerre quadrature. Since the Gamma-Gamma model employed in our paper is a particular case of the Malaga model (52), then this Gauss-Laguerre quadrature can also be applied. Thus the upper bound given in (54) and (51) (Equation (19)) can be employed.
## 3 Results and Discussion
### Imputation  
For evaluating our imputation method, we experimented and compared the results of different combinations of imputation and ensemble methods, as shown in Table 1. As regressors, we compared the outcome of using support vector (SV), decision tree (DT), random forest (RF), and simple linear (SL) regressors, in different multiple imputation ensemble model. Values marked as bold in Table 1 performed better than other regressors and, from the result, we can say that random forest produces the best result and the combination of imputation and ensemble mentioned in our proposed work (RFI-Bag, RFI-SE, MICE-Bag, and MICE-SE) performed better than other approaches. In Table 1, \"Bag\" represents bagging ensemble and \"SE\" denotes stacking ensemble.
# Improvements of the xAAL Home Automation System
## 4 The Basic Schema
### Composite Devices
#### 4.1.1 Gateways  
A gateway is a piece of software and hardware that makes the bridge between the xAAL bus and a proprietary home automation protocol used by some devices deployed in a facility (e.g., X10, Zigbee, X2D, Z-Wave, HomeEasy, KeeLodq).  
The gateway presents one xAAL device on the xAAL bus for each proprietary home automation device it manages; each one with its own xAAL address and its own dev_type. They are called _embedded devices_.  
Moreover, the gateway should present itself as an additional xAAL device, with its own address and a dev_type:\"gateway.basic\", or any relevant extended type of gateway. The gateway maintains the list of its embedded devices and can be questioned about it. Some advanced gateways may also provide information about the status of managed devices (e.g., if the gateway know that certain devices are defective or dead).  
Even if it is not recommended, a gateway can also choose to be not visible on the xAAL bus, does not announce itself, nor indicates that it has embedded devices.
## 2 Related Works  
The importance of defending data from attackers has seen more emphasis and study recently. The authors of [3] improved system security by adding a variable cyclic prefix (CP) and frequency domain jitter. The authors of [19] proposed a random user selection method. The authors of [20] proposed precoding-aided spatial modulation to improve covert performance.  
Spread spectrum technology is commonly used to improve covertness [1; 21]. Spread spectrum technologies including Direct-Sequence Spread Spectrum (DSSS) and FrequencyHopping Spread Spectrum (FHSS), and combinations of DSSS and FHSS are usually used with CDMA and Orthogonal Frequency-Division Multiplexing (OFDM) technologies for multiple access. Moreover, various studies have attempted to enhance the advantages of the spread spectrum with CDMA [5; 6; 22]. However, the implemented algorithms have the limitation that decoding the original message is possible when the signal is detected because of their proceedings combining a cryptographic method with spread spectrum.  
To overcome this problem, physical layer security methods have been studied with wireless communication technology. The authors of [4] presented a method to improve LPI performance in the physical layer. The dirty constellations algorithm is introduced as a modulation method that adds an additional covert Quadrature Phase Shift Keying (QPSK) signal to the original modulated signal with a low information transmission rate similar to Binary Phase Shift Keying (BPSK) and QPSK. This method mimics wireless communication signals with high noise and hides the signal at a high Error Vector Magnitude (EVM) point. Therefore, the dirty constellation of the transmission signal has high covert performance. However, the constellation generated by the presented algorithm does not deviate from the form of a conventional QPSK; the attackers are likely to intercept and possibly decode the original message. The multiple modulation schemes broadcasting method enhances security performance compared with a single modulation scheme system [23]. A continuously varying modulation scheme has been proposed [24; 25]. A method of shaping the symbol similar to AWGN has also been proposed [26]. However, the proposed methods have limitations in that the modulation scheme may easily be guessed and require dummy data.  
In this paper, we propose a ring-shaped signaling system algorithm that cannot demodulate the message even if the transmitted signal is detected and intercepted. The algorithm mainly utilizes the LPI/LPE communication function. In this algorithm, additional shared modifying factors are used to shape and recover the signal. This method has the advantages of being easy to implement, being adaptable for any communication system using I/Q data, and effectively decreasing attackers' probability to decode the message.
# A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
## 3 Methods
### Communication between User and Tgs  
Users who have passed authentication with the AS and obtained a digital signature for the AC can use the TGS to obtain a ticket to access the SSs connected to the SCAP. Figure 4 shows the process of issuing a ticket for a user to access the CDM data of all hospitals in the SCAP through the TGS. To request the issuance of a ticket to the TGS to access the SS of the hospital connected to the SCAP, a message \(M_{C-TGS}\), which includes token and \(sig^{AC}\), along with its signature \(sig_{M_{C-TGS}}\), is transmitted (flow 1 in Figure 4). It does not matter whether \(M_{C-TGS}\) contains the AC, as long as it is proven that the generator of \(sig^{AC}\) is the CS. The TGS delivers a message \(M_{TGS-C}\), including a cryptographically generated ticket and its signature \(sig_{M_{TGS-C}}\) to the platform users (flow 2 in Figure 4). Similar to the authentication token of the AS, this ticket is generated using a symmetric key cryptographic algorithm with information such as the user's ID and expiration time. However, unlike the AS, the key used to create the ticket is encrypted with the public key of each SS receiving the ticket and then delivered to the user along with the ticket. Therefore, the user acquires the same number of tickets as the number of SSs connected to the platform. The user cannot decrypt the verification key delivered to the ticket. That is, except for the SS, a third party who has obtained a ticket, including a user, cannot create or decrypt a ticket. The TGS is an essential component in the SCAP operating on a distributed network. Without the help of the TGS, if users want to analyze CDM data from multiple medical institutions, they should prove their identity to the administrator of each institution. Even in such an environment, each institution should operate a database that stores the identities of all users who want to use the data. The TGS creates a ticket that allows any medical institution to authenticate the user's identity, in an attempt to analyze the data and alleviate these obstacles. By verifying this ticket, the SS of each hospital can authenticate the user without a user account database.